Pharma Focus Europe - Issue 01

Issue 01 | 2022 www.pharmafocuseurope.com

Catherine Hall VP of Data and Quality, Endpoint Clinical

The Game of e-Clinical Technology in Clinical Trials Model-Informed Drug Development (MIDD) - EMA's Advancement

Cold-Chain Considerations for Manufacturing Biologics w w w. p h a r m a f o c u s e u r o p e . c o m

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Welcome & Introduction Welcome to our very first issue of Pharma Focus Europe 2022. On our tryst with this commendable milestone, I take the opportunity to thank one and all from my editorial team, my distinguished advisory board members and all the subject matter experts who have contributed with their articles.

This is truly a feather on our hats, a highly coveted Europe-centric pharma magazine adding to the list of our premium pharmaceutical publications.

With this newest issue we aspire to be a leading industry publication serving Pharmaceutical professionals with unparalleled quality, facts, data analysis and expert advises - covering trends, advancements, and all the criticalities which the industry faces from time to time.

Smarter Technology and Refined Project Management Techniques–For Tomorrow’s Clinical Trials and Rapid Drug Development From the emergence of newer techniques and tools for effective decentralised clinical trials to the use of 3D printing technology in medicine manufacturing and the industry’s subsequent shift towards 4D printing – we have a lot to narrate, share insight and unravel.

Modelling & Simulation (M&S) is used in all phases of drug development, Eva Gil Berglund and Justin Hay from Certara explain how using physiologically based PK (PBPK) and population PK (PopPK) approaches in parallel for model-informed drug development (MIDD) is proving especially valuable.

Biomarkers role in rapid drug discovery by aiding in analysing the pharmacodynamics of a specific drug and how exosomes are being considered as an indispensable part of biomarkers discovery -Ashish Wadhwani & Salvi Wahidna, JSSAHERM takes us through the journey on how identifying effective biomarkers can aid in industry shift from “one drug fits all” to personalised approach.

Manash k Paul, UCLA takes his investigation further on hybrid exosomes for Cancer therapy.

Extracellular vesicles (EV), EV-liposome hybrids are a novel nanocarrier platform that can solve the existing limitation of EVs as effective drug delivery system given their repeatable loading with therapeutic or imaging agents.

Geoffrey Gill from Shimmer, Americas presents his market acceptance and challenges research on wearables and other sensor based devices in conducting decentralised clinical trials. Wearables can measure digital endpoints and can provide continuous documentation of a patient’s progress during the trial. But, it would be better for deeper penetration of the wearables usage that the industry as a whole focus on standardised clinical end-points, use open-source algorithms and share raw data for optimal leverage.

Medical applications of nanomaterials span drug, protein and vaccine delivery, diagnostics, theranostics. David Winkler, explains that there is a need for a safe-by-design paradigm for nanomaterials, and machine learning is increasingly used to predict their properties.

If you have any views or ideas or if you want to share your feedback with Pharma Focus Europe, you are more than welcome. You may also connect with us on our social media handles for exquisite stories, articles and much more pertaining to pharma sector.

N D Vijaya Lakshmi Editor

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CONTENTS STRATEGY

MANUFACTURING

08 Model-Informed Drug Development (MIDD) EMA's Advancement

58 Additive Manufacturing as a Problem-Solving Tool in Pharmaceutical Processing

Eva Gil Berglund, Senior Director in Clinical Pharmacology and Regulatory Strategy, Certara Justin Hay, Senior Director, Certara

RESEARCH & DEVELOPMENT 16 Hybrid Exosomes: A Novel Platform for Cancer Therapy Manash K. Paul, Scientist, Principal Investigator at UCLA

22 Effective Biomarkers in Uncovering Novel Target Molecules for a Drug The Revolution in New Drug Targets Ashish Wadhwani*, Head, Department of Pharmaceutical Biotechnology, JSS Academy of Higher Education & Research - College of Pharmacy Salvi Wahidna, Student, School of Pharmacy, JSS Academy of Higher Education and Research

30 c-MET Receptor Tyrosine Kinase: A Potential Biomarker for Cancer Therapeutics Dhruv Kumar*, Senior Associate Professor, School of Health Sciences and Technology, UPES University Sibi Raj, ICMR-SRF Fellow, currently pursuing PhD in Cancer Biology, School of Health Sciences and Technology, UPES University

36 New Pharmacological Targets and Biomarkers for the Treatment of Tuberculosis (TB) Vidya Niranjan, Professor and Head of the Department, Department of Biotechnology Lead-Centre of Excellence Computational Genomics, R V College of Engineering

Chris O’Callaghan*, Innopharma Technology Ltd Caroline McCormack, Innopharma Technology Ltd Sam Solomon, Department of Chemical Sciences and Bernal Institute, University of Limerick Patrick Cronin, Department of Chemical Sciences and Bernal Institute, University of Limerick Ian Jones, Innopharma College of Applied Sciences Ltd.

66 3d Printing: What's in Store for Medicine in the Future Ashwin Kuchekar, Associate Professor, Head of Career Services and Placement, MIT World Peace University School of Pharmacy Shalmali Shirish Cholkar, Master’s Student, MIT World Peace University School of Pharmacy

73 Cold-Chain Considerations for Manufacturing today’s Advanced Biologics Cold Chain Integrity and Capabilities Lee Seungheon, Product Logicstics Specialist, Samsung Biologics

EXPERT TALK 79 Future of Clinical Trials and Technology Innovations Catherine Hall, VP of Data and Quality, endpoint Clinical

INFORMATION TECHNOLOGY 84 The Pharmaceutical Industry and Data Science

CLINICAL TRIALS

Ashwin Kuchekar, Associate Professor, Head of Career Services and Placement, School of Pharmacy, Dr. Vishwanath Karad MIT-WPU

44 Benefits and Challenges of Using Wearables in Clinical Trials

Ashwini Gawade, Assistant Professor, School of Pharmacy, Dr. Vishwanath Karad MIT-WPU

Geoffrey Gill, President, Shimmer Americas

52 Decentralising Clinical Trials for Better Drug Formulations Claude Price, Head of Clinical Data Management, Quanticate

90 Role of Artificial Intelligence and Machine Learning in Nanosafety.

David Winkler, Professor of Biochemistry &Chemistry at La Trobe University, Professor of Pharmacy at the University of Nottingham, and Professor of Medicinal Chemistry at Monash University

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Advisory Board

Paola Antonini Chief Scientific Officer, Meditrial Global CRO, Italy Shamal Jeewantha Fernando Managing Director, Slim Pharmaceuticals ( Pvt) Ltd, Srilanka Josipa Ljubicic QA Director / Principal GCP and GVP auditor, Proqlea Ltd, Croatia Joaquin D. Campbell Global Director, Managed Access Services, Spain Amine Bekkali Director, Medfields, UAE Svetoslav Valentinov Tsenov Senior Pharma Executive and Global Transformation Lead, Bulgaria Tamara Miller Senior Vice President, Product Development, Actinogen Medical Limited, Sydney Hassan Mostafa Mohamed Chairman & Chief Executive Officer at ReyadaPro, Saudi Arabia

EDITOR Vijaya Lakshmi N D EDITORIAL TEAM Sarah Richards Debi Jones Harry Callum Anusha Roopa Vani Supraja B R ART DIRECTOR M Abdul Hannan PRODUCT MANAGER Jeff Kenney SENIOR PRODUCT ASSOCIATES David Nelson Sussane Vincent Peter Thomas PRODUCT ASSOCIATE Veronica Wilson CIRCULATION TEAM Sam Smith SUBSCRIPTIONS IN-CHARGE Vijay Kumar Gaddam HEAD-OPERATIONS Sivala VNR

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Nicoleta Grecu Director, Pharmacovigilance Clinical Quality Assurance, Romania Alessio piccoli Director & Head,Business Development Europe presso Aragen, Italy Vicknesh Krishnan Associate Medical Director at Fresenius Medical Care Malaysia Sdn Bhd, Malaysia Thitisak Kitthaweesin Chief of Phramongkutklao Center of Academic and International Relations Administration, Thailand

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STRATEGY

Model-Informed Drug Development (MIDD) EMA's Advancement

Modelling and simulation (M&S) plays a crucial role in drug development. It allows information from diverse sources – pre-clinical and in-vitro data, early and late-stage clinical trial data, biomarkers, and efficacy outcomes – to be included with a dataset. Modelling enables researchers to visualize complex scenarios and determine what will happen if a parameter is changed in a clinical trial. It can optimize clinical study design, enable unnecessary studies to be waived, and inform risk: benefits in “what if” scenarios with specific patient populations or drug-drug interactions (DDI).

Eva Gil Berglund

Modelling & Simulation

Senior Director in Clinical Pharmacology and Regulatory Strategy, Certara

M&S is used in all phases of drug development in the EU, US, and UK starting from pre-clinical pharmacokinetic (PK) and pharmacokinetic/pharmacodynamics (PK/PD) studies, through clinical trials to post-marketing commitments. It is generally used to support the clinical pharmacology files and labelling for new drug applications. M&S supports early dose selection

Justin Hay Senior Director, Certara

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based on pre-clinical data, PK/PD in-vitro animal data and scaling approaches and helps to set the first-in-human dose. M&S also shows the impact of intrinsic factors, such as age, body weight, or organ function on PK, PK/PD, and dose-exposure-response relationships.

Dose-exposure-response analysis is used to support safety and efficacy evaluations. The target exposure range – the concentration range that produces efficacy at an appropriate safety level – is a particularly important part of the clinical pharmacology evaluation. Researchers try to keep all clinical trial participants within that dose exposure range. If the concentration is too high, they will lower the dose to try to bring it into that range.

Using physiologically based PK (PBPK) and population PK (PopPK) approaches in parallel for model-informed drug development (MIDD) is proving especially valuable. But it is important to start populating the models early using pre-clinical data, and then adding new clinical data throughout the development process to strengthen and refine the models. The resulting models can be used to run simulations that answer questions regarding populations and situations that are difficult to study, and optimize clinical study design, in addition to generating evidence.

M&S can also help to address major issues relating to clinical pharmacology, PK in the target population, and dose-exposure-response analyses. These issues arising during the assessment of a marketing authorisation application (MAA) could potentially lead to a negative opinion, rejection, or delays in product approval.1 By employing high quality M&S, and seeking scientific advice during the development process, sponsors can mitigate many issues.

EMA Adoption of M&S M&S has become an accepted part of the new drug development process. M&S used to be a supplemental part of the dossier, but regulators now expect it to be included in the files. In fact, MAAs are rarely submitted without modelling being used to describe the PK of a new medicine.

Research at academic institutions and software companies played a significant role in advancing MIDD. Some agencies, such as the U.S. Food and Drug Administration (FDA), have also sponsored related research. In addition, several Innovative Medicines Initiative projects in the EU are focused on applications of MIDD. During the past decade, the European Medicines Agency (EMA) has drafted many guidelines that discuss M&S approaches, published papers on MIDD, created an M&S working party (MSWP), and hosted several MIDD-centric workshops. EMA has promoted the use of MIDD in dose finding during its workshops and locally in the agencies. It has produced guidance documents on the use of MIDD approaches in paediatric drug development, DDI risk assessment, w w w. p h a r m a f o c u s e u r o p e . c o m

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renal and hepatic impairment, obesity, and pharmacogenetics. It also has clinical guidelines that reference MIDD approaches to support dosing for oncology, HIV, and antibiotic use.

In its “Regulatory Science to 2025” strategy document, the EMA mentions modelling 37 times and specifically references its desire to “Optimize capabilities in modelling, simulation and extrapolation” to drive collaborative evidence generation and improve the scientific quality of evaluations.2 While the FDA and EMA have both embraced M&S, they take slightly different approaches.

For example, the FDA always requests that sponsors provide raw data with their new drug applications (NDAs), and its internal team of pharmacometricians can reanalyse it. But the EMA generally does not currently perform its own M&S. However, the EMA did announce a pilot study in July 2022 for which it is requesting that raw data be submitted for possible re-analysis. But the Agency did not state whether it plans to conduct M&S or statistical analysis of the data.

Paediatric Drug Development Many M&S approaches are used in paediatric drug development. Underscoring its importance, the EMA published a reflection paper in 2018, which outlined scenarios under which sponsors could use M&S to extrapolate efficacy from adults to children when developing medicines for paediatric patients.3 Most importantly, M&S is used to extrapolate from adult efficacy and safety data to determine the initial paediatric drug doses for clinical studies. M&S is also employed to optimize the blood sampling times and minimize the number of blood

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samples that need to be collected from paediatric patients. M&S also helps to determine how many paediatric patients need to be recruited in each age group or body weight category for a specific clinical study. Those results are included in the Paediatric Investigational Plan, which is submitted to the Paediatric Committee (PDCO), the EMA’s scientific committee responsible for activities regarding medicines for children.

All the available data are used to create and refine a dynamic paediatric model, which shows the effect of body weight or other covariates on PK parameters and dosing. M&S enables all the available data to be used in a much more efficient way.

Furthermore, by using PBPK modelling, researchers can progress beyond scaling based on body weight and factor in the paediatric patient’s maturing physiology and enzyme levels as well. Literature data can be employed to support the maturation of different enzymes in paediatric patients.

Advancing International Harmonisation The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) strives to achieve greater harmonisation worldwide to ensure that safe, effective, and high-quality medicines are developed registered and maintained in the most resource efficient manner whilst meeting high standards.

ICH has a rich history of collaboration. ICH members have worked closely together for many years to develop comprehensive guidelines for PopPK and exposure-response analyses in multi-national clinical trials. Those guidelines include E5 which focuses on “Ethnic Factors in the Acceptability of Foreign Clinical Data,” E7 for “Studies in Support of Special Populations: Geriatrics,” E14 for “The Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs,” and E17 which outlines “General Principles for Planning and design of Multi-Regional Clinical Trials.”

In April 2022, ICH also released a draft guideline for “Paediatric Extrapolation” (E11a).4 Even more recently, ICH released draft drug interaction guidance, which provides a harmonized approach to drug interactions and references PBPK, in addition to PK, for the first time.5 In fact, PBPK is mentioned about 50 times in the extrapolation guideline, and it is referenced in the drug interaction guidance as well.

In 2021, ICH founded a MIDD Discussion Group in Europe. In March 2022, that group released a roadmap, outlining its plans. It is expected to be a particularly important platform, which regulators can use to advance the field. It enables staff from different agencies to contribute their expertise, exchange, and challenge ideas, and develop new guidance on MIDD principles. w w w. p h a r m a f o c u s e u r o p e . c o m

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A clinical trials regulation (Regulation (EU) No 536/2014) was also introduced in early 2022, which describes harmonized ways to conduct assessments of clinical trials. It is hoped that this will lead to harmonized assessments of M&S methodologies as well.

Embracing International Collaboration Several EMA working groups have also helped to advance the field. They include the MSWP, PDCO, and the PK Working Party. Recently, EMA announced the formation of a Methodology Working Party, which will include experts in biostatistics, M&S, extrapolation, PK, real-world evidence, etc. The working party will support both scientific advice and scientific committees, write guidelines and provide assessor training.

The working parties participate in cluster meetings with experts from other global regulatory agencies, including the FDA, Health Canada, Japan’s Pharmaceuticals and Medical Devices Agency, and Australia’s Therapeutic Goods Administration. The MSWP, for example, usually meets quarterly, bringing together pharmacometricians, and other clinical pharmacology assessors, to share information and discuss common issues. At the World Conference on Pharmacometrics earlier this year, representatives from the EMA and FDA met with colleagues from the African agencies, so there is scope for harmonization there as well.

A lot of cross-agency reliance models have also been implemented. For example, the regulatory agencies in Australia, Canada, Singapore, Switzerland, and the UK are members of the Access Consortium and participate in joint assessment work. Sponsors can submit a new drug application to multiple agencies in the Access Consortium. Then, the members prepare a work-sharing agreement to assess the new medicines.

The FDA has introduced a similar model, called Project Orbis, which provides a framework for the concurrent submission and review of oncology products among international partners.

Project Orbis includes all the agencies in the Access Consortium plus the Brazilian agency.

As the EU is comprised of 27 member states, it focuses on harmonization internally as well as externally with other global regulatory agencies.

The EMA MSWP is helping to share knowledge between member states and harmonize the new drug assessment. This cooperation between member states also reduces duplication of efforts and helps to ensure that vulnerable, rare patient populations are not asked to participate in multiple study designs.

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Growth of M&S in Europe The adoption of MIDD approaches in the EU is expected to continue to grow as assessors became more familiar with PopPK, PBPK and quantitative systems pharmacology (QSP) approaches and other emerging methodologies. The planned drafting of the ICH MIDD guidance documents will also help to progress MIDD science and increase regulatory acceptance.

Understanding the relationship between PK and PD has always been at the heart of clinical pharmacology yet an even greater emphasis is expected to be placed on exploring doseexposure-response relationships in future clinical trials.

Using PBPK and PopPK approaches for formulation bridging is another area that holds enormous potential. If a sponsor wants to change a product’s formulation, such as switching an injection device from vials to an auto-injector, between Phase 2 and Phase 3 clinical trials, or just prior to market introduction, when the similarity criteria are stricter, M&S can support bridging. It is also invaluable with generics and biosimilars. These forms of modelling are particularly useful in instances when it is complicated to perform conventional studies in healthy volunteers, such as when the product has a very long half-life.

Both PBPK and PopPK methods should also be used together when assessing and investigating a medicine in special populations. For example, patients with renal impairment can enroll in a clinical study if they are given an appropriate dose determined using PBPK modelling.

Then, the PK data generated from the study can be analysed, using PopPK, and a learn and confirm approach, to create more clinical data for this population for which there is usually only minimal PK data available.

QSP is another application that has been steadily growing in popularity and has gained traction. It enables biological systems to be combined with mechanistic models to predict drug safety and identify critical biomarkers. It was used heavily during the pandemic for vaccine development. A mechanistic model of the human immune system, which was originally created to determine whether a biological therapeutic would create an unwanted immunogenic response, was repurposed as a COVID-19 vaccine model. While the creators of the initial model were hoping to see low levels of immunogenicity for their candidate biologicals, those working on the revised model wanted vaccine candidates that promoted high levels of immunogenicity.

