September 20th-21st 2023 | Brussels
More than: 350 in-person attendees | 90 speakers | 40 solution-focused sessions
The 9th annual summit will address cancer control for European citizens. Convening a wide range of critical stakeholders including policymakers, healthcare professionals and providers, regulators, payers, investors, industry and patient groups.
We are thrilled to announce the release of the 2nd Edition of Pharma Focus Europe magazine 2023! It is equally a remarkable achievement as it fills us with pride and a sense of responsibility. I take this opportunity to mark the exceptional dedication, expertise, and collaborative efforts of my proficient team, advisory board panel, and our esteemed authors. Their synergy has collectively rendered top-quality industry literature that caters well to our readers and seekers.
In this edition, we bring to you a wide scope of knowledge base from industry experts ranging from Clinical trials, Data Management, Pharma Discovery & Development and much more. Our 2nd Edition also marks the beginning of two new sections – Industry Sense & Through the hourglass, aiming to bring the industry experts much more close to our readers with their POVs & insights.
A collection of carefully blended noteworthy topics are mentioned below for your quick reference and introduction.
Josipa Ljubicic, QA Director, Principal GCP and GVP auditor, Proqlea sheds insights on Clinical Trials & Innovations - Clinical trials are moving towards greater technology adoption for enhanced processes. Advanced tools, patient-focused approaches, and collaborative trial design will redefine the future of clinical trials with innovation and improved data analysis.
Shamal Fernando, Managing Director, Slim pharma brings to us Artificial Intelligence in Drug Manufacturing and Drug Discovery - AI's integration in the pharmaceutical industry has transformed drug discovery, design, and manufacturing. It has revolutionized the pharmaceutical landscape with ground-breaking approaches that were once unimaginable.
Svetoslav Valentinov Tsenov, Chair of the Board of Directors, ARPharM shares his expert advice on Digipharma aka Digital Transformation. Digital transformation has revolutionized the pharmaceutical industry, enhancing operational efficiency in areas
like clinical trials and patient engagement. Using AI, machine learning, data analytics and cloud computing companies can intelligently manage resources and adopt innovative approaches.
Michael N. Liebman, PhD, Managing Director, IPQ Analytics systematically introduces various software products and technologies that are redefining the pharmaceutical industry globally. Computers drive pharmaceutical development, analysing biology, screening compounds, and optimizing drug development. Applications like CTMS, CDMS, pharmacovigilance systems, and EDCT are heavily used in data management & analysis.
Hassan Mostafa Mohamed, Chairman & CEO, ReyadaPro speaks on Biopharmaceutical Supply Chain - Supply chain updates are crucial for bio-pharmaceutical companies to deliver safe and timely treatments worldwide. Strategies like cold chain management, serialization, and AI integration optimize efficiency and patient care.
Sheryl Caswell, HOD, Monument Therapeutics brings forth the Application of Digital Biomarkers in Neuroscience. Digital biomarkers offer the potential to customize treatment decisions, deliver more objective measures of disease activity and response to treatment, accelerate clinical trials, and optimize patient outcomes.
I hope you enjoy this latest edition of Pharma Focus Europe as much as we did producing it. If you'd like to contribute an article or interview to be featured in our next issue, you can reach me via email: editorial@pharmafocuseurope.com
The input, ideas, and feedback of our audience are driving force for us. We invite you to share your thoughts and connect with us on social media to stay informed and engaged with the Pharma industry.
N D Vijaya Lakshmi Editor06 Rare Diseases Medicine: Advantages, Challenges, and Future Developments
Shamal Fernando, Managing Director, Slim pharmaceuticals (Pvt) Ltd
14 Pooling: An Untapped Clinical Supply Chain Strategy
Catherine Hall, VP of Data and Quality, Endpoint Clinical
28 Clinical Trial Project Management Practices Review
Joab Williamson, Director, Program Management, Faron Pharmaceuticals
36 Blood to Plasma Ratio: An Important Determinant of the Pharmacokinetic Behavior of Drugs
Pallavi Limaye, Director of Consulting, BioIVT Steve McGreal, Senior Scientist, BioIVT
42 Future of Clinical Trials and Technology Innovations
Josipa Ljubicic, QA Director, Principal GCP and GVP auditor, Proqlea Ltd
48 Biopharmaceutical Supply Chain
Hassan Mostafa Mohamed, Chairman & Chief Executive Officer, ReyadaPro
58 Pharma 4.0: Advanced Continuous Pharmaceutical Tablet Manufacturing
Ravendra Singh, C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey
67 The Potential of Automation and AI/ML in Causality Assessment for Safety Vigilance
Ryanka Chauhan, Product Manager, Datafoundry
76 Digipharma – A New Era of Disruption and Transformation
Svetoslav Valentinov Tsenov, Chair of the Board of Directors, ARPharM
83 The Tsunami of Big Data for Pharma: Sink or Swim?
Michael N. Liebman, PhD, Managing Director, IPQ Analytics, LLC
90 Artificial Intelligence in Drug Manufacturing and Drug Discovery
Shamal Fernando, Managing Director, Slim pharmaceuticals (pvt) Ltd
98 Advances in Cancer Therapeutics
Svetoslav Valentinov Tsenov, Chair of the Board of Directors, ARPharM
106 Impact of Covid-19 on Pharmaceutical Industry
Gustavo Samojeden, CEO of Eriochem S.A
109 Vaccine Development and Manufacturing
Josipa, QA Director, Principal GCP and GVP Auditor, Proqlea Ltd
122 The Future of Integration of Targeted Therapy with Genomics in Personalized Medicine
Ravi Dashnamoorthy, Senior Scientist, Genosco
126 Emerging Trends and Strategic Insights in the Pharmaceutical Industry
Alessio piccoli Director & Head,Business Development Europe presso Aragen
Italy
Amine Bekkali Director, Medfields, UAE
Dmitrii Vitalievich Kriuchkov Executive Director Axon Clinical Trial Lab
Russia
Gustavo Samojeden
CEO, Eriochem S.A
Argentina
Hassan Mostafa Mohamed
Chairman & Chief Executive Officer
ReyadaPro
Saudi Arabia
Hoda Gamal
Director of Regulatory and Corporate Affairs Middle East and Africa, Allied associate, Egypt
Joaquin D. Campbell
Global Director
Managed Access Services
Spain
Josipa Ljubicic
QA Director / Principal GCP and GVP auditor, Proqlea Ltd
Croatia
Juris Hmelnickis
CEO, Grindeks
Latvia
Nicoleta Grecu
Director, Pharmacovigilance Clinical Quality Assurance
Romania
Nigel Cryer FRSC Global Corporate Quality Audit Head Sanofi Pasteur France
Paola Antonini
Chief Scientific Officer, Meditrial Global CRO
Italy
Pinheiro Neto Joao
Chief Executive Officer
Meu Doutor
Angola
Shamal Jeewantha Fernando
Managing Director, Slim Pharmaceuticals ( Pvt) Ltd
Srilanka
Svetoslav Valentinov Tsenov
Senior Pharma Executive and Global Transformation Lead
Bulgaria
Tamara Miller
Senior Vice President, Product Development, Actinogen Medical Limited, Sydney
Teresa Derbiszewska Clinical Quality Director
G42 Healthcare/IROS
Thitisak Kitthaweesin
Chief of Phramongkutklao Center of Academic and International Relations Administration, Thailand
Vicknesh Krishnan
Associate Medical Director at Fresenius Medical Care Malaysia Sdn Bhd
Malaysia
EDITOR
Vijaya Lakshmi N D
EDITORIAL TEAM
Sarah Richards
Debi Jones
Harry Callum
Supraja BR
ART DIRECTOR
M Abdul Hannan
PRODUCT MANAGER
Jeff Kenney
SENIOR PRODUCT ASSOCIATES
David Nelson
John Milton
Peter Thomas
Sussane Vincent
Veronica Wilson
CIRCULATION TEAM
Sam Smith
SUBSCRIPTIONS IN-CHARGE
Vijay Kumar Gaddam
HEAD-OPERATIONS
Sivala VNR
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Rare diseases, also known as orphan diseases, are a group of disorders that affect a small number of people. These diseases are often difficult to diagnose and treat due to their rarity, and patients suffering from rare diseases often face significant challenges in accessing appropriate care. However, in recent years, significant advances have been made in rare diseases medicine, and promising developments are on the horizon..
Shamal Fernando Managing Director, Slim pharmaceuticals (Pvt) LtdThe challenges associated with rare diseases are numerous and multifaceted. Due to the rarity of these conditions, they are often misdiagnosed or not diagnosed at all, leading to delayed or inappropriate treatment. Access to treatments can also be a significant challenge, as pharmaceutical companies may not see a profitable market in developing treatments for rare diseases. Additionally, research
into rare diseases is often underfunded and can be challenging due to the difficulty in finding enough patients to participate in clinical trials.
Despite these challenges, there are also advantages to studying and treating rare diseases. The rarity of these conditions often leads to close-knit communities of patients, families, and caregivers who can collaborate and share information about their experiences and treatments. Additionally, because rare diseases often require unique solutions, researchers and healthcare professionals may be more likely to develop innovative treatments and approaches. Furthermore, the study of rare diseases can provide insights into the underlying mechanisms of disease and pave the way for personalized treatments tailored to the individual patient.
Looking at the future, there is hope for improved diagnosis, increased access to treatments, and collaborative research efforts. Advances in genetics and genomics are allowing for more precise diagnosis of rare diseases, and efforts to incentivize the development of treatments for rare diseases could lead to more treatments becoming available and at more affordable prices. Collaborative efforts among researchers, healthcare professionals, patients, and industry partners could lead to a more comprehensive understanding of rare diseases and more effective treatments. Finally, patient advocacy and engagement will continue to play a critical role in driving progress and increasing awareness of rare diseases, leading
to better outcomes and improved quality of life for those affected.
The past few decades have seen significant progress in the field of rare diseases medicine.
Researchers have been able to develop new treatments and therapies that have helped improve the quality of life for people suffering from rare diseases.
Here are some of the significant advances in rare diseases medicine:
Gene therapy has emerged as a promising approach for treating rare diseases. In gene therapy, scientists use genetically modified viruses or other delivery systems to introduce healthy copies of defective genes into the patient's cells. This approach has been used to treat rare diseases such as spinal muscular atrophy, hemophilia, and certain types of inherited blindness.
Rare disease registries are databases that collect and store information on patients with rare diseases. These databases can help researchers better understand the prevalence and natural history of rare diseases, which can lead to improved diagnosis and treatment. They can also help patients connect with other patients and clinical trials that may be relevant to their condition.
Addressing Unmet Medical Needs:
Rare diseases often lack effective treatments, leaving patients with limited options for managing their symptoms and improving their quality of life. The development of new therapies for rare diseases can address unmet medical needs and provide hope to patients and their families. In addition, the development of treatments for rare diseases can lead to improvements in health outcomes and reduce the burden of disease on patients and society.
Rare diseases present opportunities for innovation in drug development, with the potential to explore new targets and mechanisms of action. The development of treatments for rare diseases can also
Regulatory agencies around the world have recognized the need for expedited approval pathways for drugs for rare diseases. This can accelerate the development and availability of treatments for patients with rare diseases. For example, the US Food and Drug Administration (FDA) has established the Breakthrough Therapy designation, which allows for expedited development and review of drugs for serious or life-threatening conditions, including rare diseases.
lead to advances in our understanding of disease biology and the development of novel technologies. For example, the development of therapies for rare genetic diseases has led to advances in gene therapy and gene editing, which have broader implications for the treatment of other diseases.
The development of treatments for rare diseases can also improve the diagnosis and understanding of these conditions, leading to earlier detection and better outcomes for patients. Improved diagnosis can also facilitate the identification of patients who may be eligible for clinical trials and other research studies, which can further advance the development of treatments for rare diseases.
Research into rare diseases can have broader public health benefits. For example, rare diseases can serve as models for understanding how the immune system works and how infectious diseases spread. Additionally, the development of treatments for rare diseases can lead to advances in drug development and manufacturing processes that can benefit patients with more common diseases.
Overall, the study and treatment of rare diseases have important advantages that go beyond helping those directly affected by these conditions. By understanding the underlying mechanisms of rare diseases and developing targeted therapies, researchers and healthcare professionals can make significant contributions to the broader field of medicine and public health.
Despite the significant advances in rare diseases medicine, many challenges still exist. Here are some of the significant challenges:
Due to the low prevalence of rare diseases, there is often a lack of research funding available to study these conditions. This lack of funding can make it challenging for researchers to develop new treatments and therapies.
Limited access to specialized healthcare providers:
Patients with rare diseases often require specialized care from healthcare providers with expertise in their condition. However, these providers can be challenging to find, particularly in rural or low-income areas. Many patients with rare diseases may not have access to appropriate diagnosis, treatment, or supportive care due to a lack of awareness, expertise, and resources in their local healthcare systems. This can result in significant disparities in healthcare outcomes for patients with rare diseases.
Limited patient population:
Rare diseases affect a small number of people, often fewer than 200,000 in the United States, making it difficult for pharmaceutical companies to recoup the costs of drug development. The small patient populations affected by rare diseases make it challenging to conduct clinical trials and to obtain sufficient data to support regulatory approval. This can result in high development costs and limited profitability for drug manufacturers. In addition, the limited patient populations can make it difficult to conduct post-market surveillance and to assess the long-term safety and efficacy of treatments.
The development of rare disease medicines can be more expensive than for more common diseases due to the need for smaller clinical
trials, specialized manufacturing processes, and the high regulatory hurdles.
Many of the treatments for rare diseases are expensive, and insurance coverage for these treatments can be limited. This can make it challenging for patients to access the care they need.
The regulatory process for rare disease medicines can be lengthy, often taking longer than for more common diseases. This can delay patient access to much-needed treatments.
Many rare diseases are poorly understood, and there may be a lack of expertise among healthcare providers in diagnosing and managing these conditions. This can result in delayed or incorrect diagnoses and inadequate treatment. In addition, the limited understanding of rare diseases can make it difficult to develop appropriate endpoints for clinical trials, which can further delay the development of treatments.
Due to the limited patient population, clinical trials for rare disease medicines can be challenging to conduct ethically. There are concerns about adequate informed consent, equitable access to the trial,
and potential exploitation of vulnerable populations.
The cost of developing and providing treatments for rare diseases can be high due to the limited patient populations, which may make it difficult for healthcare systems to provide these treatments to all patients who need them. In addition, the high costs of treatment can result in limited access to care for patients who do not have adequate insurance coverage or financial resources.
Medicine Despite the challenges facing rare diseases medicine, there is reason to be optimistic about the future. Here are some of the promising developments: The future of rare diseases holds great promise as researchers and healthcare professionals continue to make significant strides in understanding these conditions and developing effective treatments. Here are some key areas of progress and innovation that are shaping the future of rare diseases. The future of rare diseases holds great promise as researchers and healthcare professionals continue to make progress in understanding and treating these conditions. By collaborating across different fields and sectors, leveraging new technologies, and prioritizing patientcentered research, we can work towards a future where rare diseases are better understood, more effectively treated, and ultimately cured.
Artificial intelligence and machine learning technologies are increasingly being used to diagnose and treat rare diseases. These technologies can help doctors identify rare diseases more quickly and accurately, which can lead to better outcomes for patients. Advances in artificial intelligence and machine learning are already being applied to the diagnosis and treatment of rare diseases. In the future, these technologies may become even more sophisticated, with the potential to analyze vast amounts of data and identify new patterns and connections that could lead to breakthroughs in the understanding and treatment of rare diseases
Gene editing involves using CRISPR or other technologies to modify genes in a patient's cells. This approach has shown great promise in treating rare diseases caused by specific genetic mutations. Researchers are currently conducting clinical trials for gene editing in rare diseases such as sickle cell anemia and rare genetic disorders.
Advances in genomics and other technologies are paving the way for personalized medicine, which may be particularly relevant for rare diseases. Personalized medicine can allow for tailored diagnosis and treatment approaches based on an individual's unique genetic profile. This can lead to more effective treatments and improved outcomes for patients with rare diseases
Personalized medicine is an approach to healthcare that involves tailoring medical treatment to the individual patient based on their specific characteristics, such as genetics, lifestyle, and environment. Personalized medicine is particularly relevant for rare diseases, which often have a genetic basis and require unique treatment approaches.
The development of personalized medicines for rare diseases has been enabled by advances in genomics, which have allowed researchers to identify the specific genetic mutations that underlie many rare diseases. By understanding the genetic basis of a rare disease, researchers can develop targeted therapies that address
Promising developments are on the horizon, igniting optimism for improved care and outcomes in the field of rare diseases.
specific defects that cause the disease. For example, in some rare genetic diseases, such as cystic fibrosis, gene therapies have been developed that involve modifying or replacing the faulty gene to treat or cure the disease.
Personalized medicine can also involve tailoring existing treatments to the individual patient based on their characteristics. For example, in cancer treatment, personalized medicine may involve using genetic testing to identify specific mutations in a patient's tumor, which can then inform the choice of chemotherapy or targeted therapy.
Overall, personalized medicine has the potential to revolutionize the treatment of rare diseases by providing more targeted, effective, and personalized treatments that address the underlying causes of the disease. However, there are still many challenges to overcome, including the need for more comprehensive genetic testing, the development of targeted therapies for a broader range of rare diseases, and the need for more research to understand the complex interplay between genetics, environment, and lifestyle factors in rare diseases.
Improved collaboration and data sharing among researchers, healthcare providers, and patients can help accelerate the pace of research into rare diseases. This can lead to faster development of new treatments and therapies for patients.
Collaboration between researchers, industry, and patient advocacy groups can accelerate the development of treatments for rare diseases and improve access to these treatments for patients. Collaborative research can facilitate the sharing of data and resources, which can lead to more efficient and effective drug development.
Precision medicine, which involves tailoring medical treatments to the individual patient based on their specific characteristics, is becoming increasingly important in the treatment of rare diseases. As genetic testing becomes more widely available and affordable, precision medicine approaches will become more common and effective.
Rare diseases medicine presents a unique challenge in healthcare, as these conditions are often poorly understood and affect a small population of patients. Despite this, the development of new therapies for rare
diseases has the potential to address unmet medical needs and provide hope to patients and their families. Advances in technology and research, such as personalized medicine and collaborative research, offer opportunities for improving diagnosis, treatment, and outcomes for patients with rare diseases.
However, addressing the challenges of rare diseases medicine, such as the limited patient populations, lack of knowledge and expertise, cost of treatment, and access to care, will require a collaborative effort from healthcare providers, researchers, industry, and patient advocacy groups. By working together, we can continue to make progress in the development of effective treatments for rare diseases and improve the lives of those affected by these conditions.
It is important to recognize the significant disparities in healthcare outcomes for patients with rare diseases and to prioritize efforts to improve access to care and support for these patients. The development of expedited approval pathways for drugs for rare diseases and the establishment of centers of excellence for rare diseases can help address some of these challenges.
Conclusion:
In conclusion, rare diseases medicine presents both challenges and opportunities for healthcare. By leveraging advances in technology and research and working collaboratively, we can continue to make progress in the development of effective
treatments for rare diseases and improve the lives of those affected by these conditions. It is imperative that we prioritize efforts to improve access to care and support for patients with rare diseases and address the significant disparities in healthcare outcomes for this population.
Shamal Fernando is currently working as a Managing Director at Slim Pharmaceuticals (Pvt) Ltd. He is Purpose Driven Marketer, Finance and Business Leader who gets energized by the opportunity to impact patients and people Business leader with experience in a broad range of therapeutic areas including oncology, Haematology, Rheumatology, women's health, neuroscience, cardiovascular, endocrine, and infectious diseases. Proven expertise in the on-time and within-budget delivery of innovative strategies.
Over the past two decades the Clinical Supply Chain discipline within the pharmaceutical industry has sought a variety of strategies that help ensure the right medicine gets to the right patient at the right time more effectively and efficiently. One of the first transformative strategies to be developed was drug pooling. Today however, this strategy is still not used pervasively throughout the industry. This article will address the reasons why supplies pooling can be effective and efficient and the barriers to its widespread adoption.
Catherine Hall VP of Data and Quality Endpoint ClinicalOne of the most challenging responsibilities a Clinical Supply Chain Manager has is to determine a reliable supply forecast that stands up to the complexities of the clinical trial design and the various scenarios that can occur during trial conduct.
From unpredictable patient recruitment to the inevitable string of protocol amendments, the design of a clinical trial both within the protocol and in operational conduct provides a host of variables that lie outside of supply chain control, and which must be adjusted rapidly from study start-up to study close-out. However, even elements within supply chain control can be subject to unexpected changes such as delays in sourcing, manufacture, packaging, or distribution. While a good supply forecast will plan to mitigate future scenarios of significant risk and impact, no forecast can adequately prepare for all of the unknown combinations of factors without incurring the high costs of supply waste. Thus, the best and most successful supply forecasts are supported by continuous monitoring that serves to evaluate the performance of the forecast over time.
While monitoring their forecast, Supply Chain Managers will often ask questions like have we manufactured enough, have we manufactured too much, or do we have enough in the right place? When the supply is just supporting one trial, this effort to manage supplies is focused and efficient. Most clinical programs however are rarely limited to one protocol, and during Phase III development, it is more likely to be 3-5 trials in some stage of start-up, conduct, or close-out across the globe. It was from the program-level perspective and a need for
clinical supplies to be agile, that the supply strategy called Pooling was designed.
Pooling by today’s definition refers to a strategy in which supplies are packaged and labeled in such a way that they can be used across multiple studies, distributed globally, and are adaptable to change. Although the definition of pooling today is focused on this cross-study use of supplies, the concept was born from the evolution of supplies management after IRT technology began to play a prominent role in trial conduct. Not even 20 years ago, clinical supply material labels included pre-printed patient numbers and visit identifiers to properly dispense the right medication according to the randomization of patients to treatment arms. With the advent of centralized randomization through IRT technology, the supplies at the site had to be ready and able to be dispensed to any patient at any visit for which they could be assigned. As a result, patient numbers and visit identifiers were removed from the labels, and the supplies at the depot could be sent to any site and supplies at a site could be dispensed to any patient at any visit which required that drug type. In this way drug was pooled, but within one limitation. That limitation was that the supplies were only pooled within the one study that the supplies were specifically labeled for. It was natural for the clinical supplies industry to then ask could the protocol number be removed so that supplies could be used not only across patients within one study but across patients in multiple studies.
As pooling evolved, the most common implementation has been to have a stock of supplies at a depot ready and able to be dedicated to one study or another up to the point of shipment to a site. In this way, if one study was slow to enroll and another was going faster than expected, the supplies could be easily shifted from one study to another. A less common implementation of pooling is to have a stock of supplies at a site ready and able to be dedicated to one study or another up to the point of dispensation to a patient. This provides maximum flexibility of supplies such that if a site runs multiple trials within the program, the supplies could shift between studies right up to the point of dispensing. This strategy is useful because many clinical programs rely on a common set of sites representing large centers for clinical trial conduct or key opinion leader sites. In either case, depot or site pooling, the major advantages of the strategy stem from delaying the point in time in which a packaged unit is dedicated to a study and thereby maximizing the opportunity for the supplies to be used, increasing supply flexibility to cover various scenarios, and ultimately minimizing supply waste. For the Clinical Supply Manager, pooling also plays a role in simplifying the aggregation of forecasts across multiple studies as they are centrally inventoried within supply systems that control distribution and dispensation, typically an IRT system. Instead of aggregating a forecast from multiple independent study-
level reports, pooling strategies consolidate the inventory into centralized reporting for easy visualization during monitoring activities.
To facilitate drug pooling successfully, a standard packaging configuration and label design is key. The ability to use a common dosing unit in a standard form, like a 30-count bottle labeled for many countries, stakes the foundation of a pooling strategy. While standard packaging designs can also be used in supply strategies that do not employ pooling, it is the labeling strategy that is principally different within a pooled supply. There are multiple ways to adapt a clinical label that supports pooling. These include
1) listing all protocols that the supplies might be used in,
2) providing a list of protocols on the label with an added check box to be filled in when the materials are dedicated to one study over
Pooling fosters sustainable practices, minimizing waste and reducing environmental impact in the clinical supply chain.
another, 3) Just-In-Time (JIT) labeling at a depot that applies the protocol number on the supplies just before shipping to a site, 4) providing a write-in field on the label to have the study assignment applied by the depot or clinical site, and 5) providing a program identifier alone rather than a specific protocol ID. Each strategy has its benefits and risks. For example, while these labeling strategies can be effective when pooling supplies at a depot level, a JIT labeling strategy cannot be employed if supplies are then also pooled at a site level. Additionally, sites can often feel it is confusing when they see multiple protocols listed on the label especially if they are not involved in all atudies, or when only a program ID is provided. If using checkbox configuration or a write-in field, sites often require additional training on what they must do before dispensing the medication to the patient. In other cases, if the program is rapidly expanding, then a choice to list all protocols, with or without checkboxes, may mean you are continually having to reprint
your labels to accommodate additional studies. Moreover, in these cases of having all studies on the label, the approval for use of the supply in any one study is contingent on approval for use in all studies. If there is a delay in any one trial, the supplies will not be released for use in the other trials. Finally, each labeling strategy has various levels of regulatory acceptance as shown in the table below. As depot-level pooling is most often the strategy of choice, and JIT labeling is well accepted by the authorities, applying the protocol number at the point of distribution is a commonly employed strategy when pooling is adopted. See the table below for a side-byside comparison of these different strategies.
The second key to facilitate pooling is to employ a technology such as IRT that can assign the supplies to a study or a patient. GXP regulations require a method that can trace any one dispensable unit through the
course of events from manufacture to site, site to patient, and finally from return to destruction. These requirements impose a study-level accounting of supplies within the study and are the point at which the supply dedicated to the study must be documented and traced. IRT technology is a perfect choice for the job since it already traces the supplies from depot to patient and often, to the point of destruction as well. While the early step to pool supplies across patients is commonly employed, and IRT technology supplies a means to do it, taking the additional step to pool drugs across studies at either the depot level or the site level is rarely used within the industry. Instead, most supplies are labeled for each specific trial regardless of how many studies use the same packaging design. There are many factors that have prevented the more widespread use of pooling within clinical programs. One of these factors is the readiness of many regulatory agencies to embrace the strategy and adapt their laws to enable it more easily. In countries that employ import license requirements, for example, supplies must be dedicated to a specific study to gain an import license, and thus drug pooling across studies is inhibited within those countries. In the EU, the early Clinical Trial Directive provided language that supported the use of IRT to indicate labeling information and thus permitted pooling. Specifically, Article 13 Section 26 stated that “information should be included on labels, unless its absence can be justified, e.g., use
of a centralized electronic randomisation system” and listed the trial reference code as one such piece of information that could be “given elsewhere” (Directive 2001/20/ EC 2001). However, as it was a directive and not a law, there was variability across countries in supporting such strategies, and thus few readily approved label templates that did not specifically show the protocol number on them. As a result, and coupled with the fact that most companies are riskaverse when it comes to challenging regulatory pushback, few companies chose to adopt pooling strategies. There was some hope when the EU Clinical Trial Regulation was drafted to resolve these country-by-country differences, but the regulation, when it was first published, only reinforced a preference toward protocol-specific labeling.
Another factor that has been a barrier to the widespread adoption of pooling is the growing
trend of clinical supply organizations adopting ERP technology systems within their clinical supply chain. Most of these systems were designed to support commercial operations where there is one label per package and a dedicated distribution stream. As pooling requires the supplies to exist in a partially undefined state, which ERP systems do not easily support, a pooling strategy was difficult to implement when these systems were in use. Additionally, while many IRT systems took steps to incorporate pooling methodologies when the idea was first developed, the lack of interest has failed to inspire further innovation which might work to promote its use. While several IRT solutions can support drug pooling, especially at the depot level, it is often a specific customization of the system rather than a standard in configurable templates. Thus, the development timelines for IRT systems with pooling can be subject to additional build time, which most protocol teams are not in favor of.
As with any strategy, there are tradeoffs between benefits and risks. Pooling, nonetheless, has been well proven to reduce the overall cost of the clinical supply chain and mitigate the risk of stock out when multiple protocols are ongoing in a clinical program. The keys to the successful implementation of pooling strategies include a standard packaging design, a fit-for-purpose labeling strategy, an IRT system that has robust pooling functionality, thoughtful planning for what countries will best support a pooling strategy,
and most of all a strong partnership between clinical operations, the CRO, the supply chain, and the supporting regulatory group such that there is alignment that the benefits of pooling far outweigh any risks. For large phase III programs that are managing several trials at once, pooling works to ensure that the right investigational supplies are in the right place and the right time, and in the right amount and there is no greater achievement for a Clinical Supplies Manager.
