Pharma Focus Europe - Issue 03

Issue 03 | 2023 www.pharmafocuseurope.com

EMERGING ROLE OF MASS SPECTROMETRY IN BIOLOGICS DEVELOPMENT

Yu (Annie) Wang

Spo n s o r

Qingyi Wang

Thomas R. Slaney

Alexandria Emory

PAG E

PA G E

Genomic Medicine Keeps Innovating and Manufacturing Needs to Keep Pace

Using AI to Accelerate Drug Discovery

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Simplifying Progress

Welcome to a Journey of Innovation, Insights, and Industry Excellence!

We are thrilled to present the much-anticipated third edition of Pharma Focus Europe magazine for 2023.

As a beacon of knowledge and innovation in the pharmaceutical landscape, we take pride in presenting a publication that goes beyond the ordinary, providing our readers an immersive experience into the dynamic world of pharmaceuticals.

In this issue, we explore the vast landscape of the global pharmaceutical industry, offering unique perspectives from thought leaders and experts leading the way. Delving into revolutionary gene therapies and the game-changing impact of artificial intelligence, each article serves as a gateway to the evolving future of pharmaceuticals.

Our diverse range of articles covers crucial topics such as the emerging role of Mass Spectrometry in Biologics Development, the application of AI in accelerating drug discovery, and the challenges and opportunities in the evolving landscape of generic pharmaceutical manufacturing. Each piece is meticulously crafted to empower our readers with knowledge that shapes the industry.

Delve into the future with an exploration of the "digital lab" concept. Discover how a digitalfirst approach to lab transformation is reshaping the pharmaceutical landscape and unlocking unprecedented efficiencies in decision-making.

and digital twin model that promises to revolutionize the production of injectable liquid dosage forms, marking a significant leap forward in pharmaceutical manufacturing.

Explore the promises and challenges of inhaled mRNA medicines, offering a glimpse into the transformative potential of this technology in addressing unmet needs in respiratory disease treatment.

Take a deep dive into the game-changing impact of artificial intelligence on clinical trial patient recruitment. From Natural Language Processing (NLP) to predictive modeling, discover how AI is revolutionizing the landscape, ensuring diverse participant pools and enhancing trial success.

Pharma Focus Europe is not just a magazine, it's your gateway to the future of pharmaceuticals.

We invite you to join us on this captivating journey, where each page is a step toward innovation, each article a revelation, and each insight a catalyst for change.

Connect with us: editorial@pharmafocuseurope.com Join the conversation on social media #PharmaFocusEurope.

Learn how collaborations, beyond financial support, become catalysts for accelerated innovation, addressing the challenges of drug development with effective communication and shared vision.

Enter the cutting-edge realm of advanced continuous manufacturing. Explore a novel plant

N D Vijaya Lakshmi Editor

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CONTENTS STRATEGY

CoverStory

08 Strategic Partnerships in Biotech: Catalysts for Accelerated Innovation Joab Williamson, Director, Clinical Operations at Faron Pharmaceuticals Alexander Spicer, Head of Corporate Development, Ampleia

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Why are Generic Pharmaceutical Manufacturers Leaving the USA? Eric L. Wesoloski, Vice President Quality and Regulatory Affairs, USAntibiotics

RESEARCH & DEVELOPMENT 34 Unlock lab efficiencies and decision-making through a digital-first approach to lab Transformation Henal Shah, Head of the European Lab Centre of Excellence (CoE) Practice within the Life Sciences Manufacturing group, Cognizant John Patrick Dunne leads the Lab Centre of Excellence (CoE) Advisory Team within the Life Sciences Manufacturing group, Cognizant

40 Using AI to Accelerate Drug Discovery

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Emerging role of Mass Spectrometry in Biologics Development Yu (Annie) Wang Senior Scientist, CMC Analytical Team Lead, Biologics Development, Bristol Myers Squibb Qingyi Wang Senior Scientist, Biophysical Characterization Center of Excellence, Biologics Development, Bristol Myers Squibb Thomas R. Slaney Senior Principal Scientist, Biophysical Characterization Center of Excellence, Biologics Development, Bristol Myers Squibb Alexandria Emory Director, CMC Analytical Integration, Biologics Development, Bristol Myers Squibb

MANUFACTURING 60 Genomic Medicine Keeps Innovating and Manufacturing Needs to Keep Pace

Professor Amin Rostami-Hodjegan, PharmD, PhD, FCP, FAAPS, FJSSX, FBPhS, Chief Scientific Officer, Certara

Peiqing Zhang, Strategic Technology Partnership Leader, Genomic Medicine CSO, Cytiva

Professor Piet van der Graaf, PharmD, PhD, FBPhS, Senior Vice President, Head of QSP, Certara

Emmanuelle Cameau, Strategic Technology Partnership Leader, Genomic Medicine CSO, Cytiva

CLINICAL TRIALS 47 Bringing New Treatments to Patients with Rare Diseases: Designing and Conducting Effective Gene Therapy Trials Christopher Doyle, PhD, Senior Director, IBC Services & IBC Chair, WCG Clinical, Inc.

54 Addressing the Unmet Needs in Respiratory Disease Treatment with Inhaled mRNA Medicines Carsten Rudolph, PhD, co-founder & CEO, Ethris

67 Advanced Pharmaceutical Continuous Manufacturing of Liquid Dosage Forms Ravendra Singh, C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey

72 Safeguarding Sterility: Crucial Insights from 2023 Pharmaceutical Facility Audits Ajay Babu Pazhayattil, President, cGMPWorld Marzena Ingram, Senior Pharmaceutical Consultant,Validant Inc

EXPERT TALK 77 AI-Driven Patient Recruitment Dr Santhosh Kumar, VP, Enterprise Clinical Solutions, Indegene

84 INDUSTRY SENSE 109 WEBINAR REVIEW 110 EVENT PREVIEW 105 EVENTS LIST 118 NEWS 4

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Advisory Board Alessio piccoli Director & Head, Business Development Europe presso Aragen Italy Amine Bekkali Director, Medfields, UAE Dmitrii Vitalievich Kriuchkov Executive Director Axon Clinical Trial Lab Russia Gustavo Samojeden CEO, Eriochem S.A Argentina Hassan Mostafa Mohamed Chairman & Chief Executive Officer ReyadaPro Saudi Arabia Hoda Gamal Director of Regulatory and Corporate Affairs Middle East and Africa, Allied associate, Egypt Joaquin D. Campbell Global Director Managed Access Services Spain Josipa Ljubicic QA Director / Principal GCP and GVP auditor, Proqlea Ltd Croatia

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Nigel Cryer FRSC Global Corporate Quality Audit Head Sanofi Pasteur France Paola Antonini Chief Scientific Officer, Meditrial Global CRO Italy Pinheiro Neto Joao Chief Executive Officer Meu Doutor Angola Shamal Jeewantha Fernando Managing Director, Slim Pharmaceuticals ( Pvt) Ltd Srilanka Svetoslav Valentinov Tsenov Senior Pharma Executive and Global Transformation Lead Bulgaria Tamara Miller Senior Vice President, Product Development, Actinogen Medical Limited, Sydney Teresa Derbiszewska Clinical Quality Director G42 Healthcare/IROS UAE

Juris Hmelnickis CEO, Grindeks Latvia

Thitisak Kitthaweesin Chief of Phramongkutklao Center of Academic and International Relations Administration, Thailand

Nicoleta Grecu Director, Pharmacovigilance Clinical Quality Assurance Romania

Vicknesh Krishnan Associate Medical Director at Fresenius Medical Care Malaysia Sdn Bhd Malaysia

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EDITOR Vijaya Lakshmi N D EDITORIAL TEAM Sarah Richards Debi Jones Harry Callum Supraja BR ART DIRECTOR M Abdul Hannan PRODUCT MANAGER Jeff Kenney ASSISTANT MANAGER David Nelson Peter Thomas BUSINESS EVENTS Sussane Vincent CIRCULATIONTEAM Sam Smith SUBSCRIPTIONS IN-CHARGE Vijay Kumar Gaddam HEAD-OPERATIONS Sivala VNR

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The products and services advertised are not endorsed by or connected with the publisher or its associates. The editorial opinions expressed in this publication are those of individual authors and not necessarily those of the publisher or of its associates.

Copies of Pharma Focus Europe can be purchased at the indicated cover prices. For bulk order reprints minimum order required is 500 copies, POA.

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S T R A T E G Y

Strategic Partnerships in Biotech Catalysts for Accelerated Innovation

Now more so than ever, biotechnology companies face resource constraints and the perils of drug devel-opment. Strategic partnerships, fostering synergistic growth, become crucial. This article explores diverse partnership models, from licensing to equity, beyond mere finances. While replete with benefits, challeng-es like intellectual property and cultural disparities persist. Effective communication and shared vision are vital, making these collaborations a potent force for accelerated innovation in advancing medical science.

Joab Williamson Director, Clinical Operations at Faron Pharmaceuticals

Alexander Spicer Head of Corporate Development, Ampleia 8

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iotechnology, an industry at the forefront of scientific innovation and transformative healthcare solutions, has increasingly become a playground for ambitious start-ups and small enterprises.

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This has become more pronounced as large pharmaceutical companies have historically slimmed their own research and development (R&D) internally and have become reliant on external innovation with companies such as Novartis AG (SWX: NOVN) expecting up to 40% of their pipeline to come from biotech and put increased necessity on strategic partnerships between large pharmaceutical companies and biotech. Biotechnology companies are driven by the quest to harness life's molecular complexities, are aiming to disrupt tradi-tional healthcare paradigms with cutting-edge treatments and technologies. However, this journey is not without its challenges. Biotech start-ups often face significant hurdles, with the complex and expensive nature of drug development, alongside the ever-present risk of failure.

In such a competitive and high-stakes environment, the ability to forge strategic partnerships at criti-cal moments within a company’s life cycle becomes a crucial factor for survival and success. These part-nerships, which may involve collaborations with larger pharmaceutical companies, academic institutions, or research organizations, offer more than just financial support.

They represent a convergence of shared visions, expertise, and resources, creating a unified front to tackle the challenges of drug development and market entry whilst often providing credibility to the mission of the small organisations vision.

This article aims to dissect these strategic partnerships in the biotech sector, delving into their trans-formative potential, the various forms they take, the myriad benefits they offer, and the complexities and challenges they introduce. The focus is on understanding how these collaborations can serve as a driving force for innovation and growth in a field where the stakes are high, and the rewards can be revolutionary.

The Value of Strategic Partnerships Strategic partnerships in the biotech industry hold immense value for small and emerging companies. These collaborations can take various forms, each offering unique benefits and opportunities, but also offer their own complexities and downsides. For instance, partnerships with larger pharmaceutical companies often provide small biotech with essential financial backing, access to advanced R&D facilities, validation of the approach and team and unlocks wealth of industry expertise. This can accelerate the development process, from initial research to clinical trials and eventual market entry.

Collaborations with academic institutions can be equally beneficial, offering access to cutting-edge research, specialized knowledge, and a pool of skilled scientists, researchers and clinicians alike. These partnerships often lead to innovative breakthroughs, as they combine the theoretical knowledge and 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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re-search capabilities of academia with the practical, application-focused approach of the industry.

Equity investments from private equity, venture capitalists or strategic venture arms of larger corpo-rations are another critical form of partnership. These investments not only infuse much-needed capital into small biotech’s but also often bring strategic guidance, business acumen, and a network of industry contacts, which can be pivotal in navigating the complex biotech landscape.

Enhancing Resources and Capabilities One of the primary advantages of strategic partnerships in biotech is the enhancement of resources and capabilities. Small biotech firms, with their limited budgets and infrastructure, can gain access to state-of-theart facilities, cutting-edge technologies, and a wealth of industry knowledge through these collaborations. This access can dramatically accelerate the R&D process, enabling small firms to undertake projects that would otherwise be beyond their reach of their financial and human capabilities.

Partnerships also facilitate a pooling of expertise and knowledge. By collaborating with organizations that have complementary skills and capabilities, small biotech’s can overcome their inherent limitations. For example, a small firm specializing in a specific type of drug therapy can benefit immensely from partnering with an organization that has extensive 10

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experience in clinical trial management or regulatory com-pliance. Partnering with organisations who have these expertise and resources allows the internal team to remain focused on the science and business rather than unnecessary planning of building out large scale teams which would further limit already tight budgets.

Risk Mitigation: Sharing the Burden Navigating the biotech landscape is akin to treading a tightrope, with innovations on one side and sub-stantial risks on the other. Clinical trials, the sole way for proving the efficacy of therapeutic interventions, are a testament to this balance. They are notoriously expensive, time-consuming, and fraught with uncertainties. Many promising interventions never make it past the stringent barriers of clinical trials, which poses a considerable financial and developmental risk, especially for small biotech firms and the investors supporting them.

In the complex dance of drug development, strategic partnerships emerge as a riskmitigation strate-gy. When a small biotech enters a partnership with a larger pharma company or an academic institution, the inherent risks of drug development are distributed. While the smaller entity gets a safety net, the larg-er firm benefits from diversifying its portfolio and often at a financial discount proportional to the early stage of development in comparison to a later stage drug, and potential breakthroughs without starting its internal research from the start,

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mitigating an additional level of risk and time.

Essentially, it's a reciprocal relationship where the larger entity with its broader financial bandwidth and lower appetite for risk, shields the smaller biotech from potential financial pitfalls and rewards it for its pioneering risky endeav-ours.

Such a risk-sharing approach doesn't only pertain to financial dimensions. It extends to

regulatory challenges, clinical recruitment roadblocks, and even potential market reception issues. By sharing the burdens of these multifaceted challenges, partnerships ensure that the journey of drug development isn't derailed by unforeseen obstacles which can be simple to overcome for larger organisations with their ar-my of employees and internal expertise.

The Power of Synergy in Accelerating Drug Development In the dynamic realm of biotech, the fusion of diverse strengths is more than just an asset; it’s a neces-sity.

The blend of fundamental research prowess of academic institutions with the translational capabilities of small biotech companies epitomizes synergy in action. This integration enables an expedited route from discovery to delivery, bypassing many traditional roadblocks.

For instance, while academic researchers may excel in understanding molecular pathways, small biotech’s often have

Types of Partnership: Beyond Mere Financial Transactions When we think of partnerships in biotech, it's easy to fixate on monetary exchanges.

However, the landscape of collaboration is vast and varied.

Licensing Agreements: A foundational model in biotech partnerships, licensing agreements 12

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the agility and focus to navi-gate these insights through the complex pipeline of drug development. It's this shared vision and comple-mentary expertise that acts as a catalyst, accelerating the traditionally long and arduous journey of bring-ing therapeutic solutions to market.

enable small firms to offer their proprietary technologies or promising molecules to larger entities. In return, they often benefit from upfront payments, milestone-related revenues, and potential royalties upon successful commercialization. This not only provides capital but also endorses the potential and viability of their in-novation.

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Collaborative Research: Rooted in the shared objectives of discovery and advancement, collaborative research typically involves alliances between small biotech’s and academic institutions. They synergistically share costs, technical expertise, and the resulting intellectual property, creating an environment conducive to innovation.

Equity Investments: In some instances, corporate venture arms of larger entities, recognizing the po-tential of a budding biotech, might invest directly. Beyond mere financial support, these equity stakes often come with strategic mentorship, enhancing the small biotech's prospects in the competitive market.

Challenges and Considerations While strategic partnerships offer numerous benefits, they are not without their challenges.

One of the primary issues is the alignment of goals and objectives between partnering organizations. Different entities may have varying priorities, timelines, and risk appetites. Aligning these diverse expectations requires ro-bust communication channels, transparent operations, and sometimes, complex negotiations.

Intellectual property (IP) rights also present a significant challenge in biotech partnerships.

Determin-ing the ownership of IP, rights to commercialization, and addressing potential patent infringements can be complex and contentious. It is crucial to establish clear terms and agreements regarding IP at the

outset of the partnership to prevent disputes and misunderstandings later.

Cultural differences between partnering organizations can also impact the dynamics of the collabora-tion. For instance, the culture of an academic institution, which might prioritize discovery and innovation, can differ significantly from a biotech start-up focused on product development and return of shareholder value. These cultural nuances need to be recognized and managed effectively to ensure a smooth and productive partnership.

Future Outlook Looking forward, the landscape of biotech collaborations is poised for further evolution given the ex-tent of the market downturn we are currently seeing combined with the rapid advancements in technology and an increasingly interconnected global market, the potential for international partnerships and cross-border collaborations is immense.

These global alliances could play a critical role in addressing worldwide health challenges, sharing knowledge across borders, and fostering a more inclusive and di-verse research environment.

Additionally, the rise of digital technologies and data science in biotechnology presents new opportuni-ties for partnerships. Big data, artificial intelligence, and machine learning are set to revolutionize drug discovery and development processes. Collaborations that leverage these technological advancements can 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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lead to more efficient and precise approaches to tackling complex biological problems.

In essence, the future of biotech partnerships is not just about sustaining the current momentum. It's about harnessing the full potential of global collaboration and technological innovation to push the boundaries of what's possible in healthcare and medicine. These partnerships, if nurtured and managed with foresight and adaptability, could significantly shape the future of biotechnology, leading to unprecedented advancements and positive impacts on global health.

Conclusion

AUTHOR BIO

The biotech sector, interwoven with challenges and innovation, constantly seeks equilibrium.

Strategic partnerships, in their diverse forms, serve as a balancing force. They amplify strengths, mitigate risks, and foster an environment where collective efforts surpass individual capacities. While these collaborations

come with their own set of challenges, careful planning and clear articulation of terms can pave the way for success. These alliances not only speed up drug development but also open new avenues for therapeu-tic advancements, ultimately benefiting society at large.

In conclusion, the biotech industry's future is inextricably linked to the effectiveness of strategic part-nerships. As the sector continues to evolve, these collaborations will likely become even more crucial. They are not just a pathway to overcoming the inherent challenges of the biotech industry but a cornerstone for future innovation and progress. Through these synergistic relationships, small biotech firms can transcend their limitations, tapping into a broader pool of resources, expertise, and opportunities. As such, strategic partnerships are not merely a component of the biotech ecosystem; they are a driving force behind its continued growth and success.

Joab is the Director, Clinical Operations at Faron Pharmaceuticals, a clinical stage biotech focusing on building the future of immune-oncology. He has a vast amount of experience in clinical operations and program/project management and is also focused on continuing academic pharmacoeconomic research.

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Alex recently joined Ampleia, a venture studio in Paris as Head of Corporate Development after spending nearly five years within Faron, an immuno-oncology biotech as their Business Development Director. Alex has developed a rounded understanding of the biotechnology business and continues to develop research within the economics of the sector.

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Why are Generic Pharmaceutical Manufacturers Leaving the USA? Generic pharmaceutical manufacturers are leaving the USA due to factors like fierce competition, stringent regulations, pricing pressures, and increased manufacturing costs. These challenges have driven companies to seek more favorable conditions elsewhere, impacting the domestic generic industry and leading to a shift in manufacturing operations to other countries.

Eric L. Wesoloski Vice President Quality and Regulatory Affairs, USAntibiotics

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n recent years, the United States has witnessed a remarkable phenomenon in the pharmaceutical industry: the departure of several major generic pharmaceutical manufacturers.

The rationale behind this exodus is a subject

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of great interest, as it potentially carries profound implications for the nation's healthcare system and economy. This article attempts to delve into the reasons why these generic pharmaceutical manufacturers are moving their operations away from

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the US, exploring key factors such as regulatory challenges, intellectual property protection, cost pressures, and emerging international markets. By understanding the motivations behind this trend, stakeholders can make informed decisions to ensure the sustainability and growth of the generic pharmaceutical industry in the United States.

Regulatory Challenges: One primary reason behind the relocation of generic pharmaceutical manufacturers from the US is the increasingly complex regulatory landscape. The rigorous approval process imposed by the Food and Drug Administration (FDA) requires significant time and resources. Generic drug manufacturers face an arduous path to obtain the necessary approvals, which can take several years and consume substantial funds. The longer approval timelines and escalating regulatory requirements have led to growing frustration, pushing manufacturers to seek more favorable regulatory climates abroad.

Furthermore, inspections by regulatory agencies, such as the FDA, can be burdensome and costly. Stringent compliance measures increase the risk of regulatory non-compliance, leading to hefty penalties and potential reputational damage. In contrast, some international markets offer more streamlined approval processes and inspections, reducing costs associated with compliance and facilitating market entry.

Cost Pressures: The rising cost pressures in the US healthcare system are another driving force behind the departure of generic pharmaceutical manufacturers. A convergence of factors, including the rising costs of energy, ingredients, capital investment costs to build and maintain production capacity keep rising while there are structural impediments to increasing prices. (medicare, Medicaid, PBMs, etc.). Litigation costs, and price regulations, have hindered the profitability of generic drug manufacturing within US borders.

The cost of labor in the US, especially compared to emerging economies, is significantly higher. Countries like India and China, with their expanding skilled workforces, offer a substantial cost advantage regarding salaries and wages. The cost of compliance, though essential for maintaining the highest quality standards, adds to the manufacturing overhead. Production facilities consume a significant amount of energy. With fluctuating energy prices in the US, many companies find it cheaper to operate in countries where energy costs are more stable or lower.

The generic pharmaceutical market is fiercely competitive. As more companies worldwide enter this arena, there's continuous pressure to reduce prices and stay competitive.

Relocating to a country with lower operational costs is one strategic move to remain viable in a cutthroat market.

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their products sooner while benefiting from reduced R&D and clinical trial costs.

However, this advantage is gradually being eroded as the complexity and costs associated with proving bioequivalence have increased.

The need for extensive testing, especially in the case of complex generics, has led to prolonged launch timeframes, diminishing the incentive for manufacturers to remain in the US market.

Additionally, litigation costs have surged as pharmaceutical companies grapple with patent disputes and legal battles to defend their market share. These legal proceedings strain resources and deter investment in the US market, prompting generic manufacturers to explore jurisdictions with more favorable intellectual property protection environments.

Intellectual Property Protection: The lack of robust intellectual property protection in the US has become an area of concern for generic pharmaceutical manufacturers. Protecting intellectual property rights is crucial to fostering innovation, incentivizing investment, and maintaining market competitiveness. However, certain aspects of the US patent system have created challenges for generic manufacturers.

Complex and unclear patent regulations have led to prolonged patent battles, delaying market entry for generics. These disputes place a significant financial burden on generic manufacturers, which struggle to 18

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allocate sufficient resources towards R&D, production, and competitive pricing strategies.

In contrast, some foreign jurisdictions possess more efficient patent systems that protect intellectual property rights without unnecessarily impeding market entry, thus attracting these manufacturers.

Emerging International Markets and Supply Chain Advantages: As the global healthcare landscape evolves, emerging international markets present significant growth opportunities for pharmaceutical manufacturers. Countries such as India and China offer expansive markets with rising healthcare demands and increasing purchasing power. By establishing operations in these regions, generic pharmaceutical manufacturers can tap into these burgeoning markets and mitigate risks associated with market saturation in the US. Emerging markets also offer favorable labor costs, enabling manufacturers to achieve greater cost efficiencies. The availability of skilled labor pools and well-established pharmaceutical manufacturing infrastructure in these regions further entices companies to relocate their operations.

Several active pharmaceutical ingredients (APIs) and intermediates are primarily sourced from Asian countries. Manufacturing units closer to these suppliers can reduce transportation costs and lead times. Countries like China and India have rapidly developed their pharmaceutical supply chains, making

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it convenient for companies to source everything from raw materials to packaging in close proximity.

Furthermore, partnerships and collaborations with local manufacturers and distributors facilitate market access and knowledge sharing. This allows generic pharmaceutical manufacturers to navigate foreign markets more effectively, benefit from local expertise, and expand their customer base.

Implications and Considerations: The departure of generic pharmaceutical manufacturing from the US brings several implications: Job Losses: The direct consequence of this exodus is the potential loss of thousands of jobs that these industries provide.

Economic Impact: The ripple effect on the ancillary industries, including those supplying raw materials, machinery, and logistics, can be significant.

National Security Concerns: Relying heavily on imports, especially for essential medicines, can pose a security risk during times of global strife or pandemics.