Model-based meta-analysis, which allows researchers to leverage peer-reviewed clinical trial data from studies of similar medicines which are then combined with a pharmacology model, is also proving to be a powerful approach for the development of medicines. Data from the literature and other sources are combined in a PK/PD model, to enable researchers to bridge across different studies and conduct indirect comparisons with either the standard of care or a competitor drug. w w w. p h a r m a f o c u s e u r o p e . c o m

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M&S continues to be used extensively in paediatric studies in orphan indications when there are too few patients, in situations where there are only indirect biomarkers, or no biomarkers, such as with vaccines or gene therapies, and in new and growing fields such as biologics, bi-specific antibodies, and oligonucleotides.

PBPK modelling has also recently been proposed for assessing mother and child drug exposure during pregnancy and lactation. This information is vitally important if women who are pregnant and lactating need to be included in a clinical study. These data can be used to inform both the study design and risk assessment. This approach also aligns closely with one of the M&S objectives outlined in the EMA strategy plan.2 The EMA Regulatory Science to 2025 strategy also mentions other fields where they will be optimising capabilities, including the use of artificial intelligence in M&S, real world data and M&S in special populations, maternal-foetal health, enhancing collaboration with external parties on M&S for example through IMI research projects, and developing data sharing platforms.

AUTHOR BIO

STRATEGY

Eva Gil Berglund, PhD, is a Certara Senior Director in Clinical Pharmacology and Regulatory Strategy with 20+ years of experience as a Clinical Pharmacology Reviewer in the Swedish Medical Products Agency working under the EMA umbrella.

She has been a member of the Pharmacokinetic Working Party, contributing to many guidelines.

Conclusion M&S can be employed to confirm a drug’s early dose selection and dose-exposure-response, inform study design and risk assessments, determine the potential for DDIs, and help bridge between adult and paediatric populations and between different drug formulations.

Global regulatory agencies, such as the FDA and EMA, recognize the value provided by M&S during drug development and assessment and are working diligently with the ICH and focused working parties to develop harmonised guidance to optimise its use.

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Justin Hay is a Senior Director at Certara with 15+ years of clinical pharmacology experience. Career highlights include working as a Senior Pharmacokinetics Assessor and Deputy Unit Manager at the Medicines and Healthcare products Regulatory Agency, UK. He has also been a member of the EMA's Modelling and Simulation Working Party.

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HYBRID EXOSOMES: A Novel Platform for Cancer Therapy In the absence of an effective nanocarrier vehicle, traditional cancer treatment, tumor imaging, and immunotherapy remain ineffective. Artificial liposomes have been used to encapsulate bioactive compounds and release the payload continuously and stimuli-responsively, but they suffer from multiple drawbacks. Extracellular vesicles (EVs) are natural endogenous macromolecule transporters used for drug delivery, but adequate and repeatable loading of the EVs with therapeutic or imaging agents still limits their application as drug delivery systems. EV-liposome hybrids are a novel nanocarrier platform that can solve the challenges existing with individual nanocarrier systems and provide precise cancer detection and tailored treatment.

Manash K. Paul Scientist, Principal Investigator at UCLA

Introduction Effective cell-to-cell communication is essential for normal physiological homeostasis in multicellular organisms like humans. All bodily functions involve the coordinated interaction of cells. Extracellular vesicles (EVs) are one of the several processes through which cells interact with one another. EVs are extracellular, membrane-limited, mobile, cell-derived 14

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vesicles discharged in the extracellular space and, based on their biosynthesis, size, and membrane makeup, they can be categorized into three broad categories: micro-vesicles microvesicles MVs), apoptotic bodies (ABs), and exosomes. EVs harbor the respective parent cell's physiological state-specific proteins (such as transcription factors, surface receptors, heat-shock proteins), lipids, and nucleic acids (DNA, messenger RNA or mRNA, microRNA or miRNA, and long non-coding RNA or lncRNA), circulate in the blood and other bodily fluids. Once EVs attach to or are absorbed by other cells, the EV payload is released, facilitating information transfer-mediated pleiotropic physiological effect and establishing cell-cell communication.

EVs are released by most cells, including the tumor cells. Nonetheless, little is understood about the specific mechanisms and intricate signaling pathways underlying EV-mediated cell-cell communication, especially in disease conditions. Hence, current research focuses on gaining a greater understanding of the biogenesis and function of EVs, highlighting the difference of the EV cargo in normal vs. disease conditions, and engineering EVs to exploit their potential therapeutic applicability in diagnostics, therapeutic delivery, and theranostics.

EVs also play a critical role in cancer diagnosis and therapy.

Extracellular Vesicles and liquid biopsy Over the last two decades, scientists and researchers devoted to revolutionizing cancer therapy have acquired a vast amount of information about cancer and have realized that early diagnosis has the potential to revolutionize cancer management. Tissue biopsies depend on invasively resected representative tumor tissue sections and are fundamental for diagnosis, pathological interpretation, treatment planning, and response prediction to targeted therapy.

Though tissue biopsies remain the gold standard for confirming a cancer diagnosis, they suffer from multiple challenges and remain unrealistic for early cancer detection. Liquid biopsy or fluid phase biopsy is a simple and non-invasive alternative to tissue biopsy and identifies and evaluates liquid-state biological matter (often blood, urine, saliva, or other fluids) for diagnosis, monitoring, and treatment surveillance of cancers. The foundation of liquid biopsy is the discovery and characterization of tumor-derived exosomes (TEX)/ extracellular vesicles (EVs), circulating tumor cells (CTCs), and circulating tumor DNA (ctDNA). Among the three types of liquid biopsy targets, the wild-type target is in far higher quantities than the tumor-derived components, making it challenging to use [4]. The advent of advanced state-of-the-art technologies such as single molecular array, Surface-enhanced Raman spectroscopy, Luminex, AlphaLISA, droplet-sequencing, and Electroluminescence ELISA has sparked great enthusiasm and optimism regarding the faster and less intrusive identification of liquid biopsy-based disease biomarkers at an early stage. The discovery of w w w. p h a r m a f o c u s e u r o p e . c o m

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cancer-associated mutational changes and tumor antigens in EVs has offered the potential to use EV-based liquid biopsy as a cancer diagnostic and therapeutic-targeting platform and can complement the tissue biopsy approach. Several publications have established the role of EVs in cancer immunology, which can also be used for immunomodulation and suppression of tumor growth. EVs are key players not only in cancer diagnostics but also in therapeutic delivery.

Extracellular Vesicles and therapeutic delivery Traditional chemotherapy has shown some efficacy, but its primary drawbacks include limited absorption, large dose requirements, and development of multiple drug resistance, unexpected side effects, low therapeutic indices, and non-specific targeting. In order to overcome such hurdles, scientists are in pursuit of an effective drug delivery vehicle with efficient delivery and targeting potential. Several artificial nanotechnology (NT)-based formulations (e.g., liposomes, polymeric nanoparticles (NP), albumin NP, and inorganic NP) have exhibited promise, and few of them have reached clinical application. Various research teams have also examined the therapeutic potential of EV-mediated drug delivery due to their endogenous cellular origin, minimal immunogenicity, and intrinsic capacity to penetrate the blood - brain barrier, enhanced target selectivity, and significant biocompatibility. EV surface proteins influence their absorption/uptake by tumor cells and can modulate therapeutic delivery and dispersion. EV-based delivery of small molecule drugs, pathway inhibitors, plasmids, proteins, siRNA, and miRNA have successfully attempted to ameliorate tumor growth and promote antitumor immunity. Many EV-based formulations are under clinical investigation in light of their vast range of cancer-specific applications and are summarized in other reviews. Although significant progress has been achieved with respect to EVs, other NT-based products, especially liposomes, are already in clinical use.

Liposome and drug delivery Among all the nanotechnology-based delivery vehicles, liposomes are the most well-characterized and successful, and several clinical trials are undergoing. Liposomes are artificially synthesized lipid nano-vesicles delivery vehicles, with a lipid bilayer (can enclose hydrophobic therapeutics) enclosing a hydrophilic aqueous core (for hydrophilic small molecules/ biotherapeutics). In addition, liposomes protect the cargo, are nano-sized, exhibit colloidal stability, are biocompatible, have enhanced bioavailability, permeate cell membrane, exhibit minimal toxicity, and can be surfacefunctionalized to be cell-type specific. A wide range of bioactive chemicals can be transported via liposomes, including anti-cancer small molecules, genetic materials, peptides, hormones, enzymes, 16

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Protein

proteins, CRISPR/Cas9, and vaccines. Currently, over seventeen separate liposomal formulations are available for treating a wide range of pathological causes, and of these, many are currently used for targeting various cancers. The FDA-approved liposomal trade names are Marqibo, Doxil, LipoDox, Onco TCS, Onivyde Caelyx, Myocet, DepoCyt, Mepact, Daunoxome, Vyxeos, and Lipusu. In recent years, cancer immunotherapy has made considerable advances, and liposomal formulations for the delivery of immunotherapeutics, cancer vaccines, and immunomodulators are being tested in preclinical and clinical trials. Hence, it is eminent Liposome Exosome Lipid that nanocarriers such as EVs and Lipid Ligand liposomes have the potential to improve conventional cancer therapy, immunotherapy, and Hydeophobic mRNA Drug combination therapy significantly.

DNA Both EV and liposome platforms Lipid-lipid fusion have benefits and limitations, and Hydeophobic Receptor hybrids may assist conventional Drug and cancer immunotherapies in overcoming major obstacles.

Hybrid extracellular vesicle and cancer therapy Due to the short retention in blood, poor specificity of tumor cells, and fast clearance by the mononuclear phagocyte system, it is tough to deliver cancer therapeutics to the tumor.

EV-liposome hybrids created by membrane fusion, sonication, and freeze-thaw cycles are an attractive next-generation platform that combines the strengths of both the EVs and the liposomes. Figure 1 presents the

Generation of Exosme-liposome hybrid

Figure 1: Diagram describing the schematic of the process to engineer the exosome-liposome hybrids.

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outlying concept of the EV-liposome hybrid. A distinct advantage of using the hybrid platform is the modulation of the membrane lipid composition of the EV-liposome hybrid for better celltype-specific uptake, enhanced stability, biocamouflage, long-term systemic circulation, and reduced immunogenicity. Using the knowledge of membrane fusion, a fundamental biological process, we can generate merged membrane architecture by fusing two distinct lipid bilayers.

This opens up an exciting domain of membrane engineering that may be a potential method for designing rationally tailored hybrid nanocarriers for improved drug delivery and theranostics.

A few examples are discussed in the following section.

A significant paper in this direction was published by Sato et al. in 2016, where they made exosome-liposome hybrids and evaluated cellular uptake. Recently macrophage-derived immunoexosome were hybridized with synthetic liposomes to administer doxorubicin to target and treat breast cancer. Cheng et al. created hybrid nanovesicles with gene-engineered exosomes and drug-loaded thermosensitive liposomes, which blocked the CD47 "don't eat me" signal limiting the tumor cell's ability of immunological escape using combined photothermal therapy and immunotherapy. Li et al. have used similar hybrids to co-deliver triptolide (a natural product with an anti-cancer effect) and miR497 to attack chemoresistance in ovarian cancer. Another study used engineered, exosomes-thermosensitive liposomes hybrid NPs to target metastatic peritoneal cancer. Belhadj et al. used hybrid c(RGDm7)-LS vesicles to study nanomedicine tumor accumulation and uptake both in vitro and in vivo. A recent preprint paper by Liu et al. used paclitaxel-loaded hybrid exosome to target triple-negative breast cancer and showed that the liposome-EV hybrids infiltrated the tumor and attenuated tumor proliferation with no signs of systemic toxicity. New approaches are also being tested to generate intelligent EV-liposome hybrids using polyethylene glycol-mediated fusion. Studies with EV-liposome hybrids showed that engineered EVs hold great promise in establishing an approach to better diagnostics, dramatically increasing anti-cancer treatment effects, and helping tackle the existing challenges of conventional delivery systems.

Hybrid extracellular vesicle and future directions It is anticipated that several avenues for hybrid-engineered EV-liposome nanovesicle will become available shortly and include clinical research translation. An important aspect is the source of EVs, and the hybrid production method also needs considerable attention. Novel surface bioengineering design strategies of EV-liposome hybrids may include stealth methodology for avoiding phagocytosis and long-term circulation of therapeutic hybrids. In addition to the surface alterations, the physical features of the hybrids should be carefully considered, including size, shape, and total charge of NPs, and can critically impact systemic clearance, 18

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AUTHOR BIO

cellular internalization, and immune activation.

Research should also be focused on hybrids that can aid and monitor the effects of immunotherapy in cancer patients. Further investigation is required to study the EV-liposome hybrids in creating theranostic applications and personalized tailored medicine.

Moreover, other artificial platforms can also be hybridized with the EVs to generate novel systems and need further investigation. NP-based cancer diagnosis, treatment, and theranostics have snowballed, emphasizing the need for absorption, distribution, metabolism, and excretion (ADME) studies for FDA clearance.

Manash K. Paul works as a Scientist, Principal Investigator at UCLA, USA. He has contributed significantly to stem cell biology, extracellular vesicles, early diagnostics, and lung cancer. He has published in over 65 peer-reviewed journals and is the recipient of many awards, including the UCLA Vice-Chancellor’s award, AAISCR-R Vijayalaxmi Award, and a senior member of the IEEE.

References are available at www.pharmafocuseurope.com

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Effective Biomarkers in Uncovering Novel Target Molecules for a Drug The Revolution in New Drug Targets Biomarkers have always played a pivotal part in the detection and treatment of diseases. However, recent studies suggest that the use of biomarkers in drug discovery would unquestionably facilitate and accelerate the process of drug development through analysis of the pharmacodynamics of the specific drug.

This article focuses on the role of effective biomarkers in uncovering novel target molecules for a drug.

Salvi Wahidna and Ashish Wadhwani* Faculty of Health Sciences, School of Pharmacy, JSS Academy of Higher Education and Research

B

iological markers or the more commonly used term ‘Biomarkers’ was first heard of in the 1950s. In the coming years, this term gained unprecedented popularity as myriad studies were carried out regarding the efficacy of biomarkers in the drug discovery and development process as well as in the medical diagnosis of diseases. However, the use of biomarkers is not limited to solely these two fields; chemistry, geology, and astrobiology are only a few of the other domains in which the term ‘biomarker’ makes an appearance.

Over the years, the Food and Drug Administration (FDA) has approved a plethora of biomarkers to be used in drug development through the three-stage submission process of the Biomarker Qualification Program (BQP), an occurrence which has revolutionized the process of drug discovery from being a tedious, time-consuming task to one which is significantly less challenging. Owing to this, there have been several research which has been 20

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conducted related to drug targets for better therapeutic development of diseases such as Type 2 Diabetes, AIDS, and neurological disorders like Alzheimer’s disease, among others.

Roles of Different Biomarkers A biomarker, in accordance with the Biomarkers, Endpoints and other Tools (BEST) Resource, is said to be “a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes, or responses to an exposure or intervention, including therapeutic interventions.” The seven categories in which these measurements can be majorly classified into are susceptibility/risk, diagnostic, monitoring, prognostic, predictive, and pharmacodynamic/response, and safety biomarkers.

Susceptibility/Risk Biomarkers These characteristics serve to encompass the possibility of contracting a particular disease, prior to the development of that disease in the individual. In the case of Alzheimer’s disease, changes in the genome of Apolipoprotein E (APOE) demonstrate the onset of this neurological disorder in the patient. To further illustrate the use of susceptibility biomarkers, patients with w w w. p h a r m a f o c u s e u r o p e . c o m

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elevated levels of C-Reactive Protein (CRP) in the blood are at more peril of being diagnosed with coronary heart disease.

Diagnostic Biomarkers As its name suggests, a diagnostic biomarker plays a leading role in establishing the presence of a disease in a patient coupled with the subtype of the medical condition. The most basic example to denote a situation in which a diagnostic biomarker is employed is in the detection of essential hypertension, where repeated patterns of raised blood pressure will be observed.

On the other hand, Type 2 Diabetes Mellitus is concurrent with a high blood glucose level.

Monitoring Biomarkers This signifies continuous measurement to evaluate and judge the rate at which the disease is progressing or to oversee the way the body reacts to a drug. Monitoring biomarkers are especially beneficial in gauging the response in a Hepatitis C-positive patient through the quantification of Hepatitis C virus ribonucleic acid (HCV RNA) levels. Prostate cancer can also be monitored by indulging in the use of prostate-specific antigens as a biomarker.

Prognostic Biomarkers This biomarker’s purpose is to define the odds of a recurring or progressing disease in a specific group of people. A shift in the gene sequence of BReastCAncer genes 1 and 2 (BRCA1/2) are likely to be used as a prognostic biomarker to show the possibility of relapse in breast cancer in women.

Predictive Biomarkers After administration of a drug or exposure to environmental factors, predictive biomarkers distinguish between people who are inclined to react to it from those who do not. One such example is the use of Human leukocyte antigen allele (HLA)–B*5701 genotype to judge the severity of adverse effects on the skin of HIV positive patients prior to exposure to abacavir.

Pharmacodynamic Biomarkers It is also known as a response biomarker which shows the effects of a drug on the processes occurring in the body. The International normalized ratio (INR) of a patient is checked as a pharmacodynamic biomarker when the latter is administered warfarin to minimize the risks of a thrombus.

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Safety Biomarkers This biomarker has the crucial role of measuring whether a certain drug is noxious to the body, and if so, the degree of it. Bilirubin is one such example when used to check for hepatotoxicity.

Biomarkers in Target Validation Following target identification, the drug development process undertakes target validation as the next substantial step. This phase is vital in gaining knowledge about the medical effects encountered when a drug binds with its target molecule. This is essential to assess the efficacy of the drug while it is still under the development process.

Pharmacodynamic biomarkers as mentioned above, help establish the pharmacodynamics and pharmacokinetics profile of a specific drug during pre-clinical trials. Lack of effective biomarkers in this phase would lead to insufficient information obtained regarding the properties of the drug, hence affecting the dose and dosage form selected. The choice of target molecule will also be negatively influenced.