Catherine Hall is currently the VP of Data and Quality at endpoint Clinical during her 20+ year career She has notably diverse expertise in clinical supply chain management, learning and development, continuous improvement, strategic partnerships, product development, quality, and data privacy and security.
Treating disorders of the central nervous system (CNS) is notoriously challenging, particularly in the fields of psychiatry and neurology. Despite substantial investment, many drugs have failed to demonstrate efficacy in late phase clinical trials, leaving sponsors with few viable options for advancing their compounds, physicians struggling to decide which treatments to give to their patients, and patients in need of effective therapies. Digital biomarkers, which capture physiological and behavioural data have the potential to revolutionize CNS drug development and clinical practice. In this article, we explore the benefits of digital biomarkers for clinical development, physicians, and patients, as well as the challenges presented by their use.
Sheryl Caswell Head of Development Monument TherapeuticsDisorders of the CNS, which include a wide range of psychiatric and neurological conditions such as Alzheimer’s disease, schizophrenia and depression, have a profound impact on individuals and their families. Despite significant investment in research and
development, the provision of new effective treatments for these clinical indications remains a significant challenge. One major issue is the difficulty in objectively measuring treatment response and disease progression. Another, and less frequently discussed issue, is how to identify the specific underlying biology in a heterogenous pool of patients presenting with similar symptoms. Traditional clinical diagnostic tools and endpoints, such as clinician ratings or patient-reported questionnaires, are subjective, time-consuming and cannot fully capture the complex and heterogeneous
nature of these disorders. In recent years, the use of digital biomarkers has emerged as a promising approach to address these challenges. In addition to the well-known electronic methods of data capture, such as brain imaging and EEGs, digital tools such as wearables and smartphones capture physiological, behavioural, and environmental data that can provide objective and continuous measures of disease activity and treatment response. This article provides a short summary of the many benefits that digital biomarkers can bring to CNS drug development, clinical practice and to the patients themselves, while acknowledging the challenges to be overcome to enable their universal acceptance and use.
One definition of a biological marker (biomarker) is that it is a “characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention” [National Institutes of Health]. Even more simply, a biomarker can be described as a medical sign (rather than symptom, which can be subjective). A useful biomarker needs to be objectively measurable, have good sensitivity and specificity for the process or response that it is predicting or measuring and be shown to be reliable.
We have become familiar with the concept of analysing biological samples to identify biomarkers which can be used for a variety of purposes. In cancer for example, biomarkers can be used for screening to check cancer status (e.g. prostate surface antigen from blood), to confirm a specific diagnosis (immunohistochemistry of tissue), predict a response to treatment so the appropriate chemotherapy can be used (eg HER2 expression in breast cancer), monitor response or progression (eg CEA in breast cancer). A digital biomarker is a measurable indicator of a biological, physiological, or behavioural process that is captured using digital technologies, such as computers, imaging devices, smartphones, wearable devices, or sensors. Like biomarkers from biological samples, digital biomarkers can be used to monitor health and disease, evaluate
treatment response, and predict clinical outcomes. Examples of digital biomarkers include tools to measure heart rate variability, activity levels, sleep quality, speech patterns, and cognitive function. These biomarkers can be used to monitor a wide range of health conditions, including cardiovascular disease, diabetes, neurodegenerative diseases, and mental health disorders.
Unlike oncology there are few psychiatric and neurological conditions with accepted biomarkers, either from biological samples or digital data collection. Diagnosis and monitoring of patients is largely based on clinical assessments such as the WHO’s International Classification of Disease or the American Psychiatric Association’s Diagnostic and Statistical Manual for psychiatric illnesses, the Mini-Mental State Examination or Montreal Cognitive Assessment for dementia screening, and a wide range of self-report questionnaires and pencil & paper-based tests to measure psychiatric symptoms and provide cognitive evaluation. Even with the availability of neuroimaging for some indications, diagnostic decisions are still mainly based on patient self-report and subjective clinical assessments.
There are several challenges associated with these current methods. Importantly, diagnosis and monitoring based on patient symptoms does not account for the specific biological processes that underpin the patient’s
disease. This becomes even more complex when patients have co-morbidities, such as depression and anxiety. In oncology for example treatment is no longer based on the location of the tumour but on diseasespecific markers and patients are stratified based on the specific biological processes causing the symptoms. This approach is beginning to be applied to CNS disorders and represents an opportunity for more successful psychiatric and neurological drug development and subsequent patient care. A second challenge is that as some of the traditional tests in psychiatry and neurology are based on patient reports of symptoms, the patients are required to remember and describe how they have been feeling or what they have experienced since the last visit to the clinic. This method, particularly considering the cognitive symptoms experienced by many patients with psychiatric conditions, provides subjective and unreliable data which in any case cannot be verified. Also, the variability of some neuropsychiatric symptoms means that testing over multiple days can be beneficial to correctly evaluate the true health status of the patient, by testing on both “good days” and “bad days”. Finally, the traditional tests, including EEGs and brain scans, in addition to being limited to being conducted at the clinical site, can be time-consuming both to conduct and to analyse requiring significant healthcare resources. The traditional methods therefore place a heavy burden both on the clinical site and the patient.
Digital biomarkers can provide solutions to these challenges. Digital cognitive biomarkers have been used for some years within drug development to monitor drug activity within the central nervous system. These biomarkers, which provide objective, repeatable and reliable indicators of cognitive performance, can include measures such as attention, memory, processing speed, and executive function, and are obtained through various digital-based tasks such as reaction time tests, working memory, and visual searches. Performance on these tests has been shown to correlate with activity in specific processes and pathways in the brain and so provide an opportunity for identifying patients with specific disease sub-types and evaluation of treatment response in areas of psychiatry and neurology. Patients could then be prescribed the correct treatment for their condition based on its underlying biology.
With regards to unreliable reporting by patients and opportunity for testing being limited to clinic visits, digital biomarkers allow automatic or scheduled daily data collection over multiple days and more easily and reliably demonstrate the true health status of the patient. Wearables and at-home devices that can monitor, for example, patient activity or sleep biomarkers and that can be correlated with patient symptoms are showing promise in clinical studies. Reduced activity in a patient may, for example, be a marker of low mood. Such software is easily delivered
through commonly used devices such as smartphones, smart watches or other smart activity monitors. Digital cognitive testing can similarly be delivered either in a clinic setting or while the patient is at home using smartphones, tablets and wearable devices. The continuous or repeated nature of the data collection captures the day-to-day variation in patient symptoms more accurately, including the more subjective responses by patients about their symptom states, allowing trends across weeks or months of treatment to be better identified. This automatic data collection provided by digital biomarkers both allows the clinical sites to see more information without needing significantly extra resource and also minimises patient burden.
Conducting late-phase clinical trials is timeconsuming, costly and complex; and the risk of failure when developing treatments for psychiatric and neurological conditions is high, as shown by a number of recent clinical trial failures. Assigning the correct differential diagnosis is particularly challenging in psychiatric conditions where co-morbidities are often present, such as both depression and anxiety. Utilising digital cognitive biomarkers to group patients with similar behavioural profiles and underlying pathology may reduce the inherent heterogeneity of clinical trial populations and so increase the likelihood of the success of the clinical trial. Stratification and objective measures of treatment response also provide opportunity for reduced sample sizes.
The collection of data between clinic visits can enable more efficient clinical trials by reducing the need for frequent site visits by the patient and facilitates the implementation of decentralised clinical trials by allowing for remote monitoring of patients by both the site monitor and the physician. This can reduce patient burden, reduce costs, and increase the speed of data collection. Reduced patient burden can, further, improve the rate of enrolment to a trial.
Digital biomarkers can also provide several benefits for physicians in clinical practice. First, they can help to personalize treatment
Digital biomarkers empower patients, engaging them in their own care and improving adherence and overall well-being.
Digital biomarkers can provide several benefits for patients. The ability to predict likely response to a drug can enable more personalized treatment decisions, ensuring the patient is on the right drug and leading to improved outcomes and reduced side effects. Remote monitoring of patients may improve patient engagement and satisfaction by providing more continuous and objective measures of disease activity and treatment response
as well as reducing the need for frequent site visits.
Within the context of clinical trials reducing the burden of frequent and long site visits may enable patients to participate in clinical trials more easily, particularly where the assistance of a carer is needed when travelling.
decisions by providing objective measures of disease activity and predicted and actual treatment response for individual patients. Where this is e.g., a cognitive biomarker that is predictive of treatment response or side effects it can inform the physician’s decision on choice of treatment while that patient is at their appointment, as they can be completed within just a few minutes
Second, digital biomarkers can facilitate remote monitoring of patients, allowing for more frequent and accurate assessment of disease activity and treatment response. This can enable more accurate and timely adjustments to treatment regimens, leading to improved outcomes and reduced side effects. It may also enable the earlier detection of disease relapse or progression, allowing for timely intervention and potentially improving long-term outcomes.
Despite the potential benefits of digital biomarkers, their use in clinical development and practice also presents challenges. One of the main challenges is ensuring the validity and reliability of digital biomarkers, which requires rigorous validation studies and standardization of measurement protocols. This is particularly important given the wide range of digital biomarkers under development and the potential for variability in data collection across different devices and platforms. The advantage of digital cognitive biomarkers is that much validation work is already complete and they have been shown to be reliable, robust, and consistent across different devices.
The FDA and EMA have already provided guidance and commentary on the use of digital biomarkers within drug development
although definitive guidelines on acceptability and standardised definitions are still required. The first regulatory qualification of a digital endpoint from wearable devices; a measure of stride velocity to quantify ambulation ability of patients with Duchenne muscular dystrophy; was announced in August 2021. Collaborative groups such as Critical Path Institute’s eCOA Consortium and Patient-Reported Outcome Consortium as well as the European Federation of Pharmaceutical Industries and Associations are attempting to engage regulatory agencies and other stakeholders for the purpose of standardising terminology and understanding the regulatory environment for digital biomarkers.
In conclusion, the use of digital biomarkers in neuroscience presents significant opportunities for advancing drug development and improving clinical practice. Digital biomarkers have the potential to personalize treatment decisions, provide more precise and objective measures of disease activity and treatment response, enable more efficient clinical trials, and improve patient outcomes. Addressing the challenges of digital biomarkers will require collaboration across stakeholders, including regulators, industry, clinicians, and patients. With continued innovation and investment, and considering the high interest and activity by academics, commercial
R&D and regulators digital biomarkers have the potential to revolutionize the field of neuroscience and improve the lives of millions of patients.
Treating disorders of the CNS is challenging, particularly in psychiatry and neurology. For sponsors when promising drugs fail in late phase clinical research, physicians deciding which treatments to give to their patients and ultimately for the patients who need effective treatments. This article suggests that digital biomarkers could be an important part of the solution.
Sheryl has clinical development expertise from a long career working on new, reformulated and repurposed products for almost thirty years. As one of the founders of Monument Therapeutics, an independent UK-based biotechnology company, Sheryl is currently focused on utilising digital markers within the clinical development of effective treatment options for areas of high unmet need in psychiatry and neurology.
Clinical trial project management is a complex and multifaceted process that involves the coordination of various activities and stakeholders to ensure the successful planning, execution, and completion of clinical trials. Effective project management is essential to ensure that clinical trials are conducted efficiently, safely, and within budget. The 5 stages of project management can be applied to clinical trials and be formed into an effective clinical project lifecycle.
Joab Williamson Director Program Management Faron PharmaceuticalsThe practices associated with project management of clinical trials is crucial to the success of that trial.
As the primary point of contact between the sponsor, study team, and regulatory authorities, the project manager is responsible for ensuring that the study is
conducted in compliance with all relevant guidelines and regulations. They are also responsible for managing the study timeline, budget, quality, and for overseeing study teams to ensure that objectives are met. The project manager plays a key role in ensuring that the study is conducted efficiently and effectively, and in securing that the study data is accurate and reliable. This article will explore the key aspects of clinical trial project management, placing the clinical trial lifecycle into the principles of the Project Management Institute (PMI), while suggesting some potential best practices for each project stage.
Regardless of project type, per the PMI, projects can be seen to have five key stages in its lifecycle: initiation, planning, execution, monitoring, and closeout. The PMI’s approach is seen as effective regardless of the industry due to its overarching end-to-end framework. This approach is particularly well-suited to clinical trials, where there are many complex regulatory and logistical considerations,
and where project success relies heavily on effective planning, communication, and risk management. By following the PMI's 5-stage approach, clinical trial project managers can ensure that their projects are executed efficiently, effectively, and in compliance with all relevant guidelines and regulations. A potential project lifecycle, when following the PMI’s framework is shown in Figure 1.
The project initiation phase is the first stage of clinical trial project management, where the study is conceptualized, and initial planning is undertaken. It involves defining the study objectives, developing a study protocol, identifying potential study sites and investigators, and obtaining the necessary regulatory approvals. Below are some of the key aspects of the project initiation stage that a clinical project manager should pay attention to:
Prior to the initiation of a clinical trial, the feasibility of the study should be assessed to
determine if it is viable to conduct the study within the proposed timeline and budget. This assessment should take into consideration the study design, patient population, availability of suitable study sites, and regulatory requirements.
The study protocol is a detailed document that outlines the study design, study objectives, inclusion and exclusion criteria, study procedures, data collection and analysis methods, and other relevant information. The protocol should be developed in accordance with regulatory requirements and GCP guidelines.
The selection of study sites and investigators is a critical aspect of clinical trial project management. Potential study sites and investigators should be identified based on their experience, qualifications, and availability, and should be thoroughly evaluated to ensure that they meet the study requirements. In addition, the entire study team should be defined with clear roles and responsibilities for all team members, utilising tools such as a RACI.
Clinical trials must obtain regulatory approval from the relevant regulatory authorities before they can begin. The process of obtaining regulatory approvals can be lengthy and
complex, and project managers must ensure that all necessary approvals are obtained in a timely manner, prior to the next stage of the project lifecycle.
The development a study budget and timeline are important aspects of clinical trial project management. The budget should take into consideration all study costs, including personnel, equipment, and other expenses, and should be developed in accordance with the study protocol. The timeline should be developed based on the study objectives, site
Project
Proficient project planning is an essential component of successful clinical trial conduct. Project planning should involve the development of a comprehensive project plan that outlines the project scope, objectives, sequence, timelines, budget, resources, and risks. Additionally, that project plan should be reviewed and approved by all stakeholders to ensure that there is a shared understanding of the project goals and expectations. Below are some key aspects of the project planning stage of clinical trial project management:
1. Detailed project plan: The project plan should outline all study procedures, timelines, budgets, and resources required to complete the study. The plan should
selection, regulatory requirements, and other factors.
By effectively managing the project initiation stage, project managers can ensure that the clinical trial is well-designed, feasible, and compliant with regulatory requirements, which sets the foundation for the successful execution of the trial.
The project execution phase of clinical trial project management involves the implementation of the project plan, the collection of study data, and the monitoring
of study progress. Below are some key project management practices in the project execution phase:
1. Stakeholder management: Effective stakeholder management is essential to ensure that clinical trial projects are conducted successfully. The literature suggests that stakeholder management should involve the identification and engagement of all stakeholders, including partners, regulatory authorities, investigators, trial participants, and trial staff. Effective stakeholder management involves developing
consider the risks and bring together all the key study tasks into a detailed timeline for the study duration.
2. Resource allocation: The resources required by a clinical trial should be considered to ensure sufficient resources are available to complete the study within the proposed timeline and budget. This includes allocating personnel, equipment, and other resources required for the study. Resource allocation should take into consideration the study protocol, site, operational, and regulatory requirements.
3. Risk management: Project managers must identify, assess, and manage risks associated with a clinical trial in accordance with regulations and guidelines. The International Council for Harmonisation
(ICH) guideline E6 (R2) provides guidance on risk-based approaches to clinical trial design, conduct, and reporting Project managers must ensure that all risks are identified and evaluated, and that appropriate risk mitigation measures are put in place to minimize or eliminate potential harm to study participants, as well as risks to data quality and trial integrity3. This includes developing contingency plans, establishing protocols for dealing with adverse events, and ensuring that study personnel are trained on risk management procedures. Risk management requires effective communication and collaboration among stakeholders, including sponsors, investigators, and regulatory authorities.
communication strategies, managing expectations, and addressing any concerns stakeholders may have. The project team should then develop a stakeholder engagement plan that outlines the communication strategy, roles, and responsibilities of each stakeholder, and the frequency and mode of communication. Regular meetings should be held with stakeholders to keep them informed of project progress, address any concerns they may have, and obtain feedback.
2. Patient recruitment and retention: Project management must develop strategies for patient recruitment and retention. This includes developing recruitment materials, identifying eligible patients, and implementing retention strategies to keep patients engaged in the study.
3. Change and performance management: Trial changes are common and need close management to ensure they are planned and implemented in a timely and regulatory compliant manner. This includes identifying potential impacts of changes, obtaining necessary approvals, and communicating changes to study personnel and stakeholders. Metrics such as key performance indicators (KPIs) can be utilised as a tool to quantify performance standards.
Project monitoring and control are critical aspects of clinical trial project management,
as they involve tracking and managing project progress, performance, and outcomes. The literature suggests that project monitoring and control should involve the regular collection and analysis of project data, including project timelines, budget, and quality metrics. To effectively monitor a clinical trial, project managers should consider the following practices:
Project management is responsible for ensuring that study data is accurate and reliable, utilising necessary technology to streamline study processes and improve efficiency and accuracy. Data management
should be overseen by the clinical project management, below are some tools utilised to ensure good oversight:
a. Data management plan: Develop a data management plan that outlines how study data will be collected, stored, and analysed.
b. Data standards: Ensure that data is collected and managed according to industry standards, such as CDISC or SDTM.
c. Data quality control: Implement data quality control procedures to ensure that data is accurate and reliable.
d. Technology integration: Integrate technology solutions, such as electronic data capture (EDC), interactive response technology (IRT) or clinical trial management systems (CTMS), to streamline study processes, utilise automation and improve efficiency.
e. Data security plans: Ensures that study data security is considered, and that access is restricted to authorized personnel only.
Regular site visits must occur in clinical trials, these ensuring that study procedures are being followed. The regular review of site’s processes ensures that good clinical practice is followed, and a good quality of data is being maintained. These visits can be a good way for the study team to keep the communication and site engagement positive; this should be closely overseen by project management.
All trial procedures must be conducted in accordance with the study protocol, regulatory requirements, and good clinical practice. This includes reviewing data quality, conducting site visits, and ensuring that study personnel are trained on study procedures. It is the responsibility of clinical project management to ensure these practices are followed on the trial.
Adverse events must be closely tracked, and when needed are reported according to regulatory requirements. This includes promptly reporting adverse events to the sponsor and the relevant regulatory authorities.
Trial closeout is the final stage of clinical trial project management. This stage encompasses the activities of finalizing study data, completing study documentation, and communicating the study completion with regulatory agencies. Project managers should consider the following practices when planning for trial closeout:
All study documentation must be properly archived and retained in accordance with regulatory requirements. Clinical project management must oversee this process and ensure that study records, case report forms
and other key documentation are archived in a timely manner.
Clinical project management must conduct a final review of study data and documentation to ensure that all study procedures were conducted in accordance with the study protocol and good clinical practice. This entails verifying that all data has been entered and verified, conducting a final site visit, and ensuring that all study personnel have completed their responsibilities.
A clinical study report is prepared for clinical trials which summarizes the study results and provides an overview of study conduct. This report should include information on study recruitment, data quality, adverse events, and other study metrics. Project management should closely manage the timeline associated with this, ensuring stakeholders such as medical writers are regularly communicated with.
Project management must ensure that study results are disseminated in accordance with regulatory requirements and best practices. The results of a study may be published in peer-reviewed journals, presented at scientific conferences, or provided in-full or in-summary to study participants and other stakeholders.
Literature suggests that effective clinical trial project management involves the development of a comprehensive project plan, ongoing risk management, effective stakeholder engagement, high-performing team management, and regular project monitoring and control. Through utilising the PMI’s 5-stage approach, an effective lifecycle can be formed for project managing clinical trials. By considering these practices in the planning of their own studies, clinical trial project managers can ensure good oversight of their clinical trials, which leads to safe and efficient studies.
References are available at www.pharmafocuseurope.com
Joab Williamson is the Director, Program Management at Faron Pharmaceuticals, a clinical stage biopharmaceutical company developing novel treatments for medical conditions with significant unmet needs caused by dysfunction of our immune system. He has a range of experience in pharmaceutical project management, holds an MBA and is a certified lean six sigma black belt.
High affinity of a compound for red blood cells can lead to its sequestration in the cells but this property of certain drugs may be overlooked in the process of drug development. This article will illustrate that measuring drug concentration in both compartments - plasma and red blood cells is required for accurate pharmacokinetic assessment of drugs.
Pallavi Limaye Director of Consulting, BioIVT Steve McGreal Senior Scientist, BioIVTThe blood-to-plasma ratio (B/P ratio or R b) is an important parameter in pharmacokinetics (PK) that reflects the distribution of a drug between red blood cells (RBCs) and plasma. It is defined as the ratio of the concentration of a drug in whole blood (CB) to the concentration of the drug in plasma (CP).
Drugs that have a B/P ratio greater than 1 tend to accumulate in RBCs. Approximately 55% of the whole blood is composed of plasma and the remaining 45% consists of various blood cells including RBCs, white blood cells, and platelets. When a drug reaches the bloodstream, it can either reside in plasma or it can partition into blood cells. Among the blood cells, RBCs take up approximately 99% of cellular space and therefore RBCs are the only major compartment in blood to be accounted for apart from plasma for PK assessment. However, as a standard practice, drug concentration is typically only measured in plasma. For a drug with a high B/P ratio, PK parameters derived solely from the drug concentration in plasma may be misleading because the portion of the drug that is sequestered in RBCs is not accounted for. The intrinsic clearance of drugs with high B/P ratio, which has been estimated only based on drug concentrations in plasma, may significantly overestimate the volume of distribution and overall blood clearance. This may also lead to calculated clearance values exceeding the hepatic blood flow. For drugs with a high affinity for RBCs, the drug concentration in the whole blood will be a more accurate measurement for PK studies. In reality, drugs with a high B/P ratio may have a longer half-life due to slow release from blood cells, affecting dosing regimens and the time to achieve steady-state concentrations. Hence, it is of critical importance to decipher the distribution of a drug in plasma vs. RBCs for appropriate estimation of a drug’s PK.
Drugs can partition into RBCs in several different ways. Lipophilic organic compounds will dissolve in the lipid bilayer of the membrane and enter the RBCs. Small hydrophilic compounds can enter by diffusion. Transporters or carrier proteins can also play a role in a drug’s transport into RBCs. As a general rule, nonionized and basic drugs tend to distribute evenly between plasma and RBCs with Rb values close to 1, whereas zwitterionic and acidic drugs tend to be excluded from RBCs with Rb values of roughly 0.55 (1-hematocrit). The B/P ratio can vary greatly among different drugs depending on their physicochemical properties, binding affinity of a drug to plasma proteins, and the health status of the individual.
Understanding the drug’s partitioning in RBCs, kinetics of reversibility of such partitioning, and any species-specific differences early in the process of drug development are very useful in determining whether the PK should be estimated using the B/P ratio, selecting the right matrix for routine PK sampling if needed, confirming toxicology species selection, dosing interval determination, and so on. An additional aspect of the B/P ratio determination is its inclusion in the calculations recommended by regulatory agencies that help with the in vitro to in vivo prediction of potential clinical drug-drug interactions (DDI) among co-medications.
There are several examples of drugs that are approved for use despite their high B/P ratios that are discussed in more detail below. Certain drugs, in fact, have leveraged RBCs as
a vascular carrier for targeted drug delivery. Overall, the B/P ratio turns out be an important parameter influencing PK behavior, efficacy, toxicity, and DDI potential, unintentionally or intentionally.
The extent of distribution of a drug into RBCs can be determined in vitro by incubating the drug with fresh whole blood. Prior to the procedure, whole blood is collected in tubes with the anticoagulant K2EDTA and an aliquot is pre-warmed in a water bath at 37 °C. The test samples are incubated with the drug candidate at various concentrations for the designated time period (e.g., 60 minutes). At the end of the incubation, an aliquot is collected and transferred to a tube containing water and immediately added to the precipitating organic solution (e.g. 70:30 v/v acetonitrile:methanol) containing the appropriate internal standard. The remaining whole blood incubation is centrifuged to separate the plasma. After centrifugation, an aliquot of plasma is transferred to a tube containing water and added to the precipitating organic solution (e.g. 70:30 v/v acetonitrile:methanol) containing the appropriate internal standard as in the previous step. All blood and plasma samples are then centrifuged to separate the precipitated protein and the supernatants are analyzed by LC-MS/ MS to quantitate analyte/internal standard area ratios. Simultaneously, zero-time blood and plasma samples are subjected to the same
procedure as the test samples. In addition, incubations with a high affinity control like chloroquine and a low affinity control like metoprolol are also included in the assay. Hematocrit is also measured by adding an aliquot of whole blood into a capillary tube and centrifuging it. Upon the collection of LC-MS/MS based concentration data, the drug’s metabolic stability and B/P ratio are calculated as shown in Figure 1.
B/P ratio can be measured using this procedure in various animal species such as mouse, rat, dog, monkey, etc., in addition to human. The choice of the species can be determined based on the potential nonclinical toxicology study species and would be beneficial to determine any species-specific differences in PK owing to R b differences. Typically, the assay is conducted using a range of concentrations of the candidate drug (at least three concentrations) to determine potential
Overall, the B/P ratio turns out be an important parameter influencing PK behavior, efficacy, toxicity, and DDI potential, unintentionally or intentionally.
saturation phenomena. Stability assessment over the incubation time determines whether the drug candidate is metabolized or degraded enzymatically or non-enzymatically in whole blood. Generally, the extent of cellular drug partitioning in blood observed in vitro is similar to in vivo. However, there may be instances with drugs such as metformin where the in vivo system may be very different from the values obtained in vitro when repartitioning from blood cells is far slower than clearance of drug in plasma.
Understanding the drug’s partitioning into RBCs is also beneficial to determine the appropriate matrix for PK i.e., whole blood vs. plasma or inclusion of stabilizers such as inhibitors of hydrolases or glutathione to scavenge free radicals in the plasma. RBC partitioning may vary for certain drugs depending on the temperature. Reports
have indicated that the RBC partitioning is not affected if the assay is run at 4 0C for most drugs. However, for drugs that have high affinity to RBC components such as carbonic anhydrase, the 4°C approach may underestimate R b values and alternative approaches should be used. In these situations, using appropriate inhibitors to minimize instability issues can be considered. RBC partitioning may also vary as a result of pH-dependent binding of a drug to plasma proteins and/or RBCs. In these situations, and other instances where the limit of quantitation is not low enough to quantitate a drug in plasma due to the majority of the drug being in RBCs, whole blood may be a rational choice of matrix for PK quantitation.
R b can be applied to convert drug PK parameters in plasma to the respective parameters in whole blood and to develop in vitro–in vivo correlations for DDI
determination. The equation Rb = CB/CP can be rearranged as C B = C P x R b in order to determine the whole blood concentration of a drug. In the static model used to predict clinical potential of a drug for interactions with hepatic uptake transporters such as organic anion transporting peptide (OATP) 1B1, OATP1B3, and organic cation transporter, the relevant in vivo concentration is the unbound maximum hepatic inlet concentration (Iin,max,u) of an orally administered test drug. The three regulatory agencies, the United States Food and Drug Administration, European Medicines Agency, and Japan’s Pharmaceuticals and Medical Devices Agency, define the Iin,max,u with some nuances, however, with the use of Rb, all these values of unbound maximum hepatic inlet concentration are the same.
There are several drugs reported in the literature that exhibit R b greater than 1, indicating higher distribution to RBCs compared to plasma. They include sirolimus, everolimus, rivaroxaban, dorzolamide, metformin, gemcitabine, indapamide, acetazolamide, methazolamide, doxycycline, topiramate, doxorubicin, chlorthalidone, phenprobamate, romifidine, cyclosporine, quinidine, just to list a few.
Understanding the mechanistic aspects such as linearity of RBC partitioning and kinetics of reversibility of RBC partitioning is important before using Rb to interconvert
values of drug concentration in blood and plasma and/or clearance from blood and plasma. The following examples illustrate these differences.