Quality Control: While the FDA does inspect foreign manufacturing plants, the frequency and rigor might differ from domestic inspections, raising potential concerns about the quality of imported generics.

Consumer Costs: Ironically, even though one of the reasons for the exodus is the cost of production, it doesn't always translate to lower prices for American consumers. Market dynamics, distribution costs, and other factors might keep prices elevated.

The flip to the original question is why generic pharmaceutical manufacturing should remain in the US: Amidst increasing globalization, USAntibiotics has prudently chosen to maintain its manufacturing facilities within the United States. Quality control and stringent safety regulations are at the core of USAntibiotics' operations.

By keeping production rooted within the US, the company ensures that every stage of the manufacturing process meets the rigorous standards set by the Food and Drug Administration (FDA). Patient safety is paramount, and maintaining control over the manufacturing process allows USAntibiotics to consistently meet and exceed these stringent criteria.

A major advantage of USAntibiotics' domestic manufacturing is its ability to foster strong relationships with leading research institutions and universities.

Close proximity allows for seamless collaboration, knowledge exchange, and access to cutting-edge research.

Such partnerships enable USAntibiotics to remain at the forefront of medical advancements, resulting in the development of innovative medicines that address complex health challenges.

This commitment to continuous improvement and collaboration contributes significantly to the company's competitive edge. 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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Conclusion: The exodus of generic pharmaceutical manufacturers from the US market can be attributed to a combination of factors, including regulatory challenges, cost 20

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pressures, intellectual property protection concerns, and the allure of emerging international markets. To address this trend, stakeholders must work collaboratively to streamline the regulatory landscape, promote IP protection, and foster an environment conducive to innovation and competitiveness.

By doing so, the US can retain its position as a global leader in the pharmaceutical industry and ensure access to affordable medicines for its US citizens.

AUTHOR BIO

USAntibiotics' decision to retain pharmaceutical manufacturing operations within the US has far-reaching economic implications. By supporting domestic production, the company generates direct employment opportunities for skilled workers, stimulating local economies.

Additionally, indirect jobs related to the pharmaceutical industry, such as logistics and distribution, further contribute to economic growth. USAntibiotics understands the brand value and customer loyalty that arise from being a responsible corporate citizen, actively invested in the socioeconomic development of the communities in which it operates.

With any type of pandemic exposing vulnerabilities in global supply chains, USAntibiotics' commitment to domestic production ensures a stable and resilient supply of critical medications. By having manufacturing facilitiy within the United States, the company mitigates the risks associated with over-dependence on foreign suppliers and potential disruptions in international logistics. This ability to supply essential medicines during times of crisis bolsters the reputation of USAntibiotics as a trusted and reliable partner in healthcare.

Eric Wesoloski is a global quality leader in the pharmaceutical sector, known for constructing best-in-class quality teams and organizations. With expertise in cGMP, Regulatory, Compliance, and Quality Assurance, he excels in driving robust operational performance, spearheading worldwide quality collaborations, and leading global strategies for top-tier achievements. A strategic change agent, Eric seamlessly integrates cross-functional units, ensuring regulatory compliance with agencies like FDA, MHRA, and EU. His proficiency spans Six-Sigma, Risk Management, and Agile methodologies

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Emerging role of Mass Spectrometry in Biologics Development Mass spectrometry (MS) has become an indispensable tool during manufacturing process development, also known as CMC (Chemistry Manufacturing and Controls) development, for biologics. Information about product-related variants (such as post-translational modifications (PTMs)) and process-related impurities (such as host cell proteins (HCPs)) can be extensively characterized by MS to understand process performance and product quality. The in-depth data provided can play a key role in defining and understanding the Critical Quality Attributes (CQAs) which need to be well-controlled by the manufacturing process and closely monitored by the analytical testing panel to ensure specifications are met. MS attribute data is also a key component in regulatory submissions, including the Elucidation of Structure and Manufacturing Process Development (especially process comparability) sections. With recent development of new methods and instrumentation, MS is finding a wider usage in the regulated release testing of Good Manufacturing Practices (GMP)-produced drug substance, such as for identity testing, target attribute or impurity monitoring, or multi-attributemethods (MAM). State-of-the-art MS technologies also benefit proteomics studies with higher sensitivity, lower detection limit, and faster throughput.

Well-established roles of mass spectrometry (MS) in biologics development Manufacturing Process Development After a lead protein design is selected for clinical trials, biologics manufacturing (also known as chemistry manufacturing and control, or CMC) development activities commence. CMC development encompasses 22

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the entire manufacturing process of the drug substance (DS) and formulation of the drug product (DP) at a scale and quality sufficient for human use in clinical trials or as a commercial product. The initial application for use of a lead protein in a clinical trial requires the submission of an Investigational New Drug (IND) or Clinical Trial Application (CTA) to the respective

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Yu (Annie) Wang Senior Scientist, CMC Analytical Team Lead, Biologics Development, Bristol Myers Squibb

Qingyi Wang Senior Scientist, Biophysical Characterization Center of Excellence, Biologics Development, Bristol Myers Squibb

Thomas R. Slaney Senior Principal Scientist, Biophysical Characterization Center of Excellence, Biologics Development, Bristol Myers Squibb

Alexandria Emory Director, CMC Analytical Integration, Biologics Development, Bristol Myers Squibb

government agency where the trial is planned. The process and analytical development teams at the sponsor company ensure the quality and safety of the product to be used for clinical trials by a comprehensive workflow of identifying Critical Quality Attributes (CQAs), setting product specifications as part of a control strategy for 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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Cell line development

Process development

Comparability study

MS Critical quality attributes

Formulation development

Method development

Figure 1: MS supports multiple activities of CMC development for biologics.

ensuring the CQAs are at required levels, and demonstrating process consistency by comparability studies. As shown in Figure 1, MS is a valuable tool supporting most activities of Biologics CMC development, including but not limited to cell line development, process development including upstream (bioreactor expression) and downstream (purification) stages, formulation development of the drug product (the final presentation, including vials and devices, as well as biotherapeutic and excipients), critical quality attribute (CQA) characterization, comparability studies, and any additional studies such as investigations of process outliers or lot failures.

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Mass Spectrometry Supports Process and Formulation Development Mass spectrometry is a versatile tool for answering questions like “What does the molecule look like?”, or more specifically, “what is the amino acid sequence/primary structure of the protein molecule?” Peptide mapping and intact mass are two of the most common approaches for MS assays that support extended characterization of drug candidates. For peptide mapping experiments, protein samples undergo digestion by incubating with protease(s) prior to separation by liquid chromatography (LC) and mass

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analysis by MS. For intact mass experiments, protein samples do not undergo proteolysis; instead, samples are diluted, and the mass analyzed for the intact molecules or subunits.

Intact MS is useful because it quickly verifies the primary sequence and most-abundant post-translational modifications (PTMs) of the protein. The accurate measurement of masses by most modern MS instruments ensures reliable identification of the major component of the drug and most deviations from its expected structure due to the impact on molecular mass of the protein.

MS is used to detect sequence variants (SVs) to support cell line development in the early stage of CMC development. SVs are unintended errors in the amino acid sequence of the biotherapeutic protein caused by substitutions, deletions, and/or insertions of nucleotides in the DNA encoding for our protein within the host cell line.

DNA mutations can result in missense (incorrect amino acid being substituted), non-sense (a truncation site for expression of the protein) or frame shift mutations (where the reading frame shifts, dramatically changing the amino acid sequence), potentially impacting the quality, safety, and efficacy of biotherapeutic proteins. It should be noted that MS cannot detect silent mutations, therefore SV screening by MS as well as orthogonal nucleotide sequencing methods such as Next Generation Sequencing of mRNA (NGS) or Sanger sequencing of cDNA are routinely performed during the clone selection

stage to ensure that the final lead clone used has no encoding mutations. Using both approaches simultaneously is beneficial over nucleotide sequencing approaches alone, as MS provides a direct measure of SV impact on the final protein product, whereas nucleotide approaches may not indicate if the mutant form is expressed or co-purified.

MS also provides valuable insights to support both upstream and downstream manufacturing process development. During upstream development, many bioreactor culture conditions such as pH or temperature may lead to differences in analytical attributes of the therapeutic protein, such as the charge profile distribution (measured by isoelectric focusing or ion exchange chromatography).

MS can be leveraged to identify the specific modification differences of the therapeutic causing a shift in the profile, such as deamidation of a specific asparagine, or modification of the C-terminus of the protein.

This knowledge can allow both a quality risk assessment and understanding of the root cause of the difference.

Feed is another upstream parameter that may directly impact product quality.

For example, saccharide supplementation of the feed impacts the glycan structure of the final product. MS measurements can provide rapid glycan measurement while optimizing the feeding strategy to achieve the desired glycosylation profile. Sub-optimal levels of amino acid feed additives may lead to misincorporation of a specific amino acid 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 the protein sequence by “mischarging” of the incorrect amino acid onto a tRNA during mRNA translation into protein. Therefore, beyond screening for mutations, MS is often used for detecting SVs resulting from feed parameters and ensuring their appropriate control from the final process.

Biologic formulation development includes optimization of buffer, pH and excipients to ensure optimal stability of the protein delivered in the desired form and route of administration to the patient, be-it a glass vial for intravenous infusion, or an autoinjector for subcutaneous self-administration at home. Real-time stability studies test the protein response to various condition changes, including the concentration of the therapeutic and excipients in solution, or the delivery devices intended to be used. MS plays a key role in structural characterization of any degradation products observed such as covalently induced aggregation and clipped species. It can provide high-accuracy information about degradation products such as molecular weight of dimer aggregates, and cleavage site locations. Furthermore, MS can be used for host cell protein (HCP) characterization. HCPs are endogenous proteins from the host organism used to express the therapeutic protein which may co-purify with the desired protein product.

Lipases are a class of HCP that may cause degradation of polysorbate 80, a common excipient which may be needed for stability of the therapeutic, especially in formulation 26

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with high concentration DS. Trace level lipases may pose a common risk to the drug quality.

MS-based HCP identification methods can be used to determine if lipase contamination is responsible for polysorbate degradation observed during formulation development.

Mass Spectrometry Supports Characterization of Critical Quality Attributes (CQAs) and Informs the Control Strategy It is well understood that for a new drug product, certain physical, chemical, or biological properties should be within an appropriate range to ensure the desired product quality. These attributes, according to the Quality-by-Design (QbD) framework regulatory agencies have set, are known as Critical Quality Attributes (CQAs). Therefore, a CQA study/ assessment is an important exercise during CMC development. During early and clinical drug development, degradation pathways and potential CQAs are explored using accelerated stability and forced-degradation experiments, as well as integrating knowledge learned during formal stability studies and process development.

A good understanding of CQAs is especially important for a QbD methodology, as well as process validation, to achieve desired product quality through risk assessment and valid control strategies.

During CQA assessment studies, various analytical assays are used to examine molecular changes of the therapeutic in terms

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Mass spectrometry revolutionizes biologics development, offering unparalleled insights from process optimization to regulatory filings. As a versatile and indispensable tool, MS ensures the quality, safety, and efficacy of biopharmaceuticals, shaping the future of pharmaceutical innovation.

of high molecular weight species (HMW), low molecular weight species (LMW), charge variants, oxidation, deamidation, isomerization of aspartic acid, deconjugation of drug payloads (for antibody-drug conjugates, for example), etc. With peptide mapping and intact-mass MS, modifications of the protein’s amino acid residues or clipping products are determined, and their levels quantified. The question of “How does the molecule degrade?” can generally be answered, which can then be correlated with our understanding of structure-function relationship and functional assays (sometimes including in vivo studies) as deemed necessary as part of an attribute risk assessment. During post first-in-human (FIH) commercial process

development, further forced degradation studies are often carried out to assess multiple degradation pathways and impact to product quality and functionality.

Mass Spectrometry Supports Method Development Separations methods are commonly used for release testing assays for a Good Manufacturing Practices (GMP) manufacturing campaign, producing material for human use. This is attributed to their ability to measure product-related variants accurately and sensitively. These methods are set with specifications to monitor CQAs and to ensure process consistency. MS instruments are heavily used to support analytical method development, providing insights such as chromatographic/electrophoretic peak identification, and correlating the attributes measured with the structure-function understanding of the therapeutic.

Size exclusion chromatography (SEC) is developed to monitor the amount of HMW aggregates within biologics. MS can support characterization of the biologics to potentially help understand the mechanism of aggregation (high oxidation, crosslinks, clipping, etc.).

Additionally, MS data can confirm where an LMW truncation site is located and thus assess its location to its potential impact on potency. MS is also used in profiling PTM variations relative to binding sites and helps with understanding their impact on mechanism of action (MOA). 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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From unraveling protein mysteries to ensuring process consistency, mass spectrometry emerges as the cornerstone of biologics development, paving the way for safer, more effective pharmaceuticals.

MS is also helpful for peak characterization and identification for capillary gel electrophoresis (CGE) methods. A good example is the confirmation of a shoulder peak identity as non-glycosylated protein, which directly informs whether this peak may be considered part of the “main peak”, or a product-related impurity, when integrating electropherograms. The high-resolution of CGE can also separate and quantify specific cleavage products of the protein backbone, whose identity can be confirmed by MS. MS is also used to correlate levels of PTMs of a biotherapeutic in charge-based separation methods such as isoelectric focusing (IEF) and cation exchange chromatography (CEX).

Some common reasons for the increase of acidic peaks are deamidation of asparagine and glycation, while oxidation, C-terminal lysine, and aglycosylation lead to increase of basic peaks. Using MS to identify these modifications is a critical step to establishing phase-appropriate specifications of these release methods as these chemical changes may or may not be CQAs. 28

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In addition, it is common to use MS instruments to support the development of hydrophilic interaction chromatography (HILIC) methods for measuring released N-glycans. Individual glycan peaks can be directly identified by their masses, which is important as some classes of N-glycans such as high-mannose, sialylated, afucosylated, or alpha-Gal-containing glycans are known to impact patients by their effect on pharmacokinetics (especially half-life), potency, or immunogenicity and should thus be well-characterized and controlled.

Mass Spectrometry Use in Regulatory Filings (INDs/CTAs and BLAs) MS plays an essential role in the extended characterization of any new biologic, deciphering the chemical structure down to single amino acid residue level. Data are documented in the IND/CTA filing sections, especially in Module 3, Section 3.2.S.3.1 Elucidation of Structure, as part of the DS Quality section. Extended

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characterization testing including MS is also usually performed upon qualification of a new reference material.

A formal analytical comparability study is reported when filing an IND/CTA or biologics license application (BLA) amendment for a manufacturing process change (change in cell line, upstream/downstream process change, location change, etc.). Analytical comparability provides justification supporting the process change by demonstrating no impactful changes to the attributes of the biotherapeutic.

Similar to the characterization section of the initial IND filing, LC-MS data – both peptide mapping and intact mass – support the extended characterization portion of the comparability study and are captured in Module 3 of the filing, Section 3.2.S.2.6 Manufacturing Process Development for DS and Section 3.2.P.3.5 Process Validation and/ or Evaluation for DP. Comparability studies are performed as side-by-side extended characterization for the DS or DP material between the originally filed process and the new process. Comparability parameters to monitor using LC-MS include product-related variants and molecular masses of intact and reduced protein subunits, among other assays.

Together, MS data combined with structurefunction understanding and other analytical methods can provide a strong argument for process comparability and avoid the need for additional clinical trials to demonstrate comparability, especially in early clinical studies.

Novel Application of Mass Spectrometry in Biologics Development Host Cell Protein (HCP) Profiling by Mass Spectrometry In the past decade, LC-MS-based proteomics approaches have emerged as informative impurity profiling methods for biotherapeutics, with extensive studies and applications in the field of HCP characterization. Trace levels of certain HCPs may elicit immune responses in patients, affect drug efficacy, or degrade the product over time. It is a requirement from regulatory agencies to demonstrate effective removal of these impurities in the final DS by the downstream process steps.

HCP content is also monitored as an in-process parameter to understand the clearance of each downstream processing step. LC-MS “shotgun” proteomics is adopted in addition to the well-established and widely used enzyme-linked immunosorbent assay (ELISA) for total-HCP measurement.

Proteomics approaches identify HCPs by digesting protein samples into peptides followed by tandem MS (MS/MS) and database searches against the entire proteome of the host cell species. Semi-quantitation of individual HCP species can be achieved by employing the “Hi-3” quantitation strategy.

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species (through different purification stages, etc.), and picking up sub-ppm levels of HCPs in so-called “clean” samples such as final DS. However, one of the biggest challenges of MS technology to achieve the desired sensitivity for HCP detection is the limitation of the dynamic range required for HCP test samples. HCPs are typically present within biotherapeutics at an approximately millionfold excess of the therapeutic protein, posing a challenge for most analytical approaches.

To address this challenge, innovative technologies for sample preparation are developed for enriching HCPs to minimize the difference between the amounts of HCPs present versus biotherapeutic. Meanwhile, improvements on MS instrumentation offer better sensitivity for detection of low abundance species (i.e., HCPs) in the presence of a complex matrix.

Mass Spectrometry Has Increasing Application in the Quality Control Environment.

With the advancement and accessibility of technology and high throughput capability, there is a recent trend of employing MS in quality control (QC) environment. In previous years, MS was rarely used in the QC environment due to its complexity and the lack of MS-trained personnel. MS application in QC is rapidly expanding as no-configurationneeded MS instrumentation and QC-compliant data acquisition and processing software become more readily available.

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One such application is the use of intact mass analysis for identity (ID) confirmation of the biologics, especially in the early phase of the program development. Compared to conventional ID methods like ELISA and peptide mapping, intact MS ID method has the advantage of less-labor intensive workflow and specificity. Intact MS can be used as one of the ID methods in the scenario where health authorities require orthogonal ID strategies.

The advantages of MS over other conventional chromatographic and electrophoretic assays become more significant when it comes to characterization of complex biologics molecules such as fusion protein, bi- and tri-specific antibodies, complex glycoproteins, antibodydrug conjugates, viral vectors, peptide therapeutics, and other novel modalities.

The complexity of these molecules often mean that conventional approaches do not provide as informative results since many PTMs may contribute to multiple peaks simultaneously, making these approaches less sensitive to changes in the product. Meanwhile, MS can provide site-specific PTM information even with highly complex constructs. These PTMs, if identified as CQAs, could potentially impact product efficacy and safety (as described previously). Implementation of MS based methods for release and stability testing has been reported for monitoring various attributes such as oxidation and deamidation levels.

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Another emerging application is leveraging MS for multi-attribute-method (MAM) in the QC environment. MAM is an LC-MS based method which enables the detection and monitoring of multiple product quality attributes (PQAs) simultaneously while replacing more well-established chromatographic or electrophoretic assays.

The MAM workflow includes the discovery phase for targeted peptide library generation based on retention time and mass information of all the quality attributes, followed by the monitoring phase based on the library and new peak detection and analysis. Recently, validation of MAM as a QC method has been reported for relative quantitation of target PQAs.

Better MS Instrumentation Improvements in the performance and availability of high-end commercial instrumentation have made MS an increasingly popular approach for biologics characterization in the pharmaceutical industry.

Through the past decade or so, MS instrumentation has witnessed great advancement towards higher sensitivity, better resolving power, lower detection limit, and faster throughput. State-of-the-art technologies effectively boost the capability of mass spectrometers for handling complex therapeutics samples. The latest Orbitrap mass spectrometer model, branded as “Astral”

by Thermo Scientific, combines three mass

analyzers within one instrument, including the novel Astral analyzer (featuring ion mirrors) for fast and sensitive measurements.

The greatly enhanced sensitivity, throughput, and data quality from this instrument may provide potential for more comprehensive characterization of host cell proteins in biotherapeutics products.

Ion mobility mass spectrometers couple a high-resolution mass analyzer with ion mobility separation, making it particularly useful for the analysis of isomers and conformers. The recently launched timsTOF technology from Bruker, which incorporates a Trapped Ion Mobility Spectrometry (TIMS) device at the front of a quadrupole timeof-flight (QTOF) mass spectrometer, is claimed to achieve a near 100% duty cycle for high sensitivity, high speed proteomics.

In addition, collision-induced unfolding (CIU) workflow of native-like protein ions followed by ion mobility spectrometry (IMS) continues to expand its utilization due to its ability to rapidly characterize protein conformation and stability. It has proven useful in probing the higher order structure of antibody therapeutics.

Furthermore, enhanced MS/MS technologies also facilitate ion fragmentation for increased sensitivity and deeper coverages for proteomics studies. The electron activated dissociation (EAD) fragmentation technology employed on the SCIEX ZenoTOF system is marketed for its extended utility on all molecules type from singly charged 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 multiply charged ions, making it outperform either CID/HCD (collision-induced dissociation/ high-energy collisional dissociation) or ECD (electron capture dissociation) technology alone. EAD can also aid in identification of labile PTMs such as glycosylation, phosphorylation, sulfation, etc. Overall, continuing improvement of MS instrumentation performance is expected to meet the challenges of complex next-generation therapeutics.

Conclusion In conclusion, mass spectrometry techniques are under continuing development to enable more routine and specialized support of all phases of biologics CMC development. Not only has MS been enabling faster, higher-accuracy measurements in areas like process and formulation development, CQA control strategy, method development and regulatory filings, but it has also been expanding to more specialized applications such as HCP profiling and GMP release testing within the QC environment.

As MS instrumentation and performance continue to improve, MS will undoubtedly be playing a more important role for biologics development in the pharmaceutical industry.

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AUTHOR BIO

Yu (Annie) Wang, Ph.D. is a Senior Scientist, Analytical Team lead of CMC Analytical Integration in Biologics Development at Bristol Myers Squibb. In 2017, she received her Ph.D. in Analytical Chemistry from Northeastern University. In her current role, she leads a matrix team to support the analytical activities for biologics clinical development.

Qingyi (Emma) Wang Ph.D. is a Senior Scientist, Biophysical Characterization Center of Excellence, Biologics Development, Bristol Myers Squibb, USA. In 2019, she received her Ph.D. in Analytical Chemistry from the University of Michigan.

Thomas Slaney, Ph.D. is a Senior Principal Scientist in the Mass Spectrometry Center of Excellence in the Biologics Analytical Development organization of Bristol Myers Squibb, with over 11 years of experience in the pharmaceutical industry. He currently specializes in the characterization of protein therapeutics and viral vectors.

Alexandria Emory, M.S., is the Director of CMC Analytical Integration in Biologics Development at Bristol Myers Squibb, leading a team of scientists who drive development of CMC analytical strategies for biologics clinical development.

She brings over 12 years of industry experience establishing integrated development strategies for complex biologics.

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Unlock lab efficiencies and decision-making through a digitalfirst approach to lab transformation In this article, Henal Shah, Head of Life Sciences Lab Practice Europe & M&A, and John Dunne, Lab Centre of Excellence Consultant at Cognizant, Life Sciences Manufacturing, explore the driving forces behind the “digital lab” concept and its potential to transform the pharmaceutical industry. Shah and Dunne also provide a strategic guide for organizations seeking to adopt a roadmap toward the digital lab.

Henal Shah Head of the European Lab Centre of Excellence (CoE) Practice within the Life Sciences Manufacturing group, Cognizant

John Patrick Dunne leads the Lab Centre of Excellence (CoE) Advisory Team within the Life Sciences Manufacturing group, Cognizant 34

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ecent industry pressures brought on by the global COVID-19 pandemic provided critical lessons for the life sciences industry, and labs in particular, highlighting the urgent need for accelerated speed in bringing new medicines, vaccines and diagnostic testing services to market.

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Before the pandemic hit, segments of the life sciences space, such as biopharma manufacturers, were slower to adopt new data management technology. Often working across legacy systems, companies needed to carefully orchestrate the acquisition, storage and analysis of their lab data across disparate sources to extract its full value —

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often a near-impossible task to complete in its entirety.

Now, in a post-COVID-19 world, the need for digital change remains stronger than ever. Accelerated vaccine release has changed patient expectations regarding cycle times for bringing medicines to the market.

It has become evident to many across the industry that the research, development and manufacturing life cycle must become more agile and flexible. At the same time, the process must remain compliant with the strict regulatory requirements that come with accelerated product launches.