Individual biomarkers are used in each phase of the drug discovery and development process, some of which might not necessarily be interrelated to each other. Nevertheless, the properties the biomarkers should possess to be termed as an effective biomarker remain the exact same; easily attainable, robust, readily replicated and the ability to differentiate between real and fake results. The biomarker employed should be able to show both the progression of the disease and the effects of the drug on that disease. To test whether the biomarker meets these criteria, in vivo testing of the biomarker should be performed.

Type 2 Diabetes Advanced genetic studies in 2015 by Nelson and group have shown that new drug targets can be discovered through the genomics of an organism. In this study, the gene SLC16A11, coding for monocarboxylate transporters (MCTs), was examined. MCTs act as a drug target while it is majorly involved in the transfer of monocarboxylic acids across the plasma membrane of cells. To substantiate this view, pyruvate was used in the assay, hence proving MCTs to be a valuable drug target.

The transporter has also proven to be engaged in the release of insulin by beta-cells of the pancreas when stimulated by the presence of pyruvate and in regulating the absorption of pyruvate into cells. Several questions have aroused regarding monocarboxylate transporters as a valid drug target in showing curative properties particularly about the use of MCTs in the treatment of Type 2 Diabetes. These questions can only be answered after additional 24

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research has been carried out, which will clearly be a fruitful activity, seeing that MCTs have shown their potential as a drug target.

Challenges and Opportunities for Drug Development So far, only the positive aspects regarding the development of biomarkers have been portrayed.

Regardless, it cannot be denied that there are several oppositions to be countered before launching into biomarker development. Included in the challenges is the fact that scientific explanations of biomarkers are not always verified, leading to a certain reluctance to approve the biomarker by regulatory bodies. Assessment of biomarker characteristics can also be wrongly defined, hence incorrectly setting up the link between the biomarker and a specific disease.

Another truth about biomarker development is that it can prove to be quite time-consuming, and it will additionally take up significant assets and funds to be able to contribute to an early decision-making process. More extensive and prolonged clinical trials will have to be considered to prove the greater benefits of biomarkers as compared to their risks.

Regulatory bodies as well do not make the development of biomarkers an easy task with their continuously varying requirements to qualify a biomarker for approval.

To balance out these challenges, biomarker development involves numerous opportunities as well:

The Way Forward…

The latest buzzword heard when biomarkers are discussed is exosomes, extracellular vesicles derived from the endosomal structure of cells and containing proteins and nucleic acids that symbolize the

Figure: Biomarkers: Opportunities for Drug Development w w w. p h a r m a f o c u s e u r o p e . c o m

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pathological processes occurring in the body. Being ever-present under any conditions leads to exosomes being an indispensable part of biomarkers discovery.

The effortless isolation of these vesicles has only encouraged their use as a diagnostic biomarker in several diseases like cancer, neurodegenerative diseases, as well as liver and kidney diseases.

A 2013 study by Yoshioka and team led to the revelation of lower levels of CD63, an exosomal protein marker of the tetraspanin group, in cases of benign tumor cells being present as opposed to when cancerous cells proliferate in the body. Another finding by Graner et al.established the accumulation of amyloid proteins from exosomes in the brain plaques in the case of Alzheimer’s Disease along with elevated levels of phosphorylated Tau proteins, an essential biomarker in the premature diagnosis of the disease.

Coming to exosomal nucleic acids, much research has led to the conclusion that exosomal miRNAs can be of great significance in detecting various cancers.

The year 2013 showed that increased exosomal miR-21 levels in the blood plasma of patients suffering from esophageal squamous cell cancer (ESCC) can be used as a crucial biomarker, as proven by Tanaki et al.

This goes to show that indulging in further research on exosomes is of paramount importance and it would undoubtedly prove to be a huge advancement in exposing novel drug targets.

To conclude the pharmaceutical and health sector is shifting focus from a “one-drug-fits-all” to a “personalized approach”, the state-of-the-art roadmap for biomarker-driven drug development is the way forward and the biomarkers will surely bring the revolution in new drug targets for this approach.

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AUTHOR BIO

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Ms.Salvi Wahidna is graduate scholar at the School of Pharmacy, JSS Academy of Higher Education and Research, Mauritius. Her interest lies in the vast field of research and drug development specifically the pharmacological aspects of drugs. In these lines, she has authored articles on ‘Diabetes Insipidus’ and ‘The Drug Development Process’.

Dr Ashish Wadhwani did his MPharm and PhD in Pharmaceutical Biotechnology from JSS University, Mysore. After completing his PhD he worked as a Research Associate at National AIDS Research Institute, Pune for DBT-ICMR joint project.

He has handled six projects as PI/Co-PI from the Government of India, published 67 research/review papers in peer-reviewed journals and published 03 Patents. He has received various awards at National and International conferences for his research findings. Currently, he is working as a Professor & Head at the School of Pharmacy, JSS Academy of Higher Education and Research, Mauritius.

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c-MET Receptor Tyrosine Kinase: A Potential Biomarker for Cancer Therapeutics The c-MET/HGF signaling regulates multiple cellular processes that stimulate cell proliferation, invasion and angiogenesis. The c-MET pathway has significant interaction with other signaling pathways. The deregulation of c-MET plays an important role in the formation, growth, maintenance, and invasion of tumors. It is involved in various cancers, including lung, colon, liver and stomach cancer. Many studies on the abnormal signaling of c-MET offer strong reasons to investigate the therapeutic potential of some receptor antagonists.

Therefore, more active substances must be selected and integrated effectively into well-designed clinical trials, these include predictive biomarkers such as genetic mutation, amplification and overexpression of immunohistochemical c-MET. The complex interconnection of cell control networks provides new pathways for combining EGFR inhibitors and other targeted drugs. Thus, c-MET can be used as potential biomarkers for cancer therapeutics.

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Dhruv Kumar* Senior Associate Professor, School of Health Sciences and Technology, UPES University

Sibi Raj ICMR-SRF Fellow, currently pursuing PhD in Cancer Biology, School of Health Sciences and Technology, UPES University

RESEARCH & DEVELOPMENT

c-MET is a tyrosine kinase receptor in the MET family. Normally, it is expressed on the surfaces of several epithelial cells. HGF/SF is a common ligand for c-MET receptors. Cooper et al. were the first to identify the MET oncogene in 1984. They chemically transformed the human osteocarma cell line by transfection analysis in NIH/3T3 cells and mapped the MET oncogene. The MET oncogene was identified by the 7q21-31 chromosome band, showing a genetic shift between two different loci, MET’s proto-oncogenes are on chromosome 7 and on chromosome 1’s transfer promoter regions (TPRs). After analyzing the translation product of the proto-oncogene 21-exon MET proton, it was discovered on the surface of the cells and classified as a tyrosine kinase growth factor receptor. The MET-proton-oncogene product acts as a receptor for the HGF tyrosine kinase transmembrane. The receptor is mainly present in melanocytes, epithelial cells and epithelial tissues, including the liver, the intestine, the kidneys and other organs. HGF regulates various transmission cascades on the c-MET receptor and acts on c-MET receptors in a paracrine manner (such as mitogenactivated protein kinase), phosphatidylinositol-3 kinase (PI3K)/AKT, and Janus kinase/ signal transducer and activator of transcription (JAK/STAT) pathways. The c-MET network consists of multiple signal molecules that interact with specific physiological processes.

In addition to the properties of microbial, molecular, and morphological, c-MET protects against apoptosis by interactions with PI3K/AKT (phosphate di-tyrosine triphosphate/protein kinase-B). However, the co-expression of c-MET and HGF has been observed in various wound closure, tissue regeneration and embryonic events.

c-MET c-MET is a receptor tyrosine kinase that is found primarily in melanocytes, endothelial cells and epithelial tissues such as liver, gastrointestinal tract, and kidneys. In normal cells, primary MET transcripts produce 150kDa peptides and partially glycosylated 170kDa precursor proteins.

Originally isolated from the line of human gastric tumor cells, the receptor contained a 50kDa chain and 145kDa chain and was linked to a sulfur bridge in a transmembrane αβ complex of 190 kDa. 170kDa precursor proteins are transformed by glycosylation and subsequent translation to produce mature heterodimers. The α -subunit is located on the outer layer, and the β -string, a single-pass chain, forms the core of the three main regions of the c-MET. The extracellular area of c-MET is highly adapted to HGF while a single transmembrane segment and a cytoplasmic tyrosine kinase domain are cut by juxtamembrane and carboxyend sequences. In the extracellular part, there are three types of domains. The SEMA domain, including 500 amino acids, is based on the α and β subunits in their entirety. The preserved c-MET receptor sequences are structurally similar to large families of ligand receptor pairs w w w. p h a r m a f o c u s e u r o p e . c o m

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with semaphorin and plexin. Semaphorin is a large protein family that helps to decompose cells and induce reprehensible orientation events, such as cell dispersion during neural development. Under the SEMA domain, the PSI domain is a common domain for Plexins, Semaphorins, and Integrins. It is connected to the transmembrane helix through four IPT domains (immunoglobulin-like, plexin-like, transcription factor). HGF can be connected to two locations in the SEMA area, one with a low specificity contact and a high specificity contact, and the other with a low specificity contact and a high affinity contact in the IPT region. The intracellular domain consists of three parts: the Juxtamembrane sequence, which down regulates kinase activity followed by Ser975 phosphorylation in the catalytic region for the activity of kinase leading to transphosphorylation of Tyr1234 and Tyr1235. A dimerization of the receptors is caused by the phosphorylation of the tyrosine residues in the kinase domain. This may lead to the autophosphorylation of bidentate docking sites in the carbonate terminal tail of the cytoplasm (the Tyr1349 and Tyr1356 tyrosine residues). The total length of MET as a proto-oncogene is 125,982 bp and belongs to the 7q31 chromosome.

HGF is a family of plasminogen-related growth factors known as PRGF-1. The length of HGF gene spans 70 kb on chromosome 7q21.1. HGF initially in the form of pro-HGF is further cleaved by a protease to mature HGF.

c-MET Signaling HGF is the main ligand of the c-MET receptor and acts as a biological signal. HGF is produced as an inert precursor of a single chain. The elimination by extracellular protease transforms the predecessor into two-chain functional heterodimers. HGF is mainly distributed in most tissues’ extracellular matrix in inactive forms and is separated from mature forms of proteoglycans such as heparin. HGF originates mainly from mesenchymal cells, which act paracrine in epithelial cells that express c-MET receptors. Several cytokines like interleukin 1, 6, tumor necrosis factor- and the transformation factor- induce transcriptional upregulation, both in HGFs and macrophages, and in c-METs and epithelial cells. Tumor stroma increases the expression of pro-HGF-activated proteins such as plasminogen activation systems and matriptases.

Therefore, HGF activates and produces more biological functions by controlling combinations of transcription regulation and post-transcription regulation, which also eventually leads to optimal MET activation on target cells. It is also an important part of the physiological defense mechanism against tissue damage. When HGF is bound, c-MET is activated by the activity of kinase and then the receptor dimerization is completed and followed by transphosphorylation of two catalytic tyrosine residues Tyr1234 and Tyr1235 within the kinase activation region. Subsequently, two additional phosphorylated tyrosine residues are docked 30

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in the terminal tail of the carboxylic acid (Tyr1349 and Tyr1356), which, upon activation, can serve as degeneration elements to recruit several other subsequent signaling molecules. MET serves as a c-MET plays an substrate for the phosphatase of protein tyrosine, important role in the including PTP receptor density-increasing phosphatase 1 (DEP 1) and leukocyte common formation, growth, antigen related (LAR) and the non-receptor maintenance, and PTPs, PTP1B and T cell PTP. These phosphatases invasion of tumors. may disrupt the c-MET signal by promoting the Thus, it can be dephosphorylation of catalytic tyrosine or docking used as potential tyrosine. Many scaffold proteins, associated with c-MET receptors, activate downstream signal biomarkers for cancer transmission pathway, this includes cascades of therapeutics. kinase of mitogen-activated proteins (MAPKs), kinases of extracellular signal control 1 (ERK1) and ERK2, kinases of end-amino-terminal Jun (JNKs), and p38, the phosphoinositide 3-kinase– Akt (PI3K–AKT) axis, signal transducer and activator of transcription proteins (STATs), and the nuclear factor-κb inhibitor-α (iκbα)–nuclear factor-κb (NF-κb) complex (Figure1). The C terminal tail of c-MET is linked to a wide range of Src homology domains, including PI3-K, Src non-receptor kinase, and effects the adapters of SHP2 (PTPN11) that connect protein 2 of growth factors receptors (GRB2) and SH2 domains contain converting proteins (SHC).; an upstream activator of Src and ras), phospholipase Cγ1 (PlCγ1) and the transcription factor STAT3. c-MET receptors also provide additional binding sites for other adapter proteins and are associated with the phosphorylation of binding protein 1 (GAB1) associated with GRB2.

GAB1 is connected to c-MET receptors through 13 MET binding sites of amino acids and indirectly through GRB2 linked to MET. Some transducer proteins are involved in c-MET receptor signal cascades, mainly in interactions with the GAB1 scaffold adaptor. CD44 is recognized as another protein interacting with c-MET in normal epithelial cells.

Role of c-MET in cancer c-MET was first discovered in the early 1980s as a product of chromosome rearrangement after a carcinogenic treatment with N-methyl-N′-nitro-N-nitrosoguanidine. This reorganization leads to the constitutive fusion of TPR-MET oncogene, after dimerization, a leucine-zipper w w w. p h a r m a f o c u s e u r o p e . c o m

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Figure 1. Different downstream signal pathways activated by c-MET and other interactive membrane receptors. motif is formed in the TPR molecule, which is converted to an oncoprotein. This satisfies the structural requirements for the constitutive activity of the c-MET kinase. TPR-MET is capable of transforming epithelial cells and inducing spontaneous mammary tumors when transgenic mice are generally overexpressed. These results set the basis for the ongoing efforts to discover all the cancer-related capabilities of c-MET. More than 10 years of proof of concept of c-MET’s role in human cancer have been required. The discovery of the activation point mutation in the germ line of patients with hereditary renal cell carcinoma was obvious.

However, spontaneous MET mutations occur only rarely between 2 and 3 percent. The expression of aberrant MET is widespread in various malignancies, particularly non-small cell lung cancer (NSCLC), gastrointestinal (GI) cancer, and hepatocellular carcinoma (HCC).

The interaction between MET and the HER2 family is becoming an important mechanism for the progression of the tumor and treatment resistance. MET signals also interact with 32

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vascular endothelial growth factors (VEGFs) and VEGF receptors (VEGFRs). MET activation increases the expression of VEGF-A, and promotes angiogenesis and the growth of endothelial cells.

Many reports show that altered RTK activation levels play an important role in cancer pathophysiology. Deregulation and subsequent aberrant signalling of c-MET can be caused by different mechanisms, including gene amplification, overexpression, activation of mutations, and increased stimulation by autocrine or paracrine ligands, and interaction with other active cellsurface receptors. In preclinical studies, several c-MET small molecule inhibitors and monoclonal antibodies have been evaluated. Studies have shown that the expression of c-MET is related to the expression of immune regulation molecules, such as programmed cell death ligand (PDL-1) and indoleamine-2,3-dioxygenase (IDO) in cancer cells.

This assures that c-MET can be effectively targeted for immunotherapy in clinical research. c-MET is also associated with chemotherapy by avoiding traditional clinically suppressive signals such as EGF. Gefitinib and c-MET inhibit additive synergy demonstrated in cell line models. Many studies reported that c-MET was overexpressed in various cancers, including lung, breast, ovaries, kidneys, liver, thyroid and gastric cancer. This excessive expression may be due to transcription activation, hypoxia-induced excessive expression, or MET amplification, especially in a few cancers. It is reported that a transgenic mouse overexpressing c-MET spontaneously develops liver cell carcinoma, and when the transgene was inactivated, tumor regression was reported even in large tumors.

AUTHOR BIO

RESEARCH & DEVELOPMENT

Dr. Dhruv Kumar, Senior Associate Professor at the School of Health Sciences and Technology, UPES University, Dehradun, Uttarakhand, India. His lab is currently focusing on Translational Cancer Research, mainly to understand the role of tumor microenvironment in metabolic alterations in solid tumors including Head and Neck, Breast, Brain, Prostate, Pancreatic, Gallbladder and Skin cancer and to understand the genomic and mutational heterogeneity in solid tumors.

Sibi Raj, ICMR-SRF Fellow, currently pursuing PhD in Cancer Biology under the guidance of Dr Dhruv Kumar at the School of Health Sciences and Technology, UPES University, Dehradun. Her research interest is to explore the role of c-MET in metabolic dysregulations in Head and Neck Cancer.

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New Pharmacological Targets and Biomarkers for the Treatment of Tuberculosis (TB) Two decades of rigorous study and a unifying omics strategy to TB treatment.

We are now confronted with several uncertainties as well as the unfathomable implications of multi-drug resistance. Outstanding prospective leads have emerged in recent years as a result of collaborative efforts. This includes academic research, pharmaceutical corporations, government programs, and non-profit organizations. Novel biomarkers have been identified as possible medication targets by new drug development pipelines that use machine learning methodologies.

Vidya Niranjan Professor and Head of the Department, Department of Biotechnology Lead- Centre of Excellence Computational Genomics, R V College of Engineering

M

ycobacterium tuberculosis (MTB), which has harmed people for more than 10 decades, is the cause of the chronic lung infectious disease known as pulmonary tuberculosis (TB). Modern chemotherapy has been crucial in the fight against TB, but the spread of HIV and the rise of drug-resistant TB pose a threat to its control. The success rate of the 6-month conventional treatment for developing TB is only 85%, despite the fact that the cure rate of TB has dramatically increased since the deployment of direct treatment short-course chemotherapy (DOTS), which was approved by the WHO. The main disadvantage of the present chemotherapy is how long it takes to complete—up to two years for drug-resistant TB (DR-TB) and 6–9 months for drug–susceptible TB (DS– TB) patients. Patient nonadherence, treatment failure, and resurgence are frequently the results of this. According to 2017 WHO Global TB Report, there were 1.67 million deaths,

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10.4 million new cases of active TB disease, 6,00,000 new cases of rifampicin resistance (RR-TB), of which 4,90,000 were multidrug resistant (MDR-TB), and 6.3 million new TB cases recorded in 2016.