Tacrolimus, an immunosuppressive, is one of the drugs that exhibits a high B/P ratio. In liver transplant patients, Rb may range from 13 to 114. There is significant variation in the plasma vs. whole blood clearance (CL) and volume of distribution (Vd). CL and Vd were much higher based on plasma concentrations (CL=1.7 L/h/kg; Vd= 30 L/kg) compared to whole blood (CL=54 ml/h/kg; Vd= 0.9 L/ kg). Binding to RBCs affects the disposition of tacrolimus, and plasma concentrations are indirectly and inversely related to it. In this case, nonlinear binding of tacrolimus to RBCs was found to be a major source of interpatient variation in the disposition of the drug. Tacrolimus has a narrow therapeutic index and therefore, to optimize dosing, therapeutic drug monitoring is required on the basis of systemic exposure that is typically measured in whole blood.
Some diuretic drugs such as chlorthalidone, dorzolamide, and methazolamide exhibit a high Rb. These drugs bind with high affinity to carbonic anhydrase, an abundant enzyme in RBCs. These drugs have Rb values ranging from 30 to 240, but they do not move freely between the RBCs and plasma. Hence, for these drugs—whose movement out of RBCs is restricted—it is their concentration in plasma that is relevant to their potential to cause drug interactions.
High Rb is beneficial for drugs that have RBCs as their targeted site of action, e.g., antimalarial drugs. Accumulation of the drug in RBCs increases its half-life and consequently increases the efficacy of the drug. Anti-malarial drugs such as chloroquine, amodiaquine, mefloquine, piperaquine, proguanil, quinine have Rb greater than 1.
Another interesting upcoming advance in drug development is RBC-based drug carriers. RBCs represent attractive natural carriers that can be loaded with a drug of interest either in vitro or ex vivo. Recent studies showed that RBC-based drug carriers indeed may feature
unique PK and biodistribution characteristics favorably changing the benefit/risk ratio of some cargo agents.
RBCs occupy approximately 45% of the blood space and distribution of a drug to RBCs impacts the overall PK of a drug. Therefore, it is imperative that Rb is measured in routine PK analysis. Rb and an understanding of the extent and reversibility of RBC partitioning of a drug are also necessary to delve effectively into intricate PK-based decision making.
References are available at www.pharmafocuseurope.com
Pallavi
a Director of Consulting at BioIVT and advises clients on in vitro ADME-DDI. She received her PhD in toxicology from The University of Louisiana. She has published various research articles, contributed to several book chapters, and is actively involved in scientific societies.
Steve McGreal,
is a Senior Scientist at BioIVT and serves as a study director for multiple non-clinical DDI related contract studies. He received his PhD in toxicology from the University of Kansas. He has published various research articles.
Clinical trials are scientific studies that test the safety and effectiveness of new treatments or interventions in human volunteers. Their purpose is to determine if these innovations are safe, effective, and superior to existing options. Conducted in multiple phases, each phase has specific objectives to address safety and effectiveness concerns. Successful clinical trial results can lead to regulatory approval for widespread use in the general population.
Josipa Ljubicic QA Director Principal GCP and GVP Auditor Proqlea LtdClinical trials are an essential part of the development of new medical treatments and procedures, and they help to ensure that new interventions are safe and effective for patients above all. Because of that, it is really important that all steps in that process were done with special attention to patient safety above all, but also to the
accuracy of data that is often still collected manually, which slows down the entire process, and sometimes even compromises it, which result with the fact that clinical trials are exposed to frequent audits precisely for this reason.
Although clinical trials are essential for advancing medical knowledge and improving
patient outcomes, there are several challenges associated with the current approach to conducting trials, including high costs, slow recruitment, lack of diversity, regulatory
Clinical trials are conducted according to a rigorous and well-defined protocol, which outlines the objectives, methodology, and criteria for participant selection and data collection. The protocol is designed to ensure that the study is conducted in a standardized and consistent manner and that the results are reliable and valid.
Today, clinical trials are typically performed using a combination of traditional methods and cutting-edge technology.
The following are some of the key steps involved in conducting a clinical trial:
1. Study Design: The first step in conducting a clinical trial is to design the study. This involves determining the objectives of the study, selecting the appropriate patient population, and defining the study endpoints.
2. Participant Recruitment : Once the study design is finalized, the next step is to recruit participants. This is typically done by contacting potential participants directly or through healthcare providers.
3. Informed Consent: Before participants can enroll in the study, they must provide informed consent. This involves providing participants with information about the study, including the risks and benefits of participation, and giving them the opportunity to ask questions.
burden, data management challenges, and lack of transparency. Addressing these challenges will be essential for improving the efficiency and effectiveness of clinical trials in the future.
4. Data Collection: During the study, data is collected on each participant, including their medical history, test results, and other relevant information. This data is collected using a variety of methods, including interviews, physical exams, and laboratory tests.
5. Data Analysis: Once the study is complete, the data is analyzed to determine the safety and effectiveness of the intervention being studied. This involves using statistical methods to analyze the data and determine if the intervention is safe and effective.
6. Regulatory Approval: If the intervention is found to be safe and effective, it may be submitted to regulatory agencies for approval. The regulatory agencies review the data and determine if the intervention can be approved for use in the general population.
Overall, clinical trials are a complex and highly regulated process that requires careful planning, execution, and analysis to ensure that the results are reliable and valid. The use of cutting-edge technology, such as electronic data capture and telemedicine, has helped to streamline the clinical trial process and improve the quality of the data collected.
The healthcare industry has seen a significant amount of technological innovation in recent years. Some of the most notable technological innovations include:
• Electronic Health Records (EHRs): EHRs are digital versions of patient medical records that can be accessed by healthcare providers anywhere, anytime. This technology has revolutionized the way patient data is stored, shared, and analyzed, leading to better patient care and improved outcomes.
• Telemedicine: Telemedicine allows healthcare providers to remotely diagnose and treat patients using video conferencing technology. This technology has become particularly important during the COVID19 pandemic, enabling patients to receive medical care from the safety of their homes.
• Wearable Technology: Wearable technology, such as smartwatches and fitness trackers, has become increasingly popular in recent years. These devices can monitor various aspects of a person's health, including heart rate, activity level, and sleep patterns, allowing individuals to better manage their health and wellness.
• Artificial Intelligence (AI): AI technology is being used in healthcare to analyze large amounts of data, identify patterns, and make predictions. This technology has the potential to improve the accuracy of diagnoses and treatment
plans, leading to better patient outcomes.
• 3D Printing: 3D printing technology is being used to create customized medical implants, prosthetics, and other devices. This technology has the potential to significantly improve patient outcomes by providing personalized medical solutions.
• Blockchain: Blockchain technology is being used in healthcare to improve the security and privacy of patient data. This technology allows patients to control who has access to their medical records, improving data security and privacy.
Technology innovation in healthcare has the potential to significantly improve patient outcomes by providing better access to medical care, improving data analysis, and providing personalized medical solutions.
The future of clinical trials and technology innovations is likely to be shaped by a variety of factors, including advances in data science, artificial intelligence (AI), mobile technology, and wearable devices. Some potential trends in this area include Virtual and Decentralized Trials, AI and Predictive Analytics, Wearable Devices and Remote Monitoring, Blockchain Technology and Personalized Medicine.
As the world becomes increasingly connected, and patients become more comfortable with digital communication, virtual and decentralized clinical trials are likely to become more common. These types of trials can be more convenient for patients,
allow for real-time data collection, and reduce costs associated with site-based trials.
AI and predictive analytics have the potential to revolutionize clinical trials by enabling researchers to identify the most promising drug candidates and patient populations more quickly and accurately. By analyzing vast amounts of data from electronic health records, genomic databases, and other sources. AI algorithms can help researchers make more informed decisions about trial design and patient recruitment.
Wearable devices and remote monitoring technologies are already being used in some clinical trials to collect real-time data on patient health and behavior. As these technologies become more sophisticated and affordable, they could become even more
important for improving patient outcomes and reducing the costs of clinical trials.
Blockchain technology has the potential to improve the transparency and security of clinical trial data by creating a tamper-proof record of all transactions. This could help to prevent fraud and ensure that trial data is accurate and reliable.
As researchers gain a better understanding of the genetic and molecular basis of diseases, personalized medicine is likely to become more common. By tailoring treatments to individual patients based on their genetic makeup and other factors, researchers can potentially improve treatment efficacy and reduce side effects.
While technology innovations in clinical trials have the potential to improve efficiency, accuracy, and patient outcomes, they also present several challenges that must be addressed. Some of the biggest challenges of tech innovations on trials include Data Security, Patient Privacy or Data Quality.
With the increased use of technology in clinical trials, there is a greater risk of data breaches and cyber-attacks. It is essential to ensure that patient data is secure and protected from unauthorized access. The use of technology in clinical trials can make it difficult to protect patient privacy, especially with the increased use of remote monitoring and telemedicine. It is essential to ensure that patient privacy is protected throughout the entire trial process. Also, the use of technology can result in large amounts
of data being collected, which can make it difficult to ensure that the data is accurate and reliable. It is important to ensure that data quality is maintained throughout the trial process.
Besides all standard challenges, there are more of them that will impact the whole clinical trial process; Technical Difficulties and Regulatory Compliance.
Technology failures can significantly impact the efficiency of clinical trials, leading to delays and increased costs. It is important to ensure that the technology used in clinical trials is reliable and well-maintained. The use of technology in clinical trials may require additional regulatory compliance measures to ensure that the data collected is valid and reliable. It is essential to ensure that all regulatory requirements are met when using technology in clinical trials.
While technology innovations in clinical trials offer significant benefits, they also present challenges that must be addressed to ensure the success of the trial and the safety of the patients involved. Addressing these challenges requires collaboration between stakeholders in the healthcare industry, including regulators, sponsors, and technology providers.
Over the years of performing trials all over the globe, and talking with people and healthcare professionals, I concluded that one of the primary problems that arise all over the
world is the fact that clinical trials are still a taboo topic among people. Trials are not easily reachable to the people, and there are no options to be informed about available clinical trials that would be of use and interest to the specific population.
The popularization of clinical trials is an important issue, as it can increase awareness of the importance of clinical trials and encourage more people to participate. There are several ways in which the popularization of clinical trials can be achieved: Public Education, Patient Involvement, Community Outreach, Collaboration and Simplification. All those steps were followed by some sort of technology tool and this is precisely why technological progress is necessary. One up-to-date application could make people to be informed in a timely manner and the entire procedure will become accessible to them. They will be able to apply for the trial of their choice and to pass the selection of inclusion and exclusion criteria. This certainly saves both, time and money, for the sponsors.
Also, there is an impact on the sponsor and/or auditor, who has visibly facilitated access to the data, and therefore in some cases, on-site visits are not necessary. That will also have an impact on cost reduction process. Technology applications in clinical trials are developed by a variety of stakeholders, including pharmaceutical and biotech companies, contract research organizations (CROs), academic research institutions, and technology companies.
Academic research institutions may develop technology applications for clinical trials as part of their research programs, with the goal of improving patient outcomes and advancing scientific knowledge. Technology companies may also develop technology applications specifically for use in clinical trials. These companies may specialize in areas such as electronic data capture, wearables, or telemedicine, and may work with pharmaceutical and biotech companies or CROs to integrate their technology into the clinical trial process. The development of technology applications for clinical trials is a collaborative effort that improves the clinical trial process and advances medical knowledge.
However, the popularization of clinical trials requires a multi-faceted approach that involves collaboration between stakeholders in the healthcare industry and education and outreach efforts to increase awareness and encourage participation. Technology applications in clinical trials offer significant benefits, including improved efficiency, accuracy, and patient outcomes.
Overall, the future of clinical trials and technological innovation is likely to be characterized by increased digitization, automation, and data-driven decision-making. By leveraging these technologies, researchers can potentially speed up the drug development process, improve patient outcomes, and reduce healthcare costs. The future of clinical trials is likely to be shaped by advances in technology, a greater focus on patient-centricity, and a more collaborative and innovative approach to trial design and implementation. In a very short period of time, it seems that every industry, including healthcare, will follow the lead of technological improvement and this will become an inevitable path in the development of innovative drugs. It will become inevitable to follow trends in tech development that ultimately improve every process today, including the process of clinical trials.
The biopharmaceutical industry operates under strict regulations, and effective supply chain management is crucial for patient safety and timely access to treatments. The biopharmaceutical supply chain is responsible for ensuring efficient and secure delivery of drugs and vaccines while maintaining product quality. Challenges such as compliance, risk management, and technology adoption have emerged in recent years. Cold chain management, monitoring technologies, and new regulations like DSCSA and FMD have impacted the industry. Adhering to trends and regulations, including blockchain and artificial intelligence, can improve transparency, traceability, and efficiency in supply chain management, ultimately ensuring the safe and prompt delivery of life-saving treatments to patients.
Hassan Mostafa Mohamed Chairman & Chief Executive Officer ReyadaProThe biopharmaceutical industry is a highly regulated and complex industry, where the timely and safe delivery of life-saving treatments to patients is of utmost importance. To ensure patient safety and product quality, the industry must adhere to strict regulations and guidelines
governing the manufacturing, distribution, and supply of biopharmaceutical products. In recent years, there have been several trends and regulatory changes that have impacted the bio-pharmaceutical supply chain. This article provides an overview of these trends and regulations and their implications for bio-pharmaceutical supply chain management.
Bio-Pharmaceutical Supply Chain New Regulations and Challenges and Trends
Raw Materials
Suppliers
The Bio-Pharmaceutical Supply Chain involves a complex network of suppliers, manufacturers, distributors, and regulatory bodies. This complexity can create challenges in managing the supply chain and ensuring the quality of the final product. Hence, Bio Pharmaceutical Supply Chain is subject to a range of challenges, including those related to regulatory compliance, quality control, supply chain visibility, and risk
Raw Materials
Raw Materials
Finished product
Manufacturing
Packaging
Distribution
Distribution & Dispensing Logistics Sourcing
Distributor
Hospitals/clinics/ Pharmacies
Dispensing
Patients
management. However, by implementing effective strategies and technologies, companies can overcome these challenges and ensure the safe and timely delivery of life-saving medications to patients around the world. Examples of such strategies and technologies include quality control measures, real-time monitoring systems, risk management strategies, and collaborative planning processes. By prioritizing effective supply chain management, bio-pharmaceutical companies can continue to fulfill their mission of improving human health and well-being.
In recent years, there have been several new regulations introduced that impact the biopharmaceutical supply chain. One of the most significant regulatory changes is the Drug Supply Chain Security Act (DSCSA) in the United States. The DSCSA is designed to enhance the security and traceability
of pharmaceutical products by requiring the use of unique product identifiers and serialization. Under the DSCSA, all pharmaceutical products must have a unique product identifier, which enables traceability of the product throughout the supply chain. This includes the ability to track the product from the manufacturer to the dispenser and to verify the authenticity of the product at each stage of the supply chain.
Another regulatory change that impacts the bio-pharmaceutical supply chain is the Falsified Medicines Directive (FMD) in the European Union. The FMD is designed to prevent the entry of falsified medicines into the supply chain by requiring the use of serialization and track-and-trace technologies. Under the FMD, all prescription medicines must have a unique identifier, which enables traceability of the product throughout the supply chain.
This includes the ability to track the product from the manufacturer to the patient and to verify the authenticity of the product at each stage of the supply chain.
To comply with the DSCSA, companies must implement serialization and trackand-trace technologies that enable them to capture and exchange data about drug products throughout the supply chain. This includes investing in systems for printing and verifying unique identifiers, as well as software solutions for managing serialization data and communicating with supply chain partners.
Falsified Medicines Directive (FMD)European Union the FMD requires that all prescription drug products in the European Union be serialized and tracked through the supply chain using a unique identifier by February 9, 2019. The Falsified Medicines Directive (FMD) is aimed at preventing the entry of falsified medicines into the supply chain. The law requires pharmaceutical companies to place a unique identifier on the packaging of prescription drugs, which can be scanned at various points in the supply chain to verify the authenticity of the product. The law also requires the use of tamper-evident packaging and the reporting of suspect products. This information must be maintained for at least five years and be readily retrievable upon request by the authorities.
Both the DSCSA and the FMD have had a significant impact on the bio-pharmaceutical supply chain, requiring companies to invest in new technology and processes to comply with the law. While the regulations have increased the security and safety of the supply chain, they have also increased the cost and complexity of doing business. As a result, companies are looking for new ways to streamline their supply chain processes and reduce costs, such as using automation and digitalization.
Bio-Pharmaceutical Supply Chain also faces several other challenges. One of the biggest
challenges is ensuring product integrity throughout the supply chain. Recently, there has been a growing trend of outsourcing, and the need for effective technology adoption. By staying up-to-date with the latest trends and regulations, and by implementing effective supply chain strategies and technologies, biopharmaceutical companies can ensure the safe and timely delivery of life-saving treatments to patients around the world, while also improving supply chain efficiency and reducing costs.
The Bio-Pharmaceutical Supply Chain is subject to a wide range of regulatory requirements, including those related to product safety, quality, and efficacy. Failure to comply with these regulations can result in significant financial and reputational damage.
An example of this challenge is the need to comply with strict transportation and storage requirements for temperature-sensitive products, such as vaccines and biologics. These products require precise temperature control throughout the supply chain to maintain their efficacy and safety.
the bio-pharmaceutical supply chain should ensure the quality of raw materials used in the manufacturing process. Poor quality materials can lead to quality issues in the final product, resulting in regulatory sanctions, product recalls, and harm to patients.
The Bio-Pharmaceutical Supply Chain requires visibility and transparency throughout the supply chain to ensure the timely delivery of products, minimize the risk of stockouts, and optimize inventory levels. However, achieving this visibility can be challenging due to the complex nature of the supply chain and the need to protect patient privacy.
The Bio-Pharmaceutical Supply Chain is subject to a wide range of risks, including those related to supplier quality, transportation, and regulatory compliance. Effective risk management is essential to ensure the continuity of the supply chain and prevent disruptions.
Using temperature-controlled packaging and transportation:
to maintain the required temperature range during shipping. These packages are designed to maintain a specific temperature range for a set period, often using insulated materials and gel packs or dry ice. In addition to packaging, many companies also use temperature monitoring technologies, such as data loggers or real-time monitoring systems, to ensure that products are kept within the required temperature range throughout the supply chain.
The risk of counterfeiting and product diversion:
Counterfeit drugs can pose serious health risks to patients and undermine public trust in the pharmaceutical industry. To address this challenge, many companies use to track and trace technologies, such as serial numbers or barcodes, to monitor the movement of products through the supply chain and verify their authenticity. To further improve supply chain security, some companies are also exploring the use of blockchain technology. Blockchain can provide a secure and transparent record of the movement of products through the supply chain, making it easier to detect and prevent counterfeiting and diversion.
By prioritizing supply chain management and implementing best practices and technologies, biopharmaceutical companies can ensure the safe and timely delivery of life-saving treatments to patients around the world.
Looking to the future, the bio-pharmaceutical supply chain will continue to evolve, with new technologies and regulations shaping the industry. For example, the adoption of new gene and cell therapies may require new supply chain strategies and technologies to ensure their safe and effective delivery. Similarly, the growing focus on sustainability may require biopharmaceutical companies to develop more environmentallyfriendly supply chain processes.
The use of temperature-controlled packaging and transportation, along with monitoring technologies, is crucial to ensuring
the integrity of temperature-sensitive products in the bio-pharmaceutical supply chain. These technologies help to ensure that temperaturesensitive medications are transported and stored within the desired temperature range, thus maintaining their efficacy and safety and monitoring temperature conditions to identify potential issues before they result in product spoilage.
• Temperature-controlled packaging includes various types of insulated containers and packaging materials that are designed to maintain a specific temperature range for a specified duration during transportation and storage.
• Temperature-controlled transportation involves the use of specialized vehicles, such as refrigerated trucks or cargo planes, to transport temperature-sensitive medications.
• Monitoring technologies, such as temperature data loggers, real-time
temperature monitoring systems, and GPS tracking, are used to monitor temperaturesensitive medications throughout the supply chain. These technologies provide real-time information on the temperature and location of the medication, allowing supply chain partners to take immediate action in the event of a temperature excursion.
• Product Integrity: These technologies help to maintain the desired temperature range and prevent temperature excursions, thus maintaining the integrity and efficacy of the medication.
• Customer Confidence: Customers, including patients, healthcare providers, and distributors, have greater confidence in temperature-sensitive medications when they know that these medications have been transported and stored within the desired temperature range.
• Cost-Effectiveness: Effective cold chain management can help to reduce costs in the bio-pharmaceutical industry by minimizing product waste and optimizing inventory levels.
In conclusion, the use of temperaturecontrolled packaging and transportation, along with monitoring technologies, is essential to ensuring product integrity in the bio-pharmaceutical supply chain.
In recent years, there has been a growing trend of outsourcing in the industry, as companies seek to reduce costs, improve efficiency, and
Maintaining a secure and efficient biopharmaceutical supply chain is crucial to ensure the availability and quality of life-saving medications.
focus on their core competencies. This trend has significant implications for supply chain management, as outsourcing can impact the entire supply chain, from sourcing raw materials to delivering finished products to customers.
Outsourcing can offer several benefits to biopharmaceutical companies, including cost savings, access to specialized expertise, and increased flexibility. However, outsourcing also poses several challenges to supply chain management, including the need for effective supplier management and risk mitigation strategies.
To mitigate these risks, biopharmaceutical companies must establish robust supplier qualification and selection processes, as well as implement effective supplier performance monitoring and risk management strategies.
Companies need to ensure that they have strong contracts and non-disclosure agreements in place with their vendors to protect their valuable intellectual property.
Companies need to ensure that their vendors are compliant with all relevant regulations, particularly in the areas of manufacturing and distribution. Failure to comply with regulations can result in significant fines or even product recalls.
To overcome these challenges, companies need to develop robust supply chain management strategies that consider the specific risks and challenges of outsourcing.
Quality
When outsourcing manufacturing or other supply chain activities, it can be difficult to maintain consistent quality across different vendors. This can be particularly challenging when working with vendors located in different countries with different regulatory environments and quality standards.
Intellectual
Outsourcing can increase the risk of intellectual property theft or infringement.
The bio-pharmaceutical supply chain is a complex network that involves multiple stakeholders, including manufacturers, distributors, wholesalers, pharmacies, and healthcare providers.
Blockchain is one technology that has gained significant attention. It is a distributed ledger technology that enables secure, transparent, and tamper-proof recordkeeping. In the bio-pharmaceutical supply chain, blockchain can be used to improve transparency and traceability by enabling the secure sharing of data among supply chain partners. For example, blockchain can be used to track and record the movement
of pharmaceutical products from the manufacturer to the patient, providing a secure and auditable record of the product's journey.
Artificial Intelligence (AI) is another technology that has gained attention in bio-pharmaceutical supply chain management. AI can be used to improve supply chain efficiency and decision-making by analyzing vast amounts of data and identifying patterns and trends. For example, AI can be used to optimize inventory levels, predict demand, and identify supply chain bottlenecks.
However, the implementation of new technologies such as blockchain and AI can also pose challenges to supply chain
management. For example, the integration of new technologies with existing systems and processes can be complex and may require significant investments in IT infrastructure and personnel training.
Another technology that can play a role in bio pharmaceutical supply chain management is artificial intelligence (AI). AI can be used to analyze vast amounts of data and identify patterns and insights that can help to improve supply chain efficiency and reduce the risk of disruptions. For example, AI can be used to monitor inventory levels, predict demand, and optimize logistics routes. By providing real-time insights, AI can help to improve the responsiveness of the supply chain and reduce the risk of stockouts and delays.
While blockchain and AI have the potential to improve the bio pharmaceutical supply chain, there are also challenges and limitations that must be considered. One of the main challenges is the need for interoperability and standardization across different stakeholders in the supply chain. For blockchain to be effective, all parties must be able to access and contribute to the shared ledger. This requires the development of common standards and protocols, which can be difficult to achieve in a highly regulated industry.
In terms of regulation, there are currently no specific regulations governing the use of blockchain and AI in the bio-pharmaceutical supply chain. However, companies must comply with existing regulations governing data privacy, security, and quality assurance.
For example, companies must ensure that their blockchain platforms are compliant with the General Data Protection Regulation (GDPR) in the European Union, which mandates strict rules for the collection, use, and storage of personal data. Similarly, companies must comply with the FDA's current good manufacturing practice (cGMP) regulations, which require that drugs be manufactured in accordance with strict quality standards.
For example, a biopharmaceutical company could use predictive analytics to analyze data on supplier performance, shipping times, and other relevant factors to identify potential delays or issues that could impact the supply chain. This information could then be used to
take proactive measures to prevent or mitigate any potential disruptions.
In addition to AI, blockchain technology is also being explored as a potential solution for supply chain management in the biopharmaceutical industry. Blockchain is a distributed ledger technology that allows for secure, transparent, and immutable recordkeeping. This can be particularly useful in the biopharmaceutical supply chain, where traceability and authenticity of products are critical.
Overall, the use of technology in biopharmaceutical supply chain management is becoming increasingly important as the industry continues to grow and evolve. By leveraging AI, blockchain, and other advanced technologies, biopharmaceutical companies can improve efficiency, transparency, and traceability in their supply chains, leading to better patient outcomes.
The importance of staying up-to-date with the latest trends and regulations in bio-pharmaceutical supply chain management, to ensure the safe and timely delivery of life-saving treatments to patients.
Staying up-to-date with the latest trends and regulations in bio pharmaceutical supply chain management is crucial for ensuring the safe and timely delivery of life-saving treatments to patients. The rapidly evolving nature of the industry means that new challenges and opportunities are constantly emerging, and those who can adapt quickly are more likely to succeed.
Effective supply chain management requires a deep understanding of both the regulatory environment and the latest technological advancements. As we have seen, new regulations such as the DSCSA and FMD have created a need for greater transparency and traceability in the supply chain and have led to the adoption of new serialization and trackand-trace technologies. In addition, the growing trend of outsourcing has made it increasingly important to have robust supplier management and risk mitigation strategies in place.
Staying up-to-date with the latest trends and regulations requires ongoing education and training, as well as a willingness to embrace new technologies and approaches. Companies that prioritize these areas are more likely to remain competitive and successful in the rapidly evolving bio-pharmaceutical industry.
In conclusion, the bio-pharmaceutical supply chain is a complex and rapidly evolving ecosystem, with numerous challenges and opportunities for those involved. Bio-Pharmaceutical Companies must stay up-to-date with the latest trends and regulations in supply chain management and implement effective supply chain strategies and technologies. This includes the establishment of robust cold chain management processes, the implementation of serialization and trackand-trace technologies, effective supplier
management and risk mitigation strategies, and the integration of recent technologies such as block chain and AI. Staying up-todate with the latest trends and regulations, and leveraging the latest technological advancements, is crucial for ensuring the safe and timely delivery of life-saving treatments to patients around the world.
References are available at www.pharmafocuseurope.com
an entrepreneurial and growthdriven executive with more than 25 years of experience in pharmaceutical industries, including, Technical, sales & marketing, Production, Supply chain, Engineering/utilities, Quality, and regulatory issues. Mr Hassan is an expert in driving pharmaceutical facilities to accomplish corporate goals, building and leading technical & quality aspects with market consideration for rapid growth, and efficient operational excellence.
Pharma 4.0, an application of Industry 4.0 concepts in the pharmaceutical industry, aims to enhance manufacturing efficiency, product quality, and consistency. It faces challenges in areas like artificial intelligence, material traceability, optimization, process control, cyber-physical security, and data management due to the complexity involved. However, by incorporating artificial intelligence and advanced model predictive control with robust cyberphysical security measures, predictive capabilities and product quality can be significantly improved. This work focuses on implementing seven Industry 4.0 components, including AI/ML, modeling, material traceability, optimization, advanced control, cyber-physical security, and data science, in continuous pharmaceutical manufacturing processes.
Ravendra Singh C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey USACurrently, Industry 4.0 concepts are being applied to the pharmaceutical industry to achieve Pharma 4.0 paradigm. It is promoted by industries, academia and regulators. Pharma 4.0 reduces the time and resources needed for continuous pharmaceutical manufacturing and improves product quality and production consistency. It has many advantages but also have bigger challenges on the applications
of artificial intelligence (AI)/machine learning (ML), material traceability, optimization, advanced process control, cyber-physical security, and data management side because of the different levels of complexities involved. The predictive capabilities and the quality of the pharmaceutical products can be improved
is needed. This is direct compaction line that have three levels. The feeders are placed at top level to feed API and excipients. The co-mill and blender are placed in middle level. The API and excipient streams passing through the co-mill before entering to the bledner. The co-mill is used for delumping purposes. The significantly via employing the artificial intelligence and the advanced model predictive control (MPC) system if an appropriate cyber-physical security defense is in place.