Today, the “lab of the future” or “digital lab” is seen as a business imperative. Based on key digital transformation concepts defined in Industry 4.0, the digital lab goes far beyond simply going digital. Instead, it is about creating an interconnected lab ecosystem that harnesses the latest technological advancements, coupled with sustainability, user adaptability and business intelligence.

For many, creating an end-to-end digital lab has only ever been a pipe dream. Its successful implementation, however, has never been more important in meeting evolving industry challenges head-on. There is a feeling that lab technology is now ready to meet the needs of the pharmaceutical industry and transform lab processes, and the industry is ready to commit to that technology.

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realizing the potential of the digital lab, and failure to act now could cost companies dearly.

Life science companies that are behind the digital curve are experiencing associated costs that reached a staggering $650 million in 2018, compared to $103 million in 2015.

These costs have only increased since the pandemic. Imminent strategic adoption of digital lab innovations is key to gaining a competitive market advantage.

Creating a digital lab can address emerging needs and help clients overcome traditional obstacles, enabling greater efficiency, cost savings and better regulatory compliance.

Benefits are also seen in information transparency, data integrity and talent retention.

What does the digital lab look like?

A digital lab is an interconnected, digitized lab ecosystem integrated with manufacturing shop floor and enterprise systems that harnesses the latest technological advancements, coupled with sustainability, user adaptability and built-in cyber security. It creates platformbased lab environments with interoperable systems and utilizes artificial intelligence (AI), machine learning (ML), augmented reality (AR), virtual reality (VR), 3D modeling and advanced analytics to simplify data access for visualization, create predictable behavior in a lab and improve business processes.

There are multiple advantages derived from digital lab strategies, including: 36

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● Accelerated product launches driven by faster access to data through interconnected applications. ● Empowered laboratory staff working more seamlessly across research and manufacturing activities and functional domains. ● Better performance and analysis of largescale parallel experiments via automationenabled lab management systems. ● Process improvements through digital labs that can help streamline operations and processes. ● Cloud adoption and automation that can deliver a more robust lab ecosystem.

Through these enhanced processes, the digital lab can have an additional business impact and deliver a competitive advantage by: ● Accelerating commercial product launches with quality by design. ● Reducing drug development and approval cycles. ● Improving manufacturing efficiencies. ● Enhancing regulatory compliance and auditready labs. ● Strengthening connectivity and seamless digital business process flow. ● Delivering a single source of data truth for data integrity and end-to-end traceability.

Considerations before embarking on adopting a digital lab Although the digital lab offers significant promise to life science companies, the

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Assessing your current position on the digital lab maturity roadmap

The digital lab is vital for post-COVID pharmaceutical success, ensuring faster launches and efficiency. Strategic tech integration is key for a competitive edge and innovation in life sciences.

industry has historically been hesitant to pull the trigger on such investments. This largely comes down to concerns around cloud security, data integrity, regulatory compliance, perceived difficulties in the transformation process, value to business and a shortage of skilled labor. But as more companies reap the benefits of the digital revolution and more startups emerge with a digital-first approach, this hesitancy is no longer commonplace. For companies ready to take the leap, there are a few considerations to bear in mind when embracing a digital transformation, such as:

To effectively move towards a truly digital lab, companies must first assess their current level of digital lab maturity. They should conduct an overview of their existing digital resources that can be incorporated into a digital lab roadmap, covering the following levels: • Standardized and harmonized lab business processes, methodologies and technologies • Lab informatics suites • Integrated lab informatics platforms • Digital lab — the digital lab of the future It is important to note that for companies to achieve digital lab transformation, these maturity steps do not require completion in a linear order. For example, companies in the initial stage can transition from paperbased lab notebooks to cloud-based electronic lab notebooks (ELN) and avoid the costs associated with servers and maintenance.

As long as the lab foundation is paperless, sequential progression is not mandatory.

Determining the current status of capital equipment During the initial planning stages, it is important to develop an action plan for integrating new or replacing any existing equipment in the lab. Some equipment may already possess digital capabilities but may require reconfiguration to enable cloud access and control. Assessment of digital abilities 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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and cloud access control can help determine how much reconfiguration is required.

Choosing the right cloud service model Embracing cloud technology reduces capital expenditure for laboratories and enables the distribution of operational costs over time. The selection of the appropriate cloud deployment mode depends on factors such as the company’s data security requirements, cost considerations and the availability of in-house infrastructure or regional cloud services.

How to ensure a smooth transition to a digital lab For a straightforward transition to the digital lab, establishing a solid foundation through 38

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the development of a comprehensive digital strategy for the entire organization is crucial.

This requires an understanding of the data already held and of its purpose, as well as the identification of key areas where technology — combined with data — can optimize overall business performance.

Leveraging advanced technology in this way can complement the skills of your workforce, freeing them to focus on critical decision-making while reducing manual steps in workflows to ensure end-to-end data integrity. Use of the latest digital technologies can also allow you to analyze work patterns and resource usage to gain further insights that can lead to waste reduction and increased efficiency in lab processes, minimizing your carbon footprint.

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In addition, it is important to clearly communicate the impact of any incoming changes to your team. Giving the personnel a chance to voice their concerns and providing them with required training in any new protocols will leave them better equipped to adapt and feel actively involved in the transformation process.

Looking ahead at a digital-first lab With a solid foundation in place, organizations adopting the digital lab concept into their facilities are poised to revolutionize their lab processes, enhance productivity and drive more sustainable practices, making it clear that this is no longer just a pipe dream.

By actively embracing change management and empowering their workforce with cuttingedge technologies, companies can ensure a seamless transition to the digital lab. By bringing together robotics, AI/ML, AR/VR and informatics, working in collaboration with human talent, life science companies can unlock greater levels of efficiency and enhance their business decision-making capabilities.

The future of life science pioneers who embrace digital transformation looks bright.

By embracing change, these companies will have the tools and processes in place to gain a competitive advantage and drive further innovation that ultimately helps improve the quality of patient outcomes.

References are available at www.pharmafocuseurope.com

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AUTHOR BIO

R E S E A R C H

John Patrick Dunne leads the Lab Centre of Excellence (CoE) Advisory Team within the Life Sciences Manufacturing group at Cognizant.

He has been working in the Life Sciences industry for the last 19 years, working across a variety of laboratory related roles from R&D to QC for several large multinational pharmaceutical companies.

Henal Shah is the Head of the European Lab Centre of Excellence (CoE) Practice within the Life Sciences manufacturing group at Cognizant. She has been working in the Life Sciences technologies industry for the last 17 years, working across the Life Sciences Quality Engineering and Assurance practice for more than a decade.

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Using AI to Accelerate Drug Discovery

Artificial intelligence (AI) helps to address two diverse scenarios in drug discovery – instances when researchers have too much data and want to make sense of it or too little data and need to derive the most realistic virtual alternative data describing the scenario. But using AI simply to optimize target affinity for virtual compounds is not addressing the key bottleneck. It is the combination of their pharmacokinetics (PK), efficacy for desired effects and avoidance of unwanted issues that are harder to predict. Physiologically based pharmacokinetic (PBPK) and quantitative systems pharmacology (QSP) models can provide those wholistic answers and predict clinical endpoints during drug discovery.

Professor Amin Rostami-Hodjegan PharmD, PhD, FCP, FAAPS, FJSSX, FBPhS, Chief Scientific Officer, Certara

Professor Piet van der Graaf PharmD, PhD, FBPhS, Senior Vice President, Head of QSP, Certara

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Q

uantitative systems pharmacology (QSP) combines computational modeling and experimental data to examine the relationships between a drug, the biological system, and the disease process. QSP converts biology into clinical pharmacology

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and clinical endpoints, and we use artificial intelligence (AI) to further that. There are also instances where we have very limited actual data, and we use AI to mine the huge amount of knowledge in the public domain to help create the databases to build the QSP models.

What makes AI useful, as opposed to just another gadget, is asking the right questions.

But what are the right questions to ask to find your way through that avalanche of data? How do you find a pathway for a drug that may or may not have an effect?

If you are researching a new mechanism of action in drug discovery, by definition, you do not have any clinical data. How are you going to predict whether that new mechanism is going to be useful in the clinic? And how do you decide how much of that drug to give so it is effective but not harmful? By employing QSP and physiologically based pharmacokinetic (PBPK) modeling, you can borrow the necessary data and knowledge from a variety of other sources to answer your questions. In effect, we use AI to build models that are a mathematical representation of our current understanding of the biology.

Managing Data Overload AI allows us to easily mine millions of documents and unstructured data sources in a systematic and meaningful manner. That is more data than any human could review and digest.

We also use AI to seamlessly couple data that is in the public domain with a pharmaceutical

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company’s proprietary data to build a unique database. As each company has lots of internal databases, unstructured documents, papers, and lab books, the output will be specific to that company, even though the algorithm used may be the same. As the starting point is different for each company, they will get their own unique answer, depending on their data.

The ability to mine unstructured data is incredibly important for pharmaceutical companies because they gather so much data over the years, especially during mergers and acquisitions, that it is very difficult to get it organized without integration tools such as Certara’s D360 system, and extract pearls from the ocean of data without AI tools such as Certara’s Vyasa.

Our AI platform mines about six million public sources, including all the massive regulatory databases and the associated filings, memos, and meetings, at a click of a button. It is too much data for a person to sift through, but with AI we can do it quickly and completely. We ask a question, and get realtime answers. We can ask for everything that has been written about a particular compound, class of compounds, or disease, and then search and mine the data.

Building a Biological Map We start by asking questions, such as “How does A go to B, and how does B go to C?” much as you would with ChatGPT, and building a biological map. We then request scientific references to support each step. 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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For example, we might begin by stating, “We have hormone X. How does hormone X go to compound Y?”, and the AI tool will populate the model with that information and the pertinent reference. Then, we might ask, “How fast does it go?” To which the AI platform might respond – “I don’t know, I can’t find it” – thus identifying a new area for research. Then we might inquire, “How much of the hormone is there?” and it

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will let us know the details and perhaps that Smith discovered it in 1973.

As there are a specific set of questions that we routinely ask when we build QSP models, such as “Does that step exist? How much of X is there, how much of Y, and how fast does it go?” it is possible to automate some steps. But once you have made the QSP model, how do you validate it?

Validating QSP Models Once you have conducted a clinical study, you can conduct statistical testing, and retrospective analyses of the data to validate it.

But mechanistic modeling is prospective, it focuses on extrapolating from something that has not been done before. Therefore, it requires a different view of validation.

It is not useful to say, “I only believe the weather forecast when I’ve seen the weather.”

When you have seen the weather, the forecast is obsolete. You can choose to either use the model or not, but not say, “It’s not validated because we haven’t seen the weather yet” because you are creating the model to get a glimpse of the future. That is what we are doing with mechanistic modeling, whether it is PBPK or QSP. Obviously, when a number of earlier forecasts have turned out to be true, a level of credibility is built around the models, but that is not a guarantee for precise prediction of any future event. The differentiations between model qualification, verification, credibility, and validation are the subject of a recent in-depth article by Frechen and Rostami-Hodjegan1.

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Thus, our model will not say exactly what is going to happen. It is not a deterministic model that delivers a single answer. Instead, it is a stochastic model that provides the most likely answer, alongside the least likely answers, and assigns a percentage value based on the likelihood of them happening. For example, it may state that the likelihood of it raining at 4:00 pm tomorrow is 70%, and then you must decide whether you should take an umbrella or walk without it!

In other words, decisions still must be made in the face of uncertainty, with or without AI and a QSP model. The models just allow you to make better informed and optimal decisions. You are determining the probability of success, which is hardly ever 100%.

Most of the models we develop now describe a set of conditions that cannot be easily accessed and evaluated prospectively and in large numbers, but they do occur in the clinic when drugs are being administered to patients.

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Providing Real-world Validation

Improving Upon the Rule of 5

AI helps to manage information overload, particularly when the facts are sparse and seemingly unconnected, by going through data and extracting elements that are useful. It also gives us confidence in models by gathering indirect evidence that verifies the model-informed decisions.

For example, Joseph Grillo and his colleagues at the FDA built drug-drug interaction (DDI) models for renally impaired people taking rivaroxaban more than 10 years ago.2 It is difficult evaluating one drug in people with renal impairment, for both practical and ethical reasons, so no one studies whether two drugs are going to have a different DDI in this patient group.

The FDA team used our tools instead to create models that predicted what would happen under those conditions. Their models showed that renal impairment put patients in a higher risk bracket for a certain combination of drugs. As a result, the FDA asked Johnson & Johnson to annotate their drug label to that effect even though they did not have clinical data to show it.

Now 10 years later, an AI analysis of real-world data from people with renal impairment who received a combination of those drugs despite the warning on the label, demonstrated indirectly through side effects of bleeding that drug interactions were occurring.

The fundamental facts being investigated in the lab during drug discovery have not changed significantly during the past 20-30 years. We still need to know whether a drug is permeable enough to pass through the wall of the gastrointestinal tract if it is going to be given orally. Will it dissolve at the dose we are giving in the liquids that are in place for this sort of administration?

Then, how is the liver going to deal with it? And how long will it stay in the body?

In the past, chemists would follow the Lipinski Rule of 5 and make decisions regarding candidate drugs based on their ranking against a small number of physical chemical properties and rules that were based on simple plots. Many drugs were killed because they failed to meet one criterion, but they may have achieved an acceptable result if the approach considered those criteria combined.

Now AI can tell us that a candidate molecule, based on its chemistry, will have a tendency toward lipophilicity, and will be acidic with a PK of x. Many of those elements are related to the molecule having a particular charge or receptor affinity for cytochrome P450.

Chemists used to assess those elements individually and make determinations. But AI allows us to take all the available data and integrate it into a model, which can be used to answer our questions, such as “How long will the molecule stay in the body? Will it turn into an undesirable metabolite?”

We now have access to millions of chemical structures in silico, generating lots of physical 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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chemical properties to feed into the model.

Then we can do a multivariate analysis of all the elements together. AI can support the application of a Rule of 50, 5,000, or 5 million!

Progressing Beyond Target Affinity Chemists initially began using AI to generate virtual chemical structures. They created millions of candidate molecules on the computer and then used AI to link them to pharmacological properties. But that approach only focused on finding affinity for targets.

They optimized compounds so they were potent against their chosen target and then took them into the clinic. But several of the first AI-designed compounds have failed in clinic trials or been deprioritized.

But finding potent molecules was never really the issue. The hardest part is optimizing 44

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the pharmacokinetics and then predicting biomarkers and actual clinical efficacy. We are now working on methods where we do all three simultaneously as the basis for translational virtual drug discovery. We can make a large number of virtual compounds in an iterative manner and optimize them not just for the pharmacology but also the pharmacokinetics, and them feed them into a QSP model that can predict clinical outcomes on the computer for a whole range of compounds.

Predicting Clinical Endpoints By combining QSP and AI, we can also predict clinical endpoints in discovery for novel mechanisms. This is remarkable because in discovery you are working with novel targets.

In the past, the best you could do is test the compound in animal models and maybe an

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organ on a chip, but certainly not in patients, and you could never get to clinical endpoints which are often soft, subjective scores, such as rating how you feel on a scale of 1 to 5.

But now we can use QSP to identify virtual biomarkers. We use QSP to capture the fundamental biology, and then simulate what happens to the biomarkers, cell types, and cytokines when we put a compound into that system.

While we cannot model actual clinical endpoints, such as “How do you feel?” in a mechanistic way, we can calibrate a QSP model with known compounds, where we do know the clinical endpoints. Then, we can put new compounds into the model and look at the virtual biomarker output and link that to known correlates with clinical endpoints.

This allows us to predict clinical endpoints for novel mechanisms in the discovery phase.

AI transforms drug discovery, from decoding data overload to predicting clinical endpoints, offering a quantum leap in efficiency and precision for the pharmaceutical industry.

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The beauty of this approach is that we start with very little actual data, and the QSP model generates a very large virtual biomarker dataset, including biomarkers that have been measured and those that have not, which is ideal for analyzing with AI.

Helping to Repurpose Drugs In the past, researchers approached a disease from one angle, exploring the redundancy of a receptor, lack of an enzyme or protein, just hitting one target. But the network that is causing the disease is more complex than a single receptor that is failing. That is why QSP models are becoming increasingly relevant.

In some cases, when researchers have redefined diseases based on biomarkers and networks as opposed to symptoms, they have discovered that conditions which were previously considered distinct, are in fact, fundamentally the same. This revelation has provided a rationale for repurposing drugs for other conditions that would previously not have been considered because they did not have similar symptoms. That is another benefit coming from AI. Consider immunology 10 years ago – there was a very narrow set of indications, essentially arthritis, psoriasis, and Crohn’s disease. But now immunology is recognized as a component in almost every disease, ranging from diabetes to Alzheimer’s disease and cardiovascular disease.

As a result, methotrexate, which was originally only prescribed for cancer, is now used to treat many immune-related diseases. 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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Furthermore, dexamethasone, which was traditionally used to relieve inflammation, has shown efficacy in treating COVID-19 because it is an immunological disease as well as an infectious one.

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and predicting clinical endpoints. It is an asset in situations where researchers are faced with data overload and data sparsity.

References are available at www.pharmafocuseurope.com

The new AI application that the United States Food and Drug Administration (FDA) is most excited about involves interpretation of QSP models. When a company submits a QSP model to the FDA for review, that model is by nature very complex, and a member of the Agency’s team needs to evaluate it.

We can approach our QSP model in reverse and go through each step, annotating it with scientific references. We can employ a traffic light system, where green signifies that we found a reference for that step and it is qualified, while red means that information may be true, but we have not found a reference for it.

This process would help regulators to quickly get a sense of whether a QSP model is based on data from quality peer-reviewed papers in the scientific literature and therefore credible.

Conclusion AI is providing tremendous support for the drug discovery process. Its applications range from reviewing millions of documents and unstructured data and gleaning relevant information, to helping build biological maps and QSP models of new mechanisms of action, validating QSP models using real-world data, 46

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AUTHOR BIO

Offering Regulatory Support

Professor Amin Rostami-Hodjegan, PharmD, PhD, FCP, FAAPS, FJSSX, FBPhS, is the Senior Vice President of Research & Development and Chief Scientific Officer at Certara. Previously, he was co-founder of Simcyp Limited, a University of Sheffield spin-off which was acquired by Certara. Amin is also a Professor of Systems Pharmacology and the Director of the Centre for Applied Pharmacokinetic Research at the University of Manchester.

Professor Piet van der Graaf, PharmD, PhD, FBPhS, is Senior Vice President and Head of QSP at Certara and Professor of Systems Pharmacology at Leiden University. He was co-founder of XenologiQ Limited, which was acquired

by Certara in 2015. Before joining Certara, Piet was the CSO of the Leiden Academic Centre for Drug Research and held various research leadership positions at Pfizer across discovery and clinical development.

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Bringing New Treatments to Patients with Rare Diseases Designing and Conducting Effective Gene Therapy Trials

Clinical trials testing gene therapies and other genebased medicines come with operational, regulatory, and biosafety-related considerations that are not required for trials testing most “traditional” drugs. By planning ahead and ensuring investigators are aware of these requirements, gene therapy trial sponsors can dramatically increase the likelihood of conducting a smooth, safe study.

Christopher Doyle PhD, Senior Director, IBC Services & IBC Chair WCG Clinical, Inc.

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oday there are more than 10,000 known rare diseases, defined by the European Union as conditions that affect less than 1 in 2,000 people and by the United States as those that affect fewer than 200,000 people. The vast majority of rare diseases are caused by or 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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associated with detrimental mutations in protein-coding genes, making them prime candidates for correction with novel gene therapies and other gene-based medicines.

Thanks to scientific advances in genetic engineering technologies over the last 20 years, clinical trials testing gene therapies and other gene-based medicines are more common than ever, with nearly 200 opening to enrollment in the United States this year alone (Figure 1). This trend in gene therapy trial starts is not unique to the United States, as similar increases have occurred in Europe and Asia. The European Medicines Agency (EMA) and Food and Drug Administration (FDA) have also each approved more than 10 gene-based medicines for use since 2021 following a long period of relative inactivity (Table 1).

Despite this growth, most trials testing gene therapies and other gene-based medicines fail.

While that is also the case for most traditional drugs, gene therapy trials often fail for unique reasons, ranging from unanticipated safety concerns to difficulties in patient recruitment and site-level operations.

Understanding the complexities associated with gene therapy trials for rare diseases is critical to planning robust, successful trials 48

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that will bring life-changing treatments to patients quickly. This article describes some of the most problematic aspects of conducting a gene therapy clinical trial and outlines strategies that trial planners and sponsors can use to design safe, successful trials.

Choose Appropriate Patients and Sites Unlike many trials where large numbers of healthy participants are desired, trials testing gene therapies for rare diseases are limited by the number of individuals living with the disease. Participant eligibility criteria should therefore be carefully crafted to include populations relevant to the disease in question without unnecessarily excluding patients for reasons unlikely to impact safety or efficacy.

Protocols should allow as much flexibility in eligibility criteria and screening processes as possible without compromising participant safety. As gene therapies advance, inclusion

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of pre-symptomatic patients should also be considered. Depending on the natural course of the disease, administration of the gene therapy before symptoms arise may provide maximal benefit – and in some cases, keep the disease at bay indefinitely. This is especially important for pediatric indications where early intervention is desirable.

Appropriate site selection is vital to planning an effective gene therapy trial. This is largely due to two factors: the limited number of individuals living with the disease within a particular geographic area, and the operational complexities that come with gene therapy

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trials. For these reasons, sponsors should seek out well-known sites with in-house gene therapy experts, established patient networks, and a history of successfully enrolling patients and completing gene therapy trial activities.

Oftentimes a “spoke and hub” approach to site selection is best, where patients are recruited at satellite sites, dosed at a central site, and return to satellite sites for follow-up assessments. If such an approach is used, satellite sites should be conveniently located for patients, and sponsors should plan on reimbursing patients for transportation and lodging costs incurred for travel to the central

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site. Whatever the case, sponsors should try to set the trial up for success by steering clear of sites that have a history of underperforming on prior trials.

For many rare diseases, enrolling patients at sites in different countries may be necessary to accrue the desired number of participants.

And with effective planning, multinational trials can yield valuable data from diverse

Know Your Vector Choosing and designing a gene therapy vector is one of the most important decisions faced by sponsors and investigators planning new trials for rare diseases. An ideal gene therapy vector should efficiently enter desired cells, drive sustained expression of the corrective transgene, and do so without compromising participant safety. Today, most rare disease gene therapies are based on adeno-associated virus (AAV), which is nonpathogenic and can drive long-term gene expression from non-integrated AAV genomes harbored outside of the nuclei of transduced cells.

Engineered capsids and transgene promoters have also emerged as powerful tools that can be used to limit vector transduction and transgene expression to cells and/or tissues of interest.

Compared to more traditional products, virally vectored gene therapies come with unique biosafety concerns related to product shedding.

Most viral vectors, like those based on AAV, are unable to replicate within participants after dosing.

For these types of vectors, shedding is a small risk. For others, however, this is not the case, and the likelihood of vector shedding and transmission to others is substantially increased. This is especially true for products capable of replicating within participants after dosing, which are most commonly used as oncolytics for the targeted treatment of various cancers.

To mitigate risks associated with product shedding and transmission to others, sponsors should develop clear post-dosing instructions

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populations. However, conducting a gene therapy trial in different countries can be very difficult to manage, as clinical trial regulations and healthcare systems can vary widely from one country to another. This, when coupled with inherent cultural and socioeconomic differences between countries, can make it extremely challenging to manage a gene therapy trial in multiple countries. for participants that outline the risks and strategies to mitigate those risks. These instructions should be based on empirical evidence wherever possible, such as preclinical observations or known routes of shedding for similar products in humans. For example, if a product is known to be shed through saliva, participants should be instructed to not share food or drinks with others. Shedding and transmission risks should be noted in informed consent forms (ICFs) as appropriate, and depending on the degree of risk, sponsors should consider creating separate ICFs or information sheets specifically for close contacts of participants, like parents, spouses, and caregivers.