Numerous novel compounds with anti-TB potential have been identified as a result of decades of worldwide study, and they are now being tested in both the pre-clinical and clinical phases of therapeutic development. It takes an hour to look for novel anti-TB medications (ATD) that can address the problems with the present TB treatment, however very few treatments reach the market. Hopefully, innovation in the quest for fresh and potent ATDs can reduce the global TB burden.

In order to highlight the impact of tuberculosis on human health worldwide, we begin this review by giving a succinct introduction to the disease. We started out by focusing on how current chemotherapy is used to treat TB and its effectiveness. The main difficulties or problems in TB chemotherapy are then discussed. The remainder of the review gives readers in-depth information on the drug targets now being used against M. tuberculosis, as well as updates on recently FDA-approved medications that can be used to improve the efficacy, bioavailability, and safety of current regimens. Since only a small percentage of compounds pass past the strict bottlenecks of TB medication development programs. This points to the urgent need for the development of new medications that can entirely.

Mycobacterium tuberculosis and host epigenetic alterations as new indicators and therapeutic targets.

The fact that MTB can divert the innate and adaptive immune systems of the human body in many ways is one of the key causes of tuberculosis' protracted dormancy period.

In order to fend off the immune attack, pathogenic mycobacteria support a number of mechanisms, including suppression of phago-lysosomal maturation, inhibition of autophagy, activation and production of cytokines, inhibition of reactive oxygen and nitrogen species (ROS and RNS), manipulation of T cell antigen presentation, and epigenetics. During MTB infection, several miRNA levels are dysregulated. Similar to miR146a, the host's level rises w w w. p h a r m a f o c u s e u r o p e . c o m

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following infection. This miRNA can affect the expression of TRAF6 mRNA to affect MTB survival in macrophage.

Conversely, the opposing regulation of certain miRNAs enhances the immune signaling pathway and promotes the immune cells' ability to recognize the pathogen.

It is known that these epigenetic modifications affect the latent active and inactive phases of TB infection. MTB latency and the reactivation of pertinent transcription and translation are impacted by epigenetics.

Ser/Thr protein kinase (STPK), a component of the Ser/Thr/Tyr kinase system found in MTB, has a significant impact on bacterial growth and engages in interactions with the host. In order to better adapt to the environment of the host, bacteria will occasionally modify their condition through phosphorylation. Bacterial replication can be aided by Ser/Thr protein kinase B (PknB), and its overexpression is detrimental to bacterial development. When MTB is active, latent, or reactivated, PknB can be phosphorylated to regulate the production of specific proteins.

In the realm of oncology or other disease models, the investigation of epigenetic medicines has advanced significantly. Malignant tumors can now be treated in a novel approach by using epigenetic targets to prevent or suppress the imbalance of epigenetic regulation.

In the drug discovery process in different disease models, our aforementioned targets or biomarkers, particularly connected microRNA, have made some success. For instance, MRG-201, which imitates miR-29 and is used to treat individuals with Keloids, has passed clinical phase II. Similar to this, there are other medications in phase I, as well as miR-21 and miR-155 inhibitors for T-cell lymphoma and Alport syndromes, respectively. Finding epigenetic medicines for the treatment of tuberculosis is made possible by the development of treatments for related diseases.

For MTB, the novel epigenetic biomarkers/targets are MamA, Rv2966c, RV1988, Rv3204, Rv3763, Rv3423.1, Rv2416c, HsdM. Similarly, in humans (host) the novel epigenetic targets are SUV39H1, SET8, MiR-29, MiR-147, MiR-21, MiR-99b, MiR-126b, MiR-144, MiR-223, MiR-424, MiR-26b, MiR-132 and MiR-155.

Scientists from academic and industry domains undoubtedly have innovative strategies to address the problem of drug-resistant tuberculosis given the growing understanding of MTB 36

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infection, which causes epigenetic changes in infected hosts. The development of modulators that target epigenetic changes occurring both before and after TB infection is both exciting and difficult.

Circular RNA as potential biomarkers and Regulate Anti-TB Defense as Potential Therapeutic Targets) A type of non-coding protein RNA called circular RNAs (circRNAs) is expressed in the majority of eukaryotic cells. Circular RNAs can loop into a single-stranded, covalently closed structure for more stable and highly conserved properties, which helps them resist or evade RNAase digestion, in contrast to linear RNAs, which have three-prime tails and five-prime caps. The first circular RNA was discovered in the 1970s in plant viroids, which had great thermal stability and a single-stranded covalently closed structure. Sequence analysis verified this discovery two years later. Thus, the study of circRNAs in various species from a functional and mechanistic perspective is now possible because of this work in biology and medicine.

Sputum specimen smear microscopy and sputum culture are currently the major methods used in the laboratory to diagnose tuberculosis (TB). Sputum culture is constrained by its long detection time, which increases the risk of missing the right treatment in time, whereas sputum specimen smear microscopy is constrained by its low sensitivity and specificity values.

CircRNAs have been shown to play a significant role in a wide range of physiological and pathological processes. The circRNAs that are aberrantly expressed in patients may provide a clue as to the possible use of circRNAs in the diagnosis of various disorders. Regarding their vast distribution and durability, circRNAs can be easily found in bodily fluids like blood, urine, exosomes, and others. CircRNAs may be used as TB diagnostic biomarkers, according to the increasing number of research in recent years. To learn about the crucial functions of circRNAs following tuberculosis infection, which can improve our comprehension of TB pathogenesis and aid in the development of TB diagnostic or therapeutic approaches, there are still a lot of facets of the topic that need to be fully researched.

As the initial line of defense against the germs after MTB infection, host macrophages are activated. It's interesting to note that MTB can successfully adapt to a number of ways to evade the macrophages' bactericidal actions, securely surviving inside the cell host, and even developing into active TB. According to an increasing number of studies, circRNAs can operate as essential components in the immune defense response during TB infection by influencing the actions of macrophages with the goal of containing and eradicating MTB invading in macrophages. Determining the significance of circRNAs that are aberrantly w w w. p h a r m a f o c u s e u r o p e . c o m

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produced in TB and delineating their potential roles in the pathophysiology of TB infection is therefore crucial.

For list of circular RNA’s reported to be involved in regulating tuberculosis are as follows It is crucial to find Hsa_circ_0001204, hsa_circ_0001747, hsa_ new TB targets circ_0001204, hsa_circ_000174, hsa_circ_0001953 that are not only hsa_circ_0009024 hsa_circ_0001953; hsa_ circ_0009024 hsa_circ_001937 hsa_circ_0043497 crucial under host hsa_circ_0001204 hsa_circ_103017 hsa_ infection but are circ_059914 hsa_circ_0028883 hsa_circ_0005836 also susceptible to hsa_circ_0001380 hsa_circ_103571 circ_051239 pharmacological circ_029965 circ_404022 SAMD8_hsa_ inhibition circRNA994 TWF1_hsa_circRNA9897 circ_ TRAPPC6B hsa_circ_0003528 hsa_circ_101128 hsa_circ_0045474 circAGFG1 circ_0001490 cPWWP2A. The majority of research has reported that different circRNAs are involved in TB through the miRNA-mRNA transcriptional regulatory axis, but their functions, such as interacting with RBPs or taking part in transcription or translation, have not been investigated. Uncovering the functions and mechanisms of circRNAs in TB will be crucial in the future study, making use of newly developed technologies. In order to discover new functions and underlying processes in TB, it would be advantageous and essential to continue developing a unique regulatory network analysis technique of circRNAs. This would ultimately be helpful and vital for the prevention, control, and treatment of TB. To sum up, more research is still needed to determine whether circRNAs are safe to use as innovative tools. The immunogenicity and biosafety of circRNAs vaccines or medications need to be further verified in vivo and in vitro. CircRNAs drugs targeting immunological disorders or tumors can be manufactured using circRNAs technology. We expect that circRNAs will continue to be recognised as playing important roles in the management of TB as more research is done on their mechanisms and activities using successively upgraded methodologies.

MTB Enzyme complexes of the electron transport chain can be plyed as potential drug targets Unlike other bacteria that rely on substrate-level phosphorylation, Mtb is an obligate aerobic 38

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bacterium that primarily depends on Oxidative phosphorylation (OxPhos). The OxPhos process uses the Electron Transport Chain (ETC) to shuttle electrons through a sequence of ETC complexes in order to produce the required PMF. Recently, numerous chemicals that are thought to be better regime tools have been reported against the Mtb energy production cycle. The development of possible therapeutic compounds using ETC complexes such as NDH-II, cyt bc1-aa3, cyd-bd MK, ATP synthase, and others has been suggested. Although BDQ (sirturo) and Q203 (telebace), which either directly or indirectly limit ATP production, are currently being tested in clinical trials against MDR-TB and XDR-TB. The drug targets for ETC are NDH-I, NDH-II, Cytochrome bc-I, MenG, MKH2, SDH and ATP synthase. Based on their contributions to the creation of the PMF, each ETC complex has been targeted as a possible druggable target. ATP synthase draws its energy from the PMF. More exhaustive research is needed in order to pinpoint the pathway's potentially more sensitive and dependable target.

The MTB deftly alters its paths to suit its needs. Enzymatic assay, inhibitory assay, and genomic organization of the genes encoding these proteins have all been investigated for each of the complexes directly connected to ETC. Currently, the energy production cycle is affected by more than 30% of the medications undergoing clinical trials. Therefore, identifying the most appropriate target would benefit from a complete understanding of the system and its metabolic adaptability under changing settings.

The components' mechanistic and functional analysis will aid in the development of anti-tuberculosis inhibitors. This review comes to the conclusion that using inhibitory action against a single component will be less effective than using a combinatorial theory of inhibition. The present medications that target the ETC pathway are effective, but only for a short time due to altered pathways and the emergence of resistance. Therefore, it is important to find the appropriate scaffolds and potential inhibitors that could work well with present medications to treat tuberculosis.

A novel concept of bait drug synergism targeting AAC proteins The four families of aminoglycoside acetyltransferases are AAC(1′), AAC(2′), AAC(3′), and AAC(6′), which are named by the position of modification on the 2-deoxystreptamine core. Aminoglycoside acetyltransferases (AACs) catalyze the synthesis of a physiologically stable amide with the aminoglycoside using intracellular acetyl-CoA as a co-substrate.

O-acetylation occurs using the acetyltransferase domain of the bifunctional enzyme AAC(6′)APH(2′′) and the mycobacterial enzyme AAC(2′)-Ic, although AACs primarily change w w w. p h a r m a f o c u s e u r o p e . c o m

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amino groups (N-acetylation). The AAC family is found to not be prone to mutation over the years.

The novel idea works on the concept of “bait-drug synergism” wherein a biosimilar molecule with a similar interaction profile with AAC as that of aminoglycosides. This bait drug molecule will competitively bind to AAC against aminoglycosides, in-turn preventing the acetylation of aminoglycosides. These aminoglycosides can prevent ribosomal activity leading to the death of pathogens.

The concept of “bait drug synergism” has to be explored more in the coming years as a potential mode to overcome antimicrobial resistance in tuberculosis.

Conclusion

AUTHOR BIO

A resurgence in interest in TB medication development during the past ten years has led to numerous important scientific advancements. The present TB-drug pipeline contains innovative chemical scaffolds and a range of targets from a novelty perspective. But despite these improvements in chemotherapy, eliminating TB is still a major issue on a global scale.

Therefore, it is crucial to find new TB targets that are not only crucial under host infection but are also susceptible to pharmacological inhibition, in addition to the aforementioned scientific research The new targets should also be focused on overcoming antimicrobial resistance. In the implementation of machine learning in pharmacological research new drug targets have been identified. The key message is that in order to develop a comprehensive control strategy against MTB, the scientific community should constantly engage the socio-political establishment, fill gaps within existing approaches, pursue newer chemotherapeutic approaches, and try to achieve a cumulative therapeutic outcome from various unrelated approaches.

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Vidya Niranjan, PhD is a leading scientist and academic researcher excelling in computational biology. She has worked extensively on genome analysis, drug discovery, tools and database development.

With an extensive research experience of over 20 years, she has published over 80 research articles. She has bagged research funding worth 40 million USD from various government agencies and pharmaceutical companies.

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BENEFITS AND CHALLENGES OF USING WEARABLES IN CLINICAL TRIALS

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earable sensors have the potential to transform healthcare. In many instances, wearable sensors can pick up signs that a person is getting sick before even they realize it. This capability is particularly relevant for chronic conditions. Imagine sensors picking up signs of worsening heart disease in Congestive Heart Failure (CHF) patients and spurring an intervention before the patient needs to be readmitted to the hospital. Wearable42

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Wearable sensors can facilitate a shift from reactive treatment of a disease to proactive prevention. In an age of chronic disease management, they can help to monitor patients’ realworld behavior. But sponsors face challenges implementing wearable sensors in clinical trials. Before sponsors can implement digital endpoints in their studies, they must first validate the endpoints (and their algorithms). To gain greater insights into patients’ health, sponsors should leverage wearable sensor platforms designed for clinical trials, collect raw data, and utilize open-source algorithms.

Geoffrey Gill President, Shimmer Americas

detected changes in a person’s gait can already predict an increased risk of falls – a major cause of hospitalization and death in the elderly – and enable proactive interventions to be made.

The potential healthcare cost reductions and quality-of-life benefits this could produce are enormous. With aging populations around the world, chronic disease management increasingly dominates healthcare spending. In fact, 86% of healthcare spending in the U.S. was on adults with chronic conditions, according to a study published in ACR Open Rheumatology in February 2020. While COVID had a major temporary impact on the proportion of resources devoted to infectious disease, there is little question that chronic conditions are the biggest long-term healthcare concern.

The ability to forecast health issues can transform healthcare from reactive treatment to proactive prevention of illness – arguably the most fundamental change in the history of healthcare. Furthermore, these capabilities can be used to assess treatments in real-world situations, potentially transforming clinical trials.

The Cost of Clinical Trials The increasing dominance of chronic diseases affects clinical trials. According to 2016 research from the Tufts Center for the Study of Drug Development, it cost $2.6 billion to bring a drug to market: a 145% increase over 10 years. While some question Tufts’ methodology, there is w w w. p h a r m a f o c u s e u r o p e . c o m

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no question about the trend. One major driver of this increase is the increased incidence of chronic diseases.

Unlike other diseases where outcomes (e.g., cure and mortality rates) can be objectively measured, chronic diseases tend to be characterized by the degree of severity. Many studies still rely on subjective measures – like doctors asking patients how they feel or how much pain they experience – to gauge the quality of life. Not only are these measures highly subjective; but they are also prone to recency bias, where patients place too much weight on experiences in the past 1-2 days.

These measures are unreliable and uncertain.

To get more reliable data, clinical trials have resorted to larger sample sizes, driving up costs and prolonging trials. Often, that is still not enough. Most pharmaceutical companies have placed their hope in drugs that looked promising in Phase 2 trials, only to witness them fail in Phase 3.

The pressure to contain costs also stems from diminishing returns in a crowded market.

Just about every condition already has an existing treatment – many of which are quite good.

As a result, the incremental increased value from new treatments is getting smaller. Profits are getting squeezed. Return on pharma R&D was at 1.9%, according to one 2018 Deloitte study. That’s less than a U.S. Treasury bond. No company will continue to invest for long at those rates of return.

Benefits of Wearables in Clinical Trials Wearables can measure digital endpoints that address these challenges. They can redefine the success of clinical trials and transform them. Occasional, often subjective, measures of patient progress – such as patient-reported outcomes or in-clinic tests – can be replaced.

Instead, wearable sensors can provide continuous documentation of a patient’s progress.

For example, an activity monitor worn by a patient with Parkinson’s disease or chronic obstructive pulmonary disease (COPD) can determine quantitatively whether their activity level is increasing or decreasing over time. It can even provide detailed gait metrics and analysis of freezing periods. This data can be measured and monitored 24 hours a day, 7 days 44

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a week. Objective measurement replaces subjective. A continuous perspective supersedes episodic glimpses.

These changes can improve the reliability of clinical trial data and reduce measurement uncertainty. They bring about manifold benefits. For example, they reduce the likelihood of incorrect assessments from clinical trials. Accordingly, this reduces the required sample size, potentially shortening the duration of the trial, and helping to contain the rising cost of drug development. Similarly, continuous monitoring makes it possible to detect an unexpected deterioration in overall health. Adverse events can potentially be identified earlier. For healthcare providers, this improved feedback can inform a potential course correction. More importantly, it improves patient safety. Furthermore, data from wearables can be monitored remotely, potentially reducing the number of visits required to clinical sites and the burden on clinical trial participants. This in turn may contribute to improvements in patient enrollment and retention and mitigate the risk of disruptions to clinical trials.

Challenges of Using Wearables Employing wearables in clinical trials is not without challenges, which fall into two categories: (A) implementation and (B) interpretation.

(A) Implementation Implementing wearables in a clinical trial comes with its own challenges: predominantly training, compliance, and troubleshooting. Trials often involve dozens or hundreds of clinical sites, which manage the deployment of wearable sensors to participants. Often, these sites only enroll a few participants a year. Keeping site staff trained and up to speed on the technology can be a significant challenge, especially if staff turnover is high. Likewise, participants’ compliance with the protocol needs to be managed. Although there are systems that track compliance, monitoring them requires time and training. Finally, technical issues must be identified, diagnosed, and rectified. Building the processes to train, manage compliance, and troubleshoot places additional burdens on sites.

(B) Interpretation The real challenge lies in using wearable-generated data. There are, currently, very few accepted digital clinical endpoints. Without an accepted endpoint, wearable data cannot be used to advance the goals of a clinical trial. As a result, the adoption of digital technologies in clinical trials is very low. A recent review of the Digital Medicine Society (DiMe) Library of Digital Endpoints identified 325 endpoints used in 155 trials out of the tens of thousands of trials conducted during the same period. w w w. p h a r m a f o c u s e u r o p e . c o m

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Further review of the DiMe data showed that almost two-thirds (198) of endpoints analyzed three basic concepts: glucose levels (73), activity levels (70), and sleep/nocturnal activity (55).