In this work, seven components of industry 4.0 namely artificial intelligence (AI)/machine learning (ML), modelling, optimization, advanced control, material traceability, cyber-physical security, and data science have been developed for the continuous pharmaceutical manufacturing process.
A continuous direct compaction tablet manufacturing pilot plant has been developed, situated at C-SOPS, Rutgers University, NJ, USA (Figure 1). This is vertical design of the plant that take advantage of gravitational material flow and therefore no convayer belt
lubricant feeder is also placed at middle level. The lubricant is directly fed to the blender without passing through the co-mill to avoid the over lubrication. The tablet press is placed on ground floor. A chute is placed in between blender and tablet press. The PAT sensor is integrated with the plant via chute interface. The pharma 4.0 related tools are shown in Figure 1.
There are different methods and tools useful to achieve pharma 4.0 paradigm. Artificial intelligence (AI)/machine learning (ML), modelling, optimization, advanced control, material traceability, cyber-physical security, and data science have been developed for the continuous pharmaceutical manufacturing process.
AI/ML algorithms have been highly successful in solving complex problems for many industries to predict the process response accurately. The pharmaceutical industry has yet to see the potential impact of a machine learning based self-trained process for efficient manufacturing of high-quality products. There are different AI approaches that may have potential applications in continuous pharmaceutical manufacturing. However, the
identification of best performing AI method for continuous pharmaceutical manufacturing process is still a challenging task. In this work, four machine learning (ML) models have been trained to predict the response of continuous pharmaceutical manufacturing process and the performance of these ML models has been compared. The investigated ML methods are artificial neural network (ANN), 1D convolution neural network (CNN), long short-term memory (LSTM), and random forest (RF).
Inspired by the nervous system and chemical-based neuron activation in a biological brain, artificial neural networks (ANN) are a multi-layered black-box approach to digitally recreating non-linear systems' functionality with a series of interconnected signals that get sent to neurons.
For example, given a 3-layer fully connected neural network, an array of input values X of size N, an array of output Y of size M, and four hidden layers of size H are shown in Figure 2. The output of X's input values is connected to each node in the hidden layer. Each connection between layers is multiplied by a unique weight wj and summed with a bias b resulting in:
The training process for all neural networks described in this paper follows the ADAM optimizer algorithm.
A convolutional neural network (CNN) is a class of ANN that uses convolution in place of multiplication. The traditional CNN extracts feature in a 2-Dimensional image, while the 1D-CNN approach observes an input that spans some window of time of an arbitrary length. Unlike the RNN approach, the 1D-CNN pre-processes the input data before training and does not require backward propagation. Essentially, the 1D-CNN treats the input time window as a single image of a larger dataset and aims to identify the step response of changing some input with a smaller set of data labels as output.
Long Short-Term Memory (LSTM) is a form of RNN (Recurrent neural network) that uses specialized cells with feedback connections and is used in long sequences of data such as time-series data in speech and weather forecasting or text-based image problems.
Random Forests (RF) are an approach to machine learning methods that use an ensemble of randomly generated decision trees to classify or regress by majority vote. Initially used for classification problems,
random forests are effective for regressive multivariate and time-series forecasting problems. A decision tree is an intuitive set of binary conditions that branch to a result based on an input's qualities. In the context of a manufacturing process, sensor data inserted into a decision tree pass through simple conditions until the most likely result is decided.
The best performing ML model is selected for the continuous pharmaceutical tablet manufacturing process for real time prediction. (Figure 2)
A flowsheet model of continuous pharmaceutical tablet manufacturing process has been also developed. The heart of the flowsheet model is the unit operation model library. The mathematical model of continuous pharmaceutical unit operations such as feeder, co-mill, blender, and tablet press have been developed. The model has been simulated in gPROMS (Process Systems Enterprise), Matlab, and Python simulation platforms.
These models can be used to generate an integrated process flowsheet model. The different configuration of the virtual CM line can be developed and investigated using this modeling toolbox.
The process flowsheet model mentioned in section 2.2 has been used to optimize the continuous pharmaceutical tablet manufacturing process. For example, a systematic framework including the methods and tools has been developed for dynamic optimization of the feeder refill strategies. The deviation of the outlet mass flow of the feeder from the targeted flow rate has been minimized to obtain the optimum value of the feeder refill parameters. The material properties also affect the refill strategy meaning that the feeder refill strategy needs to be frequently optimized if there are any changes in the materials and plant. Therefore, the developed model and dynamic optimization tool can save the time and recourses as well as improve the product quality significantly.
The automation and control are the key to fully utilize the benefits of the continuous pharmaceutical manufacturing process. For automation, the continuous manufacturing line has been integrated with two distributed control systems (DletaV, PCS7). There is a
switch to move from one operating system to another operating system. This allows us to cover wider range of industrial continuous manufacturing system. The control panel is connected to the pilot-plant through standard communication systems. Feeders are connected through field bus device, co-mill and blender are connected through serial ports, and tablet press is connected via OLE process control (OPC). There are two control panels, one for DletaV and one for PCS7. But, both control panels are connected with the pilot-plant via same communication protocols and wirings. The control panels are connected to their respective control computers where control software (DeltaV or PCS7) is installed. Therefore, the switch is useful to move from one distributed control system to another. The implementation of the feedback control system into the plant is previously described.
Pharma 4.0 represents the integration of advanced technologies and digitalization in the pharmaceutical industry, revolutionizing tablet manufacturing processes.
In the continuous pharmaceutical manufacturing (CPM) pilot-plant, the CPP’s and CQA’s are controlled in real time. The critical control variables that have been controlled using model predictive control (MPC) system are drug concertation, powder level before tablet press, main and pre compression forces, tablet weight and hardness. A novel control strategy for powder level control in a chute placed in between blender and tablet press unit operation of continuous tablet manufacturing process has been developed, implemented and evaluated. A residence time distribution (RTD) based control system has also been developed and implemented to the continuous pharmaceutical tablet manufacturing pilotplant for real time diversion of out of specs tablets to assure content uniformity (CU).
A systematic framework including the methods and tools have been developed for material traceability. A corresponding software tool has been also developed to automate the material traceability procedure. The heart of the material traceability toolbox is the RTD model. The applications of the developed methods and tools have been demonstrated for material traceability of continuous pharmaceutical manufacturing process.
The cyber-physical security is important for both plant and patient safety. There has been
a significant progress in the area of ‘cyber security’ but ‘cyber-physical security’ is still an open area of research. Much less attention has been paid toward the cyber-physical security of continuous pharmaceutical manufacturing process. A cyber-physical attack in continuous pharmaceutical manufacturing process could alter the critical quality attributes (CQA’s) of the product. There are some commercially available tools such as SNAP7, Wireshark, and Tripwire that have been used in CM plant. The Snap7 has been used for data block monitoring, Wireshark has been used for network traffic monitoring, and Tripwire has been used to assure file integrity. A novel software tool named CPS (Cyber-physical security) has been developed and integrated to the continuous pharmaceutical manufacturing pilot-plant. The CPS provides the interface to integrate commercially available cyberphysical security tools, decode the data blocks to get the plant signal, and to provide an extra level of the protection to the plant.
The data management is critical for continuous pharmaceutical manufacturing. A commercially available data hub (OSI PI) has been used to collect, store, and manage the data generated from continuous pharmaceutical manufacturing pilot-plant in real time using industry 4.0 standard. The plant operating data is captured using commercially available distributed control system (e.g. Delta V, PCS7). Process Pulse
II integrated with OLUPX has been used for managing, real time prediction of PAT data, and real time process monitoring.
The first component of industry 4.0, i.e. artificial intelligence (AI) has been used here for demonstration purposes. Similarly, the other components of industry 4.0 have been developed which is subject of future publication.
The data used in this article consists of 16-time series experiments concatenated with real-time sensor data from the continuous tablet manufacturing process. The time-series experiments all use the same formulation and measure the time delay and step response due to changes in input.
Significant events of steady-state operation or missing sensor data were removed before the AI model training process to mitigate the number of duplicate data samples during training and validation, resulting in 48781 time-series measurements. The most extensive experiment used 8401 time-series measurements with four signal data variables. Figure 3 shows an example of a step response of the main compression force (MCF) and pre compression force (PCF) achieved during tablet compaction due to a shift in fill depth (FD). MCF and PCF can be measured in real time and therefore are ideal candidates to use for the comparison of the performance of different AI models. (Figure 3)
The training process of the Neural Networks operates by sliding window. Two window sizes were used for the experimental approach to observe the benefits of varying sizes in forecasting accuracy. The consequence of increasing the size input and output is a
decreased size to the overall dataset and an increased domain range required for accurate prediction.
In the first experiment, a, short window, vector of size 30 is used as the input, and the predictive time-span extends to t+15 to keep the predicted complete step response due to change in input. The input size was selected due to the response delay in the dataset not being observed over 20 measurements, so we assume a delay of 30 is enough minimum context to predict a step change. In the second set of experiments, a 'long window' vector of size 180 is used, and we assume that the length of input provides full context for forecasting the system's response.
The data used for the Random Forest training process varies slightly, as the input is determined as the time-series signal measurements at time t. The target label is delayed 20 seconds from the labeled measurements, and predictions are made without MCF measurements at time t.
The long window case study has been used here for the illustration of the concept. Some of the result is shown in Figure 4. Machine Learning approaches are analyzed to forecast the multivariate response during steady-state and step-change events and provide accurate prediction for real-time applications. We observed each neural network’s performance
in forecasting real-time signal data in 3 groups of experiments in a continuous manufacturing plant. The first set of experimental data is used to train the models to forecast the main compression force (MCF) given fill depth and tablet thickness inputs. The next following experiment uses the same inputs and includes the pre-compression force (PCF) as well. And the final experiment involves models trained to predict the PCF.
Mean absolute error (MAE) is used as a performance indicator of AI models. MAE of DNN, CNN, and LSTM is shown in Figure 4. As shown in the Figure 4, the LSTM performed better in compared to DNN and CNN. For the target MCF prediction without PCF as input, the dense neural network and CNN performed similarly but DNN is slightly better than CNN. Meanwhile, given the PCF as input, both the DNN and CNN lost over 200% in MAE. In this case LSTM performed best followed by DNN and CNN. For the PCF prediction, all neural network models struggled to perform under 0.1 MAE, where the LSTM had the highest score at 0.1026. (Figure 4)
Different components of industry 4.0 such as artificial intelligence (AI), optimization, advanced control, material traceability, cyberphysical security, and data science have been developed for the continuous pharmaceutical manufacturing process. Within AI models, Long Short-Term Memory (LSTM) performs better for the case studies that have been
performed. Some drawbacks of relying on LSTMs are the training time and computation power needed to train LSTMs, which could be significant as the LSTM models grow in complexity. DNNs or 1D CNN can reduce these requirements with a sacrifice of training accuracy. The Random forest remains a promising alternative to neural networks for fast deployment but could compromise with the prediction accuracy. The current drawbacks of the AI approaches include the need of the substantial amounts of data to obtain a fully confident model, which requires a large amount of material and time. To overcome this limitation, a validated flowsheet model or digital twin could be used to train the AI model that can be used for real time applications.
This work is supported by the US Food and Drug Administration (FDA), through grant 5U01FD006487.
Dr. Ravendra Singh is faculty of the Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA. He is the recipient of the prestigious EFCE Excellence Award from the European Federation of Chemical Engineering. His research focus is continuous manufacturing of drug substances and products. He is PI/Co-PI of several projects funded by FDA, NSF, and companies. He has published more than 75 papers, edited one pharmaceutical system engineering book published by Elsevier, written more than 12 book chapters, and presented at over 150 conferences. He is actively serving as a Journal editorial board member, and conference session chair. References are available at www.pharmafocuseurope.com
Causality assessment, which determines the relationship between a drug and an adverse event, is critical in safety vigilance. It helps identify new signals, measure the strength of evidence, and evaluate the benefit-risk profile of pharmaceutical medicinal products. This process has traditionally been performed manually by experts, but the emergence AI/ML technologies present an opportunity to automate it. This article will explore the various AI/ML models and methods that can be used to implement automated causality assessment in safety vigilance, along with the challenges and opportunities associated with this approach.
Causality assessment is a crucial process in safety vigilance that involves determining the relationship between a drug and an adverse event or reaction. The identification of new signals,
Ryanka Chauhan Product Manager Datafoundry
evaluation of benefit-risk profile, and measurement of evidence strength for pharmaceutical medicinal products heavily rely on the factor of causality. Traditionally, this process has been performed manually
by experts, but using technologies such as Machine Learning (ML) and Natural Language Processing (NLP), there is an opportunity to automate this process.
Automated causality assessment is an emerging approach in safety case management that uses ML algorithms and other automated techniques to assess the causes of incidents and hazards. This approach has the potential to improve the accuracy and efficiency of causality assessments, leading to better decision-making and risk management. It overcomes some of the limitations of manual assessments by analysing large volumes of data and identify patterns and correlations. These algorithms can be trained on different types of data including historical data, real-world data, data from spontaneous reporting systems, social media, biomedical literature and knowledge bases with an aim to learn about the causes of incidents and hazards and to develop models that can be used to predict the likelihood of future incidents.
This article will explore various methods and AI/ML models that can be utilized in implementing automated causality assessment in safety vigilance along with the challenges and opportunities.
The World Health Organization's (WHO) Uppsala Monitoring Centre (UMC) and the Naranjo algorithm are currently the two widely used methods for determining the causality of adverse drug reactions (ADRs).
The WHO-UMC causality assessment method is a widely used and standardized method for determining the causality of ADRs. It was developed by the UMC, which is a collaborating center of the WHO. The method involves the temporal relationship and some other important criteria for assessing causality such as lab tests results, any sudden abnormalities in the reports, dechallenge and rechallenge values etc. between the drug and the ADR, the known pharmacological properties of the drug, and the presence of alternative explanations for the ADR.
The WHO-UMC method classifies the causality of ADRs into five categories: Certain, Probable/Likely, Possible, Unlikely, and Unclassified. The method provides a standardized framework for evaluating ADRs and is widely used by regulatory agencies and pharmaceutical companies. (Table 01:)
The Naranjo Adverse Drug Reaction (ADR) Probability Scale was developed to provide a standardized and systematic approach to assess the likelihood that an ADR was caused by a particular drug.
The Naranjo Scale consists of ten questions that evaluate the relationship between the drug and the ADR. The questions are designed to assess the temporality, the nature and severity of the ADR, the possibility of other causes, the drug's known pharmacology, and the response to rechallenge.
Each question is scored based on the answer, with a score of +1 indicating a positive answer, 0 indicating a neutral answer, and -1 indicating a negative answer. The scores are then totalled to provide an overall score ranging from -4 to +13. The higher the score, the more likely the drug is to have caused the ADR. (Table 02:)
While the Naranjo Scale can be a useful tool in causality assessment, it has some limitations. It relies on the accuracy and completeness of the information available, and its results can be subjective, as different assessors may interpret the questions differently. Additionally, the scale was developed for use in assessing ADRs, and may not be appropriate for assessing other types of adverse events.
The guidelines for the assessment of causality
of adverse drug reactions (ADRs) related to the use of personal care products and cosmetics have been established by the European Cosmetic, Toiletry and Perfumery Association (COLIPA). These guidelines were published in 1997 and are still used today.
The COLIPA guidelines suggest a structured and systematic approach to assess the causality of ADRs. The guidelines state that the assessment of causality should be based on the following criteria:
• Temporal relationship: The time interval between the use of the cosmetic product and the onset of the adverse reaction is an important factor to consider. The shorter the time interval, the more likely the cosmetic product is to be the cause of the adverse reaction.
• Previous history: If the patient has a previous history of allergic reactions to similar or
related products, it may suggest a causal relationship with the current cosmetic product.
• Dechallenge and rechallenge: The effect of stopping and restarting the use of the cosmetic product can help determine causality. If the adverse reaction disappears after stopping the use of the product and reappears after restarting, it suggests a causal relationship.
• Alternative explanations: Other possible causes of the adverse reaction should be considered, such as concurrent medical conditions, concomitant medication use, or exposure to environmental factors.
• Pharmacological properties: Knowledge of the pharmacological properties of the ingredients in the cosmetic product can help determine the plausibility of a causal relationship.
• Dose-response: The severity of the adverse reaction and its relationship to the dose of the cosmetic product used can also help determine causality.
The COLIPA guidelines also suggest that the assessment of causality should be performed by a healthcare professional with experience in the field of dermatology or toxicology. They also recommend that the assessment should be documented and communicated to the patient, the manufacturer of the cosmetic product, and regulatory authorities if necessary.
Overall, the COLIPA guidelines provide a structured and systematic approach to assess the causality of adverse reactions associated with cosmetics and personal care products, which can aid in the evaluation of product safety and inform regulatory decisions.
There are various AI/ML models and algorithms that can be used for causality assessment in safety vigilance, including causal inference methods, counterfactual inference, and machine learning techniques. These models can help identify potential causal relationships between risk factors and adverse events, enabling healthcare professionals and safety vigilance teams to take appropriate action to prevent similar events from occurring in the future.
The use of AI/ML models for causality assessment is particularly relevant given the large amount of data generated in healthcare and other industries. ML techniques facilitates the processing and analysis of vast datasets, enabling the identification of patterns and relationships that may not be readily discernible to human analysts. This can help improve the efficiency and effectiveness of safety vigilance programs, reducing the risk of adverse events and improving patient or user safety.
Below are the AI/ML models that can be leveraged to determine Causality in safety vigilance:
1. Decision Trees: Decision trees are one of the most commonly used ML models in safety vigilance. Decision trees are used to identify the causal relationship between a drug and an adverse event. They are used to classify adverse events into different categories based on their cause. Decision trees are useful in
identifying the most likely cause of the adverse event and in developing strategies to prevent the recurrence of the event. Rule-based ML Models may be built using Decision Trees. Decision trees are also used in the WHO-UMC and COLIPA Scales. The decision tree is converted into rules that could be expressed as normal ML expressions. This method is excellent when we don't have enough data, or the data quality is undetermined. However, the predictions are only as good as the underlying rules. Hence, utmost care is needed to define the rules and validate the results over time to build trust in the predictions.
2. Random Forest: Random Forest is a popular ML algorithm used for classification, that is used in safety vigilance to determine causality. Random forest is a type of decision tree algorithm that is used to improve the accuracy of the model. It works by creating a large number of decision trees and then combining their results to produce a more accurate prediction. Random forest is best utilised to address classification challenges, particularly in identifying rare events or events that are difficult to classify. It can be used to identify which features are most strongly associated with a particular outcome.
However, for the model to function effectively well, data set availability and data quality are of significance. The FDA supports the use of Random Forest for Causality Prediction, with a human in the loop to validate the findings in a research study in which a random forest model demonstrated the best
results in report ranking and accuracy in establishing causal relationships to suspect drugs.
3.Bayesian Networks: Bayesian networks are another type of ML model that can be used to determine causality in safety vigilance. Bayesian networks are probabilistic models that are used to represent the causal relationships between different variables. Bayesian networks are particularly useful in identifying the probability of a particular event occurring given a set of conditions. They can be used to predict the likelihood of an adverse event occurring given certain conditions, such as the patient's age, gender, and medical history.
4.Logistic Regression: Logistic regression is a statistical method used to predict the probability of an event occurring. It is a binary classification algorithm, which means that it predicts the occurrence or non-occurrence of an event. Logistic regression works by fitting a model to a dataset that contains one or more independent variables and a dependent variable that is binary (0 or 1). Logistic regression can be applied in causality assessment to determine the relationship between a drug and an adverse event. The independent variables can be demographic data, medical history, concomitant medications, and the drug in question. The dependent variable is the occurrence or non-occurrence of the adverse event. It can be used to determine the probability
of an adverse event occurring as a result of a drug.
5.Neural Networks: Neural networks can be used for causality assessment in pharmacovigilance by training the network on large datasets that contain information about patient demographics, medical history, concomitant medications, and the drug in question. The neural network can then learn the patterns and relationships in the data and use this information to predict the probability of an adverse event occurring because of the drug. One approach to using neural networks for causality assessment is to feed the network with structured data, such as the drug, the patient's age, gender, medical history, and other factors related to the adverse event. The network can learn to identify patterns in the data and use them to make predictions about causality. Another approach is to use unstructured data, such as free-text reports from healthcare providers or social media posts. Natural Language Processing (NLP) techniques can be used to extract relevant information from the text and feed it into the network. The neural network can then learn to identify patterns in the text and use them to make predictions about causality.
Neural networks can also be used to improve the accuracy of existing causality assessment methods, such as the Naranjo algorithm or the WHO-Uppsala Monitoring Centre (UMC) system. The network can learn to identify features in the data that are predictive
of causality and use them to enhance the performance of the existing method.
6. Support Vector Machines (SVMs): Support vector machines are a type of AI/ ML model that can be used to determine causality in safety vigilance. SVMs are used to identify the causal relationship between a drug and an adverse event by classifying events into different categories. SVMs work by finding the hyperplane that best separates the data into different classes. SVMs are particularly useful in identifying complex relationships between different variables of events. In a study published in Clinical Pharmacology & Therapeutics, SVM was used to assess the causality between drugs and adverse events (AEs) reported in the US Food and Drug Administration's (FDA) Adverse Event Reporting System (AERS). The authors developed a system that used SVM to classify each drug-AE pair into one of three categories: "positive," "negative," or "unclassified" for causality. The system was trained on a subset of AERS data and then tested on a separate set of data. The results showed that SVM had a high accuracy (over 90%) in classifying drug-AE pairs for causality.
Automated causality assessment using AI/ML models has the potential to revolutionize the way in which causal relationships between risk factors and adverse events are identified and analyzed. However, there are several challenges
associated with the use of AI/ML models for causality assessment that must be considered.
• The first challenge in implementing automated causality assessment in safety vigilance is the need for high-quality data. Causality assessment relies on accurate and detailed information about the drug, the patient, and the adverse event or reaction. This data may be scattered across different sources and formats and may require significant pre-processing and cleaning before it can be used for analysis. Without high-quality data, automated causality assessment may produce inaccurate or unreliable results, which could have serious implications for safety vigilance.
• Second challenge is the need to incorporate expert knowledge and judgment into the analysis. While ML and NLP can be used to process large amounts of data quickly and efficiently, they may not be able to capture the nuances and complexities of safety vigilance. Expert knowledge and judgment are necessary to interpret the results of automated causality assessment, and to ensure that the analysis is consistent with accepted safety vigilance standards and practices.
• Another challenge is the potential for bias and error. ML algorithms and NLP models may produce biased or inaccurate results if they are trained on incomplete or biased data. Additionally, these systems may not be able to capture all the relevant factors that contribute to causality in safety vigilance. As a result, automated causality assessment may produce false positives or false negatives, which could
lead to incorrect conclusions about the safety and efficacy of a drug.
Despite these challenges, there are advantages for implementing automated causality assessment in safety vigilance:
• One of the biggest advantages of automated causality assessment is its ability to process large amounts of data quickly and efficiently. This can help identify potential safety issues and adverse reactions more quickly, which can lead to faster and more effective interventions to ensure patient safety.
• Another advantage of automated causality assessment is its ability to identify patterns and trends in data that may not be immediately apparent to human experts. ML algorithms can analyse large datasets and identify correlations between drugs and adverse events that may not have been previously recognized. This can lead to new insights into the safety and efficacy of drugs and can help identify potential safety issues before they become widespread.
• A third advantage of automated causality assessment is its potential to reduce the workload of human experts in safety vigilance. With the increasing volume of data being generated in the pharmaceutical industry, it can be challenging for human experts to keep up with the demand for causality assessment. Automated systems can help alleviate this burden by processing data more quickly and efficiently, allowing
human experts to focus on more complex or nuanced cases.
In conclusion, automated causality assessment has the potential to revolutionize safety vigilance by improving the efficiency and accuracy of the causality assessment process. However, there are several challenges that must be addressed to ensure the accuracy and reliability of automated systems. High-quality data, expert knowledge and judgment, and careful attention to bias and error are all critical factors in implementing successful automated causality assessment in safety vigilance. With these challenges in mind, the pharmaceutical industry has the opportunity to leverage advanced technologies and safety platforms like DF mSafety AI to improve patient safety and advance the field of safety vigilance.
References are available at www.pharmafocuseurope.com
Ryanka Chauhan is currently working as a Product Manager at Datafoundry for DF mSafety AI, a safety platform that leverages AI/ ML for safety vigilance automation. She works closely with clients and business leaders across the organisation, being involved in multiple functional areas including technology, sales, marketing, and other functional areas, for delivering transformational new solutions to organizations in the life sciences and healthcare industry.
The pharmaceutical industry is facing a range of challenges that necessitate a significant shift in how it operates. These challenges include increasing costs, aging populations, and a growing demand for personalized medicine. In response to these challenges, many pharma companies are turning to digital technologies to help them overcome obstacles and stay competitive.
Svetoslav Valentinov Tsenov Chair of the Board of Directors, ARPharMDigital transformation in the pharma industry involves the use of technology to streamline operations, enhance patient engagement, and improve the efficiency of clinical trials. It also involves the adoption of different methods like data analytics and integration, artificial intelligence,
machine learning, cloud computing, and services, etc. to drive innovation and improve patient outcomes.
The pharmaceutical industry has been a pillar of healthcare for decades, but with the advancement of digital technologies, it is undergoing a major transformation. Digital
technologies have the potential to disrupt every aspect of the pharma industry, from research and development to marketing and sales.
Pharmaceutical companies are faced with the challenge of effectively managing and analyzing large volumes of data, which come in various formats, in order to extract meaningful and actionable insights. This is crucial for efficient drug development that balances cost and effectiveness. The use of cutting-edge technologies can help uncover the mechanisms of diseases, optimize clinical trials, and improve production efficiency and accuracy. By utilizing data analytics and automation, pharmaceutical companies can optimize their manufacturing processes and ensure timely and reliable delivery of drugs to patients. In addition, by leveraging data analytics and machine learning, pharma companies can identify inefficiencies in their manufacturing processes and take data-driven actions to improve efficiency and minimize waste. This can result in substantial cost savings and enhanced product quality. By utilizing real-time monitoring and data analytics, pharma companies can also monitor the drug's movement from production facilities to patients, reducing the risk of counterfeit drugs and improving the supply chain's overall efficiency.
As the number of players in the research and development process grows, having a centralized database that can access various sources and databases is increasingly important. Therefore, being able to manage and integrate data generated at every stage of the process, from design to end-user, is a top priority for any forward-thinking pharmaceutical company.
Artificial intelligence (AI) is a type of technology that employs complex algorithms and software to imitate human intelligence and process large amounts of complex health and medical data. AI is being used in various fields of medicine, especially in the areas of diagnosis and treatment protocols. The use of automated algorithms in pharmacy is redefining the traditional roles that used to rely on human intelligence. In recent years, AI has revolutionized the pharmaceutical industry by allowing scientists to discover new drugs, develop treatments, and find innovative solutions to some of the biggest challenges in healthcare.
Artificial intelligence has the potential to optimize production and prevent supply chain disruptions, as well as perform quality control and predictive analytics, reducing waste and correcting breakdowns in the production line. In addition to these benefits, AI can also help personalize treatment through mobile apps that track health metrics and allow for remote monitoring,
improving research and development and treatment efficacy.
Pharmaceutical companies are increasingly turning to automated data collection and analysis to tackle complex challenges in drug development. By utilizing AI algorithms, scientists can map the complex roles that hundreds of genes play in individual diseases, and monitor the effects of drug treatments on human cells starting from the preclinical phase. This early understanding of a drug's effectiveness can help improve the drug development process and potentially lead to faster approval and better patient outcomes. Additionally, facial and image recognition algorithms can be used to monitor therapy adherence and optimize treatment outcomes. AI can also detect potential side effects much earlier.
Recruiting suitable patients for clinical trials is often challenging for large pharmaceutical companies. However, AI and machine learning can be employed to extract useful data from patient records, simplifying the process. The monitoring of participants could also be improved. Wearable devices and remote monitoring technologies enable researchers to gather real-time data on patient outcomes, leading to more accurate and timely analysis of trial results. This translates into more effective treatments and better patient outcomes.