Finally, patients dosed with virally vectored gene therapies will usually mount an immune response against elements of the vector itself that may compromise efficacy of subsequent doses with the same vector type. Study assessments and screening procedures should therefore include evaluation of antibodies against the vector and transgene encoded by the vector at baseline and throughout the study. As gene therapies and similar products become more commonplace, it will be important for trial sponsors to develop novel vectors and/or immunosuppression strategies to get around pre-existing immunity. Directed evolution, library screening, and rationally-designed mutagenesis approaches have all been used in recent years to generate vectors that are antigenically distinct from others and can target specific cell types.

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Plan an Effective Study Generally speaking, randomized placebocontrolled studies are the gold standard in clinical trial design, and should be used where possible. However, gene therapy trials for rare diseases are limited by the small number of patients living with the disease who may qualify for the trial. For this reason, placebo controls and other design elements common in larger trials may be impractical or impossible. In the absence of such a placebo-controlled design, appropriate alternatives should be considered.

These include using intra-subject controls where appropriate, using historical control groups aligned with the natural history of the disease, and the use of multiple dose level cohorts. Irrespective of the specific design chosen, gene therapy trials for rare diseases should be designed in a way that has the potential to generate safety and efficacy data in support of a marketing application. Beginning with the first-in-human trial, sponsors should also attempt to collect as much participant data as is feasible, including adverse events, efficacy outcomes, and biomarker data that may be used to inform the design of additional trials.

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Wherever possible, study endpoints should be clinically meaningful for participants, including those that assess quality of life or other participant-derived subjective measures.

In many cases, surrogate endpoints unlikely to occur spontaneously in the natural course of the disease may also be suitable. Examples of appropriate surrogate endpoints include reduced frequency of vaso-occlusive crises for those with sickle cell disease, increased skeletal muscle dystrophin levels for Duchenne muscular dystrophy, and reduced organ volumes for participants with Gaucher disease.

Lastly, most gene therapies are designed to enter cells and drive long-term expression of a corrective transgene, as noted above. While extremely powerful, the sustained activity of gene therapies after dosing increases the risk of delayed adverse effects. This is especially true for products that harbor or drive corrective changes integrated within the cellular genome, as integration or gene editing at the wrong place within the genome – like in an oncogene or tumor suppressor gene – can drive the formation of new cancers. For these reasons, sponsors should design robust long

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term follow-up plans tailored to the disease and vector being used. Currently, the FDA recommends 15 years of follow-up after dosing with several classes of gene therapy products based on the likelihood of delayed adverse effects arising; similar guidance recommends at least five years of follow-up after dosing with AAV vectors. While these recommendations may sound cumbersome, they are critical to ensuring participant safety and instilling public confidence in the field as gene therapies advance. The FDA has also outlined various ways to reduce burdens on sites and participants during the longterm follow-up period; in some cases, it may not be necessary to require much more than an annual phone call to check in with participants.

Familiarize Yourself with Regulatory and Biosafety Requirements Bringing a new gene therapy to market is a challenge for many reasons, not the least of which is the litany of clinical trial regulations that can vary from country to country.

The European Union has implemented an overarching genetically modified organism (GMO) framework comprised of the Contained Use (2009/41/EC) and Deliberate Release (2001/18/EC) Directives, which together seek to address and mitigate the risks associated with gene therapies and other GMOs. However, individual member states have taken different approaches at implementing these Directives into regulations, which can 52

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make already challenging multinational gene therapy trials even more difficult.

The regulatory landscape for gene therapy trials in the United States is established by Department of Health and Human Services (HHS) through the FDA and National Institutes of Health (NIH), with biosafety guidance from other federal entities. The FDA does not distinguish between genebased and traditional products, requiring an Investigational New Drug Application and Institutional Review Board approval for clinical trials testing new biologics. However, the NIH’s trial oversight framework focuses specifically on trials involving human gene transfer research, which it defines in the NIH Guidelines for Research Involving Recombinant or Synthetic Nucleic Acid Molecules (NIH Guidelines) as the deliberate transfer of recombinant or synthetic nucleic acid molecules into a human research participant. The NIH Guidelines also outline various biosafety requirements for research with gene therapies and other genetically modified products that sponsors should consider when planning a new trial.

An important NIH requirement to take into account when planning gene therapy trials that will enroll patients in the United States is that at most sites, the trial must be reviewed and approved by an Institutional Biosafety Committee (IBC) before the study may begin at the site. These committees exist to protect clinical staff, members of the public, and the environment from accidental exposure

to the gene therapy product being tested.

Accordingly, IBC reviews of gene therapy trials focus on site-level handling procedures, safety equipment (e.g., biosafety cabinets, eyewash stations), emergency response plans, and staff training requirements. Because of this sitespecific scope, unsafe work practices described in pharmacy manuals or similar sponsorprovided handling instructions can lead to significant delays in obtaining IBC approval.

This is especially true for instructions related to needle handling, decontamination, and waste handling procedures. Wherever possible, sponsors should defer to local IBCs or similar institutional entities for such instructions.

Important caveats sponsors should also be aware of are exemptions for certain products, like siRNAs, and the fact that the NIH Guidelines and IBC requirements therein also apply internationally if NIH funds were used to develop the product or run the trial itself.

Consultation with biosafety and regulatory experts early on in the study planning process is the best way to ensure gene therapy studies are conducted safely and without unnecessary delays.

Looking Forward Peter Marks, head of FDA’s Center for Biologics Evaluation and Research that oversees gene therapies, earlier this year acknowledged that progress in bringing new gene therapies to patients has been slower than desired and expressed a desire to align global regulations to accelerate the

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process. While globally aligned regulations will certainly help, nothing will move forward without well-designed, well-controlled clinical trials that demonstrate safety and efficacy.

That is no simple task for gene therapy trials, so it will be imperative that sponsors and investigators recognize and avoid the most common problems that have plagued the field thus far.

References are available at www.pharmafocuseurope.com AUTHOR BIO

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Christopher Doyle, PhD, is Senior Director of IBC Services and an IBC Chair at WCG. He works with clinical trial sites, sponsors, and CROs to ensure research involving genebased medicines is conducted safely and efficiently. Prior to joining WCG, Christopher worked as a research fellow at the Albert Einstein College of Medicine and Montefiore Medical Center (Bronx, NY), where he explored novel mechanisms of antibody activity against Streptococcus pneumoniae. Christopher received his PhD in Molecular Genetics and Microbiology from Stony Brook University (Stony Brook, NY), where he studied the pathogenesis of Francisella tularensis in a high containment Biosafety Level 3 laboratory.

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Addressing the Unmet Needs in Respiratory Disease Treatment with Inhaled mRNA Medicines Progress in mRNA technology has paved the way for the creation of inhaled mRNA medications, offering significant potential to transform the management of respiratory diseases, addressing substantial unmet needs in this field. Yet, the key challenge in applying this technology clinically revolves around finding a delivery system that is not only safe but also effective. This article delves into the prospects and obstacles linked to inhaled mRNA medicines, poised to reshape the paradigm of treating respiratory diseases.

Carsten Rudolph PhD, co-founder & CEO, Ethris

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dvancements in medical science have continually pushed the boundaries of what is possible in healthcare. Among the most recent innovations is the development of inhaled mRNA medicines, a groundbreaking approach that holds immense potential for transforming the landscape of respiratory disease treatment. Respiratory diseases, which encompass a wide range of conditions from chronic obstructive pulmonary disease (COPD) to

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asthma and rare genetic disorders like primary ciliary dyskinesia (PCD), have posed significant challenges to both patients and healthcare providers. These diseases often require complex treatments, and there is a pressing need for therapies that are more effective, less invasive, and tailored to individual patient needs. In this article, we will take a closer look at the opportunities and challenges related to inhaled mRNA medicines, poised to revolutionize the treatment of respiratory disease treatment and address these currently unmet needs.

The Promise of Inhaled mRNA Medicines mRNA therapeutics offer several significant advantages when compared to other therapeutic approaches, including DNA-based treatments, protein-based drugs, and small molecules. One key advantage of mRNA-based medicines is their modularity, allowing for easy customization through sequence modification.

This enables the creation of tailored molecules capable of precisely targeting specific proteins or genes, directly addressing the root causes of diseases, and potentially halting or reversing their progression. The production of mRNA therapeutics is also notably rapid in comparison to the manufacturing of antibodies or protein-based drugs. Additionally, mRNA therapeutics are characterized by their predictable pharmacokinetics and the absence of genomic integration, contributing to their relative safety. This functional versatility makes them suitable for a wide range of applications,

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including the development of viral vaccines, protein replacement therapies, cancer immunotherapies, and cellular reprogramming.

Research is underway to assess diverse administration routes for enhancing mRNA transport and expression. These include tracheal inhalation, as well as intravenous, intraperitoneal, and intramuscular injections.

Inhaled mRNA medicines represent a paradigm shift in the treatment of respiratory diseases.

Unlike traditional oral or intravenous medications, which often distribute throughout the entire body, inhaled mRNA medicines offer a localized and targeted approach by delivering therapeutic molecules directly to the lungs.

Another exciting aspect of inhaled mRNA medicines is their potential for sustained therapeutic effects. For example, in the case of patients suffering from primary ciliary dyskinesia (PCD), a comparably short-lived mRNA can have a long-lasting effect due to the pharmacokinetics of the produced protein which is only generated once in the cell's lifetime (approximately 6 weeks). This not only improves patient compliance but also has the potential to reduce the frequency of treatments, making it a more convenient and cost-effective approach.

Inhaled mRNA medicines are versatile in their applications. They can be employed for both acute and chronic lung conditions, offering a wide range of treatment options.

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in patient care, allowing healthcare providers to tailor treatment plans to individual needs.

This precision in drug delivery is a gamechanger, as it minimizes systemic side effects and enhances treatment efficacy while reducing adverse reactions.

Navigating the Complex Challenges While the promise of inhaled mRNA medicines is evident, there are several significant challenges that must be overcome to bring these therapies to fruition in clinical practice.

As natural biological molecules, mRNAs are inherently highly unstable and vulnerable to degradation by RNA-cutting ribonuclease enzymes. Due to their natural biological nature, mRNAs are intrinsically unstable and susceptible to degradation by RNA-cutting ribonuclease enzymes.

One of the most critical challenges, as with all mRNA medicines, is ensuring targeted delivery to lung tissues while minimizing systemic exposure. Achieving this delicate balance is essential to prevent unintended effects of the drug on other organs and systems in the body.

Technical hurdles also exist in the development of inhaled mRNA medicines.

Achieving consistent protein expression and developing efficient nebulization methods are ongoing areas of research and development.

Lipid nanoparticle (LNP) aggregation is one such challenge that researchers are diligently working to address, especially in the case of inhaled delivery with a nebulizer. These 56

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Inhaled mRNA medicines: A breath of innovation transforming respiratory care, offering precision, sustained effects, and personalized solutions for unmet needs in treating lung diseases.

aggregations can hinder the effective delivery of therapeutic mRNA to target cells in the lungs, potentially compromising treatment outcomes and causing an immune reaction against the therapy itself.

Another significant concern in the realm of mRNA-based therapies is immunogenicity.

Some existing therapies, including mRNAbased treatments, can provoke excessive immune responses. This can lead to unwanted inflammatory reactions or immune responses against the therapy itself. Managing and mitigating immunogenicity is a paramount consideration in ensuring the safety and effectiveness of inhaled mRNA medicines.

In order to circumvent these shortcomings and enhance their therapeutic potential, mRNA molecules necessitate chemical modification and the implementation of optimized delivery systems, aiming to achieve not only maximum

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potency and efficient cellular uptake but also to minimize any potential issues related to toxicity and immunogenicity.

Delivering mRNA into human cells is a complex process, demanding specialized delivery systems to shield therapeutic mRNA from degradation and aid its cellular entry.

Selecting the appropriate delivery vehicle is crucial, as it needs to minimize immune responses and overcome the challenges posed by systemic or inhalable administration routes, which significantly impact organ distribution and treatment effectiveness.

While mRNA technologies are still undergoing long-term assessments of safety and effectiveness, recent advancements in novel materials and delivery formulations are emerging as effective solutions to improve the overall therapeutic efficiency.

Furthermore, stability is a critical challenge in mRNA manufacturing and its subsequent storage and use. One of the key aspects of the stability challenge in mRNA manufacturing and storage has to do with temperature sensitivity. mRNA is highly sensitive to temperature. Elevated temperatures can lead to the degradation of mRNA molecules, which can pose a challenge not only during the manufacturing process but also during storage and transportation.

Exploring the Vast Applications of Inhaled mRNA Medicines The potential applications of inhaled mRNA medicines in respiratory disease treatment are

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vast and promising. In addition to having the potential to treat chronic obstructive pulmonary disease (COPD), they have shown potential in addressing genetic lung conditions, such as primary ciliary dyskinesia (PCD) or certain auto-immune diseases like pulmonary alveolar proteinosis (PAP).

Inhaled mRNA has the potential to address PCD, a rare genetic condition resulting from structural abnormalities or the lack of cilia in the lining of our respiratory tract. When cilia can't perform their function, mucus containing trapped microbes, dust, and other debris cannot be efficiently cleared from the airways, often causing lasting lung damage. In this case, the mRNA therapy would deliver a corrected mRNA, directly to the lungs through inhalation that is designed to produce ciliary proteins in the respiratory tract to restore cilia function.

Inhaled mRNA also has the potential to address PAP, a rare autoimmune condition that leads to breathing difficulties by impairing gas exchange in the lungs. In approximately 90% of cases, the disease is caused by the development of autoantibodies targeting GM-CSF, leading to malfunctioning local macrophages and the accumulation of surfactant, an oily substance, in the lung's small air sacs known as alveoli.

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anti-drug-antibody following treatment with recombinant protein. In the case of PAP, an inhaled RNA therapy can be delivered directly to the lung that is designed to restore local macrophage function.

Providing a platform for immunomodulation via mRNA encoding for antibodies or cytokines offers the potential to revolutionize how we manage respiratory infections and conditions like asthma. Infections of the respiratory tract, including Influenza (Flu) and Covid-19 can vary in severity depending on a person’s level of immunity. Inhaled mRNA has the potential to provide anti-viral therapies that activate the body’s innate immune system specifically in the lungs, where the virus enters the body. By targeting the mRNA to the lung, an antiviral defence can be mounted directly in patients’ lungs and can significantly impact the viral infection process. This approach can treat both seasonal and potentially newly emerging respiratory viruses by acting to mount the patient’s own immune defense directly in the lungs, independently of the specific virus.

Another particularly exciting aspect is the potential for personalized medicine in respiratory disease treatment. Inhaled mRNA medicines can be tailored to individual patients, addressing specific genetic mutations or immune system profiles. This level of customization holds great promise for improving treatment outcomes and enhancing the quality of life for patients with respiratory diseases.

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T R I A L S

A Glimpse into the Future of Inhaled mRNA Medicines The future of inhaled mRNA medicines is bright, filled with promise, and poised to usher in a transformative era in the field of respiratory disease treatment. Some pioneering companies have made significant strides in the field by developing in-house technologies that not only enhance the delivery of therapeutic payloads to the lungs but also address key challenges that have historically hindered treatment efficacy.

Such advancements include the development of innovative technologies designed to prevent lipid nanoparticle (LNP) aggregation during nebulization, a crucial step in the administration of inhaled mRNA medicines.

By preserving the stability of these therapeutic agents during distribution and handling, these technologies ensure that the intended therapeutic effects are maximized.

Through extensive data analysis and research, the field is making significant progress in minimizing off-target delivery to other organs. This precision and efficiency in drug administration are crucial to the success of inhaled mRNA medicines.

Technology is also making great strides in storage and manufacturing. The success of mRNA therapeutics relies significantly on the stability of th supply chains and manufacturing capabilities. Research is ongoing to develop alternative storage and distribution methods that don't rely on deep freezing. This includes investigating the potential of refrigeration or

even room-temperature storage for certain mRNA products. To deliver mRNAs directly to the respiratory tract, new stabilizing technology is now enabling drug candidates with superior thermostability and high resistance to mechanical manipulation for use with a nebuliser. Additionally, companies are working on improving the supply chain for their raw materials as well as excipients.

Leaders in this space have already found a way to develop stable and scalable, HPLCfree upstream and downstream manufacturing processes that enable the production of highquality product candidates for clinical supply.

Another exciting frontier in the world of inhaled mRNA medicines is the integration of advanced technologies like gene editing and CRISPR-Cas9. Researchers are actively exploring the possibility of correcting genetic mutations that underlie certain lung conditions.

This groundbreaking approach holds the promise of not merely managing symptoms but potentially offering a cure, a prospect that was once considered beyond reach.

As these advancements continue to evolve, inhaled mRNA medicines are poised to reshape the landscape of respiratory disease treatment, opening doors to an array of possibilities. Inhaled mRNA medicines represent a transformative approach to the treatment of respiratory diseases, offering precision, sustained therapeutic effects, and versatility. These innovative therapies are on the cusp of significantly enhancing the lives of millions of patients worldwide,

T R I A L S

thereby paving the way for a brand-new era in respiratory healthcare. While there are significant challenges to overcome, the potential benefits are immense, addressing unmet needs in respiratory disease treatment.

With continued advancements, we can expect to see these medicines become an integral part of the treatment arsenal for respiratory diseases, ultimately redefining the way we approach and manage these conditions.

AUTHOR BIO

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Carsten Rudolph, Ph.D. is a co-founder of Ethris and the lead inventor of its SNIM® RNA Technology. His deep expertise lies in delivering mRNA specifically to the lungs. He is the inventor of 15 patents/applications and has authored more than 120 scientific publications. Carsten is affiliated with the Dr. von Haunersche Children’s Hospital, part of Ludwig Maximilian University in Munich. He obtained his pharmaceutical degree from the Freie Universität Berlin.

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GENOMIC MEDICINE KEEPS INNOVATING AND MANUFACTURING NEEDS TO KEEP PACE Gene editing clinical trials are increasing and the approval of genomic medicines continues to grow with 6 CAR-T therapies, 15 viral therapies (including gene-modified cell therapies), and 2 mRNA vaccines now at commercial stages. In this article we look at how manufacturing is evolving to better respond to the needs of these advanced therapies.

Peiqing Zhang Strategic Technology Partnership Leader, Genomic Medicine CSO, Cytiva

Emmanuelle Cameau Strategic Technology Partnership Leader, Genomic Medicine CSO, Cytiva

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enomic medicine — the therapeutic use of cells and genetic materials — is at the center of a medical revolution. Approvals of genomic medicines are growing each year, fueled by new precision medicine platforms that target diseases without current effective treatments.

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Viral vectors, primarily adeno-associated virus (AAV), efficiently treat some diseases caused by monogenic mutations. Genemodified cell therapies, notably CAR-T cell therapies, successfully treat some late-stage blood cancers but face obstacles for use with solid tumors. Challenges remain for both cell and gene therapies, including too-long development, manufacturing, and batch release timelines. More recently, we have witnessed the emergence of RNA-based therapies.

The COVID-19 pandemic showed how such therapies could be developed quickly and at large scale. mRNA has proven its ability to instruct cells to express antigens or proteins in a transient manner but is less efficient if the therapy requires systemic protein replacement.

Novel RNA variants and oligonucleotides, such as self-amplifying RNA (saRNA) and circular RNA (circ RNA) for the first, and short interfering RNA (siRNA), guide RNA (gRNA), antisense oligonucleotides (ASO)

for the latter are now being explored. Lastly, induced pluripotent stem cells (iPSCs) and exosomes, used as therapeutic agents or delivery tools, have gained interest in the scientific community.

All these modalities, their current and future variants, demand manufacturing solutions that can produce them at the needed scale and to stringent quality standards.

Only when effective science is matched with efficient manufacturing can cost-effective therapies be made available to patients who are waiting for them.

Manufacturing needs breakthroughs to keep pace with innovation Trajanoska et al. calculated a median time of 25 years from genetic discovery to drug approval. However, this timeline has significantly shortened over the years (Figure 1).

Figure 1. Evolution of time to market in years from genetic observation to gene therapy drug approval.

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Major health authorities have created expedited pathways for review and/or approval of priority medicines, including genomic medicines. While these pathways help to accelerate the delivery of life-changing therapies to patients, health authorities still have the same expectations of the information related to chemistry, manufacturing, and controls (CMC) as for drugs on normal approval pathways.

Therapy developers can sometimes struggle to keep up with the shorter timelines.

Therefore, manufacturing processes are often deeply rooted in their academic origins. In early-stage research, proof of concept is the priority over optimization that may better support scalable, high-yielding, and good manufacturing practices (GMP)-compliant processing. This early focus can hinder development, leading to compromises that may impact yield and manufacturing cost as the potential therapy transitions into clinical production. But challenges can be overcome. For example, ultracentrifugation to purify AAV capsids can be replaced with chromatographic methods, and single-use technology can support closed processing of CAR T therapies. In both cases, the substitutes are compatible with GMP manufacturing, and both derive from solutions that evolved to improve monoclonal antibody (mAb) production.

From a manufacturing standpoint, mAbs represent a good benchmark for genomic medicines. Since the first mAb therapeutic 62

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approval, a typical mAb platform has evolved and is still evolving 30 years later. Gram-scale production using transient gene expression (TGE), delivering low-titer cell cultures in small batch volumes, has been replaced with optimized cell lines and intensified cell culture methods that can deliver up to one thousand times the titer with some processes running at up to one hundred times the scale.

Cell line innovations and intensified processing have combined with the widescale adoption of single-use technologies to deliver a huge productivity boost over this time. Cost of goods manufactured (COGm) has significantly reduced and, with the expiration of many leading mAb product patents, the costs of numerous therapies are falling. The challenge is to ensure that genomic medicines achieve a similar goal, and faster.

Helping the seeds of genomic medicine grow with next-gen manufacturing Start with the cells Whatever the type of genomic medicine, the quality and properties of the starting material, and therefore the platform chosen, will drive the process design and manufacturing scheme.

For viral vector manufacturing, this can start by choosing the right cell line for the application. There is more than one choice.

Depending on the dose for the therapy and the disease prevalence, developers can choose between transient cell lines, packaging or even producer cell lines, typically with HEK293 as

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Genomic medicine's revolutionary advancements demand manufacturing breakthroughs – only through efficient, next-gen processes can we ensure cost-effective therapies for patients awaiting lifechanging treatments.

a common starting point. But establishing a stable cell line takes time, which isn’t always compatible with the desired drug development timeline. So, a hybrid approach can work, where development starts with one approach, such as a transient cell line, up to phase 1 and even phase 2. As clinical data is gathered, the stable cell line can be constructed and the data to show comparability generated ahead of the switch. This approach allows long-term benefits including reproducible, reliable manufacturing to decrease batch-tobatch variability and improve product quality control.

The clinical objective often drives cell choice in cell therapies. Whether the therapy is autologous or allogeneic, cell

quality is paramount to success. Through genetic engineering we’ve been able to modify T cells to increase their therapeutic potential beyond first-generation CAR-T. Gene editing has created T cell populations with improved responses to antigens, lower rates of exhaustion, and potential use in allogeneic applications. Different gene editing technologies and delivery methods are being explored.

Accelerate process development using scale-down models and data science Process development aims to establish the knowledge of manufacturing process conditions, material attributes, and their links to product quality attributes. Because genomic medicine manufacturing uses variable raw materials such as plasmids and viral vectors, generic process knowledge is lacking. Often, many experiments must be run to establish the process conditions (i.e., the design space) to ensure acceptable product quality attributes.

It would take a huge number of experiments to analyze one factor at a time in a complex operation such as AEX-based full capsid enrichment for AAV. Design of experiment (DoE) provides an efficient strategy to reduce the study size yet gain sufficient insights.

Thus, lab-scale chromatography systems with built-in DoE capabilities are a key enabler for rapid process development.

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process understanding based on existing data.

Among the many choices for digital solutions, mechanistic modeling is very appealing. In contrast to empirical modeling, which is based on regression of existing datasets, mechanistic modeling can predict chromatographic behaviors based on a small training dataset to avoid large numbers of chromatographic runs. The modeled conditions can be validated in just a few experiments to accelerate the unit operation, such as enrichment of full particles with AEX. 64

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Incorporate a holistic process intensification strategy Process intensification can improve process efficiency, reducing the waste from overprocessing, logistics, data processing, and manual handling. As soon as a highyielding process is in place, the next step is to proactively include ways to intensify.