That these indicators were widely used is not the problem; they are important measures of health, and almost any malady likely affects activity and sleep levels. The issue, however, lies with the proliferation of measures that are, essentially, of the same parameters. Many of these measures are based on proprietary (processed) metrics that must be validated independently for every disease for which they are used. And, if the algorithms are modified, validation must be repeated. See sidebar 1 for a discussion of why validating wearable algorithms/endpoints are so challenging. Should regulators and doctors be expected to understand tens or even hundreds of different measures for essentially the same value?

Validating Wearable Biomarkers Validation of diagnostic medical devices generally involves proving equivalence to an approved predicate device. For most devices, this means showing that the device provides an equivalent single measurement across all relevant populations (age, sex, race, etc.) and parameters (e.g., heart rate or oxygen level) for which it might be used. This can be a challenge, but it is manageable. Proving substantial equivalence of an algorithm to analyze wearable sensor data is infinitely more difficult. Not only do you need to address the relevant population/ parameter ranges, but you must be able to do it in every context in which it might be used.

For example, an activity monitor must provide the same results over every time interval (hour, day, month, etc.) and every type of activity (e.g., sleeping, walking, climbing stairs, climbing a mountain, and playing tennis) to be considered equivalent to another. As a result, validating a wearable algorithm is very difficult.

Addressing the Challenges Fortunately, there are ways to address these challenges. First, sponsors can work with companies whose technology platforms are specifically designed for clinical trials and who provide a full range of support services to help alleviate the implementation challenges. Using purposebuilt technology and expert support minimizes the implementation challenges.

More importantly, sponsors can capture raw sensor data, which establishes a common denominator for addressing the validation challenge. There is some debate over what constitutes raw data, because all sensor output is, strictly speaking, processed by at least an analog to digital converter. We define raw data as (a) minimally processed and (b) able to be verified by independent physical measurement. For example, acceleration measurements constitute raw data; steps are processed data. So long as sensor output can be independently measured, 46

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it does not matter that there is some pre-processing. In this context, validating that two wearable sensors provide equivalent raw data is relatively simple. Accelerometers can be placed on a shaker table; optical sensors can be assessed with respect to other validated sensors. In fact, sensor manufacturers typically do this work and provide specific accuracy specifications as part of their product specifications.

Using raw data, it is possible to develop clinical endpoints that are device independent and can be shared across multiple studies. Furthermore, a library of raw data can be used to validate new versions of algorithms without collecting new data. If open-source algorithms are used, validation work from different studies and applications can be combined to extend and support new endpoints.

Conclusion Capturing the potential of wearable sensors in clinical trials and healthcare in general can seem like a daunting task. We can make it less daunting if we, as an industry, collaborate in precompetitive areas. Two areas are particularly ripe for precompetitive collaboration.

The first is to standardize digital endpoints. Regulators and healthcare providers cannot be expected to understand and accommodate many different ways to measure essentially the same phenomena, like activity level or sleep quality. By standardizing these measures, we can leverage the work of others and reduce confusion in the field. We believe that using

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Recommendations For Sponsors:

For the Industry:

1. Capture raw data 2. Use a platform designed for clinical trials with full support 3. Utilize open-source algorithms

1. Standardize clinical endpoints 2. Use open-source algorithms 3. Share raw data

open-source algorithms is the best approach, given the challenges of proving substantial equivalence of continuous digital endpoints, but any step towards standardization would be useful.

Perhaps even more important is to find a way to systematically share raw sensor data that are connected to reliable independent health outcomes. Collecting the data is by far the most expensive part of discovering and validating digital endpoints. Much data is already being collected, but it tends to be highly siloed and never shared. As a result, new data is collected to discover and validate every digital endpoint – even though researchers are using equivalent sensors. Once collected and used for the particular study, the data are rarely reused – a tremendous waste. Although there are clearly privacy, security, and competitive concerns with data sharing, these can be overcome. The value of precompetitive data sharing is too high for us to continue to work without it.

AUTHOR BIO

References are available at www.pharmafocuseurope.com

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Geoffrey Gill, MS, is President of Shimmer Americas, leading U.S. operations and commercial efforts for North and South America for Shimmer Research, a designer and manufacturer of medical-grade wearables.

Geoffrey is also a Co-founder of the Open Wearables Initiative (OWEAR), an industry collaboration designed to promote the effective use of high-quality, sensor-generated measures of health in clinical research through the open sharing of algorithms and datasets.

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Decentralising Clinical Trials for Better Drug Formulations Achieving Patient Data Integrity & Fulfilling Diverse Pharma Needs Patient data is at the centre of the clinical drug development process. Data diversity is too. Drug developers and pharmacologists have always looked at collecting datasets from diverse patient groups to assess the efficacy and safety of medications in clinical trials. However, acquiring diverse patient data is exceedingly challenging due to a wide range of social and physical barriers that prove to limit trial participation. Fortunately, technical innovation, as well as general public access to the cloud, are helping pharma’s researchers raise trial participation rates, increase patient diversity and move the industry away from centralised site-based trial administration models.

Claude Price Head of Clinical Data Management, Quanticate

Trial decentralisation trending faster post-pandemic The pandemic significantly accelerated the decentralisation of clinical trials and saw more data being captured off-site, remotely from participants in real world settings. According to McKinsey, because health-system resources became consumed by COVID-19-related care and travel became limited by physical distancing, patients’ access to trial sites was reduced by 80%.

The impact of a pandemic on clinical trials and participation was significant. The number of trial starts monthly declined by 50% from January 2020 to April 2020, with 60% of the investigators reporting a significant reduction in trial activities by as of May 2020. This prompted drug sponsors to increasingly adopt remote consent and patient monitoring, video conference assessments, and even self-administered phlebotomies.

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The concept of conducting trials where patients live significantly predates the pandemic.

In an effort to overcome cost, complexity and participation issues, pharma’s developers began to innovate clinical trial design, seeking to improve every aspect of the patient experience.

For example, 70% of potential participants live more than two hours from trial sites, so decentralisation broadens trial access to reach a larger number and potentially a more diverse pool of patients. McKinsey notes decentralisation can help reduce trial investigator workloads because traditional clinical site activities (drug administration, assessments and data verification) can be performed remotely by technicians or by trial the participants themselves.

Faster, cost-efficient and where the patients are Clinical-trial sponsors continue to seek ways to help clinical trials be faster, and more costefficient while improving the experience for all stakeholders, especially patients. Decentralising clinical trials and managing their remote execution with patients using digital technologies has emerged as a critical tool in the pursuit of trial participation and patient data diversity goals. The move toward shifting clinical-trial administration to where patients are is being supported by innovation and a broad range of enabling technologies. Across the industry,

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sponsors are implementing virtual trial models and hybrid models to improve clinical trial efficacy, safety and above all patient access.

The following are emerging “E” enabling technologies and are helping clinical trial designers take a decentralised approach to conduct clinical trials:

eRecruitment with artificial intelligence Data on clinical trial recruitment reveals troublesome, systemic issues. A recent report revealed that 20% of cancer clinical trials fail because of inadequate patient recruitment.

Average enrolment efficiency was also reported to be less than 40% for Phase III and IV trials.

Globally, more than 80% of trials fail to enrol on time resulting in an extension of the study and/or the addition of new study sites.

Fortunately, several innovative solutions utilising artificial intelligence (AI) are emerging to help match patients to the appropriate clinical trial. AI driven information technologies can be leveraged to analyse structured and unstructured clinical data and match it to clinical trial criteria. Post analysis, makes it simpler for investigators to identify and contact potential patients for clinical trials. Platforms that integrate electronic recruitment capabilities, telemedicine with screening, consent forms and patient-reported data and outcomes, can support more efficient patient engagement, from recruitment to post-approval patient studies.

eConsent online informed digital consent Informed consent is a critical aspect of patient rights and clinical trial ethics. Performing the entire process of consenting remotely or virtually has always been a challenge to trial 52

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designers. Thanks to advanced information technologies, it is now possible for participants to consent from virtually any location and have that consent tied directly to their personal data. That scenario places consent firmly in compliance with 21 CRF Part 11 or GDPR requirements.

When deployed remotely, eConsent gives patients the time they need to read through and carefully consider critical trial information to make educated decisions. Today’s eConsent documents might include learning aids like hyperlinks or pop-ups with definitions of trial terminology for example. These applications are also likely to deliver multi-media content like videos or animations to help participants clearly understand what their trial experience is going to entail. The technology is beginning to demonstrate it has the potential to lower study drop-out rates because patients have the foresight to make a firm commitment to participate.

The eConsent process helps the site and other research staff administer trials more efficiently too. Because the technology is integrated with electronic data capture (EDC) systems and possibly electronic medical records and health records (EMR/EHRs) it is easier for clinical staff to track the process. It also eliminates paper-based workflows improving productivity as well as workload and staffing issues.

ePRO electronic patient-reported outcome assessments Collecting data directly from patients as part of patient reported outcome assessments electronically is an efficient way to improve the quality of the data collected. These electronic platforms are also integrated into EDC systems which helps site and data management teams track the completion and adherence on a real-time basis. The integration helps data flow according to the case report form (CRF) visit structure and in turn, makes the downstream data standardisation easier than integrating a thirdparty data file.

The architecture of available platforms is similar to EDC solutions—a feature that helps data managers get involved in designing and implementing the outcome assessments in compliance with regulatory expectations and protocol requirements. The user-friendly design of such tools helps patients provide accurate data without ambiguity or confusion.

The Bring Your Own Device (BYOD) model is another virtual trial tool gaining popularity and a reflection of how pervasive smartphone technology is in general society. Patients, with their familiar interface and device in hand, can use them to conveniently access trial applications with ease. From a patient perspective, BYOD has real potential to increase compliance and engagement. w w w. p h a r m a f o c u s e u r o p e . c o m

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eSource direct data capture Paper-based clinical processes are traditionally cumbersome and not just for sponsors and contract research organisations (CROs), but for clinical sites as well. From travel monitoring to secure record storage and beyond, a lot of time, resources, and budget end up going to things that have little to do with the actual process of collecting and managing patient and trial data.

By eliminating paper and enabling remote monitoring, direct data capture (DDC) reduces costs and resources, enabling clinicians to work more efficiently and increase trial volume. For hybrid decentralised clinical trial (DCT) models, DDC can be of real help for study coordinators in countries with less developed healthcare and telecommunications infrastructures. Because patient data must be collected remotely in these regions, DDC tools allow the collection of data offline and once connectivity is assured, it integrates the data.

Remote source data verification and centralised monitoring For specific purposes of remote monitoring, remote source data verification (rSDV) solutions accomplish just that, remote monitoring. In an industry that is focused on cutting the cost of clinical trials, it’s no surprise that reducing the amount of time and money spent on source data verification (SDV) in studies is imperative. Once data is collected at the sites it is important to ensure it is validated and verified with the source to ensure accuracy. rSDV is not a comparison of source data against CRF data. Rather it is a review of source documentation to check source quality, review protocol compliance and ensure critical processes and source documentation is adequate. Source data verification, on the other hand, is defined as the process by which data within the report form or from other data collection systems are compared to the original source of information. Primarily, rSDV’s purpose is to verify the information transcribed in the eCRF is complete and consistent with source records.

However, rSDV also helps to ensure the eCRF and source records together meet various compliance, protocol and clinical expectations.

Generally, rSDV involves specialists verifying data remotely or via a central location. However, it is feasible to have multiple sites’ source data to be checked at a central facility instead of having it parsed by individual sites. When centralised monitoring and an rSDV approach is implemented, the focus is usually on high-performing and low-compliance recruiting sites, and other locations with predicted or previous protocol deviations/violations. It is also common for centralised monitoring and rSDV to focus on data points related to primary endpoints, safety data and certain trial-related processes.

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Virtual telemedicine platforms Since the pandemic, the application of telemedicine and virtual, self-administered health care is revolutionising drug development. Virtual meeting applications on devices and laptops enable patients to connect with their clinical trial sites from virtually anywhere in the world.

These applications make it possible for clinical trial research to progress in an environment even if locked down. Many of these solutions provide real-time video capabilities and can take consent from patients in their own language. These technologies are a perfect way to connect patients, site coordinators and investigators while avoiding the time and cost of onsite clinical visits. Depending on the circumstances virtual clinical trial administration via telemedicine is likely to be the safest, most convenient way to deliver the trial to patients virtually.

AUTHOR BIO

CLAUDE PRICE has 18+ years of experience in clinical research, covering roles in both Clinical Data Management (CDM) and Project Management. A majority of his time has been spent in CDM at both large and small CROs. Claude has been responsible for overseeing large portfolios and managing Data Management groups, whilst working across numerous therapeutic areas and study phasess.

Conclusion: It is all about the patient Patient centricity is a key aspect to consider in the clinical development process.

When we gain an understanding of what the patient really wants and needs and then incorporate that into the expected outcomes of the treatment, we are truly becoming patient centred.

With COVID-19 in the rear-view mirror, governments and healthcare systems are working diligently to keep patients and research safe by adopting decentralised virtual trial delivery options. This approach also provides more comfort and convenience to patients. It’s a new normal for the whole world and enabling patients to participate in clinical trials wherever they are located, should be the focus going forward. Patient data and data diversity are critical to better medicines and virtual trials enabled by technology are delivering the solutions pharma needs to be more patient-centric. w w w. p h a r m a f o c u s e u r o p e . c o m

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Additive Manufacturing as a Problem-Solving Tool in Pharmaceutical Processing A hybrid raxial mixing impeller is designed and implemented to resolve dispersion mixing issues in a pharmaceutical application While process automation, a core tenet of the transition to Industry 4.0, can minimize or eliminate processing issues associated with a wide range of material variability, some variations require physical alterations to processing equipment to be implemented. In this article segregation issues caused by an API supplier-change are addressed rapidly and efficiently via another pillar of Industry 4.0, additive manufacturing.

Chris O’Callaghan*

Patrick Cronin

Innopharma Technology Ltd.

Department of Chemical Sciences and Bernal Institute, University of Limerick

Caroline McCormack Innopharma Technology Ltd.

Sam Solomon

Ian Jones Innopharma College of Applied Sciences Ltd.

Department of Chemical Sciences and Bernal Institute, University of Limerick

A

t the core of the transition to Industry 4.0 is the drive toward robust, reliable and comprehensive process automation. Three of the nine pillars of Industry 4.0 : Internet of Things, System Integration and Autonomous Robotics are particularly well suited to supporting this objective. With effective and democratised automation delivered by a range of available process digitalisation platforms, processing issues such as those caused by raw material variability from supplier or up-stream processing steps can often be addressed with no quality or time penalties.

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Some raw material variations however require more intervention than pure automation tools can provide, instead requiring physical re-configuration of process equipment. In this application another pillar of Industry 4.0, additive manufacturing, can be employed to address certain physical/ equipment issues in shorter timeframes than otherwise possible, ensuring quality is optimally maintained and production schedules can continue to be met.

In this article, a processing issue is discussed in which a change in the supply of Propranolol HCL API resulted in significant challenges coating this API onto substrate pellets in an established Wurster Coating process. When analysed the new-supplier API was found to have a multimodal particle size distribution, as compared to the broadly Gaussian distribution of the original API. This multimodality resulted in significant settling of the coating dispersion even while actively stirred throughout processing. Process and control constraints limited how aggressively this dispersion could be stirred and how much stirrer clearance could be used, ultimately requiring a change to the mixing impeller used.

“Standard” axial and radial impeller types were both trialed but solids separation issues were identified for each in their respectively weaker mixing areas. Ultimately a hybrid radialaxial or “raxial” impeller was designed and rapid-prototyped via FDM printing. This impeller was determined to resolve the separation issues identified as well as significantly improve impeller performance against secondary design objectives, allowing for further optimisation of the real-time process automation.

Identifying the issue The author’s company operates a process application and contract research laboratory in Dublin, Ireland. This lab provides a wide range of non-GMP development-scale pharmaceutical processing operations including crystallisation, fluid bed (drying, granulation and coating), twin-screw granulation / extrusion, and compression. A suite of accompanying analytical instruments is also available. As part of ongoing partner-research on advanced control of fluid bed Wurster coating, a series of experiments were to be conducted involving the API coating and subsequent functional coating of a variety of sizes of sucrose pellet cores.

Since previous experimental campaigns, a fresh supply of API (Propranolol HCL) had been sourced from a new supplier (Supplier B), meeting an equivalent material specification to that which had been used previously. All other materials and mixing parameters remained unchanged. The impeller used was an axial design.

In executing the first of these API-layering batches a similar automated process control strategy was followed to that which had been used in previous experimental campaigns. The process scientist monitoring the batch noted however that a significant sludgy deposit remained w w w. p h a r m a f o c u s e u r o p e . c o m

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around the outer edge of the coating dispersion beaker base when the API & binder dispersion had been fully sprayed. In the execution of a subsequent batch, the contents of the dispersion beaker were carefully monitored with solids settling noted despite an increase in stirrer RPM. Assay results for these batches confirmed API concentrations below 80% indicating significant losses. On a subsequent batch, a radial-design impeller was trialed with some improvement, however deposits still remained on the bottom of the beaker.

Analysis of API materials To investigate the root cause of the observed solids sedimentation, a sample from each API batch (Supplier A and Supplier B) was analysed for particle size distribution (PSD) and morphology at the University of Limerick.

A visual examination was first conducted by Scanning Electron Microscopy (SEM) at multiple magnifications. The results confirmed differences in the physical characteristics / morphological features of both API samples. API from Supplier A was found to be more needle-like in appearance Figure 1a, while the API from Supplier B appeared more cubic in shape Figure 1b, indicating the two samples are likely different polymorphic forms.

PSD analysis was conducted on both samples using Malvern’s Morpholgi G3 particle characterisation instrument. Significant differences were again observed for both API samples.

The volume distribution from Supplier A Figure 1c produced a typical Gaussian curve while Supplier BFigure 1d produced a multi-modal distribution curve with a larger maximum particle size.