The implementation of AI in the pharmaceutical industry is rapidly increasing, with a USM Systems study indicating that
around 50% of healthcare companies worldwide intend to adopt AI strategies and deploy the technology widely by 2025.3
Drug development is a time-consuming process that involves analyzing and investigating compounds for their biological and chemical properties. However, machine learning has emerged as a valuable tool for accelerating this process. With access to large databases, machine learning algorithms can extract chemical and biological information to identify compounds that are worth exploring further. This technology is being increasingly used by research teams to predict the potential of untested compounds. Through modeling QSAR (quantitative structure-property relationships) and developing AI programs, modern machine learning techniques can accurately predict how chemical modifications will affect biological behavior.
Machine learning is also used in research and development to improve pharmacokinetic and pharmacodynamic information, such as absorption, distribution, metabolism, excretion, mechanisms of action, route of administration, side effects and toxicity, demographic variations, and interactions with other drugs.
By leveraging the speed and accuracy of computers, new drugs can be developed more quickly and cost-effectively compared to traditional manual methods for example by using machine learning to speed up drug
development by predicting the efficacy of untested compounds through image analysis. These advancements in technology are paving the way for more efficient drug development and providing hope for the treatment of previously untreatable diseases.
According to a 2013 report by McKinsey, machine learning has the potential to save the pharma and medicine industry approximately $100 billion annually in the US alone through higher efficiency in clinical trials, better decision-making, and innovative tools that can help consumers, doctors, regulators, and insurers make informed decisions.8
Computers are playing an increasingly significant role in pharmaceutical development. They are becoming a more
convenient tool for analyzing biological interrelationships and therapeutic potential and providing a quick and thorough screening of large libraries of compounds to identify the desired structure. Over time, specific computational techniques such as virtual screening, de novo design, and fragment-based drug development have been introduced. These techniques are being employed to optimize the drug development process. Additionally, computing is being used to manage and analyze data in preclinical studies, resulting in increased productivity and shorter development time.
Computing has also become an essential tool for faster and more accurate communication within clinical trials. Due to the enormous amount of information generated within a trial, pharmaceutical companies are transitioning from traditional paper-based methods to electronic systems. This shift is facilitated by various software applications designed to manage clinical trials, including clinical trial management systems (CTMS), clinical data management systems (CDMS), pharmacovigilance systems for ensuring drug safety, and electronic data collection tools (EDCT). With the help of these software tools, clinical trial teams can efficiently manage and analyze data, leading to increased productivity and faster decisionmaking.
By implementing various data sharing and exchange systems, communication between different databases and teams is made
possible, enabling the analysis and export of data in different formats for the purpose of preparing reports or future planning. With the integration of smart devices and data sharing systems, clinical trial outcomes can be improved while increasing efficiency. There are different methods of integrating data, including software, tools, and services. Cloud computing services have become increasingly popular among pharmaceutical companies as they offer high computing power and allow partners to easily access, share, and manage data. This technology provides an attractive solution for pharmaceutical companies seeking to streamline their clinical trial processes and improve outcomes.
Cloud services are particularly useful in telemedicine, mobile health applications, and remote monitoring tools. Despite what many people think, this is not just a storage solution, but a network that enables other technologies such as AI, smart embedded devices, and databases to connect in realtime, with purposes like data integration, telemedicine, and robotic surgery.
Cloud computing technology offers benefits like decreased reliance on internal infrastructures and improved streamlining of processes on a global scale. Its applications are widespread, spanning the entire product life cycle. During the preclinical stage, data from multiple sources can be easily and safely collected and stored in the cloud. In clinical trials, it enables greater transparency and real-time visibility of operations and data.
The digital transformation in the pharma industry is a result of the collaborative efforts of various players, including startups, tech companies, established pharma companies, and healthcare providers. Each of these players has a unique perspective and skill set that is critical to achieving digital transformation in the industry.
Startups and tech companies are known for their innovative digital platforms and technologies that can improve patient outcomes and streamline operations. These companies often partner with pharma companies to develop and implement digital solutions that can transform the industry.
Established pharma companies are also key players in driving digital transformation. Many of them invest in digital capabilities either through internal development or partnerships with startups and tech companies. They also incorporate digital technologies into their existing operations, from drug development to supply chain management.
Healthcare providers play a critical role in digital transformation in the pharma industry. They increasingly utilize digital technologies to enhance patient outcomes and increase efficiency, ranging from telemedicine to digital health platforms. Additionally, they can provide valuable insights into patient needs and preferences, making them important partners for pharma companies.
While digital technologies have the potential to revolutionize the pharmaceutical industry, the transformation process is challenging. The challenges faced by pharmaceutical companies are numerous and complex. They are often related to the unknown nature of the technologies, insufficient experience and knowledge, and the need for substantial investments in upgrading the entire IT infrastructure.
Furthermore, to fully utilize the potential of these technologies, a lean organization within eHealth, including electronic records, is necessary. There must be a large set of accumulated data, greater transparency between individual healthcare processes, and solutions to issues related to cybersecurity and personal data access and distribution.
The digital transformation of pharma raises also some ethical concerns that must be addressed to ensure patient privacy and
autonomy. It is crucial for companies to collect and use patient data in a responsible manner, while also making digital solutions accessible to patients regardless of their socioeconomic status or location.
Real-time work and increased communication and collaboration between various stakeholders in separate processes also complicate the integration of data, while security risks increase. Despite these challenges, pharmaceutical companies are increasingly partnering with large technology firms with cloud and artificial intelligence solutions. As a result, more pharmaceutical giants are investing in technology companies.
As the pharmaceutical industry grows larger, it requires increasingly complex and advanced technological infrastructure to operate.
The applications of digital technologies in the lifecycle of medicinal products have the potential to transform the control and management systems of pharmaceutical
companies. Artificial intelligence, machine learning, cloud computing, and cloud services have all become essential components of modern pharmaceutical companies.
The main goal of this transformation is to reduce development costs, shorten cycle times, and improve the quality of medicinal products through improved data integration and real-time status updates. By connecting all stages of the information delivery cycle, pharmaceutical companies can increase their efficiency and productivity.
The benefits of digital transformation in pharma are clear: it can speed up and improve the accuracy of drug development, increase patient engagement and adherence, and enable the delivery of more personalized healthcare. However, there are significant challenges that must be addressed. Pharmaceutical manufacturing processes must adhere to strict safety and reliability requirements, and technological solutions require corresponding infrastructural and cognitive requirements for successful integration. By overcoming these challenges, pharma companies can benefit from improved patient outcomes, reduced costs, and increased innovation, thus contributing to the betterment of the industry and the overall healthcare ecosystem. Nevertheless, these challenges are unlikely to halt the trend towards increasing implementation of digital technologies in all industries.
References are available at www.pharmafocuseurope.com
Svetoslav is a Medical doctor, master in public health, and business expert with over 17 years of experience in the pharmaceutical industry. Held various positions at the level of vice president, executive director, and director in multinational companies at global and regional levels (USA, Europe, Asia, Australia) in the field of management, sales department, corporate strategies, market access, relations with institutions, marketing, R&D . In his work, he places a strong focus on leadership, market development, customer and patient orientation, innovative solutions and strategic thinking. Lecturer in international and national forums on various topics, such as gene and cell therapies, innovative models of new molecule development, healthcare economics, and trends in clinical trial development. Former member of the EFPIA Working Group for Central and Eastern Europe, Chairman of the ARPharM Management Board, and Co-Chair of the AmCham Health Commission. He is currently a member of the management board of the Bulgarian Oncology Scientific Society, National Coordinator of the European Union initiative "Europe beats cancer" for Bulgaria, lecturer at the Medical Universities, managing director of Sunlight Health - a company with a focus on the healthcare sector.
This article addresses the real-world challenges in assembling Big Data that need to be considered when developing and applying analytics to enhance drug and diagnostic development and patient management. AI/ML and deep learning tools focus on volume and velocity but the real value will come from understanding and dealing with aspects of validity that are currently being undervalued.
Michael N. Liebman PhD, Managing Director IPQ Analytics, LLCThe concepts of Big Data and Big Data Analytics have been around for some time but it has only been since the late 1990’s, with the confluence of genomic and transcriptomic data, along with increased use of EHR’s and access to claims data, that Big Data has arrived at the shores
of pharma and healthcare. This rapid and accelerating access has been frequently represented by Hokusai’s tidal wave (Figure 1) but this metaphor may be hiding the real challenges that we need to face to deliver more effective diagnoses, drugs and patient outcomes. A potential evolution of this model, to embrace the critical complexities of Big Data, can be accomplished in the transition outlined in this figure…as a “leaning tower” of books and publications, etc and outline in this article some of the “critical challenges that exist in the details”.
The size, the power and the non-predictable nature of the tidal wave metaphor served as an early warning to the healthcare and life sciences communities that major changes
were imminent. This has led to technological development and implementation in hardware (e.g. massively parallel computing, GPU’s, quantum computing), in software (e.g. AI/ML, deep learning, generative AI) and in “cloudware” to handle two of its “V’s”, the volume and velocity aspects of big data, but has not necessarily focused on its third, validity. With the increasing recognition and utilization of complex analytics to interpret Big Data, an emphasis on validity is critical but needs to be expanded beyond accuracy as currently defined for Big Data.
The tower of books metaphor can readily point to several realities that map to real world issues in Big Data analysis:
1. While many new books are published each year, there can be significant differences in quality between those which are selfpublished vs those that have gone through a traditional review and publication process;
2. Each book reflects the story that the author wants to tell, some more truthful and some more fiction, and likely none without some bias and incomplete rendition of events, both sides of the story;
3. Books are published in many languages and their translations may not accurately convey the intent of the author as expressed in their native language e.g. idioms;
4. Scientific, technical and medical books are published focused within disparate disciplines where use of specific words
Embracing big data offers a competitive advantage, allowing companies to adapt to evolving market trends and patient needs.
may have different meanings among those disciplines;
5. Books are written in different formats, e.g. novels, dictionaries, instruction manuals, poems, etc;
6. Books have different numbers of pages, words and figures;
7. Books are commonly written to describe specific periods of time related to the story;
8. Books typically reflect the state of knowledge and use of terminology pertinent to a specific time period and both may be subject to change over time
9. It is also worth remembering that “you cannot judge a book by its cover” and that extends to databases as well (as detailed below).
Evolving the tidal wave to the leaning tower of books provides a perspective on validity that highlights critical challenges and constraints that may exist in Big Data and significantly impact its subsequent analysis, interpretation and usefulness in drug and diagnostic development and patient management. As evidenced during the last two years of the COVID pandemic there has been an explosive growth of books, papers and data concerning COVID, well beyond the current capacities of the scientific and clinical publication systems, resulting in limited validation even at the journal review level. It is estimated that within the next 10-12 years, the number of scientific journals and the number of scientific publications will double the current totals. In evolving the metaphor,
the tower of books, i.e. databases, is more representative of the actual challenges to aggregation and integration of data from disparate data bases that comprise Big Data and which are more significant than the increased volume, alone.
Within each component database, several considerations include:
1. New databases are being created (or expanded) each year:
a. Each database reflects authors/ creators/curators who provide their own perspective to determine what and how data is collected and stored, i.e., emphasizing a particular clinical or experimental specialty, e.g. radiology, pathology, gene expression/ transcriptomics
b. Data collected commonly results from ease of access rather than attempting to completely populate an objective model that comprehensively addresses the problem e.g. patient journey (pre-disease to outcome), process of diagnosis.
Evaluation of gaps within social determinants of health and their potential impact on clinical practice, health and research is intended to help reduce inequities in health among disadvantaged populations. Consideration of an individual’s “zip code” or “census tract” is used as a surrogate to evaluate socio-economic factors, environmental exposures, educational access, etc. However
this does not adequately model the reality that an individual’s daily activities, e.g. work, may present additional “exposures” on a daily basis because of the complexity involved in monitoring and integrating such activity. Additionally, cultural differences among population groups may yield significantly different prioritization of factors that comprise SDOH and result in very different responses to efforts to close such gaps.
2. Biases within a database can result from:
a. Populating a data model that is incomplete, inaccurate or biased, resulting in missing critical data; Randomized clinical trials, which serve as the highest level of evidence in evidencebased medicine, establish inclusion/ exclusion criteria that commonly establishes a trial population that does not reflect the complexities of real world patients who have comorbidities, poly-pharmacy, etc. or exclude significant population groups, e.g. no pregnant women were included in COVID vaccine trials (or many others).
b. Using an accurate model but having incomplete or missing data; Missing data is a common occurrence that is sometimes handled using imputation, but this assumes a model is valid to generating the missing data. Separately, we use surrogate measures in place of complex physiological parameters, e.g. hypertension based on blood pressure monitoring. Episodic measures do
not adequately consider concurrent factors, e.g. time to rest, extant stress, meals, etc, nor the normal diurnal variation that may be more significant in its variation than the single measurement over time.
c. Inadequate specification or definition of data fields;
Different algorithms maybe used to compute specific variables like Glomerular. Filtration Rate (GFR) where >5 separate algorithms are in common use, two of which incorporate factors that consider race of patient. Additionally a patient’s diagnosis may be the result of the application different clinical guidelines and physician experience, none of which is noted. ICD-10 codes do not address this adequately as they are intended primarily to justify patient management and for reimbursement purposes.
d. Using different tests or test reagents to measure a clinical laboratory value; Her2/neu is an epidermal growth factor that is over-expressed in some breast and other cancers and serves as a specific target for therapeutic intervention. The FDA has approved tests using immuno-histochemistry. (IHC), i.e. anti-bodies, to detect expression but studies have shown differential response to anti-bodies raised to different features of the protein. Additional test using in situ hybridization (ISH) detect gene copy number differences that can lead to over-expression. This also extends to instrumentation and different on site procedures for maintenance and calibration, leading to the need to ideally
use centralized facilities for multi-center trials, etc to minimize the variability.
e. Using different thresholds (standards) to assign results into either “+ or –“ classifications; Triple negative breast cancer (TNBC) is characterized by “negative” scoring in 3 tests: for progesterone and estrogen receptors and her2/neu (as noted above). Thresholds for a “+ or –“ evaluation may vary among cancer centers and thus a TNBC patient may not receive the same diagnosis at different centers. Most recently, “+ or –“ has been expanded to consider “low her2/neu” expressing patients further suggesting that specific values be used for clinical decision making and research rather than “+ or –“.
f. Consider of temporal biases; Potential temporal biases may develop from two different sources: One may be the period of coverage of a given study and resulting database, i.e. studying the effects of a drug on pregnant women and their offspring typically considers preterm births, birth defects and initial postpartum period (1 year), but developmental processes may not reveal impact until the child is much older, e.g. might SSRI’s used during pregnancy impact neurogenesis and synaptogenesis and not show effects until adolescence with learning disabilities or behavioral issues. The second might reflect the external factors that were present when the study was done, i.e. changes may occur in standard of care, diagnostic criteria, testing procedures or
interpretation, etc that could impact the data within a specific data base and, perhaps more significantly, when multiple data bases are being integrated or federated for analysis (see below)
3. Additional considerations concerning data validity:
a. Fit for Purpose: as noted above, all databases are initially developed and commonly maintained to reflect the needs and intent of their authors/creators/curators with some also evolving to serve the expanded needs of their user communities. In Big Data, the focus on aggregating or federating large data repositories has led to accessing most readily available data to “feed the analytic
engine” most typically involving AI/ML or deep learning, or to provide statistically significant power to the analysis. It is critical, however, to recognize the purpose for which the data was generated and collected. In real world data (RWD), the preponderance of data exists within “claims” databases rather than clinical records which may be more highly secured for regulatory and privacy concerns. Claims data may vary significantly in terms of its accuracy in representing the actual pathophysiology of the patient because of its potential use for justification of patient management and reimbursement. Where private insurance is predominant, e.g. the US, claims data is most representative of the “business of healthcare”. Where healthcare is provided as a national service, claims data may more closely describe the patient’s underlying conditions when justification for reimbursement may be less critical.
b. There is increasing use of natural language processing to extract additional data from clinical notes. Clinician’s notes are not standardized, naturally reflecting the individual clinician’s patterns/expressions and entered as needed. An additional “feature” has further confounded the use of clinical notes as most systems have incorporated the ability to “cut and paste” clinical notes to expedite physician entry, assuming that editing to reflect current evaluation will be made and that leads to potential duplication or carrying over of notes rather than updating and clarification.
c. Natural language processing of published articles and reports can also present challenges as some studies only include documents that are readily accessible, e.g. using abstracts in place of full text because of free access (PubMed) vs paywalls, and also accessing publications that are “self-published”, i.e. early access to articles that may be in journal review but have not completed that process, and some which may never be accepted. Extraction of data and concepts from publications also should differentiate among the sections of the articles, i.e. data and methods sections and results section should be considered fundamentally more reliable than the discussion and conclusion sections where the author’s interpretations are provided and may exhibit less objectivity.
Big Data typically refers to the aggregation/ integration or federation of individual databases whose challenges are outlined above. In addition, there are enhanced needs for security and privacy concerns and regulations to be appropriately managed, national and international regulations for data exchange, compliance with disparate informed patient consents as to any limitations on personal data use and intellectual property considerations of analytic results. The distinction (and value) between de-identified data and anonymous data is significant especially when potential commercial products might result. Major efforts are underway at the national level, at the EU level, in the US, and within and across professional societies to
establish standards to support data exchange, e.g. FHIR, but these are mainly operational and may not address many of the underlying issues outlined above. In addition, while development, implementation and compliance to standards is laudable, it is also a long term process and will not necessarily address the current base of legacy data. It is critical to use this legacy data, with appropriate recognition and accommodation of its biases, etc to impact both research and clinical decision making now and using it to form the basis for the more standardized data future to which we aspire.
None of the issues raised here invalidate the potential use of the data for analyses, but they highlight challenges and constraints in the interpretation of the results.
No Big Data set will ever be perfect and complete. This reality provides both challenges to using Big Data but also presents opportunities to attain greater confidence in the results through incorporation of transparency in what the component databases and data actually represent. Segmenting and analyzing data provides a cascading approach for progressive addition and validation of data that may contain some of the biases noted here.
We typically use metaphors to convey complex concepts and make them more readily identifiable and relatable to a potentially varied audience. While this is well-suited to introduce new ideas, to realistically put these concepts into practice, it is necessary to
acknowledge the real-world complexity of the problem/challenge/situation/process. This does not mean that all issues must be resolved to make progress, e.g. integrate Big Data for meta-analysis, but it does require a greater degree of critical thinking and planning. Effecting greater transparency as to these potential challenges to real world use of Big Data can greatly impact the validity, not only of the data itself, but also the accuracy of analytic analyses and interpretations that the data.
The opportunity for Big Data seems to be to “sink or swim”…to sink if these challenges create too many waves for comfort or to swim by adjusting to the real world nature of the sea. In dealing with Big Data it is sometimes worth remembering the quote of Mies van der Rohe that “Less is More”.
Michael N. Liebman is currently working as a Managing Director, IPQ Analytics, LLC, has experienced both an academic and pharma/diagnostic career at Mt. Sinai, UPenn, Vysis, Wyeth, Roche where he has led programs and teams in Bioinformatics, Pharmacogenomics, Computational Biology, Cancer Biology. He has had senior advisory roles in PhARMA, HIMSS, IUPAC and leads IPQ in its international advanced analytics as a service (AAS) business in EU, China, Africa and Australia.
Artificial intelligence (AI) has revolutionized the pharmaceutical industry by providing innovative solutions in drug discovery, design, and manufacturing. In recent years, AI has become increasingly useful in drug manufacturing, offering several benefits such as increased speed, efficiency, accuracy, and costeffectiveness. AI techniques, such as machine learning, natural language processing, and deep learning, are being used to mine vast amounts of data and extract meaningful insights that aid in drug development.
Shamal Fernando Managing Director Slim pharmaceuticals (Pvt) LtdIn drug discovery, AI is being used to predict the efficacy and safety of potential drug candidates, identify new therapeutic targets, and optimize clinical trial designs. By harnessing the power of machine learning algorithms, researchers
can analyze vast amounts of biological data and identify patterns that are difficult to discern using traditional methods.
In drug manufacturing, AI is being used to optimize manufacturing processes, reduce costs, and ensure quality control. AI-powered
predictive models can analyze data from multiple sources to identify potential issues and optimize production parameters, leading to faster and more efficient drug production.
In general, AI has the potential to revolutionize the pharmaceutical industry, leading to the development of safer and more effective drugs, personalized treatments, and improved patient outcomes. As the technology continues to advance, we can expect to see even greater breakthroughs in drug discovery and manufacturing in the years to come.
Here's a comprehensive analysis of the use of AI in drug manufacturing:
AI in drug manufacturing can improve the efficiency of the manufacturing process by predicting the best conditions for drug synthesis and optimizing manufacturing parameters. It can also help identify impurities in drugs, increasing the quality and safety of the final product. AI can also analyze large amounts of data to discover patterns and insights, enabling pharmaceutical companies to make better decisions regarding drug manufacturing. AI has proven to be highly useful in drug manufacturing, enabling pharmaceutical companies to optimize their manufacturing processes, improve quality control, and reduce costs. Some of the key benefits of using AI in drug manufacturing include:
AI has the potential to reduce the costs of drug manufacturing by reducing the time and resources needed to develop new drugs. This is particularly important in the early stages of drug development, where AI can predict drug toxicity and efficacy, reducing the number of expensive clinical trials required. By optimizing production processes and reducing waste, AI can help pharmaceutical companies to lower their manufacturing costs. This can lead to more affordable drugs for patients and increased profitability for the company.
AI algorithms can analyze data from multiple sources, such as production sensors and batch records, to identify patterns and optimize production parameters. This leads to faster and more efficient drug production, with fewer errors and less waste.
AI can help identify the best medicines for specific patient populations by analyzing large amounts of patient data, such as genetic information and medical histories. This personalized approach to medicine can improve patient outcomes and reduce the likelihood of adverse reactions.
AI can significantly improve the accuracy of drug manufacturing by identifying potential manufacturing defects and providing real-time
quality control. This reduces the likelihood of manufacturing errors, which can cause delays and increase costs.
Despite its numerous benefits, the use of AI in drug manufacturing is not without its challenges. One of the biggest challenges is the lack of standardization in data collection and analysis, which can affect the accuracy and reliability of AI algorithms. Another challenge is the need for high-quality data, which can be time-consuming and expensive to acquire.
In general, AI has the potential to revolutionize drug manufacturing by increasing efficiency, reducing costs, improving accuracy, and enabling personalized medicine. However, to fully realize these benefits, pharmaceutical companies need to invest in high-quality data and standardize data collection and analysis. With these investments, AI can help pharmaceutical companies develop safer and more effective drugs, leading to better outcomes.
AI has become increasingly useful in drug discovery and manufacturing, offering several benefits such as increased speed, efficiency, accuracy, and costeffectiveness.
Drug discovery AI can significantly speed up the drug discovery process by analyzing large amounts of data and identifying potential drug candidates. This is particularly useful in the early stages of drug development, where
AI can predict drug toxicity and efficacy, reducing the number of expensive clinical trials required. AI algorithms can also identify new drug targets and pathways, leading to the development of innovative drugs.
AI is rapidly transforming the pharmaceutical industry, enabling companies to develop new drugs faster, more efficiently, and at a lower cost. AI is being used in drug manufacturing to optimize production processes, improve product quality, and reduce costs. AI is also being used in drug discovery to identify new therapeutic targets, design new drugs, screen potential drug candidates, and optimize drug properties.
In drug manufacturing, AI is being used to analyze vast amounts of data from sensors, cameras, and other sources to optimize production processes and improve product quality. AI-powered predictive models can identify potential issues and optimize production parameters, leading to more efficient production and higher yields. AI can also help identify potential quality issues before they become a problem, enabling operators to take corrective action before they result in quality problems. Additionally, AI can predict when equipment maintenance is needed, helping to prevent production downtime and reduce maintenance costs.
In drug discovery, AI is being used to analyze large datasets, including genomic and proteomic data, electronic health records, and scientific literature, to identify new therapeutic targets and design new drugs. AI can help
researchers identify patterns and relationships in the data that may be missed by traditional methods, leading to the identification of new targets for drug development. AI can also help design new drugs by predicting how different molecules will interact with specific targets and screening large libraries of molecules to identify potential drug candidates. By using AI to optimize drug properties, researchers can predict how drugs will be absorbed, distributed, metabolized, and eliminated by the body, guiding the design of clinical trials. Generally, AI is transforming drug manufacturing and drug discovery, enabling companies to develop safer and more effective drugs, personalized treatments, and improved patient outcomes. As AI technology continues
to advance, we can expect to see even greater benefits in the years to come.
Drug design by predicting the best conditions for drug synthesis and optimizing manufacturing parameters. It can also help identify impurities in drugs, increasing the quality and safety of the final product. AI can also predict drug interactions with other drugs or with the human body, reducing the likelihood of adverse reactions. Drug design is one of the key areas where AI is making a significant impact. AI can help researchers design new drugs by predicting how different molecules will interact with specific targets and identifying potential drug candidates with the greatest likelihood of success. Here are some ways in which AI is being used in drug design:
Virtual screening: AI is being used to screen large libraries of molecules to identify potential drug candidates. By using machine learning algorithms to analyze highthroughput screening data, researchers can identify molecules with the greatest potential for efficacy and safety. AI can also help researchers design more efficient screening experiments by predicting which molecules are most likely to be active against a specific target.
Predictive modeling: AI is being used to predict how different molecules will interact with specific targets, predicting the binding affinity and identifying potential drug
candidates with the greatest likelihood of success. By using machine learning algorithms, researchers can simulate the binding of molecules to target proteins, predicting the strength of the interaction and the likelihood of success.
Generative models: AI is being used to generate new drug candidates using generative models. These models use algorithms to generate new molecules that are optimized for specific properties, such as efficacy, safety, and pharmacokinetics.
Optimization: AI is being used to optimize drug candidates by predicting their pharmacokinetic and pharmacodynamic properties. By using machine learning algorithms, researchers can predict how drugs will be absorbed, distributed, metabolized, and eliminated by the body. This can help identify potential safety and efficacy issues and guide the design of clinical trials.
Overall, AI is transforming drug design by enabling researchers to analyze vast amounts of data and extract meaningful insights that aid in drug development. By using AI to accelerate drug design, we can expect to see the development of safer and more effective drugs, personalized treatments, and improved patient outcomes.
AI has the potential to reduce the costs of drug discovery and manufacturing by reducing the time and resources needed to develop new drugs. This is particularly
important in the early stages of drug development, where AI can predict drug toxicity and efficacy, reducing the number of expensive clinical trials required.
Best medicines AI can help identify the best medicines for specific patient populations by analyzing large amounts of patient data, such as genetic information and medical histories. This personalized approach to medicine can improve patient outcomes and reduce the likelihood of adverse reactions.
Accuracy AI can significantly improve the accuracy of drug discovery and manufacturing by identifying potential errors and providing real-time quality control. This reduces the
AI is being used to optimize drug candidates by predicting their pharmacokinetic and pharmacodynamic properties.
likelihood of errors, which can cause delays and increase costs.
Challenges Despite its numerous benefits, the use of AI in drug discovery and manufacturing is not without its challenges. One of the biggest challenges is the lack of standardization in data collection and analysis, which can affect the accuracy and reliability of AI algorithms. Another challenge is the need for high-quality data, which can be time-consuming and expensive to acquire. AI has the potential to revolutionize drug discovery and manufacturing by increasing efficiency, reducing costs, improving accuracy, and enabling
However, to fully realize these benefits, pharmaceutical companies need to invest in high-quality data and standardize data collection and analysis. With these investments, AI can help pharmaceutical companies develop safer and more effective drugs, leading to better patient outcomes.
Collaborative AI involves combining the expertise of humans and machines to solve complex problems. In drug manufacturing, collaborative AI could be used to identify potential drug candidates and optimize manufacturing processes. By working together, human experts and AI algorithms can come up with better solutions than either could on their own.
One of the strengths of AI is its ability to learn and improve over time. Continuous learning algorithms can adapt to changing conditions and improve their accuracy with each new data point. In drug manufacturing, continuous learning could be used to optimize manufacturing processes and improve drug quality over time.