Analogous to mAb processes, perfusion technology has enormous potential to intensify cell culture processes, as shown by dramatic titer and cell-specific productivity

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increases when changing a rAAV process from batch culture to perfusion culture. Multiple downstream unit operations can also be intensified, such as process feed concentration by single-pass tangential flow filtration (SPTFF) or rapid chromatography processbased membrane absorbers and tangential flow depth filtration (TFDF).

In cell and gene therapy, one process intensification trend relies on “biology intensification”. For autologous cell therapy this means transducing CAR-T cells more quickly and efficiently, focusing on getting the potent cells transduced and back into the patient. For gene therapy this could mean using stable producer cell lines that don’t necessarily produce more vector, but vector with a higher percent of full particles and much higher potency.

Implement advanced analytical technologies alongside the process Analytics is an indispensable part of manufacturing. Analytical testing provides laboratory evidence that product quality attributes meet the specifications, and the process performs as expected. Insufficient analytical testing or technologies could result in poor product and process understanding, and in the extreme case to chemistry, manufacturing, and controls (CMC)related regulatory setbacks. To meet process development and manufacturing objectives, a broad set of analytical technologies is needed.

Process development often prioritizes speed,

while GMP manufacturing and batch release prioritizes real-time attribute monitoring for in-process controls and validated assays for product release. The use of process analytical technologies (PATs) and highresolution release assays is expected to keep the manufacturing processes under control to manage product quality and efficacy. This vision requires a substantial investment in both instrumentation and method development. Still, developers are strongly encouraged to implement a qualityby-design (QbD) framework early in process development, to anticipate and proactively mitigate the risks associated with analytics and the manufacturing process. Several case studies have provided systematic approaches for implementing QbD in viral vectors and cell therapy products. The adoption of QbD elements is poised to gain further traction with the release of guidelines for next-gen manufacturing, ushering in a new era of methodical and quality-centric practices.

Use comprehensive solutions for manufacturing As already alluded to, genomic medicines are costly and require significant expertise to manufacture. Hence, moving to manufacturing solutions that derisk and simplify the processes will ultimately help ensure better process control, reduced risk of batch failure, and faster batch release.

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contamination risk and cleanroom validation burden. Coupled with single-use solutions, this enables very flexible manufacturing facility design, making multiproduct facilities much easier to set up and validate. Implementing digital solutions for manufacturing execution systems (MES) and quality management systems is a strategy drug developers are starting to adopt to reduce the “data burden” and enhance compliance.

Conclusion The rise of genomic medicines has opened new avenues to effectively manage diseases that were once difficult to treat. As therapeutic advances gain momentum, manufacturing processes must evolve in tandem to ensure the sustained growth of these novel medicines for the global benefit of patients. As biomanufacturing technologies mature, especially for antibody-based therapies, there’s a strategic opportunity to apply these advances to develop next-gen manufacturing platforms dedicated to genomic medicines.

What will it take to make next-gen manufacturing platforms a reality, soon? Industrywide collaboration. Working together, molecule owners, manufacturers, and suppliers will create an ecosystem that fosters innovation. A collaborative approach will ensure that solutions meet user requirements and that suppliers receive timely feedback on performance. This convergence of expertise will propel the industry forward, to get more therapies to more patients in need.

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AUTHOR BIO

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Peiqing Zhang, Cytiva's Strategic Technology Partnership Leader in the Asia Pacific, boasts 17+ years in biotech R&D and commercial manufacturing. Formerly at Novartis and Transcenta, he excelled in technical transfer, manufacturing science, and CMC management.

A former research group leader at A*STAR-Singapore's Bioprocessing Technology Institute, he holds a Ph.D. from NUS and has authored 20+ articles on various biotech topics.

Emmanuelle has more than 16 years’ experience Biotechnology Process Development and GMP production. Highly skilled in the field of cell culture applications, Emmanuelle joined Pall Biotech almost 12 years ago where she held different positions such as Bioprocess Specialist (BPS), Principal Bioreactor Specialist and lately Genomic Medicine Strategic Technology Partnership Leader for the past +2.5 years.

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Advanced Pharmaceutical Continuous Manufacturing of Liquid Dosage Forms Advanced continuous manufacturing (CM) is emerging as a preferred platform to produce full finished injectable liquid dosage forms. In this work, a novel plant and digital twin model of continuous manufacturing of full finish final liquid dosage forms has been developed. The developed continuous injectable manufacturing process takes the API synthesized and purified via a continuous API manufacturing process, either in powder form or as a solution in the final liquid ingredient of the formulation and turns it into a finished liquid product. This process is composed of three flexible modules that can be easily re-configured and adapted to manufacture a range of drugs involving liquid dosages forms. The developed digital twin model library consists of the mathematical model of unit operations involved in modules 1-3.

Ravendra Singh

1. Introduction

C-SOPS, Department of Chemical and Biochemical Engineering, Rutgers The State University of New Jersey

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and increasing productivity across various industries, ranging from automobiles to fine chemicals to food and consumer products.

Within the pharmaceutical industry, these and other benefits have provided impetus for rapidly growing implementation of continuous manufacturing off solid dose products.

This work aims to extend the utilization of advanced manufacturing methods to the second-largest category of pharmaceutical products: liquid formulations, which include oral liquids, aerosols, and liquid injectable.

Similarly, for liquid products, we anticipate that the implementation of a small-scale fully capable system that minimizes complexity while maximizing flexibility will enable many companies to pursue implementation.

The proposed system, adapted for purpose, can be an essential component of a strategy to strengthen the US supply chain and to address perennial shortages of injectable drugs.

Similarly, flexibility in the compounding/ sterilization/fill finish module will enable manufacturers to develop complete processes for the convenient manufacturing of many products.

The developed system also leverages well-known advantages of continuous manufacturing technologies to reduce footprint and environmental impact, improve quality control, facilitate process scale-up and scaleout and reduce cycle time. Moreover, the fast response to changes in parameter settings characteristic of continuous manufacturing systems will accelerate product and process 68

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Revolutionizing pharmaceutical manufacturing: A novel digital twin-driven continuous process for liquid injectables, offering flexibility, efficiency, and a strategic solution for supply chain resilience.

development, allowing for faster release of new products.

In this work, a novel continuous pharmaceutical manufacturing process for liquid dosages forms has been developed. The developed pilot-plant is situated at Rutgers University. The manufacturing process has been modelled to develop a digital twin for virtual experimentations. The work utilized advanced modeling approaches to enable a higher-level ability to understand injectable drug product manufacturing.

2. Overview of modular advanced continuous injectable manufacturing process The continuous pharmaceutical injectable manufacturing process has three flexible modules as shown in Figure 1. In module 1, the API is conditioned in a preconditioning tank. This involves either dissolving powder API, or diluting an API liquid feedstock,

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Figure 1: Modular continuous pharmaceutical injectable manufacturing process

to generate a solution with the desired concentration. Solutions of all remaining formulation ingredients are dispensed from additional refillable tanks. All ingredients are pumped at controlled mass flow rates to module 2. The module 2 consist of a static mixer, a homogenizer, an ultrafiltration, diversion system and a surge tank. The ultrafiltration membrane is used for purification and sterilization. Module 3 consist of vial filling, labelling, and caping system. The outlet stream from ultrafiltration unit is pumped into a reservoir tank (surge capacity), from where it is metered-fed into vial filling machine. The filled vials are then transferred to a capping station. (Figure 1)

3. Development and implementation of modular manufacturing platform for continuous pharmaceutical manufacturing of liquid dosages forms A continuous injectable manufacturing process has been developed as shown in Figure 2. The process takes the API synthesized and purified in a continuous

modular API manufacturing process3 either in powder form or as a solution in the final liquid ingredient of the formulation and turns it into a finished liquid product. The existing commercially available API can be also used as a starting material for this process. The process is composed of three modules as described in following section.

3.1. Module 1: Feeding and dispensing system In this module, the API is conditioned in a preconditioning tank. This involves either dissolving powder API, or diluting an API liquid feedstock, to generate a solution with the desired concentration. Solutions of all remaining formulation ingredients are dispensed from additional refillable tanks.

All ingredients are pumped at controlled mass rates. Module 1 have sub-modules. Each submodule consists of an integrated re-filling system, feed tank, and pump. This submodule can be easily numbered up depending on the number of starting materials (APIs, Excipients) to be used in the formulation. 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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3.2. Module 2: Mixing and purification system Module 2 consist of static mixer, ultrasonic homogenizer, ultrafiltration, real time diversion system, and surge tank. Static mixer is used to bring all the feeding streams from module 1 together and create a homogeneous solution via redial mixing. The main function of the ultrasonic homogenizer is to break down the agglomerates and larger particles and create a homogeneous solution of acceptable particle size distribution. The necessity of the homogenizer depends on the formulation and products, and it may or may not be needed.

The module 2 has been designed in such a way so that homogenization process can be by-passed if needed. The next unit operation of module 2 is the ultrafiltration. This unit operation assures the destroying and removal

of any surviving bacteria, viruses, and other impurities. There is a diversion system to divert the out of specification solution stream in real time. The diversion is based on inline measurement coupled with a predictive residence time distribution (RTD) model.

The acceptable product stream from module 2 goes to a surge tank. The surge tank is used to make the balance in between module 2 and module 3.

3.3. Module 3: Vials filling, caping, and labelling system The solution from surge tank is pumped to module 3. Module 3 uses a vial filling system to fill the vials. Each vial is automatically caped and labelled. The PAT is used to assure the critical quality attributes of each vial.

(Figure 2)

Figure 2: Advanced continuous pharmaceutical injectable manufacturing process and pilot-plant

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A digital twin of advanced continuous injectable manufacturing process

AUTHOR BIO

The central component of the 'digital twin' is the process flowsheet model, incorporating a mathematical representation of the three

modules outlined in the preceding section.

Additional modules can be seamlessly integrated as necessary. The primary module focuses on feeding and dispensing, encompassing models for a refill unit, feed tank, and pump.

The second module consist of the mathematical model of static mixer, ultrasonic homogenizer, ultrafiltration, diversion system, and surge tank. The last module consists of the model of vial filling system. Both digital twin model and computational fluid dynamics (CFD) models have been developed.

Conclusions

Dr. Ravendra Singh is faculty of the Department of Chemical and Biochemical Engineering, Rutgers University, NJ, USA. He is the recipient of the prestigious EFCE Excellence Award from the European Federation of Chemical Engineering. His research focus is continuous manufacturing of drug substances and products. He is PI/Co-PI of several projects funded

by FDA, NSF, and companies. He has published more than 80 papers, edited one pharmaceutical system engineering book published by Elsevier, written more than 12 book chapters, and presented at over 150 conferences. He is actively serving as a Journal editorial board member, and conference session chair.

An advanced modular pharmaceutical manufacturing process and pilot plant has been developed for the continuous manufacturing of liquid dosages forms (injectables). The process is flexible and generic and can be adapted for a variety of injectable drug product manufacturing.

A digital twin model library has been also developed that can be used for several applications including for scenario analysis, sensitivity analysis, dynamic optimization, and design of suitable control system.

Acknowledgements This work is supported by the US Food and Drug Administration (FDA) under contract number 75F40122C00122.

References are available at www.pharmafocuseurope.com

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Safeguarding Sterility Crucial Insights from 2023 Pharmaceutical Facility Audits

Critical challenges identified in 2023 US FDA audits of sterile facilities encompass environmental monitoring, cleaning, disinfection, microbiological control, and process validation. Manufacturers are urged to prioritize and enhance protocols in these areas, which is imperative for maintaining a commitment to delivering safe, high-quality drug products aligned with regulatory standards.

Ajay Babu Pazhayattil President, cGMPWorld

Marzena Ingram Senior Pharmaceutical Consultant Validant Inc 72

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T

he sterile pharmaceutical market is expected to expand significantly in the coming years due to the increasing demand for biological drugs, Advanced Therapy Medicinal Products

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(ATMPs)/Cell and gene Therapies (CGTs), and generic sterile injectable. The global sterile manufacturing market is projected to grow by more than 50% by 2028. Developing advanced aseptic manufacturing techniques has improved sterile injectable drugs' quality, safety, and shelf-life, contributing to market growth. However, challenges such as the shortage of sterile-manufacturing talent and regulatory GMP compliance may hinder the rapid expansion of sterile capacity. In sterile manufacturing, GMPs are essential for preventing contamination and ensuring the safety and efficacy of the final products.

Sterile Drug Products Produced by Aseptic Processing- Current Good Manufacturing

Practice Guidance for Industry provides the US FDA’s views on aspects the manufacturers must meet when manufacturing sterile drugs and biological products using aseptic processing for US FDA-regulated products/ processes. The article investigates GMP violations observed, particularly during FDA inspections of sterile facilities in 2023.

The 2023 dataset was sourced from the US FDA compliance dashboards. Citation data is only available for inspections where all area classifications are finalized. Citations for manually prepared 483s are also excluded.

Figure 1 presents the CFR section that generated three or more observations for 2023.

Figure 1

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The top three sterile facility audit observations from the US FDA in 2023 were related to 21 Code of Federal Regulations (CFR) 211 sections 42, 113, and 67. At the top is Section 211.42 of the CFR Title 21, addressing design and construction features. This critical aspect ensures that sterile environments are meticulously crafted to meet stringent specifications, safeguarding the integrity of pharmaceutical production. The second most observed stems from Section 211.113, which focuses on the control of microbiological contamination.

Lastly, Section 211.67, centered on equipment cleaning and maintenance, is vital in maintaining sterile conditions.

The meticulous upkeep of equipment is paramount to prevent cross-contamination and maintain the hygienic standards indispensable to sterile facility operations.

These audit observations serve as a clarion call for pharmaceutical manufacturers to prioritize these critical regulatory areas.

Delving into the infractions concerning design and construction features (Section 211.42), a noteworthy revelation emerged: a staggering 42% of violations were directly linked to inadequacies in the monitoring system for environmental conditions.

Consequently, it becomes imperative to meticulously integrate justified methodologies for monitoring environmental conditions. Additionally, 26% were attributed to the cleaning and disinfecting of the room and equipment. Developing robust 74

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processes for cleaning and disinfecting the room and equipment is therefore essential.

Activities must take place within precisely designated, appropriately sized areas. Distinct areas and controls should be established as needed to forestall any potential crosscontamination or mix-ups during the course of manufacturing operations. Using isolators where appropriate and regularly monitoring air cleanliness in critical areas are essential.

Additionally, cleanroom qualifications should include an assessment of air quality under dynamic conditions, with personnel present and operations ongoing. By meticulously integrating justified methodologies for monitoring environmental conditions and developing robust processes for cleaning and disinfecting, manufacturers can address FDA concerns and ensure ongoing compliance with cGMP’s.

To manage microbiological contamination effectively, it is essential to implement suitable written procedures. These procedures should be crafted to prevent the presence of objectionable microorganisms in drug products. This includes rationalized validation study protocols for all aseptic and sterilization processes that are involved. A control strategy should be in place to prevent potential microbial contamination in all operating conditions and the treatment of materials. This includes implementing measures such as regular cleaning, disinfection, and maintaining a high level of hygiene in every aspect of the manufacture

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qualification (Stage 2 PPQ), and monitoring

(Stage 3- CPV).

Maintaining sterile pharmaceutical integrity requires a meticulous focus on design, microbiological control, and equipment upkeep - crucial for delivering safe, high-quality drugs in alignment with regulatory standards.

of drug products. Automation and barrier technology, adherence to first-air principles, and good aseptic technique/behavior are vital parts of a prevention strategy. It's crucial to highlight that nearly one-third of observations under Section 211.113, precisely 29%, of the observations made were directly linked to the absence of comprehensive process validation studies. This indicates a significant gap in ensuring the robustness and reliability of the manufacturing process.

Process validation studies confirm that a given process consistently produces a product meeting its predetermined specifications and attributes. Addressing process validation issues requires a meticulous and thorough data-driven manufacturing science-focused approach in development (Stage 1- QbD),

Ensuring the integrity and safety of drug products necessitates a meticulous approach to managing equipment and utensils. The tools undergo regular cleaning, maintenance, and sanitization. The goal is to prevent contamination and malfunctions that could compromise the drug product's safety, identity, strength, quality, or purity. To uphold these standards, written procedures must be established and adhered to for the systematic cleaning and maintenance of equipment, including utensils, employed in manufacturing, processing, packing, or holding. Section 211.67 (a) observations, which constituted 66%, are specific to not performing the equipment and utensils cleaning and sanitization at appropriate intervals to prevent contamination. The remaining observations in this section pertained to the absence or inadequacy of procedures for equipment cleaning and maintenance. Notably, within Section 211.42, a significant number of observations were linked to the cleaning and disinfection of equipment and rooms, emphasizing the scrutiny placed on aseptic area cleaning practices as one of the most frequently observed aspects. This underscores the critical need for pharmaceutical facilities to prioritize and enhance protocols related to equipment cleaning, sanitization intervals, and maintenance procedures to align with regulatory expectations. 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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The 2023 observations from US FDA inspections shed light on critical areas requiring attention within sterile facilities. The prominence of Section 211.42, emphasizing design and construction, Section 211.113, focusing on microbiological contamination control, and Section 211.67, highlighting equipment cleaning and maintenance, reflects the multifaceted challenges faced by the pharmaceutical sector. The identified gap in process validation studies, constituting 29% of Section 211.113 observations, underscores the imperative for a comprehensive, data-driven approach throughout the manufacturing process life cycle. Furthermore, the emphasis on equipment cleaning, sanitization intervals, and the scrutiny of aseptic area cleaning practices signals a clear mandate for pharmaceutical facilities to prioritize and enhance protocols, aligning with regulatory expectations.

In navigating the evolving landscape of sterile pharmaceuticals, addressing these observations is crucial for sustaining the industry's commitment to delivering safe, high-quality products that meet regulatory standards and ensure patient wellbeing.

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AUTHOR BIO

M A N U F A C T U R I N G

Ajay Pazhayattil is a management consultant and an industrial pharmacist with pharmaceutical operational experience in the industry's sterile, solid oral, and API sectors. He has been in leadership roles with North American brand, generic, and CDMO organizations before establishing cGMP World, an organization helping pharmaceutical firms maintain regulatory compliance. He has successfully remediated warning letter scenarios and developed regulatory acceptable unique compliance solutions. He can be contacted through LinkedIn.

Marzena Ingram is an independent senior pharmaceutical consultant with extensive quality and technical operations experience in soliddose and active pharmaceutical ingredient manufacturing operations. She is responsible for delivering compliant solutions for clients addressing FDA warning letter scenarios. Ingram has spearheaded compliance programs meeting global regulatory requirements. She is the vice president of ISPE Canada. She can be contacted through LinkedIn.

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AI-DRIVEN PATIENT RECRUITMENT Dr Santhosh Kumar VP, Enterprise Clinical Solutions, Indegene

Artificial Intelligence (AI) can revolutionize clinical trial patient recruitment by swiftly identifying candidates and cutting recruitment time. Natural Language Processing (NLP) can effectively extract vital data from diverse structured and unstructured sources, whereas Machine Learning (ML) can automate labor-intensive tasks, and predictive modeling can evaluate patient enrolment probability and compliance with trial protocols. AI accelerates trials, and ensures diverse participant pools, enhancing trial success.

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1. How do you define AI-driven patient recruitment within the context of clinical trials, and what role does it play in enhancing the recruitment process compared to traditional methods?

What are the primary challenges in conventional patient recruitment methods, and how can AI-driven approaches effectively address these challenges to streamline the process?

With more clinical trials getting decentralized and the adoption of AI/ML witnessing an upward trend, the clinical development landscape is evolving at a rapid pace demanding insightful decision-making for increased predictability and efficiency.

Leveraging AI-derived insights, especially with Real World Data (RWD) fosters a safer, streamlined research environment thereby optimizing the clinical trial process and drug discovery process. In the patient recruitment space, AI-driven strategies are revolutionizing the entire process mainly by boosting the implementation of data-driven clinical trials’ design and patient enrolment activities respectively.

Some of the significant challenges pharmaceutical companies face with traditional patient recruitment methods are: • Huge patient dropouts during initial screening, especially for rare diseases’ clinical trials • Inability to meet patient enrolment timelines due to limited success in identifying potential participants • Delayed responses from clinical research sites and unresponsiveness of research sites about the availability of potential target pool of participants

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Integration of extensive structured and unstructured healthcare data from various RWD sources coupled with advanced analytics helps automate tasks while helping to elevate data quality in numerous activities across stages of clinical trials. Outlined below are a few examples of how AI/ML is improving trial design and patient enrolment in clinical trials: Improvements in Study Design: AI enhances trial design and optimization by identifying patterns in data, enabling predictions about patient behavior and drug efficacy.

AI/ML-enabled platforms analyze past studies to determine optimal patient populations, diagnosis, and prognosis requirements. This optimizes study design and improves decisions regarding country and site selection, enrollment models, and patient recruitment, eventually yielding predictable results, minimizing protocol amendments, and enhancing the overall efficiency of clinical trials.

Site identification and patient recruitment: AI and ML address challenges in site identification and patient recruitment for clinical trials. As studies focus on specific target populations of patients, achieving recruitment goals becomes more challenging, leading to increased costs and timelines. AI and ML mitigate these risks by identifying sites with high recruitment potential, suggesting effective recruitment strategies, and proactively targeting sites with predicted patient populations. This allows sponsors to prioritize sites and reach out to fewer sites with high enrolment probability.

This accelerates recruitment and reduces the risk of under-enrollment, ultimately enhancing

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the efficiency and success of clinical trials.

Synthesizing disparate data elements, ML uncovers meaningful insights for precise site identification, ensuring access to ample patient populations. This approach significantly increases global enrollment rates compared to traditional experience-based site identification methods. Manual efforts in analyzing site risks and generating action items for clinical monitoring can be alleviated as advanced analytics offer composite site rankings, enabling precise risk identification.

This accelerates decision-making, allowing for timely actions and issue avoidance in clinical trials.

2. Can you provide specific examples where AI has been successfully employed to accelerate patient recruitment in clinical trials, highlighting the key outcomes achieved? And, could you elaborate on specific instances where ML algorithms have significantly improved the efficiency of patient selection and screening for clinical trials, and how were these algorithms tailored to address specific challenges?

Integrating advanced AI and ML algorithms with existing data and domain expertise enhances clinical research efficiency. Unlike the traditional method of selecting and activating research sites based on historical data, using real-time site data enables just-in-time site activation. Prioritizing sites based on specific criteria, including target patient demographics, reduces recruitment forecasting time and resources. This approach offers a more accurate study completion timeline

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by activating sites based on current data, optimizing the clinical research process, and streamlining the path to patient enrollment.

Below are a few examples where with AI/ML some of the leading pharmaceutical companies garnered substantial process efficiencies in clinical development by meeting patient recruitment targets in record time.

This case involves leveraging AI/ML with a geofenced strategy to boost participant recruitment for a phase three trial on cytokine storm during the COVID-19 pandemic. Faced with recruitment challenges, the approach involved creating hyperlocal campaigns within a radius of targeted clinical research sites.

By analyzing real-time location data, demographics, and user content preferences, the team achieved remarkable outcomes. The geofenced strategy resulted in over 17,000 weekly unique visitors to the trial landing page. From these visits, 460 interested participants were identified, indicating a 7% conversion rate from website visits to secondary qualification - which is 50% higher than industry norms. The success demonstrates the effectiveness of AI and ML technologies in optimizing patient recruitment strategies and achieving superior conversion rates. This approach not only addressed the specific recruitment needs during the pandemic but also showcased the potential of innovative digital strategies to enhance clinical trial outcomes in a targeted and efficient manner.

AI/ML-driven methodologies have also been used extensively for ongoing clinical trials as well. In one such instance, the pharmaceutical company had already engaged a Contract Research Organization (CRO)

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AI-driven patient recruitment transforms clinical trials, optimizing efficiency, enhancing diversity, and accelerating drug development. Collaborative efforts and a supportive regulatory environment are key to unlocking its full potential, ensuring a future where innovation and patientcentricity coalesce for groundbreaking advancements in healthcare research.

to enroll patients for a drug trial. However, the traditional methods used by the CRO were not very effective in meeting the patient recruitment targets. By leveraging AI/ML and RWD the pharmaceutical company conducted a comprehensive site feasibility assessment, real-time analytics tracking, and the activation of accurate research sites.