These differences in particle size, distribution and shape morphology between the API batches would contribute to differences in mixing behaviors observed for the two API coating dispersions under similar mixing conditions. The difficulties experienced maintaining the API from Supplier B in suspension are therefore likely due to the greater percentage of larger particles present, represented by the visible right-hand spike in CE diameter values.

Stirring Constraints and Secondary Optimisation Objectives In an initial attempt to resolve the observed settling issue the axial impeller previously used was swapped for a radial impeller which altered the location of observed deposits, but was similarly unable to effectively homogenize the dispersion without the use of excessive mixing speed. Several additional constraints existed on the process in question beyond the core need to effectively homogenize the dispersion, exacerbating the mixing challenges.

• Any impeller design used had to be installed with minimal clearance to the bottom of the dispersion beaker, approximately 5 - 10mm. This was to avoid the need to mix significant 58

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Material D[v 0.1] D[v 0.5] D[v 0.9] Supplier A 14.72 35.41 67.37 Supplier B 10.44 51.23 157.9 Table 1: Volume Distribution CE diameters for both supplier APIs

Figure 1: SEM and PSD analysis of Supplier A API (a & c) and Supplier B API (b & d) overages of coating dispersion while allowing mixing to continue to the very end of the spraying phase. While non-optimal from a mixing perspective this was important in terms of overall processing efficiency.

• It was important to minimise the stirring speed of any impeller used, to limit the aeration of the suspension during the final minutes of coating. At this point, the height of the dispersion has dropped to that of the impeller, and aggressive stirring was prone to incorporate air bubbles.

• An optimum impeller design would minimize the vertical force imparted to the stirred dispersion. Automated control of the Wurster coating process implemented via Innopharma Technology’s SmartX utilized real-time data from the dispersion scale to control spray rate via a loss-in-weight approach as well as to end the liquid addition phase when the target quantity of dispersion had been sprayed. A substantial constant force or excessive oscillation w w w. p h a r m a f o c u s e u r o p e . c o m

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in measured scale values caused by the mixer would limit the precision with which these attributes could be controlled.

The following optimisation objectives were drawn from the identification of the processing issue and the constraints above: An optimum impeller design should: 1. Effectively maintain suspension of all API particles within the coating dispersion for the duration of processing 2. Operate effectively with a minimal “clearance” of ~6mm 3. Not require excessively high mixer RPM to operate / not aerate the dispersion when the level drops to that of the impeller 4. Impart minimal vertical force on the scale while in use

Assessing Mixing Performance With neither of the tested impellers delivering acceptable mixing performance it was decided that as an experiment an impeller should be designed and produced via additive manufacturing to address the areas of weaker mixing performance observed for each of the impeller designs already tested. To quantify the success level of any rapidly developed impeller solution however, a similarly rapid testing methodology would be required to allow comparative performance data to be generated and analysed.

A simple test was devised whereby a full coating dispersion beaker was elevated on a scale to allow clear visibility of the sides and underside. No agitation was applied for approximately 5 minutes, after which time the majority of solids had settled out of dispersion. After this time the impeller under test, positioned with 6mm clearance to the bottom of the beaker was started at an initial low RPM, and gradually increased until all settled material had been re-suspended.

The average vertical force caused by the impeller at this “minimum re-suspension RPM” was also noted. Finally, the areas in which settled solids deposits were last to re-suspend were noted for each impeller design as an indication of the distribution of mixing effectiveness

A Rapid Solution with Rapid Prototyping As can be observed in Figure 2 a & b the distributions of mixing effectiveness of the axial and radial impellers could be considered complimentary, with the axial impeller clearing the center-bottom of the beaker more effectively, and the radial impeller clearing the outer bottom more effectively. It was theorized that the weakness of the radial impeller resulted from the lower flow regime being effectively stalled due to the minimal impeller clearance as shown in Figure 3. It was thought that by introducing an axial flow component at the center of an 60

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otherwise radial impeller, this mixing dead-zone could be reduced or eliminated. Effectively reversing the typical flow direction of the lower flow regime of the radial impeller and solving the dead zone created by the minimal clearance. This concept of a hybrid radial and axial impeller approach was termed a “raxial” impeller.

A prototype of this raxial impeller was designed in Autodesk Fusion 360, sliced with PrusaSlicer V2.4.2, and printed in ColorFabb HT on an enclosed Prusa i3MK3S+. A parametric design was employed to allow the area ratio between axial (central) and radial (outer) components to be easily varied in future iterations. This was to allow for easy optimisation in future for use in suspensions with differing properties. An initial effective area ratio of 1:12 was chosen. Total design and printing time to deliver this prototype solution was approximately 90 minutes.

Figure 2: Approximate patterns of last settled deposits to be cleared by each impeller type. (a) axial, (b) radial, (c) raxial

Performance Testing All three impeller designs were tested using the method outlined above. Results can be seen in Table 2, with the approximate distribution of settled deposits which were last to re-suspend shown in Figure 2.

Impeller

Figure 3: Radial impeller mixing pattern: upper and lower flow regimes, with optimum (left) and minimal (right) clearance.

Axial Radial Raxial

minimum re-suspension RPM 300 72* 125

Avg.force exerted on scale (g) 12 1.1 1.2

Table 2: Performance results of 3 impeller designs w w w. p h a r m a f o c u s e u r o p e . c o m

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Figure 4: (a) axial, (b) radial, and (c) raxial impeller designs * 72 RPM technically resuspended all settled material but failed to effectively homogenize the suspension, with a visible higher density area between the center of the impeller and the bottom of the beaker, and visible stratification in the suspension density. This is likely due to the lesser vertical mixing effect of a radial impeller as compared to axial. Based on this the radial impeller could not be considered to have met the primary objective of effectively mixing the API solution for spraying.

In terms of force exerted on the scale, the radial impeller tested was noted to be superior to the axial, likely due to its force on the dispersion being primarily exerted in the horizontal plane, as compared to the axial impellers force being exerted along its vertical axis. The raxial impeller’s axial flow component likely adds slightly to the force it exerts, but could be considered a worthwhile design compromise to eliminate the radial impeller’s mixing dead-zone.

As can be seen from Table 2 the 3D printed raxial impeller scored considerably better than the axial design for both minimum RPM and force on the scale, while maintaining all API particles in suspension and visually achieving similarly effective inter-mixing throughout the suspension beaker.

Further Notes & Observations • Based on the distribution of deposits just below re-suspension RPM with the raxial impeller, it could be concluded that the ratio of axial to radial effective area is somewhat higher than required, and that a slight reduction might create an impeller that resuspends all settled deposits at a lower RPM. • FDM printing at a coarse layer height of 0.25mm was used for speed in the production of the prototype impeller, which as demonstrated proved effective. The polymer used (ColorFabb HT - Eastman Amphora HT5300) was selected for its solvent resistance and sufficient toughness to minimise the risk of brittle failure during use.

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• SLA / resin-based printing technologies do hold some advantages in this application due to the precision and smoothness of the exterior surfaces that can be produced.

• Printing in this case was performed with non-GMP materials and equipment as the application was non-GMP. FDA-approved food-safeFDM printing materials and printer configurations are available however, enabling similar problem-solving approaches in GMP applications.

Conclusions • Changes in raw material suppliers can often impact established processes to the point of no longer meeting the Quality Target Product Profile (QTPP). While advanced control and automation methodologies core to the Industry 4.0 transition can handle many of these variations seamlessly, some necessitate manual intervention.

• In this case a change in API supplier resulted in a multimodal particle size distribution, with the largest mode causing aggressive segregation of the mixed coating dispersion compared to previous batches executed.

• Additive manufacturing technologies, even in GMP applications, can present opportunities for rapid problem-solving without reliance on external suppliers and potential supply chain delays.

• Solutions customised to the specific application and problem can be very quickly developed, tested, and implemented, further supporting manufacturing efficiency.

• The low cost of failure provided by these tools enables more creative engineering solutions to be trialed. In this case the solution was engineered not just to resolve the primary mixing issue, but also to successfully optimise against several secondary objectives, ultimately allowing further improvement of the implemented real time process automation.

AUTHOR BIO

Chris O’Callaghan is head of engineering within Innopharma Technology and is responsible for process analytical technologies and control products, applications development, and customer project delivery. He and his team manage the custom engineering projects required to successfully interface control systems with new and existing product lines in the continuous and batch manufacturing spaces, as well as control strategy development and deployment

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3D Printing: What's in Store for Medicine in the Future 3D printing technology is a globally emerging technology in the field of medicine which has a great potential of improving the quality of life and changing the way in which pharmaceuticals are manufactured. By layering material into a product, 3D printing technology generates complex geometric structures through a digitally controlled process.

Ashwin Kuchekar Associate Professor, Head of Career Services and Placement, MIT World Peace University School of Pharmacy

Shalmali Shirish Cholkar Master’s Student, MIT World Peace University School of Pharmacy

3D printing technology, also known as additive manufacturing, is a digitally-controlled technique of fabricating a product by layer-wise addition of the feed material to generate complex geometric structures. This technique has wide applications in the field of mechanics, consumer goods, electronics, aeronautics, medicine, the food industry, and various other fronts.

The manufacturing process of pharmaceuticals has progressed from batch process to continuous process and now to printing.3D printing technology started gaining increased attention in the pharmaceutical field after the USFDA approval of the first 3D printed pill Spritam® by Aprecia Pharmaceuticals in 2015. This technology has been utilized for the printing of medical devices, dental implants, artificial organs, research prototypes, tailored dosage forms, drug fabrication, and specialty surgical instruments. It offers great flexibility which justifies its use in a wide range of settings including educational institutions, hospitals, and even households. Globally, numerous industry experts have predicted the use of 3D printing technology for centralized manufacturing of pharmaceuticals, veterinary medicine, and in the early phases of clinical trials over the span of the next 5 years.

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Fundamental principles of 3D printing

Principles and techniques of 3D printing:

3D printing of pharmaceuticals can be carried 3D out using feed materials like particles, particle PRINTING colloids, plastic powders, polymers, polymer solutions, gels, polymer-particle composites, or continuous thin sheets.

Powder 1. Powder solidification is carried out Extrusion solidfication by the techniques like selective laser sintering and binder jetting.

Liquid solidfication • Selective laser sintering: This 3D printing process involves the use of a laser beam to fuse the powder particles in successive layers to create the final structure. However, there are chances of degradation of the drug due to the high process temperature generated due to the laser.

• Binder jetting: It involves the use of binder material to successively join the layers of powder leveled on a powder bed.

2. Liquid solidification is carried out by the techniques of stereolithography and inkjet printing.

• Stereolithography: It involves the use of a light beam to cause layer-wise solidification of a photosensitive resin placed in a tank. The dosage form can be fabricated from bottomto-top or top-to-bottom. This technique is capable of attaining a good resolution of the printed product and also the speed of printing. As heat is not generated during the printing process, this process is suitable for printing dosage forms containing thermolabile drugs.

• Inkjet printing: This process of 3D printing involves heating the ink fluid with the help of a micro-resistor, creating a bubble of vapor that expands and forces the ink to drop out of the nozzle.

3. Extrusion of the feed material is carried out by techniques of fused deposition modeling and semisolid extrusion.

• Fused deposition modeling: This is the most widely used and investigated method of 3D printing of dosage forms. It involves layer-wise deposition of a molten polymer filament of a thermoplastic polymer. This technique is not suitable for dosage forms containing drugs that can undergo thermal degradation. The critical process and equipment parameters for this type of 3D printing include the printer nozzle size, layer height, build platform temperature, printing speed, and printing pattern.

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• Semisolid extrusion: It involves the extrusion of plastic semisolid material from a syringe by a pneumatic, mechanical or solenoid-based system. The material hardens on cooling.

Disposable or pre-filled syringes can be used for printing the dosage form.

Procedure of 3D printing: 3D printing starts with the creation of a virtual 3D design of the object using digital design software like AutoCAD, SolidWorks, Autodesk, etc. The steps involved in the 3D printing process are mentioned in the following figure.

Application of 3D printing in pharmaceuticals: 3D printing is a promising technology to help achieve the goal of precision medicine and personalized therapy. Immediate-release tablets, chewable tablets, orodispersible films, solid self-emulsifying formulations, microneedles, and hydrogel patches are some of the dosage forms which can be fabricated using 3D printing. A polypill containing multiple drugs in the same dosage form can be printed which will avoid polypharmacy and improve patient compliance.

Incompatible drugs can also be fabricated in the same dosage form. The release profiles of the multiple drugs can be modified by using the appropriate release-modifying polymers for the individual drugs. Customized implants and prosthetics can also be printed as per the individual patient’s needs.

Steps involved in 3D printing

Pros and Cons of 3D printing of pharmaceuticals: 3D printing technology is a computer-controlled process that eliminates the need for manual labor thereby decreasing the incidences of human error in the process.

This method minimizes the wastage of the feed material.

It is capable of fabricating complex dosage forms and dosage forms containing multiple drugs with much

STEP 01

STEP 01

STEP 02

Virtual computer aided design (CAD) 3D design

STEP 02

Conversion of 3D model to.stl digital file format

STEP 03

STEP 03

STEP 04

STEP 04

Movement of print head in X- Y axis to form 2D base of 3D object

STEP 05

STEP 05

STEP 06 Post-processing of printed 3D object

Conversion of .stl file to G file by slicing 3D design into series of 2D horizontal cross-sections

STEP 06

Movement of print head in Z axis to sequentially deposit layer on top of the base of 3D object

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Pros & Cons of 3D printing Low printing speed Generation of toxic nanoplastic byproducts Viscous feed — clogging of nozzle Poor mechanical strength of printed dosage form Degradation of API due to processing heat High cost of specialized equipment Training required to handle instruments Poor appearance

Flexible dosing range Cost-effective Simplify treatment Alter drug release profile in multidrug formulations On-demand fabrication of tablets; can be done directly in blister pack Fabricate dosage form with fine details and complex geometry Less wastage of feed Computer-controlled process eliminates need for manual labour Faster method of fabrication of dosage form

ease. This simplifies the treatment therapy and increases patient compliance. The dosing range can be customized as per the needs of the individual patient. It is a faster and more convenient method of fabricating a dosage form. The dosage form can be printed and handed over to the patient at the point of care. On-demand fabrication of the dosage form can be done directly in the packing material itself which decreases the unit operations involved in the packaging of the dosage form. However, this technology can potentially generate toxic nanoplastic byproducts. The mechanical strength of the printed dosage form can be weak and has a poor appearance at times, which can affect patient compliance. The viscous feed material can sometimes lead to clogging of the printer nozzle in case of extrusion processes.

The 3D printer is high-cost specialized equipment and requires trained personnel to handle the instrument.

Limitations and challenges of 3D printing: 3D printing faces certain limitations and challenges to be used for manufacturing pharmaceuticals which have hindered the full-fledged large-scale usage of this technology for producing medicines. In the case of pharmaceuticals, there is a very limited number of materials that are compatible and suitable for the 3D printing process. The polymers used in formulating pharmaceutical dosage forms do not possess the desired mechanical strength, thermal stability, 68

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and rheological behavior to be suitable for 3D printing. The process of 3D printing is a low-volume production process and the printed dosage form or medical device requires postprocessing at times. In addition to this, there is a lack of batch-to-batch equivalence in the printed dosage form which poses a challenge to compliance with the regulatory guidelines.

Challenges & Limitations of 3D printing

COMPATIBILITY & THERMAL STABILITY OF MATERIALS BATCH-TOBATCH EQUIVALENCE

POST PROCESSING

WIDE RANGE OF PROCESSING TIME

CHALLENGES & LIMITATIONS

CONTROL OF DRUG LOADING

ADHESION OF PRINTED LAYERS

LOW VOLUME PRODUCTION PLASTICITY AND RHEOLOGICAL PROPERTY OF MATERIAL FILAMENT

4D printing technology: Moving a step forward, 3D printing technology is further advancing to develop biopharmaceuticals by utilizing 4D printing technology. 4D printing technology is a modified form of 3D printing technology which uses stimuli-responsive materials, low-strength smart polymers, shapememory materials, self-healing materials, metals, and nanocomposites. Utilizing 4D printing technology, a real-time material response can be obtained. Such material is capable of responding w w w. p h a r m a f o c u s e u r o p e . c o m

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to multiple environmental circumstances and stimuli which may be internal (pH, temperature, biomolecules) or external (magnetic field, ultrasound). The material can also produce a predictable response to an event or stimuli locally. By printing biocompatible materials or living cells (organ-on-chip), 4D bioprinting can serve as a means of fabricating biological structures that can respond to external stimulation by changing shape or functionality.A wide variety of applications for this technology exist, including drug delivery systems, wound therapies, tissue engineering, and organ regeneration.

The difference between 3D and 4D printing technology lies in the feed material utilized. 3D printing utilizes thermoplastics, powders, gels, nanomaterials, etc whereas 4D printing uses stimuli-responsive, shape-memory, and self-healing materials to fabricate dosage forms and medical devices. 3D printing utilizes digital information for the fabrication of a structure. On the other hand, 4D printing utilizes digital information for the deformation of a structure.

The 4D printed product undergoes a change in its dimensions, structure, or appearance in response to a stimulus which does not occur in the case of most 3D printed products.

These advanced technologies need further development and optimization to overcome limitations concerning compliance with regulatory guidelines and large-scale manufacturing to increase their commercial applicability.

AUTHOR BIO

Dr. Ashwin Kuchekar is an Associate Professor and the Head of Career Services and Placement at the School of Pharmacy, Dr. Vishwanath Karad MIT-WPU in Pune. He completed his M. Pharm. in 2009 and Ph. D. in 2015. In 2012, he received the Senior Research Fellow (SRF) Award from the Council of Scientific Industrial and Research (CSIR). He is a trained specialist in Lean Six Sigma Black Belt (LSSBB). He has 7 years of industrial experience in reputed pharmaceutical companies such as Piramal, Lupin, and Abbott Healthcare. He has a unique blend of statistics and formulation development, Quality by Design with exposure to branded and generic product development. He has practical experience in the Design of Experiments and product formulation and process optimization Shalmali Shirish Cholkar specializes in Pharmaceutics. Being creative and thinking out of the box has enabled her to turn imagination into reality.