AI is being used to discover new biomarkers, which are measurable indicators of a biological state or condition. By analyzing large datasets, such as genomic or proteomic data, AI can identify biomarkers that are associated with specific diseases or treatment responses. This can help clinicians identify patients who may benefit from a specific treatment or monitor treatment response.
Predictive maintenance uses machine learning algorithms to predict when equipment is likely to fail, allowing maintenance teams to address issues before they become serious. In drug manufacturing, predictive maintenance could help reduce downtime and ensure that equipment is running at optimal levels.
Personalized medicine involves tailoring treatments to individual patients based on their genetic makeup and medical history.
AI algorithms can analyze large amounts of patient data to identify potential drug interactions and predict which treatments are likely to be most effective. In drug manufacturing, personalized medicine could lead to the development of drugs that are more effective and have fewer side effects. AI is transforming drug discovery and personalized medicine by enabling researchers to analyze vast amounts of patient data and extract meaningful insights that aid in the development of personalized treatments. By using AI to develop personalized medicines, we can expect to see improved patient outcomes and more effective treatments for a wide range of diseases and conditions.
In conclusion, the integration of artificial intelligence (AI) in drug manufacturing and drug discovery has the potential to revolutionize the pharmaceutical industry. AI has already demonstrated its ability to significantly accelerate the drug discovery process by reducing the time and cost required to bring new drugs to market, while also improving the accuracy and efficiency of drug design.
AI has also shown promise in developing personalized medicine, allowing clinicians to tailor treatment plans to the specific characteristics of each patient. By analyzing vast amounts of patient data and extracting meaningful insights, AI can help identify patient subgroups that may benefit from specific therapies and predict how patients will respond to different treatments. In
drug manufacturing, AI has the potential to improve the efficiency and quality of the manufacturing process, reducing costs and increasing production capacity. By analyzing data from manufacturing processes, AI can identify opportunities for optimization and predict potential issues before they occur, improving product quality and reducing the risk of recalls.
While there are still challenges to overcome, such as the need for more comprehensive data sets and the integration of AI into existing drug discovery and manufacturing workflows, the potential benefits of AI in pharmaceuticals are undeniable. As the field continues to develop and evolve, we can expect to see continued advancements in drug discovery and personalized medicine, bringing new treatments to patients faster and more efficiently than ever before.
However, there are still challenges to be addressed, such as the need for comprehensive and diverse data sets, robust algorithms, and ethical considerations surrounding the use of AI in healthcare. Additionally, the integration of AI in drug discovery and manufacturing workflows may require significant investment and changes to existing practices.
The integration of AI in drug manufacturing and drug discovery has significant potential to transform the pharmaceutical industry. As AI continues to evolve and become more sophisticated, we can expect to see several advancements in this field in the coming years. One prediction
is that AI will become more integrated into drug discovery workflows, enabling faster and more efficient drug development. AI can also help researchers identify new drug targets and design drugs with greater accuracy and precision, leading to the development of more effective treatments.
Another prediction is that AI will enable the development of personalized medicine, tailored to the unique characteristics of each patient. By analyzing patient data and genetic information, AI can help predict how individuals will respond to different treatments, enabling clinicians to select the most effective treatment for each patient.
Additionally, AI can optimize drug manufacturing processes, improving efficiency and reducing costs. AI can help identify opportunities for process optimization and predict potential issues before they occur, improving product quality and reducing the risk of recalls.
In summary, the continued development and adoption of AI in drug manufacturing and drug discovery are essential for the future of healthcare. We can expect to see several advancements in this field in the coming years, leading to the development of more effective treatments, improved patient outcomes, and a more efficient healthcare system.
Nevertheless, the benefits of AI in drug discovery and manufacturing are clear. It can enable the development of innovative
treatments, improve patient outcomes, and address some of the major challenges facing the pharmaceutical industry. As such, the continued development and adoption of AI in drug discovery and manufacturing are crucial for the future of healthcare, and we can expect to see ongoing advancements and breakthroughs in this area in the coming years.
Shamal Fernando is currently working as a Managing Director at Slim Pharmaceuticals (Pvt) Ltd. He is Purpose Driven Marketer, Finance and Business Leader who gets energized by the opportunity to impact patients and people Business leader with experience in a broad range of therapeutic areas including oncology, Haematology, Rheumatology, women's health, neuroscience, cardiovascular, endocrine, and infectious diseases. Proven expertise in the on-time and within-budget delivery of innovative strategies.
Recent advances in cancer therapeutics have transformed the field of oncology, offering new hope for patients worldwide. Precision medicine, immunotherapy, targeted therapy, and combination therapies are among the key developments that are revolutionizing cancer care. Ongoing research and collaboration, along with a patient-centered approach to care, are essential to progress.
1. In your opinion, what are the most significant challenges facing the development and commercialization of cancer therapeutics today, and how do you think these challenges can be overcome?
Svetoslav Valentinov Tsenov Chair of the Board of Directors ARPharMDeveloping and selling cancer drugs is a tough challenge due to the high costs involved in drug development and clinical trials. The process of creating new cancer drugs can take years and billions of dollars. The clinical trial process can also be lengthy and complicated, leading to further costs. To tackle this issue, it's suggested that stakeholders in the healthcare industry, including academic institutions, pharmaceutical companies, and regulatory agencies should collaborate. This collaboration can help in making the drug development process more efficient and cost-effective. Apart from this, another challenge is the complex nature of cancer biology. Cancer is a diverse
disease with various subtypes and genetic mutations, making it difficult to create therapies that are effective against all types of cancer. Developing precision medicine can be a solution where treatments are tailored to each individual patient based on their genetic and molecular profiles, improving treatment efficacy and reducing adverse effects.
The regulatory process for cancer drugs is also challenging. The approval process can be long and complicated, leading to delays in the availability of new cancer therapies. Streamlining the regulatory process and increasing collaboration between regulatory agencies and industry stakeholders can help ensure timely approval of new cancer drugs.
Lastly, reimbursement and pricing also pose a significant challenge in the commercialization of cancer drugs. High drug prices and limited insurance coverage can prevent patients from accessing essential treatments. To address this issue, increasing transparency in drug pricing and developing innovative pricing models that prioritize patient access to treatments is crucial.
2. There has been a lot of excitement around immunotherapies for cancer treatment in recent years. How do you see this field evolving, and what role do you see immunotherapies playing in the future of cancer treatment?
Immunotherapy is a new and promising approach to cancer treatment. It has already gained considerable success, with many immunotherapeutic drugs approved for clinical use. The field of immunotherapy is continuously evolving and has shown immense potential to improve cancer treatment outcomes.
Combination therapies are among the ways immunotherapy is evolving. By combining different immunotherapies or immunotherapy with other treatments can significantly improve treatment outcomes.
Immunotherapies are a key player in the future of cancer treatment. They are designed to target cancer cells specifically and this is exactly what differentiates them from traditional treatments like chemotherapy that damage healthy cells along with cancer cells.
Immunotherapy also has the potential to induce long-lasting responses and even cures. Stimulating the immune system of the patient to recognize and attack cancer cells can create a memory effect that helps prevent cancer recurrence.
3. Precision medicine and genomic testing have been touted as potential game-changer in the field of cancer treatment. Can you speak to any recent advances in this area, and what impact do you think they will have on cancer care in the coming years?
Precision medicine and genomic testing have brought a significant revolution to the field of cancer treatment. Recent advancements have led to the development of targeted therapies that can selectively attack cancer cells based on their specific genetic mutations or biomarkers.
Among the advances in this area are liquid biopsy and the use of artificial intelligence and machine learning. The first provides for less invasive blood testing and the second is a valuable tool for analyzing genomic data and identifying new therapeutic targets that would otherwise take humans years of work.
Precision medicine and genomic testing help develop more targeted therapies tailored to each patient’s profile and individual needs. This is expected to significantly improve patient outcomes and reduce the level of adverse effects. It can also help identify patients at higher risk of developing cancer and induce early detection and treatment.
4. Combination therapies have become increasingly popular in cancer treatment. How do you determine the best combination of drugs for a given patient, and what factors do you consider in making these decisions?
Choosing the best combination of drugs for cancer treatment can be a complex process that depends on various factors. Although combination therapies can enhance treatment effectiveness and improve patient outcomes, the choice of drugs and dosages must be carefully considered to minimize toxicity and adverse effects.
One crucial factor in determining the best combination of drugs is the specific type of cancer being treated. Different cancers may respond differently to certain drugs, and the choice of the combination therapy must consider the specific molecular and genetic characteristics of the tumor.
Other factors to be taken into account are the stage and severity of cancer; the patient’s overall health and medical history; the availability and cost of different drugs.
Usually, a personalized treatment plan is developed by a multidisciplinary team involving oncologists, pathologists, radiologists, etc.
5. CAR-T cell therapies have shown remarkable success in treating certain types of blood cancers. How do you see this technology evolving, and what potential do you see for its application in other cancer types?
CAR-T cell therapy is among the significant breakthroughs in cancer treatment in recent years with tremendous success in treating specific blood cancers. It uses genetically modified T cells that recognize and attack cancer cells.
As this technology continues to evolve, there is tremendous potential for its application in treating other cancer types. One promising area of research is the use of CAR-T cell therapy in solid tumors, which is more challenging than in blood cancers. Researchers are exploring new ways to engineer T cells that can penetrate solid tumors and effectively target cancer cells.
Combination therapies that include CAR-T cell therapy are also being researched to enhance the effectiveness of treatment and improve patient outcomes. Researchers are also exploring new targets for CAR-T cell therapy, including tumor-specific antigens and other components of the tumor microenvironment.
Moreover, there is potential for CAR-T cell therapy to be used in treating viral infections and autoimmune diseases, providing a new approach to addressing these challenging conditions.
6. The development of targeted therapies has been a major focus in the field of cancer therapeutics. How do you identify the best targets for these therapies, and what challenges do you face in developing drugs that are both safe and effective?
Identifying the best targets for targeted therapies in cancer treatment is a complex process that depends on various factors. Targeted therapies offer a highly targeted approach to treatment, selectively attacking cancer cells based on their specific genetic mutations or biomarkers.
One way to identify targets is through genomic sequencing of cancer cells. This process can reveal unique mutations or biomarkers that can be targeted with a therapeutic agent. Another way is through high-throughput screening techniques, which can identify molecules with potential therapeutic activity that interact with specific cellular targets.
However, identifying targets is just the first step in developing targeted therapies. Developing drugs that are safe and effective poses many challenges, including toxicity and off-target effects.
One significant challenge is the potential for the targeted therapy to also affect healthy cells that share similar characteristics with cancer cells. This can result in side effects and toxicity that limit the effectiveness of treatment.
Another challenge is the development of drug resistance, which can occur when cancer cells develop mutations that make them resistant to the targeted therapy. This can limit the long-term effectiveness of treatment and may require the development of
new targeted therapies or combination therapies to overcome resistance.
Finally, there is the challenge of drug delivery, which is particularly important for targeted therapies that require precise targeting of cancer cells. Ensuring that the therapeutic agent reaches the intended target and is delivered in sufficient quantities to be effective is critical to the success of targeted therapies.
7. The application of AI and machine learning to the treatment of cancer has received a lot of attention. What role do you envision these technologies playing in the creation of novel cancer therapeutics? How do you see these technologies developing over the next few years?
As I have already mentioned before artificial intelligence and machine learning have huge potential in the development of cancer care.
One area where these technologies can be applied is in the development of novel cancer therapeutics. By analyzing large datasets, AI and machine learning can identify potential drug targets and predict the efficacy of new compounds, enabling researchers to develop new therapies more quickly and efficiently. They can also help identify those patients who will benefit most from a particular treatment.
We can expect to see continued development and refinement of AI and machine learning algorithms for cancer treatment in the next few years. These technologies will likely be integrated more closely with other treatment modalities, such as precision medicine and immunotherapy, to create more effective and personalized treatment approaches.
8. Clinical trials are a critical part of the drug development process, but they can be lengthy and expensive. How do you see the clinical trial process evolving, and what strategies do you use to streamline this process and make it more efficient?
Streamlining the clinical trial process is crucial in the development and commercialization of cancer therapeutics. Innovative trial designs, such as adaptive trials and basket trials, can expedite the process. Adaptive trials allow researchers to modify the trial protocol in response to new information, enabling more efficient use of resources and reducing the time and cost of trials. Basket trials enable the simultaneous testing of multiple drugs on different patient populations, expediting the testing of new therapies.
The value of RWE and AI and ML is more and more recognized in designing and conducting clinical trials as well.
Collaboration among stakeholders in the healthcare industry, including researchers, pharmaceutical companies, regulatory agencies, and patient advocacy groups, is critical to streamlining the clinical trial process. Increased collaboration can reduce duplication of effort, streamline trial design and execution, and speed up the approval of new drugs.
9. What excites you the most about the future of cancer therapeutics, and what breakthroughs do you see on the horizon that you think could have a significant impact on cancer care?
Exciting developments in cancer therapeutics hold promise for improving patient outcomes and transforming the way we treat
cancer. Personalized medicine is one of the most exciting areas of research that aims to tailor treatment to the individual patient based on their unique genetic and molecular characteristics. This approach has already shown significant promise in the treatment of certain cancers, such as melanoma and lung cancer, and is expected to become increasingly important in the future.
Advances in genomic sequencing and precision medicine are also expected to have a significant impact on cancer care in the coming years. By analyzing a patient's genetic and molecular profile, doctors can identify specific targets for treatment and develop personalized treatment plans tailored to the individual patient's needs.
Other significant advances are related to combination therapies, AI and ML, CAR-T cell therapy which we already discussed.
10. Cancer heterogeneity has long been a challenge in cancer treatment. Can you speak to any recent advances in addressing this issue, and what impact do you think these advances will have on the development of personalized cancer therapies?
Dealing with cancer heterogeneity has been a challenge for cancer treatment due to the complexity of the tumor microenvironment. Recent advances have focused on developing therapies that target multiple aspects of tumor heterogeneity, such as biomarker analysis and therapies that target different components of the tumor microenvironment.
One promising approach is combination therapy that combines immunotherapy with targeted therapy. This approach targets both cancer and immune cells in the tumor
Liquid biopsy tests have emerged as a valuable tool for addressing tumor heterogeneity. These tests detect circulating tumor DNA (ctDNA) in the blood, providing a more comprehensive picture of the tumor's genetic and molecular characteristics, which enables more personalized and targeted treatment.
microenvironment, leading to better treatment outcomes.
Liquid biopsy tests have also emerged as a valuable tool for addressing tumor heterogeneity. These tests detect circulating tumor DNA (ctDNA) in the blood, providing a more comprehensive picture of the tumor's genetic and molecular characteristics, which enables more personalized and targeted treatment.
Advancements in genomic sequencing and precision medicine are also critical for addressing tumor heterogeneity. By analyzing a patient's genetic and molecular profile, doctors can identify specific targets for treatment, and develop personalized treatment plans based on the individual patient's needs.
Moreover, researchers are exploring new biomarkers to identify patients who are most likely to benefit from a particular
treatment. Recent studies have identified specific immune cell subsets associated with the response to immunotherapy, providing a more effective and targeted use of this treatment approach.
11. In recent years, there has been debate regarding the high expense of cancer medications. What measures can be taken to make sure that patients have access to these lifesaving treatments, and how do you anticipate the landscape of pricing and reimbursement changing?
The cost of cancer medications has become a subject of intense debate, with many patients finding it difficult to afford these life-saving treatments. To address this issue, several measures can be taken.
One approach is to increase transparency in drug pricing and reimbursement, ensuring that patients and healthcare providers have access to clear and accurate information about the cost of medications. This can help patients make informed decisions about their treatment options and enable healthcare providers to negotiate lower prices with drug manufacturers.
Another approach is to promote competition in the marketplace, by allowing the introduction of generic versions of cancer medications and encouraging the development of biosimilars, which are lower-cost versions of biologic drugs.
There are also some initiatives led by manufacturers, institutions, and NGOs providing financial aid to those who cannot afford treatment.
In the future, we can expect continued debate and discussion around the pricing and reimbursement of cancer medications.
There may be increased pressure on drug manufacturers to justify the high cost of their medications and more scrutiny of the pricing practices of insurance companies and government agencies.
12. One of the challenges in cancer treatment is drug resistance. Can you speak to any recent advances in understanding the mechanisms behind drug resistance and developing strategies to overcome it?
Drug resistance is a major obstacle in treating cancer, as cancer cells can develop mechanisms to evade the effects of chemotherapy and targeted therapies. Recent progress in comprehending the mechanisms of drug resistance is providing new perspectives into this problem, and scientists are devising methods to overcome it.
One key area of research for understanding drug resistance is the identification of molecular pathways that play a role in resistance. By comprehending the specific pathways that contribute to resistance, researchers can design new drugs or drug combinations that target these pathways and counter-resistance.
Another research area is the use of combination therapies, which can help overcome drug resistance by targeting multiple pathways at the same time. For example, combining chemotherapy with immunotherapy can improve the effectiveness of treatment and outcomes for patients with drug-resistant cancers.
Moreover, scientists are investigating the application of artificial intelligence and machine learning to evaluate vast datasets and discover patterns of drug resistance. This approach can help identify new thera-
peutic targets and develop more effective treatment strategies.
One promising method of defeating drug resistance is precision medicine and personalized treatment. By analyzing a patient's genetic and molecular profile, doctors can pinpoint specific targets for treatment and create personalized treatment plans tailored to the patient's individual needs. This approach has already demonstrated significant promise in treating drug-resistant cancers such as melanoma and lung cancer.
13. The field of oncology has traditionally been dominated by large pharmaceutical companies, but there has been a growing interest in cancer therapeutics among smaller biotech firms. How do you see the role of smaller companies evolving in the development of new cancer therapies, and what advantages do they offer compared to larger companies?
In recent years, smaller biotech firms have become increasingly important in the development of new cancer therapies. These companies offer several advantages over larger pharmaceutical companies, including greater agility, faster decision-making, and a focus on innovation. One of the benefits of smaller biotech firms is their ability to make decisions more efficiently and move quickly, allowing them to adapt to the fast-paced field of cancer research. They can also be nimbler in their response to new challenges and opportunities, allowing them to stay at the forefront of the field.
Another advantage of smaller biotech firms is their focus on innovation. They often have a more specialized focus on a particular area
of research, allowing them to develop deep expertise and identify new opportunities for innovation. This can lead to the development of novel therapies and treatment approaches that may not have been considered by larger pharmaceutical companies.
Smaller biotech firms can also offer a more personalized and patient-centered approach to cancer therapeutics. They may have a closer relationship with patients and healthcare providers, enabling them to better understand patient needs and develop more targeted and effective treatment options.
14. Finally, is there anything else you would like to share with our audience regarding the future of cancer treatment and the role of industry experts in this field?
The future of cancer treatment is promising, with developments in precision medicine, immunotherapy, targeted therapy, and combination therapies providing hope for patients globally. Industry experts are vital in driving these advances, through research and development, clinical trials, and commercialization of new treatments. Collaboration across disciplines and institutions, sharing knowledge and expertise, and working together to overcome challenges will be essential for continued progress in the field of oncology.
I believe the future of cancer treatment is bright, and sustained research and development, collaboration, and patientcentered care are crucial for continued progress in the fight against cancer.
Svetoslav is a Medical doctor, master in public health, and business expert with over 17 years of experience in the pharmaceutical industry. Held various positions at the level of vice president, executive director, and director in multinational companies at global and regional levels (USA, Europe, Asia, Australia) in the field of management, sales department, corporate strategies, market access, relations with institutions, marketing, R&D . In his work, he places a strong focus on leadership, market development, customer and patient orientation, innovative solutions and strategic thinking. Lecturer in international and national forums on various topics, such as gene and cell therapies, innovative models of new molecule development, healthcare economics, and trends in clinical trial development. Former member of the EFPIA Working Group for Central and Eastern Europe, Chairman of the ARPharM Management Board, and Co-Chair of the AmCham Health Commission. He is currently a member of the management board of the Bulgarian Oncology Scientific Society, National Coordinator of the European Union initiative "Europe beats cancer" for Bulgaria, lecturer at the Medical Universities, managing director of Sunlight Health - a company with a focus on the healthcare sector.
A summary of the lessons that our industry learned from the COVID-19 pandemic, and have to incorporate as best practices in the next years future for all our new developments and also commercial practices. Transparency is the axial factor for healthy growth in the next decade.
Gustavo Samojeden is currently working as the CEO of Eriochem S.A. He is a Pharma executive with more than 35 years of experience in both technical and business aspects of the industry. Has managed C-level organizations in 5 countries on 3 continents with a focus on emerging markets like South East Asia Pacific and Latin America.
The COVID-19 pandemic was a great booster for the pharma industry's innovation pipeline. At first glance, it could seem that clinical trial patients’ recruitment was delayed or exhibition batch production stopped for a time, but the R&D efforts and synergies between the
public and private sectors worldwide will continue for many years as an important growth factor for the sector.
2. With the significant increase in the demand for treatments and vaccines, how has the pandemic impacted the financial outlook of pharmaceutical companies?
Without any doubt, things are very positive; cash flow speeds up, and new investments come to our sector.
3. What changes do you see occurring in the regulatory landscape for the pharmaceutical industry as a result of the pandemic, and how do you think this will impact the industry's growth?
I believe that new regulatory pathways will be created, making it easier for the population to afford the new medicines without increasing any safety risks.
4. Do you anticipate any long-term changes in how clinical trials are conducted and managed following the pandemic, and if so, what might these changes be?
Not very much; today's clinical research is very efficient.
5. How has the pandemic affected the globalization of the pharmaceutical industry, and do you expect companies to change their approach to globalization in the future?
In a very positive way, not only globalization but also a connection between the private and public sectors everywhere creates new opportunities for the development of pharma and medical practices.
6. In your opinion, what role has technology played in the pharmaceutical industry's response to the pandemic, and how has it impacted the industry's growth?
The most significant new technology is by far the mRNA technology, now applied in COVID vaccines but with a lot of new applications in the future. The pandemic was a great test for this technology; hundreds of thousands of doses were applied worldwide in a very short period of time without major safety issues, which was very positive in every aspect.
7. With the pandemic highlighting the importance of public-private partnerships in developing vaccines and treatments, do you see this trend continuing in the future, and what impact might it have on the pharmaceutical industry?
Yes, it will definitely continue and increase.
8. How has the pandemic impacted the industry's approach to drug pricing, and what changes do you anticipate in the future?
The pressure for better medicines at affordable prices will not stop, and the pandemic will accelerate this process. Both the public sector as governments and social security players, as well as private patients at pharmacies, will actively ask for this policy.
9. With the pandemic bringing new attention to the importance of public health and preventative medicine, what impact do you see this having on the pharmaceutical industry's R&D efforts?
Public health awareness will be a booster
for our sector; more people are viewing their health as a big value that needs attention, so more patients will be in the doctor's office and later require more medicines from us. This process will be deeper in the next few years.
10. With the COVID-19 pandemic accelerating the adoption of digital health technologies, what impact do you see this having on the pharmaceutical industry's business models and revenue streams in the future?
New digital technologies are quickly adapting in our industry: electronic batch records, paperless procedures, more data integrity risk assessment, electronic signatures, and e-CRFs are only a few examples that will be met in the near future in our pharma companies.
11. Given the rapid pace of vaccine development and regulatory approvals during the pandemic, what lessons do you think the industry can learn from this experience that will improve future response efforts to emerging infectious diseases?
Time is always a factor that must be considered in the development of a new medicine or treatment. Sometimes we forget about this; we focus on efficacy and safety and sometimes forget the time factor. This will not be the case, in my opinion, in the next few years in our industry.
12. In the wake of the pandemic, do you see a greater emphasis being placed on collaboration and data sharing across the pharmaceutical industry to accelerate drug development and improve patient outcomes? If so, what challenges do you anticipate in achieving this goal? I believe that this will not happen very fast because there are still many factors, sometimes commercial competition, scientific grant contests, etc., that are not very easy to overcome and make data sharing more open and free.
13. The COVID-19 pandemic has brought unprecedented attention to public health and the role of the pharmaceutical industry in addressing the global health crisis. In light of this, do you see the industry's social responsibility and ethical considerations changing, and what actions can companies take to promote greater transparency and accountability in their operations?
Yes, it is a must—not better, a have-to. Our industry has to increase the transparency of all their operations, have a big scientific risk assessment, and show this to all key players, such as regulators, politicians, patient groups, medical associations, journalists, etc.
The integration of vaccine development and manufacturing plays a critical role in addressing global health challenges. This comprehensive approach enhances disease surveillance, enables rapid outbreak response, strengthens health systems, and maximizes the impact of vaccines. Collaboration and innovation in this field are crucial for a healthier and more resilient future.
Director, Principal GCP and GVP Auditor Proqlea Ltd1. Can you explain how mRNA vaccines work, and what are some of the advantages and disadvantages of this technology compared to traditional vaccine approaches?
mRNA vaccines work by introducing a small piece of genetic material called messenger RNA (mRNA) into the body. This mRNA contains instructions for making a specific protein. Once the mRNA is inside the body's cells, it is translated into viral protein, triggering an immune response.
Josipa QAThis immune response involves the production of antibodies that can recognize and neutralize the virus. The immune system also produces memory cells that "remember" how to respond to the virus if it is encountered again in the future.
The advantage of this model would be for sure the fact that mRNA vaccines can be developed and manufactured quickly, as they do not require the use of live viruses or the lengthy process of growing the virus in the lab. mRNA vaccines can be easily modified to address new strains of the virus, which is important given the ability of viruses to mutate and develop new variants. They are considered safe, with adverse events being rare and mostly mild, such as pain or swelling at the injection site.
Disadvantages are more logistic nature, like mRNA vaccines require special storage conditions and technology is relatively new, so there is still some uncertainty regarding the long-term safety and effectiveness of mRNA vaccines.
Overall, mRNA vaccines represent a promising new technology. While there are still some challenges and uncertainties associated with this approach, the benefits of mRNA vaccines are clear, and they are likely to play an increasingly important role in the development of vaccines and other medical treatments in the future.
2. What are some of the key considerations when developing vaccines for emerging infectious diseases, and how can the development timeline be accelerated in these situations?
Developing vaccines for emerging infectious diseases is a complex and challenging process. However, by taking a collaborative and coordinated approach and using innovative development strategies, it is
possible to accelerate the timeline and bring effective vaccines to market more quickly.
Developing vaccines for emerging infectious diseases is a complex process that involves many key considerations. Some of these considerations are:
• Understanding the pathogen: One of the most important considerations is understanding the pathogen responsible for the outbreak. This involves studying its transmission, replication, and disease-causing mechanisms, as well as any genetic mutations that may have occurred.
• Developing the vaccine platform: Once the pathogen has been identified and understood, a vaccine platform needs to be selected. This could involve using an existing platform that has been used for other vaccines or developing a new platform specifically for the emerging pathogen.
• Designing the vaccine: The vaccine design needs to take into account the specific characteristics of the pathogen, such as its structure, antigens, and replication cycle. The vaccine must also be able to stimulate an effective immune response in the target population.
• Conducting preclinical and clinical trials: Before a vaccine can be approved, it must undergo rigorous testing in preclinical and clinical trials to evaluate its safety and efficacy.
• Manufacturing and distribution: Once a vaccine has been approved, it needs to be manufactured at scale and distributed to the target population.
To accelerate the development timeline for vaccines in emerging infectious disease situations, several approaches can be taken, including funding and resources, collaboration and coordination, adaptive clinical trial designs, expedited regulatory review, and parallel processing.
3. Can you discuss the role of adjuvants in vaccine development and manufacturing, and how they can enhance the immune response to vaccines?
Adjuvants play an important role in vaccine development and manufacturing by enhancing the immune response to the antigen. By improving the effectiveness of vaccines, adjuvants can help to protect individuals and populations from a wide range of infectious diseases.
In general, adjuvants are substances that are added to vaccines to enhance the immune response to the antigen, which is the part of the vaccine that stimulates the immune system to recognize and respond to a pathogen. Adjuvants can be used in vaccine development and manufacturing to improve the effectiveness of vaccines by enhancing the immune response, reducing the amount of antigen required, allowing for antigen sparing, and increasing the duration of protection.
There are several types of adjuvants that can be used in vaccine development and manufacturing, including:
• Aluminum salts: These are the most used adjuvants in vaccines. They work by enhancing the antigen's ability to be taken up by immune cells, thereby improving the immune response.
• Oil-in-water emulsions: These adjuvants work by creating a depot of antigen and adjuvant at the site of injection, which stimulates the immune system to produce a strong and long-lasting response.