In addition to this, hyper-local, geo-fenced digital outreach campaigns prioritized patient qualification. Support by RN Concierge Services ensured swift participant engagement and handover to research sites. The result was the activation of 60+ research sites in approximately just three weeks and a remarkable 3x increase in participant enrollment rate. Thus with data-driven site priori-

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tization, omnichannel marketing, and RN Concierge Services, the company synergized with the existing CRO model to enhance outreach, refine the secondary qualification of eligible clinical trial participants, and meet the patient enrolment targets on time.

In another instance, a leading Japanese pharmaceutical company achieved a 40% improvement in both the top of the funnel and final enrollment by leveraging a hybrid nurse concierge system for screening. The primary screening process, a digital websitedriven experience, was followed by a handoff to a nurse concierge team for phone or chatbased secondary screening. Additionally, an auto-site visit scheduler minimized delays between participant expression of interest and contact from the research site, improving adherence and accelerating enrollment. The efficient workflow streamlined the screening process and ensured a prompt handoff to the research site upon participant qualification, minimizing the risk of attrition and optimizing the overall enrollment experience. This innovative approach highlights the transformative impact of combining digital technologies with human touchpoints in clinical trial recruitment.

In another example, an oncology biotech company used AI/ML and RWD for trial design optimization, feasibility assessments, and recruitment forecasting. The solution involved creating a clinical site prioritization dashboard, ranking sites based on factors like therapeutic experience, current trial status, and competing trials. Notably, considerations included the size of hospitals, recognizing that larger institutions often

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lead to longer contracting times, impacting trial startup delays. The meticulous site selection process addressed this concern, ensuring judicious choices. The result was a bespoke trial recruitment plan tailored for a successful phase two metastatic nonsmall cell lung cancer study. This strategic approach showcased the importance of informed site prioritization in mitigating delays and optimizing trial outcomes for pharmaceutical and biotech companies in the oncology field.

3. In what ways can NLP effectively extract pertinent information from structured and unstructured sources in the context of patient recruitment, and what are the benefits of utilizing NLP in this domain?

NLP has significantly improved the analysis of diverse data sources, including Electronic Health/Medical Records (EHR/EMR), insurance claims, and notably, social media data.

This plays a pivotal role in targeted marketing and educating potential trial participants.

With the explosive growth of RWD in this industry which largely encompasses information routinely collected outside clinical trial settings, spanning hospitals, labs, imaging centers, and patient-reported outcomes, NLP can be extensively leveraged to generate Real World Evidence (RWE) for many informed decisions in the clinical development space. RWE significantly impacts inclusion/exclusion criteria evaluation, ensuring the right data is collected to ease site and patient burdens. By leveraging RWE, protocol amendments in clinical trials may be

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reduced, streamlining timelines and alleviating burdens.

The COVID-19 era emphasized the importance of hybrid clinical trials, introducing flexibility with patient assessments outside research sites. Home health services and local healthcare facility visits enhance trial participation convenience without straining research infrastructure. RWE, informed by patient feedback, influences study design and protocol optimization, promoting diversity, equity, and inclusion. In summary, NLP-driven data analysis and the integration of RWE contribute significantly to clinical trial optimization. From refining inclusion/ exclusion criteria to minimizing protocol amendments, shaping study endpoints, and fostering hybrid trial configurations, the multifaceted impact of RWD on study design is evident. This comprehensive approach not only improves the efficiency of clinical trials but also ensures patient-centricity and relevance in a rapidly evolving healthcare landscape.

4. What are the critical factors that need to be considered when implementing NLP techniques for data extraction from diverse sources such as EHR and social media, and how can potential challenges be mitigated?

Addressing data privacy challenges in accessing diverse data sources, especially patient information, involves considerations of regulatory requirements and regional laws.

Amidst the scrutiny faced by social media companies, protecting patient identity and health information (PHI) is paramount.

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To mitigate concerns, a focus on patient consent should be emphasized, employing a double opt-in mechanism. Beyond initial website consent, patients should receive an email requiring explicit permission, ensuring a robust consent process in adherence to local laws and platform frameworks. The objective is to assist patients rather than treating them as retail consumers. By seeking explicit permission and consent, concerns are alleviated, healthcare information is safeguarded, and actions are taken after their consent, maintaining a patient-centric and ethical approach in the realm of data access and utilization. Apart from the issue of data privacy, the presence of extensive data often results in significant noise, underscoring the need to discern and prioritize pertinent information over extraneous details.

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6. What are the potential challenges in implementing predictive modeling techniques for patient recruitment, and what strategies do you suggest to mitigate these challenges effectively during the implementation process?

While predictive modeling enhances trial planning, it’s delayed application during the trial, rather than beforehand, poses challenges.

The significance and quality of data for predictive modeling are crucial, given the potential disparities between predictions and actual outcomes. Analysis often involves eliminating outliers, potentially skewing results towards an idealized rather than realistic scenario.

7. How important is interdisciplinary collaboration between AI experts, healthcare professionals, and regulatory authorities in the

5. How can ML techniques be utilized to automate labor-intensive tasks in the patient recruitment process, and what advantages does this automation bring to the overall efficiency of the recruitment process?

ML techniques play a pivotal role in patient recruitment by: • Analyzing patient data against eligibility criteria, efficiently identifying potential study participants, and excluding ineligible candidates • Streamlining scheduling and follow-up on appointments for clinical trial participants • Automating patient qualification and various workflow activities Further, regardless of data sources, AI/ML-enabled RWD platforms can ingest and unify datasets into a common data model. This eliminates the need for extensive time spent on data preparation, with the platform focusing on analytics and insights.

This paradigm shift - from spending 80% of time on data preparation, to 80% on analysis, brings great efficiencies in clinical trials. The solution is crafted to assist data scientists with a powerful tool for data harmonization, analysis, and actionable insights. This approach aims to accelerate the drug development process from inception to market, aligning with the overarching objective of bringing effective pharmaceuticals to market promptly and efficiently

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While change is inevitable, the industry is cautious in implementing AI/ML innovations, emphasizing incremental transformations in each aspect of the clinical trial continuum.

This approach ensures impactful changes are implemented gradually, fostering industry trust, comfort, and confidence over time, while maintaining patient safety as the paramount concern throughout.

8. Looking ahead, how do you envision the continued evolution of AI-driven patient recruitment shaping the landscape of clinical trials and healthcare research? What key steps do you believe are necessary to maximize its potential impact in the field?

The future of AI-driven patient recruitment holds promise for advancing clinical trials in continuing to accelerate drug development and improve the quality of clinical studies.

To fully realize this potential, it is crucial to establish a supportive regulatory environment, foster collaboration, prioritize data privacy, and continuously assess and refine these approaches to align with the evolving needs of the healthcare and research communities.

Dr. Santhosh has over two decades of experience in the clinical research industry, specifically clinical data management. Most recently, he was VP at Accenture Services, leading a large pharma client involving clinical study set-up activities. He has previously worked at TCS and IQVIA respectively, where he was responsible for clinical development solutioning and delivery. At Indegene, he is responsible for growing/ streamlining Indegene’s Enterprise Clinical BU Global Operations.

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development and implementation of AI-driven patient recruitment strategies? Can you provide examples of successful collaborations that have yielded significant advancements in this field?

Like with all industries, the adoption of AI/ML for any aspect of clinical development requires very strong interdisciplinary collaboration between AI experts, healthcare professionals, and regulatory authorities.

While there have been great innovations by the AI experts’ community and extensive support by Healthcare Professionals (HCPs) the life sciences industry is yet to witness direct collaboration with regulatory authorities. However, it is extremely encouraging to see the frequent guidance the industry receives from regulators pertaining to the application of AI/ML for drug development activities.

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Innovations in Pharmaceutical R&D: Navigating the Future.

Welcome readers, to our esteemed panel discussion on 'Innovations in Pharmaceutical R&D: Navigating the Future.' We are honored to have a distinguished group of experts who bring a wealth of experience and insights from various facets of the pharmaceutical industry.

Dr Lakshmi Raghavan Founder & CEO at Healios Labs LLC, United States

Mr. Somesh Sharma Executive Vice President at Aragen Life Sciences, India

Mr. Shamal Fernando Managing Director at Slim Pharmaceuticals (Pvt) Ltd, Sri Lanka

Mr. Michael N. Liebman Managing Director, Co-Founder at IPQ Analytics, LLC., United States

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Question 1. As we see an increasing emphasis on data-driven decision-making, can you discuss specific examples of how big data analytics and computational modeling are being applied to streamline and enhance various stages of the drug development lifecycle?

LAKSHMI RAGHAVAN: Data-driven decisionmaking is becoming mainstream in various stages of drug development and is transforming the drug development lifecycle, streamlining processes, reducing costs and timelines, and improving the probability of success. Critical to

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A Pharma Focus Europe’s Initiative fostering exclusive and open discussion on Industry Trends

different stages of drug development are data analytics and is primarily dependent on how good the data are. Given below are some examples of different stages of drug development lifecycle where data analytics and computational modeling are being applied to streamline and enhance various stages of the drug development lifecycle.

1. Target Identification and Validation: There are vast amounts of genomic, proteomic, and phenotypic data available, which are

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mined and analyzed to identify new potential therapeutic targets. These targets are validated by analyzing data from animal models, clinical trials and real world evidence for the drug target’s safety and efficacy.

2. Drug Discovery and Development: A majority of data analytics in the drug development lifecycle is focused on in the area of drug discovery and development as small and large pharmaceutical companies are find that data analytics and computational modeling can significantly reduce the time and resources need with conventional drug discovery process. For example, machine language algorithms are used to predict the properties of the drug candidates such as pharmacokinetics and pharmacodynamics.

Similarly, drugs designs are optimized by analyzing molecular data to identify compounds with desirable properties and how they interact with biological targets.

3. Clinical Trials: Several companies are engaged in using data analytics to shorten the clinical study timelines, reduce costs as machine language can be used to generate predictive models that can accurately predict the clinical study outcome. In addition, data analytics are being used in patient selection by identifying patients who are likely to respond to a particular treatment. Optimization of clinical trial design by identifying the most informative endpoints, selecting appropriate patient 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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populations and determining the optimal dosing regimens is another area of focus in the world of data analytics. Lastly, real-time monitoring of clinical trials data to identify potential safety concerns and optimize clinical trial outcomes is used to significantly reduce the risk, cost and time.

4. Post-Market Surveillance: Data analytics is used effectively in identifying adverse drug reactions (ADR) that are not reported through traditional methods by analyzing data from social media, electronic health records (EHRs) and insurance claims. Analysis of data from large patient’s populations can help monitor the safety and efficacy of the drugs in real world settings.

Another area where data analytics are being explored are in personalized medicine.

Pharmaceutical companies are continually finding new ways to use data analytics and computational modeling to make more informed decisions at every stages of the drug development lifecycle, paving the way to bring safer and effective drugs to market quicker and at lower cost.

Question 2. Adaptive clinical trials are gaining attention for their potential to optimize trial designs in real-time. How do you see this approach impacting the efficiency and success rates of clinical trials, and what challenges need to be addressed for widespread adoption?

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is all about flexibility, nimbleness and innovative clinical trial approach, for prospective planned modifications based on interim data analysis from subjects in trial.

Adaptive clinical designs offer enormous benefits over traditional fixed design plan when information is limited before beginning of trial. The main advantages fall under following categories as outlined in FDA guidelines – i) Statistical efficiency: An adaptive design can provide a greater chance to detect a true drug effect than non-adaptive design in group sequential designs, and adaptive modifications to the sample size. ii) Ethical considerations: Discontinue or stop a trial early if interventions reflect ineffectiveness of investigational treatment, and in course helps to reduce the number of patients exposed to the unnecessary risk. iii) Improved understanding of drug effects: An adaptive design strategy enriches the information from small population of patients and estimates better understanding of experimental treatment. iv) Acceptability to stakeholders: An adaptive design is more amenable to stakeholders as it allows planned design modifications based on accumulating information.

Adaptive design approach can enhance the chance of successful trials and translation of information across all phases of clinical development – from dose finding (Phase-1) to confirmatory Phase III trials.

However, all of this presents some difficulties that must be thought out and

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considered before being widely adopted.

The adaptive design method results in longer trial start duration and more work during the design phase. There aren't enough particular, user-friendly statistical tools to prevent bias and incorrect results. In comparison to a non-adaptive design, an adaptive design may have a larger maximum sample size but a smaller minimum and predicted sample size.

Furthermore, it is difficult to guarantee the timely availability of high-quality interim data, moreover, important scientific limitations or specific clinical contexts may restrict the potential for efficiency through adaptation.

To sum up, adaptive clinical trials can optimise trial designs in real time, which would increase trial efficiency and appeal to enrolled participants. But before adaptive clinical trials become widely used, several issues must be resolved. These issues include the need for regulatory guidelines, a clear statistical approach, and a greater comprehension of the ethical implications of adaptive clinical trials.

Question 3. The shift towards patientcentric approaches is transforming the traditional R&D paradigm. How can pharmaceutical companies effectively incorporate patient insights and experiences into the drug development process to ensure more targeted and successful outcomes?

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is crucial for pharmaceutical companies to enhance the effectiveness and relevance of their products. Here are several strategies that companies can employ to effectively integrate patient-centric approaches into the R&D paradigm:

Early and Continuous Patient Engagement: Involve patients from the early stages of drug development, including protocol design, endpoint selection, and study planning.

Establish ongoing communication channels to ensure continuous engagement throughout the research process.

Patient Advisory Boards: Form patient advisory boards comprising individuals with the target condition to provide input on various aspects of the drug development process. Seek input on study design, recruitment strategies, and patientreported outcomes.

Patient-Reported Outcomes (PROs): Incorporate patient-reported outcomes into clinical trial designs to capture the impact of the disease and treatment from the patient's perspective. Use validated instruments to measure symptoms, daily functioning, and quality of life directly from the patient's point of view.

SHAMAL FERNANDO: Incorporating

Real-World Evidence (RWE):

patient insights and experiences into the drug development process

Leverage real-world evidence from sources such as electronic health records, wearables, 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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and patient registries to complement traditional clinical trial data. Gain insights into the long-term safety and effectiveness of treatments in real-world settings.

Share study results with patients in a clear and accessible manner. Foster open communication to build trust and demonstrate a commitment to transparency.

Digital Health Technologies:

Partnerships and Collaborations:

Explore digital tools, such as mobile apps and wearables, to collect real-time data on patient experiences, adherence, and outcomes. Use technology to facilitate remote monitoring and data collection, reducing the burden on patients.

Collaborate with patient advocacy groups, non-profit organizations, and other stakeholders to access a diverse range of patient perspectives.

Establish partnerships to co-create research agendas and prioritize areas of focus.

By implementing these strategies, pharmaceutical companies can enhance the patient-centricity of their drug development processes, leading to more targeted, relevant, and successful outcomes. This approach not only aligns with ethical considerations but also contributes to the development of therapies that better meet the needs and preferences of the individuals they are intended to benefit

Education and Empowerment: Educate patients about the drug development process, clinical trials, and the importance of their participation. Empower patients to actively contribute by fostering a collaborative relationship between researchers and patients.

Diversity and Inclusion: Ensure diversity in patient populations participating in clinical trials to reflect the broader patient demographics. Consider cultural and socioeconomic factors that may influence patient experiences and preferences.

Regulatory Collaboration: Collaborate with regulatory agencies to incorporate patient perspectives into the regulatory decision-making process.

Understand and align with evolving regulatory guidelines that emphasize patient engagement.

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Question 4. Artificial intelligence is increasingly being utilized for drug repurposing. In your view, what role does AI play in identifying new therapeutic uses for existing drugs, and how does this contribute to the acceleration of drug development timelines?

SOMESH SHARMA: The process of finding novel therapeutic applications for already-approved medications is known as "drug repurposing," and artificial intelligence (AI) tools are becoming more and more significant in this

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regard. AI systems have the capacity to evaluate humongous data and spot trends that humans might miss, which speeds up and simplifies the process of discovering new therapeutic uses for existing drugs more quickly and efficiently.

Repurposing medications is a low-risk approach since it involves less financial commitment, fewer unknowns, less undesirable side effects than developing new drugs as the drugs to be examined have already been approved. A few successful examples of medications that have been repurposed in the past include Rituximab, which was first used to treat cancer but has shown to be effective in treating rheumatoid arthritis, and Sildenafil, which was first developed as an antihypertensive medication and later proved to be effective in treating erectile dysfunction. Even during COVID-

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19 pandemic, already approved drugs like Remdesivir (a drug for treating Ebola virus disease), Ivermectin (anthelmintic drug), Dexamethasone (anti-inflammatory drugs) are being studied for their efficacy against the disease and proved quite beneficial. The main advantage of drug repurposing is to reduce time and cost in developing new drugs and lower the safety risk assessment of new medications.

Machine learning and Deep learning tools can integrate heterogeneous data and predict drug-drug and drug-target interactions. As these tools become more predictive and authentic on availability of substantial pre-clinical and clinical data, and guide us for a synergistic drug combination for a disease, it will definitely reduce the overall drug development cost.

SHAMAL FERNANDO: Artificial intelligence (AI) plays a pivotal role in drug repurposing, contributing to the acceleration of drug development timelines by leveraging advanced computational techniques and data analytics.

Here are key aspects highlighting the role of AI in identifying new therapeutic uses for existing drugs:

Data Integration and Mining: Diverse Data Sources: AI algorithms can efficiently integrate and analyze vast amounts of diverse data, including biomedical literature, electronic health records, genomics data, and drug databases. 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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Pattern Recognition: AI systems excel at recognizing complex patterns and relationships within this diverse data landscape, helping identify potential connections between drugs and diseases.

Prioritization of Compounds: AI algorithms can prioritize existing drugs based on their potential efficacy for a new therapeutic indication, reducing the time and cost associated with experimental screening.

Predictive Modeling:

Personalized Medicine and Biomarker Discovery:

Machine Learning Algorithms: AI employs machine learning algorithms to build predictive models based on historical data. These models can predict potential drug-disease associations by identifying hidden patterns and correlations.

Feature Extraction: AI algorithms can extract relevant features from various data types, providing insights into the biological mechanisms underlying diseases and potential drug actions.

Network Pharmacology: Systems-Level Analysis: AI facilitates network pharmacology approaches, where drugs, proteins, and diseases are considered as interconnected components in biological networks.

Identifying Targets: AI helps identify new therapeutic targets by analyzing the relationships between drug targets and diseaserelated proteins.

Patient Stratification: AI contributes to the identification of patient subgroups that may respond differently to existing drugs, supporting the development of personalized treatment strategies.

Biomarker Discovery: AI algorithms can identify potential biomarkers associated with drug response or disease progression, aiding in the selection of appropriate patient populations for repurposing efforts.

Drug Safety and Toxicity Prediction: Risk Assessment: AI models can predict potential safety concerns and toxicities associated with repurposed drugs, guiding decision-making in the drug development process.

Optimizing Formulations: AI can assist in optimizing drug formulations to enhance safety profiles and reduce adverse effects.

Rapid Hypothesis Generation: High-throughput Screening and Virtual Screening: Computational Screening: AI enables virtual screening of large chemical libraries to identify potential drug candidates for a specific disease.

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Iterative Learning: AI enables an iterative and learning process, allowing researchers to rapidly generate and test hypotheses regarding potential drug-disease relationships.

Reduced Trial and Error: By providing data-driven insights, AI minimizes the need

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for extensive trial and error in the drug repurposing process.

indications and potential treatments in a cost-effective manner.

Resource Optimization:

Question 5. In light of technological advancements, regulatory agencies are evolving their approaches. How do you see innovations like real-world evidence, patient-reported outcomes, and digital biomarkers influencing the regulatory landscape, and what adaptations should the industry make to align with these changes?

Cost and Time Savings: AI-driven drug repurposing can significantly reduce the time and cost associated with the early stages of drug development.

Focused Experimental Validation: AI helps prioritize candidates for experimental validation, directing resources towards the most promising repurposing opportunities.

In summary, AI expedites drug repurposing by leveraging its capacity for data integration, predictive modeling, and advanced analytics.

By accelerating the identification of new therapeutic uses for existing drugs, AI contributes to more efficient drug development timelines, enabling the exploration of novel

AI's role in drug repurposing is transformative, with its ability to integrate diverse data, recognize patterns, predict associations, and expedite the identification of therapeutic uses. This not only reduces development costs but also minimizes trial and error, ushering in a new era of efficient drug development.

MICHAEL N. LIEBMAN: I see challenges that face regulatory agencies in dealing with rapid technology development and have some concerns that the tendency is to rely, at least initially, on using the current standard of care as the gold standard. This can impact new technology that may be generating data and/or measuring parameters that are not just incremental improvements over existing methods. For example, continuous blood pressure monitoring that uses non-auscultatory methods should be evaluated based on what the new data may represent and whether the existing methods should be considered the gold standard because of inconsistent lack of clinical adherence to existing ciinical guidelines and whether point in time measures adequately describe the full circadian pattern of blood pressure variation.

It is also incumbent on the industry, however, to design studies to appropriately show the value of such new technologies as to how they will improve patient care. 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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Question 6. Collaboration between pharmaceutical companies and technology firms is becoming more prevalent. Can you share insights on successful collaborations you've witnessed or been a part of, and how these partnerships are driving innovation in R&D processes?

LAKSHMI RAGHAVAN: While pharmaceutical companies are strong in developing drugs and coming out with safe and effective drugs, they rely on technology companies to provide insights on how data can be utilized and analyzed effectively to make the drug development process more efficient and reduce the risks, costs, and timeline. There are a few examples where pharmaceutical companies have collaborated with Technology companies driving innovations in research & development.

Some of these collaborations are given below.

Boehringer Ingelheim and Google: Boehringer Ingelheim (BI) formed a collaborative partnership with Google Quantum AI, focusing on researching and implementing cutting edge use cases for quantum computing in pharmaceutical research & development. The partnership was formed with the expertise of BI’s expertise in computer aided design and in silico modeling combined with Google’s outstanding resources in the field of quantum computing and algorithms.

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with conventional computing, and thus can create opportunities for newer therapies and treatments, which otherwise is difficult to treat.

AstraZeneca and IBM: AstraZeneca’s Bioventure Hub and IBM, Sweden formed a collaborative partnership primarily to drive innovation through simulating growth of Small and Medium Enterprises (SMEs), support knowledge exchange between life sciences and digital technology industries and strengthen digital health expertise. AstraZeneca’s aspire to embrace data science, AI and Machine Language technologies to advance its fundamental understanding of the diseases, increase productivity and get faster access to medicines.

Roche and Flatiron Health: Roche acquired Flatiron Health, which is a Healthtech company that is a market leader in oncology specific electronic health record software as well as in the curation and development of real-world evidence for cancer research. Flatiron Health is an independent affiliate of Roche and with their respective strengths in the oncology space, the digital technology solutions from Flatiron is expected to hasten Roche’s drug development in the oncology space.

There are several other pharmaceutical and technology partnerships that are evolving and changing the way the drug development process is understood and developed. They

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are primarily based on either corporate level strategy or product level strategy. There are also several other startups that are collaborating with other startups, providing an avenue for large pharma companies to test some of these concepts, which are more difficult to justify pursuing in-house.

Question 7. With the integration of advanced technologies, ethical considerations become paramount.

How should the pharmaceutical industry navigate ethical challenges associated with innovations such as AI, CRISPR, and personalized medicine, and what frameworks can ensure responsible and transparent R&D practices?

SOMESH SHARMA: First of all, a paradigm shift in healthcare is being made possible through modern technical platforms like AI, CRISPR, and personalized medicine. As pharmaceutical industry envisages to integrate these technologies in their research and development practice, the ethical considerations of these tools have a propensity to draw attention due to lack of framework to ensure transparent, responsible practices and accountability of these technologies.