Pharmaceutical polymeric excipients, 3D printing of pharmaceuticals, hot melt extrusion, packaging material of pharmaceuticals, QbD in pharmaceutical process and product development, and Lean Six Sigma in pharmaceutical process optimization and performance enhancement are some of her research interests. Currently, she is developing a robust polymer combination platform for the 3D printing of pharmaceuticals.

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Cold-Chain Considerations for Manufacturing today’s Advanced Biologics Cold Chain Integrity and Capabilities The ways CDMOs exhibited flexibility and agility to overcome the challenges of mRNA vaccine production. Examining the lessons learned, he provides his perspective on the strategies taken to deliver a better program and highlights the importance of remembering key lessons as we move forward.

Lee Seungheon Product Logicstics Specialist, Samsung Biologics

T

he biopharmaceutical industry’s reliance on the technical experience and manufacturing capacity of contract pharma continues to grow as demand for all biologic drugs surges around the world.

Manufacturing advanced biologics compliantly requires mastery of aseptic containment, as well as temperature-controlled cold-chain systems for product integrity and quality. Most biologic modalities require temperature-controlled environments and packaging solutions to preserve product integrity and assure safety quality and efficacy. It also requires operational excellence and an understanding of every aspect of commercial biologic drug substance and product manufacturing to it.

The COVID-19 pandemic highlighted the challenges of ultra-low temperature storage and distribution as messenger RNA (mRNA) technologies required exacting cold-chain temperature controls at every step and highlighted the challenges of ultra-low temperature storage and

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distribution. Other biologic modalities including autologous and allogeneic cell and gene therapies (CGTs) are also coming into the scene. These therapeutics involve several steps to accomplish and require exacting cold-chain control throughout production and distribution.

Monoclonal antibodies need a reliable cold-chain Monoclonal antibodies (mAbs) are synthetically derived molecules that are used to mimic the body’s own natural antibodies. In 2021, the global mAbs market was valued at $185 billion in 2021 and is projected to exhibit a compound annual growth rate (CAGR) of 11.30% from 2022 to 2030. Rising investment to treat chronic diseases such as cancer and cardiovascular disease is increasing the demand for all biologics, but especially mAbs. Similarly, increased application of targeted mAb therapies and rising awareness among patients and physicians is expected to significantly contribute to the market’s tremendous projected growth.1 Used to treat many immune-compromising conditions, mAbs antibodies are relatively fragile and if not stored properly, they can become irreversibly damaged. Most of the industry agrees that temperatures above 65°C will likely result in damage. Most antibodies however should retain functional activity if kept refrigerated at 2 to 8°C for up to 12 months.2

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Emerging mRNA technology demands cold-chain integrity mRNA vaccines are one of the rare commercial drug products that must be stored and shipped frozen within a precise temperature range. From a cold-chain perspective, having to maintain such extremely low temperatures while storing and shipping the finished drug product has challenged the contract development and manufacturing organization (CDMO) industry to a significant degree.

Seasonal vaccines, such as those that use adenoviral vectors, for example, are typically stored and shipped at 2°C - 8°C (35.6°F - 46.4°F). mRNA vaccines, on the other hand, are far more challenging, needing sub-zero storage and shipping, potentially down to -80°C (-112°F) or even lower. Some emerging biologics are expected to require temperatures as low as -180°C (-292°F).3

The crux of cold-chain custody The crux of cold-chain supply management is to maintain desired product quality through strict temperature controls from warehousing to final shipment. In the case of ultra-low temperature storage and transhipment, it can be technically and operationally quite daunting to flawlessly deal with every storage and logistical requirement these products' temperature requirements demand.

Maintaining a consistent temperature in storage or processing can be particularly challenging. Temperatures have to be precisely controlled for long periods and generally can be vulnerable to even the slightest incursion of ambient air.

Furthermore, more rigorous controls are required when mass-producing commercial vaccines. In this case, finding experienced and well-equipped contract partners wellversed in cold-chain supply management will be critical to the success of any biotech trying to deliver their mRNA-based vaccine or therapeutic safely and successfully to patients.

Among industry peers, Samsung Biologics played a notable role in the global, collective effort to end the pandemic. By outsourcing messenger RNA (mRNA) vaccine production to prominent CDMOs, Pfizer/BioNTech, Moderna, AstraZeneca, J&J, Novavax, and others were able to manufacture and distribute more than 14 billion doses to the world in less than a year.4 Several advanced CDMOs were already well-versed in sophisticated lipid formulation techniques, a key element of mRNA vaccine delivery. This is the kind of expertise CDMOs had at the ready and it is likely that without this experience built-in, the industry’s swift and effective response would not be the reality it became.

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Flexibility and experienced operations deliver cold-chains that work Depending on the incoming drug product, various temperature conditions for storage and shipment are required as each product type needs to be inspected in a different environment.

Cold-chain facilities must be capable of meeting each molecule’s unique storage and shipping temperature requirements. For best cold-chain outcomes, flexible cold-chain systems adaptable to fickle environments are required to provide a relevant and competitive offering. This can range from precisely controlling storage temperatures end-to-end to state-of-the-art blast and control-rate freezers that can precisely and rapidly freeze the various types of biologic therapeutics being developed by pharma.

When contracted to manufacture mRNA COVID-19 vaccines for Moderna, one of the first orders of business for our operational teams was to thoroughly evaluate and inspect cold-chain capabilities to make sure Samsung Biologics’ control-rate and blast-rate freezing systems and ultra-low temperature storage facilities were prepared and operating as validated.

Facilities for storing and transferring intermediate products during production as well as facilities for freezing completed biomedicines are a must-have. It is also important to calculate the appropriate facility size considering the production cadence of biomedicines, capacity of refrigeration facilities, and efficient shipping procedures for mass-produced biomedicines.

It is imperative to keep the temperature at an appropriate level between storage and shipment while consistently checking and collecting instant temperature data through statistical analysis and Mean Kinetic Temperature (MKT) calculation.

The goal is to maintain a temperature in every section within a required range.

The crux of cold-chain Any change in shipment temperature is supply management is to converted into data during shipment, and maintain desired product real-time monitoring and data archiving quality through strict are utilized to record temperature temperature controls changes during storage. Temperaturerelated technology has greatly influenced from warehousing to final the validation work for the cold chain. shipment Technologies that affect the MKT and air circulation inside the equipment and inside shipping containers are utilized during the validation process.

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Maintaining critical parameters is especially challenging Keeping the temperature within a certain range from storage to shipment is crucial. However it’s especially challenging to keep the product at the right temperature while shipping. Just because it left the facility’s control in a frozen condition, shipments are vulnerable to temperature excursions at any time. That’s why it’s vital to minimize the number of entry-and-exit events as well as any external exposure in transhipment.

Shipping container technology has a pivotal job As biologics need to be stored and then shipped within a limited time to ensure their stability, container technology has also evolved. Shipping cold-chain integrity depends on this aspect of product logistics and distribution to an important degree. Samsung Biologics has acquired enough lightweight, compact shipping containers from its partner to help maintain seamless, cost-efficient cold-chain management. Overcoming the challenges of the COVID-19 pandemic required the global distribution of vaccines. Every dose was transhipped via this key enabling technology, and the industry continues to refine the cold-chain logistics contemporary biologics require to ensure patient access to these extremely beneficial therapeutics.

Most products required to be stored at a refrigerated temperature are shipped by RKN-type packaging solution via air. Essentially portable refrigerators, RKN units are a solution optimized for air shipment designed to fit into aircraft holds, monitor conditions, and precisely maintain temperature. Products required to be stored at a freezing temperature often need individual packaging solutions. w w w. p h a r m a f o c u s e u r o p e . c o m

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As storing and shipping biologics needs to be completed within a constrained timeline, keeping a seamless cold-chain system operating cost efficiently can challenge even the most adept organizations. Express international shipment via air freight, which usually occurs within less than a week, is growing more expensive due to rising fuel and labour costs confronting air shippers.

Labelling: Challenging the cold chain further

AUTHOR BIO

From a cold-chain perspective certain regulatory requirements offer specific challenges to manufacturers. For example, it is particularly technically challenging to conduct labelling in low-temperature environments on frozen primary packaging. Often requiring long manual processes, it can cause both manual processes and product temperatures to rise. It can be operationally daunting to reattach a label at a low-temperature setting once it has been detached.

It is also difficult to visually identify the right product among other unlabelled ones, which can cause a quality deviation. Labelling under cold-chain custody is truly problematic to accomplish and requires systems and process expertise to accomplish successfully. Product serialization is another aspect of cold-chain operations adding complexity to the mix. As IDs are added through multiple serializations, operators are mandated to check the batch, quantity, and storage location of the product, often through the organization’s enterprise resource planning (ERP) system. This too adds time complexity and the potential for deviations.

All things considered for the cold-chain biologics need now

Lee Seung-Heon is a product logistics specialist in charge of validating storage and shipment of frozen biologics at Samsung Biologics. Lee holds a bachelor degree in aerospace mechanical engineering at Korea Aerospace University.

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Delivering comprehensive cold-chain integrity for biologics in development and being marketed commercially is challenging but certainly well within the capabilities of most of today’s biologics-focused CDMOs. What will always distinguish one from another is how well the organization pays attention to the details technically and organizationally. Leveraging risk-based, compliant, controlled environments, freezing processes, and standard operating procedures will continue to enable the industry to deliver the desired market and patient outcomes it seeks for its advanced biologic drug products.

EXPERT TALK

FUTURE OF CLINICAL TRIALS AND TECHNOLOGY INNOVATIONS As Clinical trials evolve, the processes within the trials evolve and this in turn encourages the technology that supports to change. The reverse is however also true, as technologies innovate and evolve, they propose improved models of how to conduct clinical trials. In an Interview with Cat Hall from endpoint clinical we explored the current and future trends in technology.

1. Please provide us with some background information about endpoint Clinical, including the products and services it offers to the pharmaceutical sector.

Endpoint Clinical provides technology solutions to the pharmaceutical sector predominantly focused on randomization and trial supply management (IRT). Endpoint’s flexible technology platform enables sponsors to design and deliver software that enables patient enrollment, dispensation, streamlined supply logistics, and advanced analytics facilitating the conduct of Phase I – Phase IV clinical trials. The technology is paired with experienced service and project management staff that facilitate the delivery and support of the study enabling sponsors, sites, CROs and other technology providers to effectively utilize or integrate with the platform. All products and services are geared towards enabling an integrated, seamless experience for everyone participating in the trial.

2. On issues like patient-centricity, drug delivery systems, and global manufacturing, what guidance would you offer to companies as they start to develop biopharmaceuticals?

Each sponsor is unique. Different considerations go into the technology selection and direction of each company starting to develop biopharmaceuticals. What are my organizations main indications? What is my existing vendor and technology landscape? How large is my company? Do I leverage a full-service CRO? Do I have an experienced internal supplies or IT department? Understanding the current and future state of your organization will help you make the right-sized technology decisions. It is important to find good w w w. p h a r m a f o c u s e u r o p e . c o m

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partnerships that will help enable your company to succeed. The e-Clinical technology landscape contains many vendors and integrated solutions so having someone to help plan your technology footprint is important. By selecting the right systems, your end-toend clinical trial process will be smoother resulting in high quality data, simplified user experiences, better insights, quicker analysis, and ultimately a better outcome for your clinical trial.

3. What modifications do you believe the use of DCT has brought about in patient attitudes?

Clinical trials will continue to be a hybrid combination of at-home and on-site experiences for the patient. Increased accessibility, high convenience, and telemedicine will help alleviate burdens placed on patients participating in studies. The ability to conduct trials in a hybrid fashion, provides patients with the best of both worlds, convenience when needed, but also the comfort of a doctor to patient relationship faceto-face. Technology will enable these experiences to occur while maintaining the quality and integrity of the trial design.

4. Are there any prominent technologies that are being used in trials now? What would their impact be on the future?

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Many different technologies are currently being utilized in clinical trials, but some common industry trends are leaning towards advanced processing and automated understanding of vast amounts of information. The volume and velocity of clinical data is continu-

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ing to increase as more software systems are introduced into the clinical workflow, so incorporating technology to enable access, integration, and processing of this information is vital. Cloud providers, such as Microsoft Azure, make processing and integrating data easy by providing simple interfaces for AI, ML, NLP, integrations, data transformation, and movement. Operating in a cloud native or hybrid framework is important for technology providers to ensure they can easily adopt new technologies as they come online.

5. How can inaccuracies in real-time data collection be reduced?

First and foremost, it is having a clear understanding of your data lineage, and knowing what is considered source data, where having an integrated set of technology providers helps to eliminate the burdens of data reconciliation. Secondly, it is developing the right skill set to understand how the technology works and where the risks to the data lie. Technology helps to accelerate and augment processes, but if not intelligently designed in the ecosystem of data exchange, technology can introduce issues that compromise the integrity of the data. It demands those that know how best to design the system so that they seamlessly work together and reinforce the integrity of the data. Finally, to reduce issues, there must be the governance of the data in place.

What are the quality indicators of the data, how will it be measured, and how often to support confidence in the data from input to output.

6. Are there any barriers preventing clinical studies from adopting new technology?

The biggest hurdle to overcome in adopting new technology is the unknown. The industry is traditionally risk adverse especially around data because at the end of the trial it is all about the data and what it tells us if the trial was a success. Stepping into a new technology raises fears of the unknown that may not be understood until the end of the trial. The other major hurdle is resistance to change.

7. In which areas of clinical trials does Endpoint Clinical see room for innovation?

In short, everywhere. IRT was one of the first technologies to help support how a clinical trial is conducted and has only grown from there. There is an interesting dynamic between clinical trial design and IRT. Because of a need for the IRT to do something new, clinical trial designs changed and because clinical trial designs changed, IRT has changed. For example, the first IRT systems only managed Randomization, but because they did, clinical supply management evolved from manual shipments of

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Endpoint Clinical understands the pain points in a variety of trial designs that inspires us to think outside the box and bring new solutions to bear

patient-specific and visit-specific bottles to on-demand, automated shipments of bottles with kit numbers. And because of this change supporting a global trial across hundreds of sites was made possible, which lead to changes in how patients are recruited, that then lead to gathering patient feedback on trial designs. Thus, for each evolution of trial design, IRT evolves to help support them.

8. What are the product innovation trends of the future that will influence clinical trials?

Commercialized software has paved the way for product innovation within clinical trials. Advancing clinical trials will start with applying best practices and forwardleaning cloud technology towards chal-

lenges within the clinical trial industry. As patient-centricity, convenience, and trial designs evolve, the need for mobility, interoperability, and flexibility will continue to drive products forward. Sponsors, patients, and doctors will experience consumer grade software solutions that will seamlessly work with their everyday jobs. As part of this process, data will more easily be able to be leveraged across systems in an automated, intelligent fashion providing insights into trial designs, logistics, outcomes, and analysis.

9. What are some of the major changes you see in clinical trials over the next few years, based on your experience?

The industry has faced major disruptors recently with the COVID pandemic, devastating hurricanes, and the Ukraine/Russia conflict. Each event has had a lasting impact on the industry, and will inevitably bring further changes that will provide increased flexibility in trials. We have only begun to see how we shift to allow a trial participant the flexibility they need in their lives whether due to placement in quarantine or a need to evacuate their home. Today sponsors focus on picking technology before the trial has started and feel stuck when it isn’t the right as the trial progresses. The need for flexibility to rapidly adapt to changes will inevitably spark further innovation.

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10. Do you anticipate any other market problems or developments that will have an impact on this industry sector? Please elaborate.

We see this already. Due to the need for countries to invest in other areas such as defense and economic support, basic research funding has diminished. This then feeds into the industry to have to invest in this area themselves which will further increase the cost of new medicines in a world that is under constant pressure to decrease those costs. Another trend to watch is the concern over personal data and the differences in regulations across the world. Many countries are asking that data never leave their geographical borders.

This is a challenge for a global clinical trial that must aggregate worldwide data for trial analysis. From a clinical supply perspective, the change in global temperatures introduces a new challenge of how to ensure trial medication is being stored according to the label, especially when it is delivered directly to the trial participant.

11. What are some future industry challenges that pharmaceutical companies might experience that Endpoint Clinical is ideally suited to handle?

At Endpoint we focus on the voice of our customers and their stakeholders. By working closely, and listening objectively, we better understand the pain points in a variety of trial designs which inspires us to think outside the box and bring new solutions to bear. As a result, we can promise our clients that no matter how their trial design evolves, our system and our teams will support it. Achieving seamless integration across new and evolving technologies takes an investment of resources, and as a result, we actively engage with other providers to anticipate future trends that shape our product roadmap. We also are continuously exploring the landscape to bring transparency, analytics and insights to the data within the IRT.

Catherine Hall is the VP of Data and Quality at endpoint Clinical During her 20+ year career, she developed notable expertise not only in clinical supplies but also in the design and implementation of e-clinical technologies. Cat has a passion to bring the customer voice into shaping the future of IRT.

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THE PHARMACEUTICAL INDUSTRY AND DATA SCIENCE Data can be used to analyze genomes, prescribe pharmaceuticals in the pharmaceutical and biotechnology industries, and a variety of other applications.

Now that data has become "BIG DATA," analyzing it has become a whole new field of "Analytics and Data Science." The cost of genome structure analysis has decreased from ten million dollars in 2007 to one thousand dollars thanks to analytics. On the basis of big data analysis, a cancer treatment was prescribed.

It can be used in a variety of industries and enterprises. There are numerous firms that are solely dependent on analytics, as well as numerous start-ups in the same field. There are ten reasons why pharmaceutical needs Big Data.

Drug Discovery, Research and Development and Electronic clinical research, dossier filing and increase business area are just a few of the causes. The pharmaceutical sectors are benefiting from data mining since it is opening up new business options. Big data, data science, tools of data science, and drug discovery, clinical research are some of the terms used in this paper.

Ashwin Kuchekar Associate Professor, School of Pharmacy Dr. Vishwanath Karad MIT-WPU

Ashwini Gawade Assistant Professor, School of Pharmacy Dr. Vishwanath Karad MIT-WPU

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he application of data science in the pharmaceutical industry has far-reaching consequences for individuals considering a degree in this discipline. Important statistical data acquired from several sources is regularly used by the pharmaceutical sector. What is the

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definition of data science? What role does data science play in the pharmaceutical industry and other similar fields? How could a data science degree help you advance in your career?