• Liposomes: These are tiny spheres made of lipids that can carry the antigen and adjuvant directly to immune cells, improving the immune response.
• Particulate adjuvants: These are adjuvants that are made up of particles, such as virus-like particles or nanoparticles that can
improve the immune response by mimicking the size and shape of the pathogen.
4. What are some of the challenges in developing vaccines for diseases that have a high degree of genetic variability, such as influenza and HIV? Development of such vaccines can be challenging for several reasons:
• Antigenic variability: One of the main challenges in developing vaccines for these diseases is their high degree of antigenic variability. The antigens on the surface of the pathogen can rapidly mutate, making it difficult to develop a vaccine that can effectively target all strains of the pathogen.
• Lack of a correlate of protection: Another challenge is the lack of a clear correlate of protection, which is a specific measure of immunity that can be used to predict vaccine effectiveness. For diseases like influenza and HIV, it is difficult to identify a specific measure of immunity that is strongly associated with protection against infection.
• Immune escape: Viruses like influenza and HIV can evolve rapidly and develop mechanisms to evade the immune system. This can lead to the emergence of new strains of the virus that are resistant to the immune response elicited by the vaccine.
• Limited animal models: Animal models for these diseases may not fully reflect the pathogenesis or immune response observed in humans, making it challenging to predict vaccine efficacy in humans.
•Manufacturing challenges: Manufacturing vaccines for these diseases can be challenging due to the high degree of variability of the antigen, as well as the need for large-scale production and purification of the vaccine.
To overcome these challenges, vaccine developers use several strategies, including developing vaccines that target multiple strains of the pathogen, using novel vaccine
platforms, developing broad-spectrum vaccines (this approach has shown promise in preclinical studies for both influenza and HIV). Also, developing adjuvant vaccines can improve the immune response to the vaccine and may be particularly useful for diseases with a high degree of antigenic variability. Overall, developing vaccines for diseases with a high degree of genetic variability is a complex and challenging process. However, with the use of novel vaccine platforms and innovative development strategies, progress is being made toward developing effective vaccines for these diseases.
5. How do manufacturers ensure the stability and shelf life of vaccines, and what are some of the factors that can impact vaccine stability?
Manufacturers usually have several strategies to ensure the stability and shelf life of vaccines, as the efficacy and safety of vaccines heavily depend on their stability. Some common practices are formulation optimization, cold chain management, aseptic manufacturing, and quality control testing.
These tests assess the potency, purity, and stability of the vaccine, ensuring that it meets the required specifications for efficacy and safety.
Factors that can impact the stability of vaccines are temperature, light, oxygen and moisture, pH and Chemical Stability, and time. Over an extended period, even under ideal storage conditions, vaccines can undergo gradual degradation.
By considering these factors, implementing appropriate manufacturing practices, and adhering to strict quality control measures, manufacturers aim to maximize the stability and shelf life of vaccines, ensuring their effectiveness when administered to patients.
6. Can you explain the concept of “vaccine hesitancy,” and how it can impact vaccine uptake and public health?
"Vaccine hesitancy" refers to the reluctance or refusal of individuals or communities to receive vaccines, despite the availability of vaccination services. It is a complex phenomenon influenced by various factors, including attitudes, beliefs, and concerns about vaccines. It was strongly pointed out in the time of the COVID-19 pandemic.
Vaccine hesitancy can have significant implications for vaccine uptake and public health in reduced vaccination rates, increased disease burden, loss of trust in vaccines and public health authorities, and social and economic consequences. Vaccinepreventable diseases can have social and economic consequences. They can lead to increased healthcare costs, lost productivity due to illness, and disruptions to education and work.
By contrast, high vaccine uptake contributes to improved public health, reduces healthcare expenses, and fosters societal well-being.
7. Can you discuss the role of “reverse vaccinology” in vaccine development, and how it has changed the way vaccines are developed?
Reverse vaccinology is an innovative approach to vaccine development that has revolutionized the way vaccines are created. Traditionally, vaccine development involved cultivating and inactivating pathogens or using attenuated strains to induce an immune response. However, reverse vaccinology takes a different path by leveraging the power of genomics and bioinformatics to identify potential vaccine targets. Reverse vaccinology works and its impact on vaccine development in many ways: Genomic Analysis, Bioinformatics Screening, Antigen Selection, Expression and Purification, and Immunological Evaluation.
Reverse vaccinology has significantly changed the vaccine development landscape by offering several advantages like Broader Pathogen Coverage, Rational Vaccine Design, Speed and Efficiency, Customization and Adaptability, and Reduced Safety Risks.
That process has proven successful in the development of several vaccines, including the vaccines against meningococcus B, Streptococcus pneumonia, and the human papillomavirus (HPV). It continues to be a valuable approach in the quest for new vaccines against various infectious diseases.
8. How can vaccine development and manufacturing be made more efficient and cost-effective, and what are some of the emerging technologies and approaches in this field?
Efficiency and cost-effectiveness are critical considerations in vaccine development and manufacturing. Advancements in technology and innovative approaches have the potential to enhance efficiency and reduce costs in the following ways:
1. Process Optimization: Streamlining and optimizing manufacturing processes can significantly improve efficiency and reduce costs. This involves identifying and eliminating unnecessary steps, optimizing cell culture conditions, implementing continuous manufacturing approaches, and adopting advanced process analytical technologies for real-time monitoring and control.
2. Vaccine Platform Technologies: Developing vaccine platform technologies that can be adapted to multiple pathogens can enhance efficiency and cost-effectiveness. Platforms like viral vectors, virus-like particles (VLPs), or nucleic acid-based vaccines offer the flexibility to modify the genetic payload to target different pathogens, thereby reducing the need for developing individual manufacturing processes for each vaccine.
3. Computational Modeling and Artificial Intelligence (AI): Computational modeling and AI can aid in optimizing vaccine development and manufacturing. These technologies can help predict vaccine candidates, design experiments, analyze data, and optimize
production processes. They enable faster and more informed decision-making, reducing development timelines and costs.
4. Advanced Vaccine Manufacturing Facilities: Modernizing vaccine manufacturing facilities with state-of-the-art equipment and technologies can enhance efficiency. This includes implementing single-use bioreactors, automated systems for process control and monitoring, and integrated manufacturing platforms to reduce setup times and increase production flexibility.
5. Vaccine Formulation and Stabilization: Improving vaccine formulations and stabilization methods can enhance efficiency and reduce costs. Stable formulations can eliminate the need for cold chain storage and transportation, reducing logistics complexities and costs. Additionally, advancements in lyophilization (freeze-drying) techniques and novel stabilizers can improve the stability of vaccines and extend their shelf life.
6. Manufacturing Collaborations and Public-Private Partnerships: Collaborations and partnerships between vaccine manufacturers, academic institutions, and government organizations can foster knowledge sharing, resource pooling, and cost-sharing. Such collaborations can leverage expertise, infrastructure, and funding to accelerate development and manufacturing efforts, making them more efficient and cost-effective.
7. Regulatory Advances: Regulatory agencies have been exploring ways to streamline vaccine development and approval processes without compromising safety and efficacy. Initiatives such as accelerated approval pathways, regulatory harmonization efforts, and expedited review procedures can facilitate faster and more cost-effective vaccine development and manufacturing.
These emerging technologies and approaches have the potential to revolutionize vaccine development and manufacturing,
making it more efficient, cost-effective, and adaptable to evolving public health needs. Continued research, investment, and collaboration are key to realizing their full potential and ensuring the availability of safe and effective vaccines to protect global populations.
9. What are some of the key ethical considerations in conducting clinical trials for vaccines, such as informed consent, risk-benefit analysis, and vulnerable populations?
When conducting clinical trials for vaccines, several key ethical considerations must be considered to ensure the protection and well-being of trial participants. Some of the primary ethical considerations include for sure Informed Consent. Informed consent is a fundamental ethical principle in clinical trials. Participants must be provided with comprehensive and understandable information about the trial, including its purpose, procedures, potential risks and benefits, alternative options, and their rights as participants. Informed consent should be obtained voluntarily, without coercion, and individuals should have the freedom to withdraw from the trial at any time.
Then we need to consider Risk-Benefit Analysis. Before initiating a clinical trial, researchers and ethics committees must conduct a thorough risk-benefit analysis. This analysis involves evaluating the potential benefits of the vaccine in terms of its effectiveness in preventing or treating the targeted disease and comparing them to the risks associated with participation in the trial. The risks must be minimized, and the potential benefits should outweigh the potential harms. Also, special attention must be given to protecting vulnerable populations, including children, pregnant women, the elderly, and individuals with cognitive impairments or limited decision-making capacity.
Ethical guidelines emphasize the need for additional safeguards and considerations for these populations, ensuring that their inclusion in the trial is justified, and their rights, well-being, and autonomy are protected.
Equitable Participant Selection needs to be pointed out. The selection of participants should be fair and equitable, without discrimination or exploitation. Factors such as age, gender, race, and socioeconomic status should not be used to exclude individuals from participating in trials unless scientifically justified. Ensuring diversity in trial populations is important to understand the vaccine's effectiveness across different demographic groups and to prevent inequities in access to potential benefits.
In the end, regular steps and rigorous monitoring of participant safety is essential throughout the trial. Researchers have an ethical obligation to promptly report any adverse events or side effects to the appropriate authorities and to provide necessary medical care to trial participants. This includes implementing mechanisms for ongoing safety monitoring and data analysis. Also, Ethical Review and Oversight must be done. Clinical trials must undergo ethical review by independent institutional review boards or ethics committees. These bodies evaluate the trial protocol, participant protection measures, informed consent documents, and overall ethical considerations. They ensure that the trial design is scientifically sound and ethically justified before granting approval. Ethical considerations extend beyond the trial itself to the dissemination of results. Researchers have an ethical obligation to be transparent and to publish the trial findings in a timely and transparent manner, regardless of the outcome. Transparent reporting ensures that the scientific and broader communities have access to accurate information, allowing for informed decision-making and further research.
These ethical considerations provide a framework for ensuring the rights, well-being, and autonomy of participants in clinical trials for vaccines. Adhering to these principles is essential in conducting ethically sound research and maintaining public trust in the development of vaccines.
10. Can you discuss the role of international collaboration and cooperation in vaccine development and manufacturing, and how can it be improved in the future?
International collaboration and cooperation play a crucial role in vaccine development and manufacturing, particularly in addressing global health challenges. Some key aspects of their role and potential improvements for the future would be for sure knowledge sharing and expertise. International collaboration allows for the sharing of knowledge, expertise, and best practices across borders. It enables researchers, scientists, and manufacturers from different countries to pool their collective knowledge and experience, accelerating the progress of vaccine development. Enhanced collaboration can lead to more efficient processes, improved technologies, and better vaccine outcomes. Then I would point out resource mobilization. Collaborative efforts can help mobilize resources, including funding, research facilities, and manufacturing capabilities. Global partnerships enable the pooling of financial resources and infrastructure, supporting the development and manufacturing of vaccines. This is particularly important for diseases that disproportionately affect low-income countries and where local resources may be limited. Access to a diverse population would have an impact too. International collaboration facilitates access to diverse populations for clinical trials, ensuring the representation of different demographics and geographic regions. This inclusivity allows for a more comprehensive
understanding of vaccine safety, efficacy, and effectiveness across populations and helps identify potential differences or challenges. Collaborative efforts among regulatory agencies can promote the harmonization of regulatory standards and processes. Aligning regulatory requirements and streamlining approvals can facilitate the timely and efficient development, evaluation, and deployment of vaccines globally. It helps prevent duplication of efforts, reduces regulatory burdens, and ensures that vaccines meet high safety and efficacy standards.
International collaboration can support the establishment and scaling up of vaccine manufacturing capacities in different regions. Sharing manufacturing technologies, expertise, and knowledge can enhance local production capabilities, reduce dependency on a limited number of manufacturers, and ensure wider access to affordable vaccines. International collaboration is vital for pandemic preparedness and response. It involves coordinating research efforts, sharing data, and rapidly developing and distributing vaccines during outbreaks. Collaborative platforms and mechanisms facilitate a coordinated global response, ensuring the availability of vaccines to control and contain infectious diseases.
Improving international collaboration and cooperation in vaccine development and manufacturing requires sustained political commitment, adequate funding, and the establishment of frameworks that prioritize equity, transparency, and knowledge sharing. Multilateral organizations, such as the World Health Organization (WHO), play a vital role in fostering global partnerships and coordinating collaborative initiatives. By strengthening these efforts, the global community can better address emerging health threats and ensure access to safe, effective, and affordable vaccines worldwide.
11. How can vaccine research and development be used to address global health disparities and improve health equity for marginalized populations? In our ongoing pursuit to bridge global health disparities, vaccine research, and development have emerged as a crucial avenue for promoting health equity, particularly among marginalized populations. By targeting diseases that disproportionately affect underserved communities, inclusive clinical trials, and ensuring equitable access, vaccines can become powerful agents of change in improving the health and wellbeing of vulnerable groups.
To truly tackle global health disparities, research efforts must be directed toward diseases prevalent in low-income countries, neglected tropical diseases, and ailments affecting marginalized populations such as children, pregnant women, and refugees. By prioritizing these areas, vaccine candidates are tailored to address the specific health needs of those often overlooked.
Ensuring inclusivity in clinical trials is equally critical. By actively involving diverse populations, including marginalized and underrepresented groups, vaccine efficacy and safety can be thoroughly evaluated. These trials provide robust data on vaccine response across populations with different genetic, physiological, and socio-cultural factors, allowing us to make informed decisions that cater to specific needs.
However, research and development alone are not enough. Equitable access to vaccines is the linchpin to achieving health equity. Barriers such as affordability, geographical constraints, and limited healthcare infrastructure must be dismantled. Through collaborative efforts with local healthcare providers and community leaders, targeted vaccination campaigns can reach marginalized communities, ensuring that no one is left behind.
Strengthening healthcare systems in lowincome countries and underserved areas is pivotal. Investments in infrastructure, healthcare worker training, and immunization programs are necessary to extend routine vaccination services to marginalized populations as part of their regular healthcare. By empowering these healthcare systems, we create sustainable avenues for equitable access to vaccines and improve long-term health outcomes.
Partnerships and collaborations between governments, international organizations, research institutions, and vaccine manufacturers are pivotal in addressing health disparities. Through joint efforts in research, technology transfer, capacity-building, and knowledge sharing, we can leverage collective expertise to overcome obstacles and uplift marginalized populations.
Vaccine diplomacy plays a vital role in fostering health equity. Advocating for fair allocation and distribution mechanisms that prioritize marginalized populations, supporting global vaccine-sharing initiatives, and engaging in international cooperation are necessary steps to reduce health inequities. By ensuring access to vaccines for the most vulnerable and in need, we create a more just and equitable world.
Furthermore, education and community engagement are crucial. Tailored health education campaigns that respect cultural sensitivities, provide accurate information, and involve community leaders and organizations can combat vaccine hesitancy. Building trust and actively involving communities in decision-making processes foster health equity and pave the way for lasting change.
Harnessing the power of vaccine research and development, we have the potential to transform the lives of marginalized populations worldwide. By focusing on their specific health needs, improving access, strength -
ening healthcare systems, and engaging communities, we can bridge global health disparities and create a future where everyone has the opportunity to thrive.
12. How can vaccine development and manufacturing be adapted to address emerging public health threats, such as bioterrorism or pandemics, and what are some of the challenges in this area?
In the face of emerging public health threats, such as bioterrorism incidents or pandemics, the world is increasingly recognizing the need to adapt vaccine development and manufacturing to effectively respond to and contain these challenges. By employing innovative approaches, researchers and manufacturers can enhance their ability to swiftly develop and produce vaccines tailored to combat these emerging threats. However, significant challenges remain on this transformative journey. To address these challenges head-on, one key strategy involves the establishment of rapid response platforms. These platforms utilize cutting-edge technologies like mRNA or viral vectors, enabling scientists to quickly adapt vaccine production to tackle new or emerging pathogens. By leveraging these platforms, vaccine development can be expedited, ensuring a rapid response during emergencies. Nonetheless, the development and validation of such platforms require substantial investment in research and infrastructure. Striking a delicate balance between speed and safety while adhering to regulatory standards poses an ongoing challenge for scientists and regulatory authorities alike. Nevertheless, progress in this area has the potential to revolutionize our ability to combat emerging threats.
Another critical component in addressing these challenges is strengthening global surveillance and early warning systems. By bolstering surveillance networks at national and international levels, we can swiftly detect
and identify potential threats. The timely identification of emerging pathogens serves as a catalyst for scientists to initiate vaccine development promptly, thereby reducing the impact of outbreaks or bioterrorism incidents. However, limited resources and infrastructure in certain regions hinder the establishment of effective surveillance systems. Moreover, achieving harmonization and coordination among different countries and organizations to enable timely information sharing remains a complex task. These hurdles necessitate global collaboration and support to ensure a robust early warning system that leaves no one behind.
Flexible regulatory approaches are also crucial in adapting to emerging threats. Regulatory authorities need to adapt their processes to ensure safety and efficacy while expediting the evaluation and authorization of vaccines during emergencies. Striking the right balance between speed and adherence to regulatory standards is a formidable task, as maintaining transparency and public trust in the regulatory process remains paramount.
Promoting collaborative research and development efforts among researchers, governments, and manufacturers is an essential element of tackling emerging threats. By fostering partnerships, sharing data, and collaborating on vaccine development, the
global community can pool resources and expertise effectively. However, addressing issues related to intellectual property rights, information sharing, and equitable distribution of benefits is crucial to ensure fair and efficient collaboration.
Preparing for emerging threats also involves strategies for vaccine stockpiling and distribution. Adequate stockpiling of vaccines specific to potential pathogens enables a rapid response to outbreaks or bioterrorism incidents. Nonetheless, the financial investments and ongoing maintenance required for vaccine stockpiling pose significant challenges for governments and organizations. Striking a balance between costs and potential risks remains a critical consideration.
Finally, enhancing vaccine manufacturing capabilities and scalability is vital. The ability to quickly ramp up production in response to emerging threats ensures timely access to vaccines. Nevertheless, scaling up manufacturing capacity is complex and resource intensive. Manufacturers must invest in infrastructure, equipment, and personnel training to meet increased demand during emergencies. Ensuring the quality and consistency of vaccines during rapid scaling presents another significant challenge.
Addressing these challenges requires strong coordination among governments, international organizations, researchers, manufacturers, and regulatory agencies. Investments in research and infrastructure, enhanced global surveillance systems, flexible regulatory frameworks, and collaborative partnerships are critical for the effective adaptation of vaccine development and manufacturing to address emerging public health threats.
Through collective efforts and a commitment to innovation, we can strengthen our global response capabilities, protect vulner-
able populations, and ensure the availability of life-saving vaccines in the face of bioterrorism and pandemics. By constantly evolving and adapting, we can better safeguard public health and secure a brighter, healthier future for all.
13. Can you discuss the potential of using gene editing technologies such as CRISPR/Cas9 in vaccine development, and what are some of the challenges and ethical considerations involved?
Gene editing technologies, particularly CRISPR/Cas9, are ushering in a new era of vaccine development, promising precise and efficient tools to combat diseases. With the potential to modify the genetic material of pathogens and host cells, these advancements hold great promise in revolutionizing vaccine research and production. However, as we embrace the possibilities, it is vital to address the challenges and ethical considerations associated with these cutting-edge technologies. The potential of gene editing in vaccine development is vast. By employing CRISPR/Cas9 and other gene editing tools, scientists can engineer targeted antigens, creating safer and more effective vaccines. These modified antigens can trigger robust immune responses, ensuring enhanced protection against pathogens. Additionally, gene editing enables the manipulation of viral or bacterial vectors used in vaccine delivery, optimizing their efficacy, stability, and safety profiles. The ability to fine-tune host immune responses through gene editing also holds promise for creating more potent and longerlasting vaccines.
Despite the remarkable potential, challenges in gene editing for vaccines must be addressed. One significant concern is the potential for off-target effects. Gene editing techniques may inadvertently introduce unin-
tended modifications in the genome, leading to unexpected changes that could impact vaccine safety and efficacy. Overcoming this challenge requires meticulous research and rigorous testing to ensure precise and accurate modifications. Another hurdle lies in efficient delivery methods. The successful deployment of gene editing tools to specific cells or tissues is critical for achieving desired outcomes. Scientists are actively exploring innovative delivery mechanisms to optimize the effectiveness of gene editing in vaccine development, which requires continuous research and development.
From an ethical standpoint, the use of gene editing technologies raises important considerations. One such concern is germline editing, which involves modifying the genes of reproductive cells. The ethical dilemmas surrounding germline editing are significant, as they encompass long-term effects on future generations and the potential for unintended consequences. Comprehensive ethical oversight and global consensus on the boundaries of germline editing are imperative to navigate these complex issues. Informed consent is another crucial ethical consideration. Individuals participating in clinical trials involving gene editing technologies must be fully informed about the risks, benefits, and potential long-term implications. Transparent and comprehensive communication, along with robust ethical oversight, ensures that individuals make informed decisions regarding their participation in such trials.
Equity and access are paramount when implementing gene editing technologies in vaccine development. The costs associated with gene editing may pose challenges to ensuring equitable distribution and access. To prevent exacerbating existing health disparities, it is essential to prioritize affordability and develop strategies that ensure fair access for all.
Navigating the ethical landscape of gene editing in vaccine development requires responsible use and oversight. Robust regulatory frameworks are needed to address safety, efficacy, and ethical concerns associated with gene editing technologies. Striking a delicate balance between scientific advancement and ethical considerations is vital in shaping regulations that promote responsible use and safeguard public health. Furthermore, international collaboration is crucial. Harmonizing regulations and guidelines regarding the use of gene editing technologies in vaccine development fosters responsible research practices and avoids regulatory discrepancies. By working together, the global scientific community can establish ethical standards and guidelines that uphold safety, efficacy, and public trust.
As gene editing technologies continue to evolve, careful consideration of the benefits and challenges is essential. Responsible research, comprehensive ethical oversight, and inclusive public discourse are vital to harnessing the potential of gene editing in vaccine development while upholding scientific integrity and ethical principles. By addressing these challenges, gene editing technologies have the potential to transform vaccine development and lead to improved public health.
14. How can vaccine development and manufacturing be integrated with other public health strategies, such as disease surveillance, outbreak response, and health systems strengthening, to improve overall health outcomes?
In the pursuit of better health outcomes, the integration of vaccine development and manufacturing with other public health strategies plays a vital role. By combining these efforts, we can enhance disease surveillance, improve outbreak response capabilities, and
strengthen health systems, ultimately leading to more effective and comprehensive public health interventions. This integration can benefit global health in several steps:
•Disease Surveillance: Integrated efforts between vaccine development and disease surveillance systems offer valuable insights and data-driven decision-making.
•Precise Vaccine Targeting: A robust disease surveillance system enables the identification of priority diseases for vaccine development, ensuring that limited resources are allocated to the most pressing public health challenges.
•Early Outbreak Detection: Effective surveillance systems facilitate early detection of outbreaks, enabling prompt vaccine development and deployment to affected areas.
•Monitoring Vaccine Effectiveness: Disease surveillance mechanisms play a crucial role in monitoring vaccine effectiveness, allowing for continuous evaluation and adjustments to vaccination strategies.
•Outbreak Response: Integrating vaccine development and outbreak response efforts paves the way for swift and targeted interventions during public health crises.
•Rapid Vaccine Development: Seamless collaboration between vaccine developers and response teams accelerates the production of vaccines tailored to the specific pathogen causing the outbreak, minimizing the impact of infectious diseases.
•Streamlined Regulatory Processes: Closer cooperation between regulatory agencies and vaccine manufacturers ensures expedited review and approval processes, facilitating the timely deployment of life-saving vaccines.
•Strategic Vaccination Campaigns: By integrating vaccine development with outbreak response, vaccination campaigns can be strategically planned and imple -
mented, reaching the most vulnerable populations in a timely manner.
•Health Systems Strengthening: The integration of vaccine development and manufacturing with health systems strengthening initiatives is crucial for sustainable and resilient healthcare infrastructures.
•Reinforcing Cold Chain Infrastructure: Strong collaboration ensures that adequate resources and infrastructure, such as proper vaccine storage and transportation, are in place to maintain the efficacy of vaccines throughout the supply chain.
•Capacity Building for Healthcare Workers: Integrating vaccine development with health systems strengthening involves training healthcare workers on vaccine administration, surveillance, and adverse event monitoring, bolstering their expertise and preparedness.
•Efficient Supply Chain Management: Coordinated efforts between vaccine manufacturers and health systems to optimize supply chain management, minimizing stockouts and ensuring a reliable and timely vaccine supply.
•Research and Development Collaboration: Integrating vaccine development with broader public health research efforts fosters innovation and knowledge sharing.
•Promoting Data Sharing and Collaboration: Closer collaboration among researchers, public health agencies, and vaccine manufacturers encourage the exchange of vital information, resources, and expertise, accelerating progress in vaccine research and development.
•Harnessing Epidemiological Research: Integration allows for the integration of epidemiological research findings into vaccine development strategies, guiding vaccine design and prioritization based on disease patterns and transmission dynamics.
•Conducting Health Impact Assessments: Integrating vaccine development with public health strategies enables rigorous health impact assessments, evaluating the potential benefits and cost-effectiveness of vaccines to inform policy decisions. By integrating vaccine development and manufacturing with comprehensive public health strategies, we strengthen health systems, improve outbreak response capabilities, and maximize the impact of vaccines. This collaboration between scientists, healthcare professionals, and regulatory bodies paves the way for a healthier future, with stronger defenses against infectious diseases and improved overall health outcomes for communities worldwide. Together, we can build a resilient global health landscape.
Josipa is QA Director, Principal GCP and GVP Auditor at Proqlea Ltd. She has over 18 years of experience in the pharmaceutical industry and is an acknowledged expert in the QA (Quality Assurance) field. Therefore, she has helped more than 150 companies set effective QMS (Quality Management Systems) and hosted more than 100 sponsor audits over the past 15 years. Her expertise is vaccine production, and she is dedicated to the idea of innovative products for rare diseases that will be accessible equally to the whole population. She has extended knowledge and experience in the clinical trial process and has advised many leading pharmaceutical companies in all phases of that process.
Targeted therapies guided by genomics have revolutionized medical care by providing medicine tailored to the biology of each patient, rather than one-size-fits-all treatments. As these new therapeutic paradigms are still developing in medical practice, this opinion discusses the challenges associated with implementing genomics, targeted drugs, and personalized medicine.
Ravi Dashnamoorthy Ph.D. Principal Scientist Biology, Genosco IncOne in five people will be diagnosed with cancer in their lifetime, according to the International Agency for Research on Cancer. The number of new cancer cases will increase from 15.1 million in 2020 to 28 million by 2040. Similarly,
mortality from cancer is expected to increase from 9.9 million deaths in 2020 to 16.2 million deaths in 2040. Additionally, cancer treatments impose a significant financial burden on patients, with the National Cancer Institute estimating $21 billion in financial losses in 2019. As the incidence of cancer is expected to double within the next two decades and the cost of healthcare is expected to skyrocket, it will be both an economic and a human catastrophe. Additionally, unprepared governments, a lack of significant expansion in healthcare facilities, and insufficient healthcare workers have put mankind on a path to unsurmountable tragedy. Further, the escalating incidence rate of cancer makes inpatient services at healthcare facilities extremely challenging, as current therapeutic paradigms are predominantly based on chemotherapy, radiation, surgery, etc. As a result, reducing patient foot traffic within hospitals becomes crucial in providing appropriate clinical care to patients in terminal or advanced stages of disease. Most targeted therapeutics designed to block cancer-causing molecules are developed as oral formulations, which can be taken at home under minimal medical supervision. Therefore, expanding targeted therapy use in clinical practice would reduce the strain on healthcare facilities, be cost-effective, and most importantly, deliver cancer treatments with lower toxicity and better efficacy.
For the clinical decision-making process in targeted therapy to be successful, advanced scientific knowledge and interpretation
of results are necessary. A crucial part of precision medicine is genomics, which analyzes a patient's DNA or RNA to identify abnormal genetic drivers. As the human genome contains more than 20,000 genes, it becomes extremely challenging to identify which genetic abnormalities are central to cancer progression and then identify the most effective drug candidate. Currently, providers of genomic tests include algorithms that recommend potential drug candidates for each patient based on their molecular profile, leaving it up to the physician to
make the decision. It is therefore necessary for physicians to research the literature or consult someone with prior experience on the same drug or patient with similar molecular profile before making their treatment plan. Although the drug manufacturer defines the matching of their targeted drug with the corresponding molecular abnormality, every patient's molecular profile is unique, and with multiple molecular abnormalities common in cancer, the entire process of adapting genomics guided precision medicine becomes overwhelming.