Though, AI has revolutionised the medical field, viz. imaging and electronic medical records, laboratory diagnosis, robotic surgeries, treatment, new drug discovery, repurposing of existing medicine, preventive and precision medicine, biological extensive data analysis, data storage and access for

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health organizations. It's interesting to note that it faces ethical and legal issues because of existing legislation is insufficient to protect people's health records, a could lead to an unchecked and inaccurate flow of information on social networks, endangering people's security and privacy.

Similar questions are being raised about personalised medicines about privacy, safety, phenotypical expression, drug interactions, and genetic vs. social group identities and fairness in subject selection.

In addition, personalized medicine will change the economics of drug production and distribution.

CRISPR is a novel gene editing tool for treatment of cancer and many other diseases.

Recent success in treatment of sickle cell anaemia in a patient has opened up a new horizon in healthcare. Interestingly, the use of genome editing for research and commercial purposes has too sparked debates on the effects of human genome editing on the patients themselves, and for future generation.

The manipulation of these techniques can bring imbalance in social-economic behaviour of society.

To ensure data privacy and protection, pharmaceutical industries can adopt responsible research innovation and ethics assessment framework to demonstrate socially responsible and ethical practices.

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these technologies for betterment of human society and future generations. General data protection regulation and informed consent and autonomy are steps towards privacy protection and ethical disclosures.

SHAMAL FERNANDO: The integration of advanced technologies in the pharmaceutical industry, such as AI, CRISPR, and personalized medicine, raises significant ethical considerations. Navigating these challenges requires a commitment to responsible and transparent research and development (R&D) practices. Here are key considerations and frameworks that can help the pharmaceutical industry address ethical challenges:

Ethical Considerations: Informed Consent and Autonomy: Framework: Implement robust informed consent processes, ensuring that patients and research participants fully understand the implications of innovative technologies and personalized treatments.

Consideration: Respect individual autonomy and provide clear information about the potential risks, benefits, and implications of participating in studies involving advanced technologies.

Data Privacy and Security: Framework: Adhere to stringent data privacy and security standards to protect patient 94

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information in the era of personalized medicine and AI-driven analytics.

Consideration: Clearly communicate data usage policies, provide options for data sharing consent, and take measures to prevent unauthorized access.

Equitable Access: Framework: Develop strategies to ensure equitable access to innovative treatments, considering factors such as affordability, geographic location, and socioeconomic status.

Consideration: Address potential disparities in access to advanced technologies and treatments, working towards inclusive and affordable healthcare solutions.

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Transparency and Accountability: Framework: Establish transparent communication channels with stakeholders, including patients, healthcare professionals, and regulatory bodies.

Consideration: Clearly communicate the goals, methods, and potential risks of R&D involving advanced technologies, and hold organizations accountable for ethical conduct.

Bias and Fairness: Framework: Mitigate biases in algorithms used in AI applications by regularly auditing and validating these systems.

Consideration: Ensure that personalized medicine approaches do not inadvertently reinforce existing health disparities, and strive for fairness in treatment outcomes across diverse populations.

Genetic Privacy and Consent: Framework: Develop clear guidelines for the ethical use of genetic information, including informed consent processes for genetic testing and sharing of genetic data.

Consideration: Safeguard against potential misuse of genetic information, including unauthorized access or discrimination based on genetic predispositions.

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independently review and approve research protocols involving advanced technologies.

Consideration: Ensure ERBs have diverse expertise and include members knowledgeable about the specific ethical implications of AI, CRISPR, and personalized medicine.

Guidelines and Standards: Role: Develop and adhere to industry-wide guidelines and standards for ethical conduct in R&D. Consideration: Regularly update guidelines to reflect evolving ethical challenges associated with emerging technologies.

Global Harmonization: Role: Encourage international collaboration and harmonization of ethical standards to ensure consistency across borders.

Consideration: Navigate global regulatory frameworks and respect cultural differences while upholding fundamental ethical principles.

Stakeholder Engagement:

Frameworks for Responsible R&D Practices:

Role: Actively engage with patients, advocacy groups, healthcare professionals, and the public to gather diverse perspectives.

Consideration: Solicit input in the development of ethical guidelines, R&D priorities, and dissemination of research findings.

Ethics Review Boards (ERBs):

Continuous Ethical Training:

Role: Establish and strengthen ERBs to

Role: Provide ongoing ethical training for 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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researchers, clinicians, and other professionals involved in R&D. Consideration: Keep individuals abreast of the latest ethical considerations associated with advanced technologies and personalized medicine.

Ethical Impact Assessments: Role: Conduct thorough ethical impact assessments before initiating research projects involving advanced technologies.

Consideration: Evaluate potential social, cultural, and economic implications of the research, and address any identified ethical concerns proactively.

By adopting these frameworks and considerations, the pharmaceutical industry can navigate the ethical challenges associated with innovations such as AI, CRISPR, and personalized medicine. This approach helps to build trust among stakeholders, ensures responsible conduct in R&D, and ultimately contributes to the ethical advancement of healthcare technologies.

Question 8. Digital therapeutics are gaining traction as standalone treatments. How do you see the integration of digital therapeutics with traditional pharmaceuticals, and what opportunities and challenges does this convergence present for R&D strategies?

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technology being developed to an emphasis on what is the critical problem that needs to be addressed. This aligns with the concept of patient-centered research and development where identification of the problem and potential root cause analysis is applied. This could help shift some of the focus from the current paradigm of “treating a disease” to also integrate additional prevention measures that the combination of digital therapeutics plus traditional pharmaceuticals could uniquely provide.

Question 9. Quantum computing holds promise for solving complex problems in drug discovery. How might quantum computing reshape the computational aspects of pharmaceutical R&D, and what potential breakthroughs can we anticipate in drug design and optimization?

SHAMAL FERNANDO: Quantum computing has the potential to revolutionize the computational aspects of pharmaceutical research and development (R&D) by addressing complex problems that classical computers struggle to solve efficiently. Here are several ways in which quantum computing might reshape pharmaceutical R&D and potential breakthroughs in drug design and optimization:

MICHAEL N. LIEBMAN: The

1. Molecular Simulation and Drug Discovery:

fundamental challenge will be the need to shift the emphasis on the

Current Challenge: Classical computers often face challenges simulating the quantum

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behavior of molecules accurately, limiting their ability to model complex biochemical interactions.

Quantum Impact: Quantum computers excel at simulating quantum systems, enabling more accurate modeling of molecular structures and interactions.

Breakthrough: Improved molecular simulations could lead to more precise predictions of drug behavior, efficacy, and potential side effects, expediting the drug discovery process.

2. Optimization Problems: Current Challenge: Drug design involves solving complex optimization problems, such as finding the optimal molecular structure for a desired therapeutic effect.

Quantum Impact: Quantum algorithms, such as quantum annealing, have the potential to solve optimization problems exponentially faster than classical algorithms.

Breakthrough: Accelerated optimization could lead to the discovery of novel drug candidates with improved potency, selectivity, and reduced side effects.

3. Quantum Machine Learning: Current Challenge: Classical machine learning approaches play a crucial role in drug discovery, but some problems, such as analyzing high-dimensional datasets, can be computationally intensive.

Quantum Impact: Quantum machine learning algorithms, such as quantum

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support vector machines and quantum neural networks, could enhance the efficiency of data analysis tasks.

Breakthrough: Faster analysis of large datasets could uncover hidden patterns in biological and chemical data, facilitating the identification of new drug targets and biomarkers.

4. Drug Interaction Prediction: Current Challenge: Predicting potential drug interactions and understanding their effects on the human body is a complex task.

Quantum Impact: Quantum computers can model the interactions between multiple molecules more accurately, enabling better predictions of drug-drug interactions.

Breakthrough: Enhanced understanding of drug interactions could lead to safer and more effective drug combinations, reducing the risk of adverse effects.

5. Protein Folding and Structure Prediction: Current Challenge: Predicting the threedimensional structure of proteins accurately is computationally demanding, and classical methods face limitations.

Quantum Impact: Quantum computers can efficiently explore the vast conformational space of proteins, aiding in more accurate predictions of their structures.

Breakthrough: Improved understanding of protein structures could facilitate the design of drugs that target specific proteins 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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higher precision, leading to more effective therapies.

6. Materials Discovery for Drug Delivery: Current Challenge: Discovering and optimizing materials for drug delivery systems involves extensive trial and error.

Quantum Impact: Quantum computing can accelerate the discovery of new materials with specific properties, such as controlled drug release.

Breakthrough: Faster identification of optimal drug delivery materials could enhance the efficiency and effectiveness of drug administration.

7. Cryptography for Secure Data Sharing: Current Challenge: The pharmaceutical industry requires secure sharing of sensitive data for collaborative research.

Quantum Impact: Quantum cryptography can provide secure communication channels, protecting sensitive information from quantum attacks.

Breakthrough: Enhanced security measures could foster greater collaboration and data sharing among pharmaceutical companies and research institutions.

Challenges and Considerations: While the potential breakthroughs are promising, it's crucial to acknowledge the current challenges and uncertainties in developing practical and scalable quantum computers. Factors such as 98

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error rates, decoherence, and the need for error correction pose significant hurdles.

Additionally, the integration of quantum computing into existing pharmaceutical R&D workflows requires careful consideration of hybrid approaches and compatibility with classical systems.

In conclusion, quantum computing holds great promise for reshaping the computational landscape of pharmaceutical R&D, potentially unlocking new avenues for drug discovery and optimization. However, the realization of these breakthroughs will depend on the continued progress in quantum hardware, algorithm development, and the successful integration of quantum technologies into the pharmaceutical research pipeline.

Question 10. The microbiome's influence on health and disease is increasingly recognized. How is microbiome research influencing drug development strategies, and what role might it play in the future of personalized medicine?

LAKSHMI RAGHAVAN: There is a big push towards a paradigm shift in healthcare towards personalized and precision medicine through better understanding of the interindividual differences that determines how drugs are tailored to individuals based on genetic and environmental factors. The microbiome, which are trillions of bacteria that live in our bodies, are found to play an important

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role in drug development, affecting both the safety and efficacy of the drugs.

Microbiome impacts the efficacy of the drugs through their interactions affecting how the drugs are absorbed, metabolized, and excreted. For example, some bacteria can break down drugs before they have a chance to reach their target, while other bacteria can produce enzymes that activate drugs. The microbiome can also affect the safety of drugs by causing side effects. For example, some bacteria can produce toxins that can harm the body, while other bacteria can interact with drugs to produce byproducts that are a significant risk to the patient.

Researchers are developing innovative therapies that target the microbiome to treat a variety of diseases. A particular example of such therapy is Fecal microbiota transplantation (FMT). FMT is a procedure in which stool from a healthy donor is transplanted into the colon of a patient

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with a disease. FMT has been shown to be effective in treating recurrent Clostridium difficile (C. diff) infection, a serious type of diarrhea. Other examples include prebiotics and probiotics that are non-digestible fibers and live bacteria respectively. While prebiotics are shown to improve gut health and may be beneficial to prevent and treat diseases such as irritable bowel syndrome and irritable bowel disease, probiotics help to treat diseases such as diarrhea, vaginal infections and allergies.

The microbiome also plays a significant role in the development of personalized medicine.

Personalized medicine is an approach where the individual's genetic, environmental, and lifestyle factors are utilized to tailor treatment plans. Such personalized medicine strategies are being adopted for a variety of diseases, such as cancer, autoimmune diseases, and mental health disorders.

The microbiome is a complex and dynamic ecosystem that vary from individual to individual and the mechanism of how and why it differs in different individuals is yet to be understood. However, as newer therapies are innovated and researchers are making progress in the understanding of microbiome’s role in health and disease, the future holds a promise to target personal medicine.

Some potential benefits of using the microbiome to develop personalized medicine over traditional treatment strategies are, improved efficacy, reduced side effects and new treatment options for diseases that are 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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currently difficult to treat, for example, cancer and autoimmune diseases.

The use of the microbiome to develop personalized medicine strategies is still in its infancy. Challenges still remain in the understanding of the interaction of microbiome with drugs that are already approved and in the market. However, future is bright for use of microbiome to radically transform healthcare and how patients are individually treated based on their specific genetic and microbiome environment.

Question 11. Continuous manufacturing is being explored as an alternative to traditional batch processing. How can the pharmaceutical industry leverage continuous manufacturing to enhance efficiency in R&D, and what impact does it have on the scalability of production processes?

SOMESH SHARMA: For an extended period, the pharmaceutical business has restricted its innovation to the exploration and creation of novel active compounds. Meanwhile, the manufacturing structure, which is mostly reliant on batchwise technology, has remained unchanged. In the recent years, the main regulatory agencies (FDA) has recognized the need for a change in drug production and started to promote continuous manufacturing technologies, and encourage pharmaceutical companies to develop and adapt such processes. Process intensification or continuous manufacturing has many 100

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As neuroscience reveals the intricacies of the brain, breakthroughs in treating neurological disorders emerge.

Targeted drugs, optogenetics, deep brain stimulation, and innovative therapies like gene therapy and nanoparticle delivery signify a promising era in neuro pharmaceutical R&D.

advantages over batch processes, for instance, speed, safety, waste generation, cost effectiveness and building a sustainable process. Continuous manufacturing is also an initiative towards green chemistry with lower consumption of raw materials and generates less waste material. It has made significant progress in pharmaceutical manufacturing from raw materials to the final dosage forms, though, true integration still demands lot of investigation and optimisation.

Continuous manufacturing provides flexibility of manufacturing of products in shorter time with high control and quality as transition from laboratory to commercial scale is quite easy. Further, establishment of facilities requires less space in comparison to batch manufacturing plants with reduced overhead costs and helps to meet the global demand of medicine with less disruptions. In summary, the pharmaceutical industry can

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leverage continuous manufacturing to enhance efficiency in R&D and improve scalability of production processes with precise control over the manufacturing process, reduce the amount of space and raw materials requirements.

MICHAEL N. LIEBMAN: It would seem that continuous manufacturing affords benefits in economy of scale and efficiency and provide potentially greater control of quality. I would expect, however, that this may further limit aspects of flexibility or adaptability and these are key elements in research and development so I see continuous manufacturing more as an operational benefit than a research or strategic benefit.

Question 12. Given the global nature of health challenges, how can the pharmaceutical industry enhance international collaboration in R&D, and what role do multinational partnerships play in addressing global health threats?

SOMESH SHARMA: Covid 19 pandemic has accelerated international collaboration in R&D and build an early warning system for disease outbreak to address global health threats with continuous and transparent partnership to develop new treatments and vaccines. There are various factors driving collaborative environment – i) government funding and partnership with private sector, ii) enhanced international collaboration to address cross border health risks, iii) emerging technologies

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and conducive environment for data sharing, and iv) increased academia-industrial collaborations.

One example of a multinational partnership is the Coalition for Epidemic Preparedness Innovations (CEPI), which is a global partnership that aims to accelerate the development of vaccines against emerging infectious diseases. Another example of a multinational partnership is the Global Health Innovative Technology Fund (GHIT), which is a public-private partnership that aims to promote the development of new drugs, vaccines, and diagnostics for neglected diseases in developing countries.

Global partnerships can play a crucial role in addressing global health needs by sharing knowledge, resources, technologies to develop new treatments and vaccines. The collaborations at government level, industryacademia and industrial partnerships to meet unmet demands is crucial for a health society.

Question 13. Neuropharmaceuticals pose unique challenges in drug development. How are advancements in neuroscience and neuropharmacology influencing R&D strategies, and what breakthroughs can we expect in the treatment of neurological disorders?

LAKSHMI RAGHAVAN: The bloodbrain barrier (BBB) serves as a highly selective barrier separating the central nervous system from the systemic circulation. The main challenge with developing treatments for neurological 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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disorders is that the blood -brain barrier restricts the ability of drugs to reach their site of action. Development of delivery strategies for neurological disorders is primarily dependent on the advances made in the understanding of the complexity of the barrier. Some of these advancements are listed below.

As our understanding of the complexities of the brain has advanced, more targeted and effective treatments for neurological disorders are being examined. For example, we now know that many neurological disorders are caused by problems with specific neurotransmitters, such as dopamine or serotonin. This knowledge has led to the development of drugs that can target these neurotransmitters and improve symptoms.

Some of the newer technologies, such as optogenetics and deep brain stimulation, are couple of new techniques that are used to treat neurological disorders. Optogenetics 102

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is a technique that uses light to activate or deactivate neurons. Deep brain stimulation is a technique that uses electrical impulses to stimulate or inhibit brain activity. These are particularly useful to treat a wide range of neurological disorders, including Parkinson's disease, epilepsy, and depression.

Biomarkers are biological molecules that can be used to measure the severity of a disease or the response to treatment.

Biomarkers are becoming increasingly important in the development of new drugs for neurological disorders. By using biomarkers, researchers can identify patients who are most likely to benefit from a particular drug and track the progress of the disease over time.

Still challenges remain in the advancements in treating neurological disorders and the more we understand the neurological barriers, the better will be the outcome of some of the novel treatments that are being developed.

Some of the advanced treatments that we can expect in the treatment of neurological disorders is gene therapy, stem cell therapy and nanoparticle technology that delivers drugs directly to the brain. Moreover, developing a greater understanding of the cellular and molecular mechanisms which control the BBB will enable scientists to optimize the delivery of small molecules, biological therapeutics and diagnostic agents to target sites within the CNS. Michael N. Liebman: I believe that diseases/conditions in neurological disorders are an example of the real world complexity in disease that is not being adequately addressed

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at the clinical level. This not a criticism of clinical medicine but more a reflection on the gap between clinical practice and clinical research. I consider the major challenge is the ability to accurately, and reproducibly, diagnose complex diseases with respect to critical stratification and the need to consider disease as a process, not a state, and how this affects the accuracy of a diagnosis. Of course this affects patient treatment and management but it also impacts the ability to effectively identify clinical targets for drug development because of the inherent non-homogeneity of patients diagnosed with a specific condition. In neurological disorders this reflects not only the spectrum nature of many current diagnostic classifications but also the challenge in considering the impact of comorbid conditions both on diagnosis and potential response to therapy.

Question 14. Predictive analytics is becoming more sophisticated in predicting drug safety and efficacy. How can the industry leverage predictive analytics to mitigate risks and optimize decision-making throughout the drug development process?

SHAMAL FERNANDO: Leveraging predictive analytics in the drug development process can significantly enhance decision-making and mitigate risks. Here's a systematic approach that the pharmaceutical industry can follow to optimize decision-making using sophisticated predictive analytics:

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1. Data Integration and Quality Assurance: Objective: Ensure access to diverse and high-quality data.

Application: Integrate data from various sources, including preclinical studies, clinical trials, real-world evidence, and -omics data. Implement robust data quality assurance processes to enhance the reliability of predictive models.

2. Early Target Identification and Validation: Objective: Improve the selection of drug targets with a higher probability of success.

Application: Utilize predictive analytics to analyze genetic, omics, and pathway data to identify potential drug targets. Validate and prioritize targets based on predictive models to increase the likelihood of success in later stages.

3. Compound Screening and Optimization: Objective: Prioritize and optimize lead compounds.

Application: Employ predictive models to assess the pharmacokinetics, toxicity, and efficacy of lead compounds. Identify and optimize promising candidates while reducing the likelihood of late-stage failures.

4. Clinical Trial Design and Optimization: Objective: Design and optimize clinical trials for efficiency and success.

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to model patient populations, predict enrollment rates, and optimize trial parameters. Implement adaptive trial designs based on real-time data analysis to address emerging insights during the trial.

5. Patient Recruitment and Retention: Objective: Enhance patient recruitment and retention in clinical trials.

Application: Implement predictive models to identify suitable patient populations and predict recruitment rates. Tailor recruitment strategies based on predictive analytics to improve enrollment and retention.

6. Biomarker Identification and Validation: Objective: Identify and validate biomarkers for patient stratification.

Application: Use predictive analytics on omics data to identify potential biomarkers associated with drug response. Prioritize biomarkers based on their predictive power and validate their relevance for patient stratification.

7. Adverse Event Prediction and Risk Mitigation: Objective: Early detection and prediction of adverse events.

Application: Implement predictive models to analyze preclinical and clinical data for potential safety concerns. Proactively address safety issues and optimize risk mitigation strategies.

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8. Real-world Evidence (RWE) Analysis: Objective: Enhance understanding of drug safety and effectiveness in real-world settings.

Application: Analyze real-world data using predictive analytics to assess long-term safety and efficacy. Identify potential safety signals and optimize post-market surveillance strategies.

9. Market Access and Commercialization Strategies: Objective: Optimize market access and commercial success.

Application: Use predictive analytics to model market dynamics, forecast drug demand, and assess pricing and reimbursement strategies. Anticipate potential market challenges and adjust commercialization plans accordingly.

10. Continuous Monitoring and Learning: Objective: Iteratively improve decisionmaking based on ongoing insights.

Application: Establish a continuous learning loop. Regularly update predictive models based on new data, outcomes, and emerging trends to refine decision-making processes.

Considerations for Effective Implementation: Interdisciplinary Collaboration: Foster collaboration between data scientists, biostatisticians, clinicians, and domain experts to ensure the development and application of relevant predictive models.

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Regulatory Compliance: Align predictive analytics practices with regulatory requirements. Demonstrate the validity and reliability of predictive models for regulatory acceptance.

Ethical Data Use: Implement ethical data practices to protect patient privacy and comply with data governance regulations.

Model Interpretability: Ensure that predictive models are interpretable, allowing stakeholders to understand and trust the results.

Validation and Calibration: Regularly validate and calibrate predictive models using new data to maintain their accuracy and relevance.

By following these steps and considerations, the pharmaceutical industry can harness the power of predictive analytics to make informed decisions, reduce risks, and enhance the overall efficiency of the drug development process.

Question 15. 3D printing has applications in drug formulation and personalized medicine. How might 3D printing technologies revolutionize drug delivery systems, and what implications does this have for patient-specific treatment regimens?

LAKSHMI RAGHAVAN: 3D printing has been in existence from the 1980s and since then has increasingly found applications in drug delivery and pharmaceutical development. Three-

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dimensional printing is a layer-by-layer, automated process, which enables the manufacturing of complex, personalized products on-demand. 3D printing applications are several fold and some examples are given below.

Personalized drug dosage and release: 3D printing allows for the fabrication of customized drug formulations with precise dosage and release profiles tailored to individual patient needs. This can be particularly beneficial for patients with complex medical conditions or those who require multiple medications.

Improved drug efficacy and reduced side effects: By controlling the distribution and release of drugs within the body, 3D printing can enhance drug efficacy while minimizing side effects. This is particularly important for drugs with narrow therapeutic windows, where the difference between effective and toxic doses is small.

Development of novel drug delivery systems: 3D printing enables the creation of intricate and innovative drug delivery systems, such as implantable devices, transdermal patches, and inhalers. Specific example is in Microneedles, which have huge applications if the 3D print design appropriately adopted.

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controlled drug release, improving patient compliance and reducing the frequency of administrations.

Fabrication of complex drug formulations: 3D printing can combine multiple drugs and materials within a single dosage form, creating complex formulations that would be difficult or impossible to produce using traditional methods. This can lead to the development of more effective combination therapies and personalized treatment regimens.

3D printing holds tremendous potential for personalized medicine, enabling the creation of patient-specific drug formulations tailored to individual genetic, physiological, and disease characteristics

Implications for patient-specific treatment regimens: 3D printing holds tremendous potential for personalized medicine, enabling the creation of patient-specific drug formulations tailored to individual genetic, physiological, and disease characteristics. This level of personalization can optimize treatment efficacy, minimize side effects, and improve patient outcomes.

Some specific examples of how 3D printing is being used to revolutionize drug delivery systems: • Personalized oral tablets: 3D printing is being used to create customized oral tablets with precise dosage and release profiles, allowing for individualized treatment regimens. This will benefit a large number of people who would require specific dose regimen based on their physical condition, 106

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• Implantable drug delivery devices: 3D-printed implantable devices can deliver drugs directly to the site of action, providing sustained and controlled release, reducing the frequency of administrations, and improving patient compliance.

• Transdermal drug patches: 3D-printed transdermal patches can deliver drugs through the skin, offering a non-invasive alternative to oral or injectable medications.

• Inhalers for lung diseases: 3D-printed inhalers can deliver drugs to specific areas of the lungs, improving drug efficacy and reducing systemic side effects.