The pharmaceutical industry is a sector that is constantly growing. As more technologically advanced prescription pharmaceuticals become available, the demand for experts who can comprehend and use relevant data and statistics has risen dramatically. What is the role of data science in the pharmaceutical industry? Consider some of the various forms of data that are needed on a regular basis in this profession.

Today’s need of data Science Data is worthless until it is transformed into useful information. Data science is the study of large collections of structured and unstructured data with the goal of revealing hidden patterns to generate valuable insights. The interdisciplinary field of data science includes computer science, statistics, inference, machine learning methods, predictive analysis, and emerging technologies.

Different software used in data science A data scientist is responsible for information extraction, manipulation, pre-processing, and prediction from data. To do so, one needs a number of statistical tools and programming languages.

Data Base

Data Transformation

Data Modeling

Data Visualization

SQL

Excel

Sci-kit Learn

Tableau

MongoDB

Python

TensorFlow

Power BL

MySQL

R

Weka

Excel

Oracle

Spark

Spark

R/Phython SAS

Why Pharmaceutical need data science In the pharmaceutical sector, data analysis has become a crucial tool. In fact, pharmaceutical companies are continuously seeking for ways to improve their chances of bringing new treatments to market as fast and inexpensively as possible. Big data applications, such as data governance, data mining, and predictive analysis, are assisting pharmaceutical businesses and researchers in making better judgments about which medications to pursue. w w w. p h a r m a f o c u s e u r o p e . c o m

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Signal Data Analysis

• Age group • Gender • Drug efficacy • Sentiments

Patents, Grants

Symptoms

Patient Research Papers

Drug Review

• KOL's • Grant surns • Impact factor • Citations

Innovation

Drugs • Drug ephase • Licensees • Enrikee number • Drug stataus

Company profile

New Drugs

Business Clinical Trials

Financlals

• Partnership • Revenue • Investment • News • Conferences

Figure 1: Need of Data Science in Pharmaceuticals

Data Science in different pharmaceutical area Much of the money spent in big pharma goes toward screening before a drug even reaches the clinical trial stage. This may be a time-consuming and costly process, especially for sick people who are waiting for new treatments to be approved that could help them. Now, data science is being utilised to speed up and hopefully reduce the cost of this previously lengthy procedure. Companies can use predictive analytics to focus on specific goods and ingredients in pharmacological therapies that are most likely to be effective. These selections will be based on a variety of facts acquired to assist them in selecting from the hundreds of options available. In addition, practically all sales and marketing teams rely significantly on targeted analytics to increase sales, improve spending, and improve the bottom line.

Drug Discovery Bringing a new medication to market is one of the most dangerous and costly ventures a corporation can undertake. Average cost of a successful drug in the market is roughly $4 billion, but it can reach $11 billion. Given that only 10 to 12 percent of new pharmaceuticals make it from the early stages of drug development to the market, the modern pharmaceutical industry has every motive to use data analysis to achieve a competitive advantage. When it comes to avoiding poor outcomes, data analysis can be utilized in clinical drug trials to 84

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quickly detect safety or operational signals that need to be addressed in order to avoid major and possibly costly concerns like adverse events and excessive delays. Companies can also discover which groups would work best in trials by combining multiple data sources, such as social media and public health databases.

Clinical Research Data scientists can assist pharma companies in lowering the expenses of clinical trials by allowing them to use: • Patient Selection Based on Data • Real-Time Monitoring.

• Drug Safety Assurance

Big data applications, such as data governance, data mining, and predictive analysis, are assisting pharmaceutical businesses and researchers in making better judgments about which medications to pursue

Formulation Development Provides real-time analysis Real-time data is now available, which is a feature that will substantially improve trials. This makes it easier to respond to difficulties quickly and provide more precise safety measures for trial participants, all of which leads to improved R&D success rates. Furthermore, data from real-world sources such as health records, insurance claims, and even social media can now be acquired. This gives evidence on how drugs operate in an uncontrolled situation and across a larger demographic to drug makers. This allows them to make changes and improvements to the medications. Data from studies and trials across many diseases is now collected by dedicated teams at major pharmaceutical corporations. Their study of this data allows them to improve the potency and effectiveness of their products while reducing the costs of traditional clinical trials and parallel development projects.

Simplify production plans A product must be manufactured and distributed after it has been developed. For the highest return on investment, you must understand the suitable targets. Companies can establish a more solid production plan, save labor costs, remove waste, reduce the need for superfluous inventory, and optimize equipment usage with the appropriate data. This simplicity of w w w. p h a r m a f o c u s e u r o p e . c o m

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manufacturing will only grow in the future, both in the healthcare industry and in related businesses. With the pharmaceutical business expected to rise to $1.57 trillion in value, data will play an even bigger role in streamlining production processes.

Smoother supply chains Prescriptive data analytics with IoT data will be used by 50% of pharmaceutical and biotech industries to optimize their supply chains. Pharmaceutical firms are moving away from traditional procedures and adopting digital transformation and pharmaceutical data analysis on a much larger scale today. As we discussed in our previous article on how to improve customer experience, this move allows them to understand and cater to the demands of both their consumers and stakeholders. By readily validating data, detecting abnormalities, benchmarking processes, and accessing mobile and logistic reports, you can increase your supply chain efficiency with data analytics. Furthermore, data analytics for pharmaceutical development allows for real-time route optimization and better inventory management, freeing up man-hours that would otherwise be spent watching and monitoring business operations.

The use of data in the development of pharmaceutical products is extremely advantageous since it aids in the prevention of health problems and the strengthening of the patient care sector.

Analytical Predictive models have been increasingly popular in recent years. There is widespread interest in being able to use current data to accurately forecast future trends and results, allowing businesses to meet future demands before they exist. Much of the money spent in big pharma goes toward screening before a drug even reaches the clinical trial stage. This may be a timeconsuming and costly process, especially for sick people who are waiting for new treatments to be approved that could help them. Now, data science is being utilized to speed up and hopefully reduce the cost of this previously lengthy procedure. Companies can use predictive analytics to focus on specific goods and ingredients in pharmacological therapies that are most likely to be effective. These selections will be based on a variety of facts acquired to assist them in selecting from the hundreds of options available.

Market Research In previous years, pharmaceutical medication companies' sales and marketing were mostly conducted on foot by paid personnel who would tirelessly visit doctor's offices and medical centres across the country. Because of developments in data science, this action is no longer 86

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essential. Drug businesses now do at least 25% of their marketing through digital channels.

In addition, practically all sales and marketing teams rely significantly on targeted analytics to increase sales, improve spending, and improve the bottom line. Based on data obtained and evaluated in advance, predictive analytics allows companies to determine which medical experts are more likely to be interested in a given drug. This can lead to the development of highly targeted sales strategies that are more likely to succeed. Additionally, today's drug salespeople are equipped with smart electronic gadgets that provide real-time analytics to assist them in closing the deal. This maximizes their productivity and increases their chances of success.

Conclusion The pharmaceutical industry can only benefit from the massive amounts of molecular and clinical data housed in proprietary networks (as well as the seemingly limitless amount of consumer data on the Internet) if it can turn it into relevant business intelligence. Data scientists can significantly impact this. In order to create a publicly accessible private cloud where the pharmaceutical industry can safely connect around clinical trial data, several drug researchers and manufacturers are working with data management companies.

AUTHOR BIO

Dr. Ashwin Kuchekar is an Associate Professor at the School of Pharmacy, Dr. Vishwanath Karad MIT-WPU in Pune. He completed his Ph. D. in 2015. In 2012, he received the Senior Research Fellow (SRF) Award from the Council of Scientific Industrial and Research (CSIR). He is a trained specialist in Lean Six Sigma Black Belt (LSSBB). He has 7 years of industrial experience in reputed pharmaceutical companies such as Piramal, Lupin, and Abbott Healthcare. He has a unique blend of statistics and formulation development, Quality by Design with exposure to branded and generic product development. He has practical experience in the Design of Experiments and product formulation and process optimization.

Dr. Ashwini Gawade is an Assistant Professor at the School of Pharmacy, Dr. Vishwanath Karad MIT-WPU in Pune. She completed her Ph. D. in 2020. She has 2 years of industrial experience in reputed pharmaceutical companies such as Lupin and Sai Life Sciences. She has knowledge with pharmaceutical product technology transfer. She has excellent hands-on documentation related to pharmaceutical formulation and development.

She has excellent knowledge of Quality by Design with exposure to branded and generic product development. She has technical expertise in formulation development of oral and topical dosage forms, solubility and bioavailability enhancement, sustained release drug delivery by Quality by Design Approach.

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Role of Artificial Intelligence and Machine Learning in Nanosafety Medical applications of nanomaterials span drug, protein and vaccine delivery, diagnostics, theranostics. There is a need for a ‘safe-by-design’ paradigm for nanomaterials, and machine learning is increasingly used to predict their properties. I briefly review nanomaterials machine learning modelling and provide examples where model predictions have been validated by experiments.

David Winkler Professor of Biochemistry & Chemistry at La Trobe University, Professor of Pharmacy at the University of Nottingham, and Professor of Medicinal Chemistry at Monash University.

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anomaterials have been one of the most exciting scientific and technical innovations of the past few decades. Due to their very high surface to volume ratios, they exhibit properties that can differ dramatically from those for the same material in bulk. This, and their ability to be designed and synthesized with multiple surface functionalities, has seen them used for a myriad of bespoke applications in industry and medicine.

Their medical applications span delivery systems for drugs, proteins and DNA/RNA to diagnostics, targeted cancer treatments, to theranostics. They have been used very successfully

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for the first-time to deliver mRNA in vaccines against SARS-Cov-2 and have massive potential for revolutionizing vaccine therapies for a wide range of diseases in the future. In industry, nanomaterials are used in hundreds of applications that exploit their unique physicochemical properties, and they are finding use as antimicrobial materials for sanitizing water, clothing, refrigeration etc.

However, commercial and medical applications can outrun the ability of the relevant agencies to regulate nanomaterials. The unusual properties of nanomaterials can potentially generate adverse effects in the workplace, public, and environment and this is particularly an issue for nanomaterials that are introduced to the body, as exposure levels are much higher than the adventitious exposure of workers and the public.

Consequently, there is a growing need for nanomaterials that are ‘safe-by-design’ (SbD) that is driving research efforts to achieve that goal. (Guinée et al., 2022; Oksel Karakus et al., 2021)

Why are machine learning models needed?

Experimental assessment of the biological effects of the myriad of commercial and medically applied nanomaterials is infeasible due to time, cost, and resource issues.

Computational methods, especially machine learning, are being increasingly used to leverage smaller quantities of experimental data on nanomaterials to predict properties of material not tested or synthesized. Such models can also be invaluable for implementing the SbD paradigm. w w w. p h a r m a f o c u s e u r o p e . c o m

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Nanomaterials pose particular challenges for computational studies. These materials are much larger than small, discrete molecules like drugs, and their composition is usually not precisely known. Most nanomaterials are distributions of sizes, shapes, surface functionality, and charge. Their surface properties are significantly modulated by the coating of proteins or other macromolecules The so-called nanoparticle corona) that often accumulate on the surface of nanomaterials. (Tomak et al., 2021) Several key factors determine how effective machine learning models of nanoparticle properties are, and most of these can be problematic for nanomaterials. The first is the quantity, quality, and diversity of data available to train models. There is still a paucity of high-quality data sets for this purpose. The second factor is related to descriptors or features, mathematical entities that capture important physicochemical, provenance, or corona properties of nanomaterials. Given the complexities of nanomaterials mentioned above, converting these structures into meaningful mathematical features is more problematic than for small organic molecules, for example.

The rise of nanosafety consortia The increasingly large amount of research into modelling the biological (and other) properties of nanoparticles has stimulated the formation of consortia that bring together researchers with complementary skills. In particular, the European Union has supported the development of research networks that aim to provide rapid ways of informing regulators of potential risks of nanomaterials, and create the technologies required to design safety as well as functionality into nanomaterials. This was initiated by EU COST support for a 2011 Maastricht workshop on the use of quantitative structure-activity relationship (QSAR) methods (largely machine learning-based) for predicting the biological effects of nanomaterials. This generated a COST Action, MODENA that generated a network of an impressive core group of researchers in this field, mainly from Europe, but also including Australia and the US.(Winkler et al., 2013) More recently, the EU has provided a large amount of financial support for multiple large nanosafety-related Horizon 2020 projects, the most relevant ones for the theme of this paper being SABYDOMA and NanoSolveIT. These have aimed to generate larger datasets relevant to nanosafety, to explore new ML methods for modelling, and predicting biologically relevant properties and to develop better mathematical descriptions of nanomaterials for training models. (Lynch et al., 2020).

Examples of the application of machine learning to nanosafety One of the first reports of quantitative modelling of biological properties of nanomaterials 90

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Introducing Abu Dhabi’s new state-of-the-art

pharma facility

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was by Puzyn’s group in Gdansk. (Puzyn et al., 2011) They showed that for a series of 17 metal oxide nanoparticles, they could predict cytotoxicity to bacteria using a single, albeit arcane property, the enthalpy of formation of a gaseous cation having the same oxidation state as that in the metal oxide structure. Subsequently, in a collaboration with Harvard University and the Massachusetts General Hospital, we showed that the smooth muscle apoptosis of different types of metal ion oxide nanoparticles could be predicted from their size, core composition, and surface coating. (Epa et al., 2012) This study also showed that, for a set of 109 nanoparticles whose surfaces were functionalized with small organic molecules, the ability to penetrate pancreatic cancer (PaCa2) and human umbilical vein endothelial cell (HUVEC) cells varied markedly depending on the surface chemistry (Figure 1). This was of particular interest in targeting nanoparticles to specific tissues or tumours. Using machine learning models, it was possible to accurately predict the uptake in these cells (within a factor of 2) for both the training set used to create the models and a separate test set used to validate predictions.

Figure 1. Cell uptake of nanoparticles as a function of surface chemistry Later work on the nanophase behaviour of amphiphilic, lyotropic liquid crystalline lipid nanomaterials used as drug delivery vehicles (Figure 2) showed that machine learning could predict the existence of multiple, coexisting phases as a function of drug type and concentration, temperature, and time. (Le et al., 2013; Le and Tran, 2019) These models could successfully predict the coexistence of nanophases for new drugs not used to generate the models (Figure 3). As only some of these nanophases are useful for drug delivery, it is important to be able to predict the phase behaviour over time and temperature to ensure drug delivery is optimal and safe.

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Figure 2 Types of selfassembled nanophases that form from amphiphilic lyotropic liquid crystalline lipid molecules such as phytantriol and monoloein.

Structures of the inversebicontinuous diamond QIID (Pn3m), gyroid QIIG (Ia3d), and primitive QIIP (Im3m) cubic phases.

Figure 3. Individual phases, QIID (Pn3m) cubic (blue), hexagonal HII (yellow), and primitive QIIP (Im3m) cubic (purple), predicted by the best machine learning model for monoolein nanoparticles loaded with drugs at 25 and 37 °C. This model made zero prediction errors for these phases.

Although most of the work to date has focused on the safety of nanomaterials for commercial applications or generally, there is a growing interest in applying ML methods to ensure the safety of medical nanomaterials. For nanomaterials used in medicine, safety is of course a critical issue as the materials are deliberatively introduced to the body at higher concentrations than that acquired from the environment. Singh et al. recently reviewed how AL and ML invitro and in vivo datasets are used to generate in silico models for nanomedicine. (Singh et al., 2020) They described how physiologically based pharmacokinetic and ADMET (absorption, w w w. p h a r m a f o c u s e u r o p e . c o m

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distribution, metabolism, excretion, and toxicology) in silico methods and dosimetry models can be used to support safety in nanomedicine. Shin and co-workers reviewed the application of ML to multi-omics (transcriptomics, proteomics, metabolomics and phosphoproteomics) data. (Shin et al., 2021) As a complete understanding of the nanoparticle-induced toxicity and other biological changes induced in cells is very challenging, rich omics data combined with ML elucidate mechanistic details, identify beneficial and detrimental nanomaterials properties, and allow prediction of likely biological properties of new medical nanomaterials.

Fig.4. Overview of the ML approaches to modelling multi-omics data. Creative Commons Attribution (CC BY) license. (Shin et al., 2021) 94

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What is needed to generate better models of nanomaterials properties?

Machine learning is a data driven method and the quality and utility of models are strongly dependent on the data and mathematical features used to train them. Recent efforts to synthesize nanoparticles with controlled sizes, shapes, cores, and coatings and adoption of high throughput synthesis and characterization methods should alleviate the current paucity of quality data sets. More extensive use of genomic and proteomic data will also assist in unravelling the mechanisms of biological effects of nanomaterials on organisms and provide additional data sources for modelling. A particularly difficult roadblock is the need for more effective mathematical features (nano-specific descriptors) to describe complex nanomaterials.

(Winkler, 2016; 2020; Wyrzykowska et al., 2022) New developments in convolutional neural networks may partially alleviate this problem and lead to enhanced interpretability of models. If more metadata relating to the synthesis and processing of nanomaterials can be captured, then machine learning models can also contribute to the optimisation of bespoke nanomaterials. Other important issues include better data integration at the nanoscale, the need for development of central repositories and databases of nanomaterialsbiodata, robust standards for information storage and exchange, improved nano-ontologies, and better tools for decision support. Ultimately, combined models predicting adverse biological effects and those predicting commercially useful properties will lead to more quantitative, useful, and rational SbD approaches.

AUTHOR BIO

David Winkler is a Professor of Biochemistry & Chemistry at La Trobe University, a Professor of Pharmacy at the University of Nottingham, and a Professor of Medicinal Chemistry at Monash University.

He applies computational chemistry, AI, and machine learning to the design of drugs, agrochemicals, electrooptic materials, nanomaterials, and biomaterials. He is ranked 227th of 81,000 medicinal chemists worldwide and has written >250 journal articles and book chapters (4 ISI Highly Cited) and is an inventor on 25 patents.

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