The field of precision medicine is still in its infancy. The development of more targeted drugs continues, with new drugs constantly entering clinical trials. Hence, we will continue to add to our compendium of successes and failures discovered during clinical trials and therapy, as it unfolds. Further, developing novel drugs and getting them approved for clinical use takes almost a decade, so establishing precision medicine will take a long time to mature and delaying the implementation for global clinical practice. Furthermore, it is important to recognize that only specialized centers can provide precision medicine care, while community oncology practices are still lagging.
As we face a race against time, advancements in precision medicine must accelerate to confront the impending disaster of an estimated 16.6 million deaths per year by 2040. To implement the clinical practice of precision medicine globally, a blueprint that
estimated 16.6 million deaths per year by 2040.
integrates genomics with targeted therapy principles is urgently needed. Although organizations around the world are working to establish such principles for implementing precision medicine care, there is a lack of clarity in the public about these efforts.
Personalized medicine based on genomics is gaining popularity in the field of oncology and cancer therapy but is now also widely applied to rare diseases. A major challenge for integrating genomics-based diagnostics and personalized medicine care with targeted therapies is that the biology behind complex networks of genes and pathways operating in any disease, its dynamics and progression, has not been adequately understood. The power of targeted therapy comes from blocking a single disease-associated gene/protein or pathway. However, at global levels, individual genes, proteins, and pathways rarely exist on their own, but rather interact extensively
Advancements in precision medicine must accelerate to confront the impending disaster of an
with vast biological circuits. In biological networks, parallel connections can take over and rewire away from gene-specific targeted mechanisms. Our biological network could mitigate the benefits of targeted therapy in personalized medicine by acting like the “Charlotte Web". Therefore, to overcome the "Charlotte Web" effect and reap the full benefits of target therapeutics, every disease model must outline biological circuitry and its vulnerabilities. Genomic biology assesses one gene's influence on many outcomes (genes, proteins, pathways) as opposed to one gene influencing one action (gene, protein, pathway). Without a complete understanding of disease-related biological networks and dynamics, genomic and targeted therapies would only end up as fishing expeditions.
Other imminent challenges include, establishment of clear guidelines for implementing the practice of precision
AUTHOR BIO
Ravi Dashnamoorthy is a Ph.D. cancer biologist who studies molecular pathways of cancer progression. As a Principal Scientist at Genosco, Billerica, USA with 25 years of academic-industry research experience in basic and translational oncology. In this column, the author represents his personal opinion, not that of any organization.
medicine in rural communities, which is presently available for ultra-urban community only, communicating the availability of such options with patients (see Hamilton), cost factors, necessity for real time data accumulation for improving the quality of evidence based practice, ensuring data privacy, community partnership, reporting success and failures, associating with individual biology , big data analytics to implementation of treatment decision algorithms, all these requires enormous investments, and whether governmental agencies and corporates set to make profit from the implementation of such clinical advancement are willing to contribute or support these efforts to fruition remains as a big question. Investors must also recognize that as a member of this society, many will eventually experience diseases that would require personalized medicine care, thus ensuring their investments delivering the best care for themselves is utmost important, while expecting to live and enjoy their anticipated monetary returns from their so that they could live to enjoy the returns. Personalized medicine investments are currently valued at 64 billion USD but are expected to reach 166 billion USD by the end of this decade. In light of the enormous expectations already set regarding integrating genomics with targeted therapy and delivering personalized medicine, it is essential to develop the foundational knowledge of disease-associated biological networks in order to ensure profitability for both life and money in the future.
Dear Readers,
It is with great pleasure and anticipation that I welcome you all to the latest panel discussion on Topic - "Emerging Trends and Strategic Insights in the Pharmaceutical Industry".
Our panel of experts and leaders from the industry have shared their valuable insights and expertise on the current landscape and future roadmap of the European Pharma industry.
Wishing you a happy read!
Allow me to introduce our esteemed panelists:
1. Dr. Vicknesh Krishnan
Associate Medical Director, Fresenius Medical Care Malaysia
2. Svetoslav Valentinov Tsenov
Chair of the Board of Directors, ARPharM, Bulgaria
3. Shamal Jeewantha Fernando
Managing Director, Slim Pharmaceuticals (Pvt) Ltd, Sri Lanka
What do you see as the most significant trends currently shaping the pharmaceutical industry, and how are they impacting business strategies?
Dr. Vicknesh Krishnan: Advanced Therapies - Gene therapies, cell therapies, and tissue engineering. These innovative treatments are being explored as potential cures for previously untreatable diseases and this will revolutionize healthcare. Channel investment into research and development and explore partnerships to advance the capabilities of companies/businesses
Data & Analytics: High leverage on big data, AI, and ML to improve decision-making across the drug development lifecycle. Also helps in drug discovery, clinical trial optimization, and personalized meds. Incorporate data-driven approaches into business strategies to enhance efficiency and drive innovation.
Regulatory Landscape: Increased focus on safety, transparency, and data integrity. The introduction of the European Union’s Medical Device Regulation (MDR) and In Vitro Diagnostic Regulation (IVDR) impact the development and approval processes for pharmaceutical products.
With the rise of personalized medicine and targeted therapies, how do you think pharmaceutical companies should adapt their research and development processes to meet these new demands?
Svetoslav Valentinov Tsenov:
One key aspect is the incorporation of genomic and biomarker information into their R&D strategies. By leveraging genetic data and identifying specific biomarkers, pharmaceutical companies can develop treatments that are tailored to individual patients or subgroups, maximizing efficacy and minimizing side effects. Additionally, collaboration with academic institutions, research organizations, and technology companies becomes crucial for accessing diverse datasets and advanced analytical tools. This collaborative approach enables the integration of multidisciplinary expertise, promoting innovation and accelerating the discovery and development of personalized medicines and targeted therapies.
Shamal Jeewantha Fernando:
In order to meet the demands of personalized medicine and targeted therapies, pharmaceutical companies should adapt their research and development processes in several key ways. First, they should prioritize the development of biomarkers and diagnostic tools that enable the identification of patients who are most likely to benefit from specific therapies. This would allow for more targeted clinical trials and more efficient drug development. Second, companies should invest in technologies that facilitate the discovery and development of precision medicines, such as genomics, proteomics,
and bioinformatics. These tools can help identify specific molecular targets and design drugs that selectively act on them. Finally, collaboration and data sharing between pharmaceutical companies, academic institutions, and regulatory bodies should be encouraged to foster innovation and accelerate the translation of research findings into clinical practice.
Digital health technologies are gaining momentum. How can pharmaceutical companies leverage these technologies to enhance patient engagement, improve outcomes, and stay competitive?
Dr. Vicknesh Krishnan:
Develop Patient-Centric Digital Solutions - directly engage patients via mobile apps, wearable devices, and online platforms that provide personalized health information, medication reminders, symptom tracking, and virtual support communities. This is about empowerment - leading to improved adherence, better treatment outcomes, and increased patient satisfaction.
Personalized Medicine and Biomarker Tracking: Enable personalized medicine approaches by incorporating biomarker tracking and genetic profiling. Digital tools can assist in monitoring biomarkers, genetic variations, and treatment responses, allowing for tailored therapies and optimized treatment plans. This can lead to improved patient outcomes and targeted drug development.
Svetoslav Valentinov Tsenov :
Firstly, they can develop mobile apps or web platforms to provide personalized health information, medication reminders, and virtual consultations, fostering greater patient involvement and adherence to treatment plans. Secondly, by utilizing wearable devices and remote monitoring tools, pharmaceutical companies can collect real-time patient data, enabling more accurate assessments of drug efficacy and safety. This data can also be used to identify potential adverse events early on and intervene promptly. Finally, leveraging artificial intelligence and big data analytics can enhance research and development processes, allowing for more targeted therapies and better prediction of patient responses, ultimately improving treatment outcomes.
The demand for affordable and accessible healthcare is increasing globally. How can pharmaceutical companies balance profitability and affordability while ensuring access to life-saving medications for all?
Shamal Jeewantha Fernando:
Pharmaceutical companies can balance profitability and affordability while ensuring access to life-saving medications through several strategies. Firstly, they can invest in research and development of cost-effective manufacturing processes to reduce production expenses. Secondly, they can establish partnerships with generic drug manufacturers
to facilitate the production of affordable generic versions of their medications. Additionally, engaging in price negotiations with healthcare payers and governments can help achieve fair pricing for their products. Moreover, companies can explore differential pricing models, offering lower prices in low-income countries while still maintaining profitability in high-income markets. Lastly, fostering collaboration and knowledge sharing among industry stakeholders can lead to innovative solutions for reducing costs and improving access to medications worldwide.
Data security and privacy are major concerns in the healthcare industry. How can pharmaceutical companies address these concerns while leveraging the potential of big data analytics and AI in drug discovery and development?
Dr. Vicknesh Krishnan: Data Governance Framework / Ethical use of AI: A robust framework to define the policies, procedures, and responsibilities for data management and security - should include data collection, storage, access controls, encryption, and data anonymization techniques to protect patient privacy. Clear guidelines for the use of AI algorithms and data analytics in drug discovery and development. Ensure that algorithms are transparent, explainable, and unbiased
Informed Consent and Transparency: Obtain consent from patients or participants
before collecting their data for research purposes. Clearly communicate how their data will be used, shared, and protected and the AI processes involved, including any automated decision-making algorithms.
Data Anonymization and De-identification: Remove or encrypt personally identifiable information (PII) to ensure that individual identities cannot be traced back to the data. This allows for the use of aggregated and de-identified data for analysis while preserving privacy.
Shamal Jeewantha Fernando:
Pharmaceutical companies can address data security and privacy concerns while leveraging the potential of big data analytics and AI in drug discovery and development by implementing robust measures. Firstly, they should adhere to stringent data protection regulations, such as the General Data Protection Regulation (GDPR), to ensure the privacy and security of patient information. Implementing encryption, access controls, and anonymization techniques can further safeguard sensitive data. Additionally, companies can adopt a layered approach to data security, incorporating secure cloud storage and implementing regular vulnerability assessments and penetration testing. Moreover, establishing transparent data governance policies and obtaining informed consent from patients for data usage can enhance trust and accountability. Collaborating with cybersecurity experts and
fostering a culture of data security awareness among employees are also crucial steps to mitigate risks and protect valuable healthcare data.
The COVID-19 pandemic has accelerated the adoption of telemedicine and virtual healthcare. How can pharmaceutical companies integrate these digital healthcare services into their overall business strategies?
Svetoslav Valentinov Tsenov:
They can partner with telemedicine providers or develop their own telehealth platforms to enable virtual consultations and remote monitoring for patients. This would allow them to extend their reach, enhance patient engagement, and gather real-time data for research and development purposes. Another option is to collaborate with technology companies to leverage artificial intelligence and data analytics to gain insights from patient data and improve treatment outcomes. Lastly, incorporating telemedicine into clinical trials can facilitate remote patient monitoring and enhance efficiency in data collection.
Collaboration and partnerships have become crucial in the pharmaceutical industry. How can companies effectively collaborate with other stakeholders, such as academic institutions, healthcare providers,
and technology companies, to drive innovation and address complex healthcare challenges?
Svetoslav Valentinov Tsenov:
To effectively collaborate with other stakeholders, pharmaceutical companies can employ several strategies. Establishing open communication channels and building relationships based on trust and mutual goals is essential. Regular meetings, joint workshops, and conferences can foster collaboration and idea exchange. Sharing resources and expertise can lead to innovative solutions. Companies can pool their knowledge and resources with academic institutions, leveraging their research capabilities. Collaboration with healthcare providers can provide valuable insights into patient needs and clinical expertise. Partnering with technology companies can enable access to advanced tools and data analytics, accelerating research and development.
Shamal Jeewantha Fernando:
To effectively collaborate with other stakeholders in the pharmaceutical industry, companies can adopt several strategies. Firstly, they should foster open communication channels and establish collaborative networks with academic institutions, healthcare providers, and technology companies. This can be achieved through joint research projects, sharing of expertise, and regular meetings or conferences. Secondly, companies can engage in public-
private partnerships to pool resources and expertise in tackling complex healthcare challenges. Thirdly, implementing data-sharing initiatives while ensuring privacy and security can facilitate collaboration and drive innovation in areas such as precision medicine and realworld evidence generation. Additionally, companies can actively seek out strategic collaborations with technology companies to leverage advancements in areas like artificial intelligence, big data analytics, and digital health. Lastly, creating a culture of collaboration and knowledge sharing within the organization and providing incentives for collaboration can encourage stakeholders to work together towards transformative healthcare solutions.
Regulatory frameworks and compliance requirements are evolving. How can pharmaceutical companies navigate these changes and ensure adherence to regulations while pursuing innovative strategies?
Dr. Vicknesh Krishnan: Regulatory intelligence + early engagement with authorities –proactive monitoring and analysis of changes in regulatory frameworks and compliance requirements to be updated on the business/industry. With intelligence data/ findings, engage with regulatory authorities early on to seek their input and guidance on innovative strategies or expanding existing strategies. Look into ways of collaboration that suit all parties
Cross - Functional Collaboration: Collaboration between medical affairs, regulatory affairs, research and development, quality assurance, legal, and other relevant departments within the organization. Involve these teams in decision-making processes and strategy development to ensure compliance is considered from the early stages of innovation. Cross-functional collaboration helps align innovative strategies with regulatory requirements.
Svetoslav Valentinov Tsenov:
By implementing a proactive approach. They need to stay updated on regulatory changes and engage in ongoing dialogue with regulatory authorities to gain clarity and guidance. Establishing robust compliance programs that integrate regulatory requirements into their processes is crucial. This includes training employees, conducting regular audits, and implementing internal controls. Collaboration with regulatory consultants and legal experts can provide valuable insights and ensure compliance. Additionally, engaging in early and transparent communication with regulators about innovative strategies can help address potential concerns and ensure alignment with regulatory expectations.
The global market for generics and biosimilars is expanding rapidly. How can pharmaceutical companies position themselves to take advantage of this
growth while managing competition and maintaining quality standards?
Dr. Vicknesh Krishnan: Continuous Improvement and Innovation: Embrace a culture of continuous improvement and innovation to enhance the quality and effectiveness of generics and biosimilars. Invest in technology and process optimization to drive operational efficiencies and cost savings. Explore opportunities to improve drug delivery systems or develop novel formulations to differentiate products in the market. Product development, supply, and commercialization – improve and upskill company capabilities in these segments
Expand R&D to look have an increased focus on preventive medicine/specialty drugs and a focus on market segmentation is needed.
Shamal Jeewantha Fernando:
Pharmaceutical companies can position themselves to take advantage of the rapid expansion in the global market for generics and biosimilars by implementing several key strategies. Firstly, they should invest in robust research and development capabilities to develop high-quality generic and biosimilar products. This includes thorough analytical and clinical comparability studies to demonstrate similarity and efficacy. Secondly, companies can focus on securing a strong regulatory and intellectual property strategy to protect their products and navigate
complex approval processes. Thirdly, building a strong manufacturing and supply chain infrastructure is crucial for ensuring costeffectiveness and reliable production of generic and biosimilar drugs. Additionally, companies can engage in strategic partnerships and collaborations to access new markets and leverage distribution networks. Lastly, investing in market intelligence and competitive analysis can help companies identify untapped opportunities, differentiate their products, and effectively manage competition. By balancing quality standards with competitive pricing, pharmaceutical companies can establish themselves as trusted providers of high-quality generics and biosimilars in the growing global market.
Here are a few points to be considered:
1. Continuous innovation: Pharmaceutical companies should focus on continuous innovation in the development of generics and biosimilars. This can involve improving formulation techniques, enhancing drug delivery systems, or exploring novel therapeutic applications. By staying at the forefront of innovation, companies can differentiate their products and maintain a competitive edge.
2. Market segmentation: Identifying specific therapeutic areas or niche markets where there is high demand for generics and biosimilars can allow companies to target their efforts effectively. By tailoring their
offerings to meet the specific needs of these segments, companies can capture a larger market share and establish themselves as leaders in those areas.
3. Regulatory compliance: Maintaining strict compliance with regulatory standards is vital in the generics and biosimilars market. Adhering to Good Manufacturing Practices (GMP), ensuring product quality, and meeting all regulatory requirements will help build trust with healthcare professionals and patients, ensuring continued market access and sustainable growth.
4. Global expansion: Exploring opportunities for global expansion can be a key growth strategy. Different regions have varying levels of market penetration
for generics and biosimilars, and companies should assess and enter markets with favorable regulatory environments, growing demand, and fewer competitors. Strategic partnerships or acquisitions in new geographic regions can also accelerate market entry and growth.
Overall, by combining quality products, continuous innovation, targeted marketing, regulatory compliance, and global expansion, pharmaceutical companies can effectively position themselves to capitalize on the expanding market for generics and biosimilars while managing competition and maintaining high-quality standards.
Thank you for your participation!!
and opportunities for cross-industry collaboration and exchange.
Medium-Large Enterprise Stream
• Understand & achieve the true value of real-time monitoring & end-to-end network visibility
Join us at the Temperature Control & Logistics North American Summit, taking place 19 - 21 September in Falls Church, VA, as we cut through the complexity to identify which approaches will work for you, bringing genuine value to your supply chain, your business, and your patients. Key Speakers Include:
• Bill Mcgillian, Associate Director, Supply Chain Management, Merck
• Victoria Wilmore, Global Temperature Control Support Director, Johnson & Johnson
• Tonino Antonetti, Executive Director, Regulatory, Affairs & Quality, Roche Diagnostics
The temperature-controlled supply chain is a hotbed of innovation, with pharmaceutical and biotechnology companies continuously finding new ways to mitigate the threat posed by potential temperature excursions and supply chain disruption. New visibility tools, packaging technologies and logistics services are redefining what is possible, allowing the industry to confidently protect and transport life-saving products.
It is crucial we discover how to demonstrate both reliability and true business value of any given technology and/or approach. That is why we have built a program built around the unique needs of medium-to-large pharmaceutical manufacturing and small-to-medium biotechnology companies, with bespoke learning outcomes
• Drive environmental sustainability across your supply chain without increasing risk & cost
• Enhance your logistics network design & forecasting to bolster resilience, security & ensure success
• Partner across the network to confront an increasingly complex operating & regulatory environment
• Discover the implications for your logistics network of disruptive technologies & new therapies
• Small-Medium Size Enterprise Stream
• Discover the logistics strategies that will allow you to navigate the path from clinical to commercial
• Identify & partner with the vendors capable of delivering the service your products require
• Implement best GDP practice to achieve & demonstrate long-term regulatory compliance
• Scale the size of your operations to put supply chain & logistics at the heart of your growth strategy
• Build your supply chain & logistics strategy around the unique requirements of your products
Date: September 19 - 21, 2023
Location: Falls Church, Virginia, USA
Website: https://www.pharma-iq.com/events-temperature-control-and-logistics/agenda-mc
Email: enquire@iqpc.co.uk
This webinar was presented by Yoann Saucereau, Ph.D. from Excellgene.
Recombinant Adeno-associated viruses (rAAVs) are vital for gene therapy, with over 255 clinical trials conducted in the past 25 years. However, assessing the titers and quality of AAV vectors during manufacturing is challenging. ExcellGene, in developing its AAV platform, faced the need for a rapid and robust assessment method. They turned to a label-free approach, which reduced matrix effects and enabled high-throughput, automated analysis of up to 96 samples in under 30 minutes. This innovative solution revolutionized QC workflows in AAV development and manufacturing, offering a reliable and efficient method for evaluating viral particle titers and quality attributes, propelling advancements in gene therapy.
Designation: Research Associate Analytics
Organization: ExcellGene
Yoann Saucereau obtained his Ph.D. in microorganism interactions and infection at the Claude Bernard University in Lyon in 2016, where he explored the molecular and immune mechanisms of microbial interference in mosquitoes before completing a post-doc at the Department of Biochemistry at the University of Cambridge, UK, where he continued to study this topic using biophysical approaches to characterize these interactions. With this experience in studying interaction models and using state-of-the-art techniques such as BLI and SPR, he joined Excellgene as an Analytical Research Associate to bring his experience to integrate and develop new innovative analytical methods.
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July 12 - 14, 2023
Bangkok, Thailand
https://www.cphi.com/sea/en/home.html
About Event: CPHI South East Asia will be your one-stop shop to source cost-effective pharma solutions under one roof. More than an exhibition, the event also facilitates finding the right partners with its online matchmaking tool, and encourages knowledge gathering and sharing with its online conference.
Listed Under: Manufacturing
August 30 - 01 September 2023
Seoul, South Korea
https://www.cphi.com/korea/en/home.html
About Event: CPHI Korea is a dynamic meeting place where pharmaceutical suppliers, purchasers and decision makers get together for three days of uninterrupted business. Exhibiting companies showcase products from across the entire pharma supply chain: from ingredients and contract services, through to machinery and biopharmaceuticals.
Listed Under: Manufacturing
Pharmaconex
September 3 - 5, 2023
Cairo, Egypt
https://www.pharmaconex-exhibition.com/en/home.html
About Event: Pharmaconex is Africa’s pharmaceutical manufacturing hub, connecting the entire supply chain in Egypt, the largest producer of the pharmaceutical market in the MENA region. Offering a comprehensive experience for the pharma community to network and build knowledge around the latest industry trends.
Listed Under: Manufacturing
September 20 - 21, 2023
Newark, New Jersey, United States
http://pharmaciconference.com/usa/
About Event: The Pharma CI Conference & Exhibition is THE INDUSTRY’S GOLD STANDARD for senior level pharma, biotech, and device professionals seeking the latest news and the rare chance to network with all the industry’s luminaries. If you’re interested in where your company is headed this year and beyond, you can’t miss this big event of the year!
Listed Under: Manufacturing
September 21 - 22, 2023
San Francisco, USA
https://www.ipharmaexpo.com/
About Event: The expo will showcase the latest trends and technologies in pharmaceuticals, drugs, and formulations. The expo is expected to witness approx. 150 exhibitors and 1500- 2000 visitors footfall from pharma industry & management. Direct access to highly targeted senior pharma executives, buyers, procurement managers, contract manufacturers, hospital administration, and many more Meetings with manager and business development managers who are looking for new supplies, building strategic partnerships, or entering into new ventures.
Listed Under: Research & Development
October 24 - 26, 2023
Barcelona, Spain
https://europe.cphi.com/europe/en/home.html
About Event: CPHI Barcelona unites industry leaders, pharma professionals and businesses of all sizes under one roof for three days of powerful connection and collaboration. This is the event to witness emerging pharma trends, discover innovative solutions and products to stay in front of the marketplace and drive your business forward!
Listed Under: Manufacturing
October 10 - 12, 2023
Kuala Lumpur, Malaysia
https://www.lab-asia.com/
About Event: LabAsia is Southeast Asia’s leading laboratory exhibition, serving as the region’s trade platform for laboratory equipment & services suppliers to engage with trade buyers from across the ASEAN region.
Listed Under: Research and development
January 24 - 25, 2024
Paris, France
https://www.pharmapackeurope.com/en/contentprogramme/call-for-speakers-2024.html
About Event: Pharmapack is the European hub for the pharma packaging and drug delivery device industry. Taking place annually in Paris, the event unites over 5,000 attendees and more than 300 exhibitors for two days of innovation, networking, and education.
Listed Under: Manufacturing
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Roche has discovered the first and only bispecific antibody Columvi® (glofitamabgxbm) for the treatment of adult patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL) not otherwise specified or follicular lymphoma (LBCL) recurring after two or more systemic therapies.
The approval for this indication is granted under accelerated approval, taking into consideration the response rate and durability observed in phase I/II NP30179 study. Further approval for this specific use will be dependent on the confirmation and description of clinical benefits through a confirmatory trial. Columvi is expected to be made available in the United States in the upcoming weeks.
Diffuse large B-cell lymphoma (DLBCL) is a prevalent and challenging disease, representing the most common type of nonHodgkin lymphoma in the United States. Although initial treatment response is favorable for many DLBCL patients, a significant portion experience relapse or become refractory to subsequent therapies, leading to unfavorable prognoses.
FDA accelerated approval of Columvi® is backed by positive outcomes from phase I/II NP30179 study. 132 relapsed/refractory DLBCL patients, including prior CAR T-cell therapy recipients, received Columvi. Notably, 83% were refractory to their last treatment. Results showed fixed-duration Columvi led to durable remission, with 56% overall response rate and 43% complete response rate.
ACROBiosystems, in collaboration with Diagnostic Biochips, introduces ACRO Certify's Aneuro brand, unveiling in vivo electrophysiology solutions for neuroscience research. This partnership aims to accelerate drug discovery and commercialization in the field of neurology.
Electrophysiology plays a vital role in neuroscience research, offering insights into the intricate neural network. By monitoring field potential and neuron-level electrical signals in various brain regions, researchers gain valuable information about neural network function and the underlying biological mechanisms of neurological diseases.
In vivo electrophysiology poses challenges due to the complex nature of the neural network. These challenges include data acquisition and the use of animal models, primarily rodents. ACROBiosystems addresses these challenges by offering probes designed to work without a headstage, minimizing the physical burden on animal heads while maintaining optimal signal channels. Additionally, their deeparray electrodes provide a recording depth of 90mm across 128 channels, enabling comprehensive neural activity analysis.
To enhance the capabilities of the probes, ACROBiosystems offers data acquisition systems and AI/Cloud-based data analysis software. These tools facilitate structural and functional analysis of neural circuits, biomarker discovery through electrophysiology, and drug screening. Additionally, ACROBiosystems provides beginner's guides and training for researchers venturing into the field of neurosciences, ensuring a smooth start to their experiments.
Alentis Therapeutics, a clinical-stage biotechnology firm focused on developing treatments for Claudin-1 positive (CLDN1+) tumours and organ fibrosis, has gained FDA approval for ALE.C04 as an Investigational New Drug (IND).
The approval enables for the testing of ALE.C04 in a first-in-human clinical trial as a monotherapy and in combination with pembrolizumab.
The trial, which will begin in the second half of 2023, will particularly target recurrent or metastatic Head and Neck Squamous Cell Carcinoma (HNSCC).
FDA's IND approval is a major milestone for Alentis Therapeutics in developing innovative therapies for CLDN1+ tumors and organ fibrosis. It enables clinical trials to evaluate ALE. C04's safety and efficacy for HNSCC treatment, as monotherapy and in combination with pembrolizumab. This advancement signifies a critical step in improving treatment options and advancing novel medications for patients.
ALE.C04 is a novel monoclonal antibody that targets cancer cells expressing CLDN1. It acts by remodeling the extracellular matrix to enhance immune cell trafficking and directly kill tumor cells. ALE.C04 shows promise as a monotherapy and in combination with checkpoint inhibitors.
HNSCC is the sixth most frequent type of cancer in the world, and its prevalence is increasing. In addition to surgery, chemotherapy, and/or radiation therapy, authorized therapies include cetuximab and pembrolizumab.
Novo Nordisk has recently announced a significant investment of 15.9 billion Danish kroner to expand its existing Active Pharmaceutical Ingredient (API) production facility in Denmark. This expansion aims to cater to the growing demand for medications in the field of serious chronic diseases. Coinciding with the company's 100th anniversary, this investment demonstrates Novo Nordisk's commitment to its Danish roots, as more than half of its global workforce of 21,000-plus employees are based in Denmark.
The expansion in Hillerød, Denmark, will create additional production capacity and enhance Novo Nordisk's ability to meet future market demands. It will serve as a key enabler for the company to develop its future clinical late-phase product portfolio. Henrik Wulff, the executive vice president of Product Supply, Quality & IT, emphasized the importance of this investment in delivering essential medicines to patients worldwide. He also expressed gratitude to the Danish Government and Parliament for their support in ensuring good infrastructure and supplies, enabling the company's production capacity expansion.
The new facility will cover an area of approximately 65,000m2 and will be designed as a multi-product facility, allowing for flexibility and accommodating new processes. It will incorporate state-of-the-art technology and provide a modern and efficient working environment. The construction will prioritize high-quality production while focusing on reducing water and energy consumption in an environmentally sustainable manner.
Construction has already begun, and the facility is expected to commence API production by early 2029. Once completed and fully equipped, the project is anticipated to create 340 new jobs.
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