While the success is limited with FDA in the applications of 3Dprinting, the tremendous

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potential that this technology offers allows the opportunity to explore and optimize this technology for commercial applications. As this technology continues to develop, it is only matter of time for more innovative and effective drug delivery systems to emerge, offering hope and providing relief to a large number of people suffering from diseases that is otherwise difficult to treat.

Question 16. As we conclude our discussion on 'Innovations in Pharmaceutical R&D: Navigating the Future,' I'd like to invite each panelist to share their perspective on the most critical factor or innovation that they believe will define the future of pharmaceutical research and development. Additionally, what advice would you give to the next generation of researchers and leaders entering this dynamic and evolving field?

LAKSHMI RAGHAVAN: Conventional drug development is a long, tedious, and expensive process.

With digital health exploding in the last few years, understanding and use of artificial intelligence and machine learning to different stages of drug development will be critical to the development of new drugs in a much shorter time and at the same time highly precise. Starting from drug discovery, artificial intelligence will play a big role in product development, clinical trials to manufacturing, real world evidence and commercial settings.

The key challenges that still exist are the trust or the lack of it in the AI models and the

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inertia for a change from within the pharmaceutical companies and regulatory agencies. It is heartening to see that perspective changing with lots of initiatives in the pharmaceutical industry as they start to collaborate with technology companies to come up with innovative solutions. The future holds a lot of promise for the next generation to continue exploring digital applications in not only development but also they should focus on precision and personalized medicine, which is critical to a large number of patients suffering from diseases that would require specific dosing regimen for a successful outcome.

SOMESH SHARMA: There are several elements that could influence the pharmaceutical industry's future as it is constantly evolving.

Everyone primary goal is to benefit society, which calls for creative solutions, knowledge and data sharing, storage and analysis, developing ML and AI models which are more friendly and predictive, enhanced industryto-industry cooperation, government sponsorship, and academia-industry partnerships. The future of treatment is going to change with more digital therapies, precision medicine and personalised therapies instead of universal treatments.

To reduce its influence on environment, the pharmaceutical business is also embracing green initiatives and sustainability. This includes lowering carbon emissions by using 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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renewable energy sources, energy-efficient production, continuous manufacturing, minimising waste, utilising eco-friendly packaging materials, creating more environmentally friendly chemical synthesis techniques (green chemistry), and consuming less water throughout the manufacturing process.

For the next generation, a transformational mindset for steadfast collaborative endeavors will be essential, given the complexity of today's healthcare concerns and the speed at which technology is developing.

Shamal Fernando: Critical Factor: Integration of Artificial Intelligence.

Advice: Embrace interdisciplinary collaboration, stay agile in technology adoption, and prioritize ethical considerations.

Critical Factor: Advancements in Personalized Medicine.

Advice: Master genomic technologies, prioritize patient-centric approaches, and adapt to evolving technologies.

Critical Factor: Real-world Evidence and Data Analytics.

Advice: Develop strong data analytics skills, advocate for robust RWE integration, and champion transparent communication.

Critical Factor: Quantum Computing in Drug Discovery.

Advice: Understand quantum principles, collaborate with experts, and stay adaptable in the evolving quantum landscape.

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Critical Factor: Predictive Analytics for Risk Mitigation.

Advice: Hone predictive modeling skills, prioritize data quality, and foster a mindset for continuous learning in drug development.

Michael N. Liebman: I believe that the most critical factor for improving the development of effective drugs and reducing the current rate of failure in drug development will require greater emphasis on accepting the complexities of the real world. Efforts to address patient complexity with a focus on diversity is a positive step but limited as it focuses on age, gender, ethnicity, disability, etc but does not address the real world issues of co-morbidities, poly-pharmacy, etc.

Complexity in defining disease and diagnosis needs to address the complexity that disease is a process and not a state and the temporal development of disease needs to be considered to enhance diagnosis and stratification.

This impacts target identification, drug development and clinical trial design as well. And it is also critical, early in drug development, to understand the factors impacting clinical practice, e.g. guidelines, biases, experience, reimbursement issues, training. At the end of the day in pharma R&D it is not sufficient to only have a drug that works in clinical trials and can be approved by regulatory agencies for use, it is critical to have a drug that physicians are willing to prescribe and patients are willing to take.

Thank you for your participation!!

W EBIN A R

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Arab Health 2024: Where Healthcare Innovations Converge Join us at the forefront of healthcare advancements as Arab Health, the premier event for the global healthcare industry, unfolds from January 29 to February 1, 2024, at the prestigious Dubai World Trade Centre.

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What to Expect in 2024?

• Professional Visits: Over 110,000 professionals exploring the latest in healthcare.

• Exhibiting Companies: Connect with 3,450+ companies at the forefront of innovation.

• Participating Countries: A truly global event with representatives from 180+ countries.

• Conference Delegates: Engage with 3,600+ conference delegates shaping the future of healthcare.

About Arab Health

Arab Health serves as the epicenter for addressing global healthcare challenges. It's not just an event; it's a platform where regional and international policy drivers, thought leaders and healthcare professionals converge to explore groundbreaking solutions, share insights, and foster collaborations. The event showcases the progress and achievements of the healthcare sector and facilitates networking with industry leaders from across the globe.

Arab Health 2024: Where Healthcare Innovations Converge Date:

29th Jan - 1th Feb 2024

Location: Dubai World Trade Centre Website: https://www.arabhealthonline.com/en/ Home.html Email:

arabhealth@informa.com

Why Attend?

• Networking Opportunities: Meet and connect with leaders in the global healthcare community.

• Insights into the Future: Gain valuable perspectives through curated features and live sessions.

• Innovations Unveiled: Explore groundbreaking solutions from 3,450+ manufacturers.

• Interactive Experience: Witness live demonstrations and directly engage with global innovators.

Don't miss this opportunity to be part of the future of healthcare. Register now for Arab Health 2024 and elevate your understanding of the latest trends, innovations, and collaborations in the dynamic world of healthcare. Your journey into the future of healthcare begins here.

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"41% of event professionals are putting on more events in 2023 than they originally planned."

Don't be left behind...

We are here to help!

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Where the world of healthcare meets 68,000+

healthcare professionals

3,600+ delegates

3,450+

exhibitors

180

visiting countries

$1.81 billion**

total value of business generated

Register for free before 4 Jan 2024

29 Jan – 1 Feb 2024 | Dubai World Trade Centre

arabhealthonline.com

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Medlab Middle East 2024: Pioneering Growth in Medical Laboratories Unlocking Opportunities in Laboratory Medicine, February 5-8, 2024, Dubai World Trade Centre In its 23rd year, Medlab Middle East stands at the forefront of advancing laboratory medicine, fostering growth, and catalyzing innovation. As the epicenter for global industry professionals, this event, slated for February 5-8, 2024, promises to be an unparalleled hub where challenges transform into opportunities, tailored solutions emerge, and strategies unfold to shape the future of medical laboratories not only in the Middle East but across the globe.

Global Reach, Local Focus: Redefining Healthcare Growth Medlab Middle East is evolving, embracing a paid-for visitor ticketing system, effective from 8 Jan 2024. However, you have the exclusive chance to register for free before this date.

This strategic shift reflects the commitment to sustaining and elevating the quality of business and learning opportunities within the industry.

Key Event Highlights: A Glimpse into 2024 • Venue: Dubai World Trade Centre • Floor Space: 15,000 sqm • Attendees: 30,000+ • Exhibitors: 900+ • Visiting Countries: 180+ • Exhibiting Countries: 40+ • Country Pavilions: 12

Why Attend Medlab Middle East 2024?

Experience a dynamic convergence of knowledge, networking, and business-building

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opportunities. With an expected attendance of over 30,000, Medlab Middle East 2024 is poised to be a transformative journey, fostering connections and pioneering advancements in laboratory medicine.

Dedicated Product Categories: Showcasing Value to Your Business Explore a vast spectrum of products at Medlab Middle East, tailored to bring the utmost value to your business, including Disposables, Healthcare/General Services, Imaging, Infrastructure, IT, Laboratory, Medical Equipment, and Pharma/Nutrition.

Why Exhibit?

• Build Relationships: Connect with the entire spectrum of medical laboratory professionals, propelling existing relationships forward and unlocking new networks and channels.

• Professional Engagement: With over 20,000 professional visits and 3,700+ delegates, seize the opportunity to engage with representatives from 180+ countries.

• Showcase Innovations: Be at the forefront of the industry by showcasing your products and latest innovations, benefitting from face-to-face networking, live product demos, and the chance to seal deals while attending the show.

As Medlab Middle East 2024 marks a pivotal moment in the industry's evolution, seize the opportunity to be part of this transformative experience. Register for your free visitor badge before January 7, 2024, and join us in Dubai for an event that is set to shape the future of medical laboratories worldwide.

EVENT S

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Fuelling medical laboratory growth across the Middle East region and beyond

15,000

30,000+

900+

180+

40+

12

sqm floor space

attendees

exhibitors

visiting countries

exhibiting countries

country pavilions

Register your interest to attend

5-8 Feb 2024 | Dubai World Trade Centre medlabme.com

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BIOMARKERS 2024 29 February - 01 March 2024 London, UK Europe’s flagship event for Biomarker Research Returns: the must-aaend forum to engage with the latest trends transforming biomarker and translational research

Download the agenda via the QR code Contact Miles Blakey with any queries at: m.blakey@oxfordglobal.com

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World Summit on Pharmaceutics and Drug Designs 23rd Jan 2024 San Diego | USA https://www.iarfconference.com/conf/index. php?id=2125048 About the Event: World Summit on Pharmaceutics and Drug Designs Conference will witness the participation of the Academicians, Universities, Professionals and Industry experts gathering on a global platform to discuss the future prospects of Pharmaceutics and Drug Designs.

International Conference on Pharmaceutical Formulations

Listed Under: Research & Development

7th Dec, 2023 Sao Tome, Africa https://iiter.org/conf/index.php?id=2089073 About the Event: The aim of the International Conference on Pharmaceutical Formulations (ICPF-2023) is to provide a platform that brings academicians, scientists, professionals, and practitioners under one roof. These opportunities will allow attendees to focus on current trends, achievements, solutions, and new fields.

Listed Under: Manufacturing

International Congress on Drug Discovery and Pharmacy Practices

Pharmapack Europe

14th Dec - 15th Dec, 2023

24th – 25th, Jan 2024

Amsterdam, Netherlands

Paris, France

https://www.iierd.org/events/index.php?id=1879628

https://www.pharmapackeurope.com/en/home.html

About the Event: International congress on Drug Discovery and Pharmacy Practices is delighted to welcome professionals, academicians, and practitioners from the field to attend high-level conferences. The IIERD is organising meaningful discussions focusing on solutions, future scope, and ongoing trends. This is your chance to acquire knowledge, learn, and improve.

About the Event: Pharmapack is the European hub for the pharma packaging and drug delivery device industry. Taking place annually in Paris, the event unites over 5,000 attendees and more than 300 exhibitors for two days of innovation, networking, and education. Join us in Paris to connect with other pharma professionals and expand your network at the heart of pharma.

Listed Under: Research & Development

Listed Under: Manufacturing

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Biopharmaceutics and Biologic Drugs February 19-20, 2024 London, UK https://biopharmaceutics. pharmaceuticalconferences.com/ About the Event: Biopharma 2024 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in Biopharma 2024.

Listed Under: Research & Development

Pharma and Pharmaceutical Sciences February 22-23, 2024 Zurich, Switzerland https://pharma.pulsusconference.com/exhibition About the Event: The 3rd Global Conference on Pharma and Pharmaceutical Sciences will exhibit the products and services from commercial and non-commercial organizations like Drug manufactures, Clinical Trial Sites, Management Consultants, Chemists, Pharmacists, Business delegates and Equipment Manufacturers.

Listed Under: Clinical Trials

International Conference on Pharmaceutical Formulations February 27th 2024 Las Vegas, USA

European Pharma Congress

https://itar.in/conf/index.php?id=2156646

February 22-23, 2024

About the Event: Pharma Europe 2024 is comprised of keynote and speakers sessions on latest cutting edge research designed to offer comprehensive global discussions that address current issues in Pharma Europe 2024

About the Event: International Conference on Pharmaceutical Formulations will offer researchers, delegates and scholars an incredible chance to interact with each other and share their experience and knowledge of technology application. In order for true progress to be achieved in Pharmaceutical Formulations, it simply isn't enough for professionals in the field to gather at boilerplate events that have been designed as part of a "one-size-fits-all" approach to academic event organizing.

Listed Under: Research & Development

Listed Under: Manufacturing

Zurich, Switzerland https://europe.pharmaceuticalconferences.com/

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Kynexis Takes Center Stage in Precision Therapeutics for Brain Disorders

FDA Grants Fast Track Status to AviadoBio's AVB-101 for Frontotemporal Dementia

Kynexis, a biotech company specializing in precision therapeutics for brain diseases, officially launched today after securing US $64 million in Series A funding.

AviadoBio, at the forefront of gene therapy for neurodegenerative disorders, has gained FDA clearance for the Investigational New Drug (IND) application of AVB-101 in frontotemporal dementia (FTD) patients with progranulin (GRN) gene mutations.

The funding round, spearheaded by Forbion and joined by Ysios Capital and Sunstone Life Science Ventures, positions Kynexis to advance its leading therapeutic candidate, KYN-5356.

This potential first-in-class KAT-II inhibitor is designed to target cognitive impairment associated with schizophrenia (CIAS) and is now set to progress into clinical development.

The foundation of the KYN-5356 program rests on the groundbreaking scientific contributions of Professors Robert Schwarcz, Ph.D., from the University of Maryland School of Medicine, and Carol Tamminga, M.D., from the University of Texas UT Southwestern Medical Center. Dr. Schwarcz, a trailblazer in the study of the kynurenine pathway in the brain, has revealed the significant role of kynurenine in cognition and the pathophysiology of schizophrenia. Meanwhile, Dr. Tamminga, a distinguished psychiatrist and neuroscientist, is renowned for her pioneering translational research in psychosis. Her work has provided crucial insights into the mechanisms underlying schizophrenia, its genetics, and associated biomarkers.

Positioned at the intersection of human genetics, biomarkers, and deep phenotyping, Kynexis is strategically poised to embark on an innovative precision psychiatry-focused strategy for the development of KYN-5356 in addressing cognitive impairment associated with schizophrenia (CIAS).

This promising candidate, KYN-5356, stands out as a potent and highly selective KAT-II inhibitor, granted under license from Mitsubishi Tanabe Pharma Corporation (MTPC).

As per the terms of the agreement, Kynexis holds an exclusive global license from MTPC, empowering the company to undertake the worldwide development and commercialization of KYN-5356—a pivotal step forward in advancing precision therapeutics for brain diseases.

READ THE COMPLETE POST

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Additionally, AVB-101 has been designated Fast Track status by the FDA for treating FTDGRN, streamlining its development and review to swiftly address the critical medical needs of patients.

AVB-101 is developed as a potential one-time therapy, aiming to arrest disease progression by introducing a functional copy of the GRN gene.

This seeks to restore appropriate progranulin levels in the brain regions affected by Frontotemporal Dementia (FTD). In 2022, both the U.S. FDA and the European Commission (EC) granted orphan designation to AVB-101 for FTD treatment.

The company has recently initiated enrollment in European countries for ASPIREFTD, an open-label, multi-center doseescalation study assessing the safety and preliminary efficacy of AVB-101 in FTD-GRN patients.

Frontotemporal Dementia (FTD) is a devastating form of early-onset dementia, often resulting in death within seven to 13 years from symptom onset and three to 10 years after diagnosis. Individuals with FTD commonly exhibit personality changes, behavioral disturbances, language loss, apathy, and decreased mobility. It is a prominent cause of dementia in those under 65, with an estimated prevalence of up to 4.6 cases per 1,000 people at any given time.

About one-third of FTD cases are genetic, with autosomal dominant mutations in three genes, including the progranulin gene, being the most frequent.

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Genevant Sciences and Novo Nordisk Partner on Gene Editing Treatment for Hemophilia A Genevant Sciences, a prominent nucleic acid delivery firm renowned for its state-of-the-art platforms and extensive lipid nanoparticle (LNP) patent portfolio, revealed a collaboration and nonexclusive license pact with Novo Nordisk.

The partnership aims to integrate Genevant's exclusive LNP technology with Novo Nordisk's innovative mRNA-based megaTAL technology, focusing on developing an in vivo gene editing solution for hemophilia A.

Boehringer's Dual Therapy Excels in Phase II Trial for Chronic Kidney Disease Boehringer Ingelheim reports promising Phase II Results for BI 690517, a Selective Aldosterone Synthase Inhibitor, in Combination with Empagliflozin for Chronic Kidney Disease.

The 14-week trial demonstrated a significant up to 39.5% reduction in albuminuria, a key kidney damage marker, compared to placebo. This marks the first clinical trial evaluating this innovative treatment class alongside standard care, including empagliflozin, for individuals with chronic kidney disease (CKD), impacting over 850 million people globally.

This collaboration further extends the ongoing research and development collaboration in hemophilia A between Novo Nordisk and 2seventy bio, as previously disclosed by 2seventy bio.

The agreement disclosed results from the utilization of an option under a preceding deal between Genevant and 2seventy bio. This option was then transferred from 2seventy bio to Novo Nordisk.

The financial terms of the agreement mirror those outlined in the option agreement negotiated between Genevant and 2seventy bio, as officially communicated by 2seventy bio in January 2022.

BI 690517 employs a unique mechanism of action to efficiently and durably inhibit aldosterone synthase, the enzyme governing the crucial final steps in aldosterone synthesis. Elevated aldosterone levels contribute to organ damage and foster cardiorenal-metabolic conditions like hypertension, chronic kidney disease, or heart failure.

While inhibiting aldosterone synthase can result in a modest increase in serum potassium, this research indicates that the mechanism of action of empagliflozin has the potential to alleviate the risk of hyperkalemia when used as a foundational therapy.

This finding holds significant clinical relevance, as severe hyperkalemia may necessitate alterations in medical treatment or hospitalization. The addition of the novel drug class BI 690517 on top of empagliflozin may offer a solution to this pressing unmet medical need.

In the Phase II trial, a pivotal secondary endpoint focused on achieving a clinically significant reduction in UACR (≥30%). Remarkably, up to 70% of patients treated with BI 690517 in conjunction with empagliflozin successfully reached this endpoint. Analyzing albuminuria changes as predictive indicators, these observed alterations may potentially translate into risk reductions for clinical kidney disease events, estimating at least a 30% decrease.

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QIAGEN offers customers utilizing the AVITI System comprehensive Sample to Insight NGS workflows.

These workflows include validated QIAseq panels and integrated bioinformatic solutions, featuring CLC LightSpeed and QCI Interpret software.

QIAGEN and Element Biosciences Collaborate on AVITI NGS Workflows QIAGEN and Element Biosciences, Inc. have unveiled a strategic alliance to provide all-encompassing next-generation sequencing (NGS) workflows for the AVITI™ System. This innovative sequencing platform utilizes Element's groundbreaking Avidity sequencing chemistry.

The AVITI System by Element, a versatile benchtop sequencer renowned for its exceptional performance and cost-effectiveness, generates high-quality, affordable data for various applications across different scales.

Inhalon Biopharma Acquires hMPV Antibodies License from the University of Georgia

Inhalon Biopharma, a clinical-stage firm developing an inhaled antibody platform for treating acute respiratory infections (ARI), has entered into a licensing agreement with the University of Georgia.

The agreement involves obtaining a panel of Human Metapneumovirus (hMPV) antibodies. This

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QIAGEN's QIAseq panels facilitate precise and efficient NGS library preparation. Validated on the AVITI sequencer, the QIAseq miRNA Library Kit, QIAseq xHYB Actionable Exome Panel, and QIAseq Targeted DNA Pro Panel demonstrate robust performance, offering high specificity, uniform coverage, and reliable variant detection.

Introducing LightSpeed, a novel addition to the QIAGEN CLC Genomics Workbench Premium, which empowers AVITI System users to conduct highly efficient secondary analysis for cost-effective whole genome sequencing (WGS) with impressive runtimes.

Meanwhile, QCI Interpret, a fully adaptable software solution, streamlines NGS variant interpretation and reporting for oncology and hereditary applications, boasting a track record of over 3 million issued reports.

licensing arrangement enables Inhalon Biopharma to choose specific antibody candidates for progression into clinical development, aiming to address hMPV treatment.

Human metapneumovirus (hMPV) is an acute respiratory infection marked by symptoms such as cough, fever, nasal congestion, and shortness of breath. It leads to over 1 million clinical visits, 260,000 pediatric emergency department visits, and 20,000 hospitalizations annually in the U.S. The virus is most prevalent during the winter and spring and spreads through coughing, sneezing, contact with infected individuals, and contaminated surfaces.

Similar to respiratory syncytial virus (RSV), hMPV belongs to the Pneumoviridae family and poses a significant risk to young children, older adults, and individuals with weakened immune systems. Clinical manifestations of hMPV infection may progress to bronchitis or pneumonia, resembling other viruses causing upper and lower respiratory infections.

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Cessation Therapeutics' CSX1004 Granted FDA Fast Track for Fentanyl Overdose Prevention Cessation Therapeutics, Inc., a biotechnology firm in the clinical stage focusing on biologics targeting substances of abuse, has received Fast Track designation from the U.S. Food and Drug Administration (FDA) for CSX-1004. This investigational therapy aims to prevent fentanylrelated overdoses.

iOnctura's Roginolisib Granted FDA IND Clearance iOnctura, a clinical-stage biotech focused on targeted cancer therapies, has received FDA approval for clinical investigations of roginolisib in the United States.

Roginolisib (IOA-244) is undergoing development for both solid and hematologic malignancies, including uveal melanoma—a rare cancer originating in the uveal tract of the eye. In cases where the cancer metastasizes, occurring in about 50% of patients, treatment options are scarce, and the expected overall survival is only one year.

CSX-1004 is a human IgG1 monoclonal antibody designed to specifically target fentanyl and its analogs. Its mechanism involves sequestering fentanyl molecules upon entry into the bloodstream, neutralizing them before reaching the brain, and thus preventing their harmful effects.

Fast Track designation accelerates the development and review process of drugs aimed at treating severe and life-threatening conditions, enabling the swift introduction of investigational products to the market. This designation entails regular engagement with the FDA review team and, if certain criteria are met, possible qualification for Accelerated Approval and Priority Review.

The Fast Track designation was granted, in part, due to nonhuman primate data showing that a single dose of CSX-1004 can prevent the life-threatening respiratory depressant effects of high doses of fentanyl for up to 28 days. Cessation has commenced a Phase 1a, first-in-human study (NCT06005402) to assess the safety, tolerability, and pharmacokinetics of CSX-1004 in healthy volunteers.

Roginolisib, the pioneering allosteric modulator of PI3K , signifies a groundbreaking phase in drug development for this class. Its distinctive binding mode, coupled with a strong selectivity for PI3K , is anticipated to result in an enhanced safety and tolerability profile compared to earlier generation inhibitors.

Roginolisib is currently under investigation in the DIONE-01 trial, a two-part Phase I study (ClinicalTrials.gov, identifier NCT04328844). The trial is fully enrolled, and final data is anticipated in Q1 2024. Across all treated patients thus far, roginolisib monotherapy has demonstrated 7% Grade 3/4 toxicities, with no dose-limiting toxicities, drug-related serious adverse events (SAEs), or drug-related adverse events (AEs) leading to dose interruption or discontinuation.

Although the median Overall Survival has not been reached, 62% of patients were alive at 12 months, showing a favorable comparison to historical controls (34%) in the same setting.

The extended use of roginolisib has been welltolerated, with patients undergoing treatment for up to 40 months in the study. Encouraging clinical activity has been noted among patients with both solid and hematological cancers.

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PRODUCTS & SERVICES

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CLINICAL TRIALS Roquette Asia Pacific Pte Ltd............................................................. OBC Sartorius..............................................................................................IFC

MANUFACTURING

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To receive more information on products & services advertised in this issue, please fill up the "Info Request Form" provided with the magazine and fax it.

1.IFC: Inside Front Cover 2.IBC: Inside Back Cover 3.OBC: Outside Back Cover

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