AI's Impact on Regulatory Affairs: Enhancing Drug Development

FINAL ASSIGNMENT PRESENTATION SUMMARY Submitted By: Kaustubh Panda Latesh Yogesh Chaudhari RGA 6463 70866 REGULATORY STRATEGY FOR PRODUCT DEVELOPMENT AND LIFE-CYCLE MANAGEMENT Part I : Artificial Intelligence and Regulatory Industry Part II: Post Market Approval Plan for Anti-Viral Drug in Pandemic

PART: I Artificial Intelligence and Regulatory Industry

Introduction to AI in Regulatory Affairs  AI’s Role in Regulatory Affairs  Artificial Intelligence (AI) is transforming Regulatory Affairs (RA) in pharmaceuticals by accelerating drug approval processes, ensuring compliance with regulations, and refining market strategies. Harper & Patel (2023) emphasize AI's multifaceted role in various RA aspects.  AI applications streamline administrative tasks, enhance dossier preparation efficiency, and aid decision-making processes. Rivare (2023) highlights AI's crucial role in auditing and compliance analysis within RA.

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AI Tools and Applications  Automation and Data Extraction  AI-driven automation efficiently handles complex tasks, including responding to letters, managing accounting records, and extracting medical product data from unstructured documents. Harper & Patel (2023) provide insights into AI's prowess in automating RA processes.  - In Regulatory Chemistry, Manufacturing, and Controls (CMC), AI facilitates simultaneous global submissions, collaborative reviews, and post-approval optimizations. Rivare's study (2023) showcases AI's substantial contribution to post-approval RA tasks.

Perspectives of Health Authorities  World Health Organization (WHO) Stance on AI -The WHO advocates six principles for ethical AI use in healthcare, emphasizing human autonomy and inclusion (WHO, 2021). - Both the FDA and EMA acknowledge AI's potential while addressing concerns regarding bias, transparency, and algorithmic risks (FDA, 2023). They aim to collaborate and ensure safe and effective healthcare innovations.

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Positive Outcomes and Effective Integration  Factors for Effective AI Integration -Successful AI integration in RA requires quality data, revised operating models, and cultural shifts (Finch, 2019). -Specific guidelines and robust governance structures are vital to ensure responsible and effective AI use in medical products (EMA, 2021).

AI's Complementary Role in RA  AI and RA Professionals - AI supports RA professionals by coding regulatory documents, performing semantic searches, and analyzing drug-adverse event relationships (Liu et al., 2021). - Collaboration between AI and human professionals enhances the regulatory process, leveraging AI's strengths while preserving human expertise, ensuring better outcomes in the pharmaceutical industry.

References  Harper, J. K., & Patel, R. (2023). Accelerated Drug Development during Emergencies: Lessons from the COVID-19 Pandemic. Drug Discovery Today, 28(5), 103700. https://doi.org/10.1016/j.drudis.2023.103700.  River, A. (2023, June). Artificial intelligence and digitalisation in pharmaceutical regulatory affairs. Retrieved from https://helda.helsinki.fi/bitstream/handle/10138/359556/Rivare %20Alise%20LitRev_AI%20and%20digitalization%20in%20RA.pdf?sequence=1  World Health Organisation. (2021, June 28). WHO issues first global report on Artificial Intelligence (AI) in health and six guiding principles for its design and use: Growing use of AI for health presents governments, providers, and communities with opportunities and challenges [News release]. Retrieved from https://www.who.int/news/item/28-06-2021-who- issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use.  FDA Voices. (2023, May 10). FDA releases two discussion papers to spur conversation about artificial intelligence and machine learning in drug development and manufacturing. Retrieved from https://www.fda.gov/news-events/fda-voices/fda-releases-two-discussion- papers-spur-conversation-about-artificial-intelligence-and-machine.  European Medicines Agency. (2023, July 19). Reflection paper on the use of artificial intelligence in the lifecycle of medicines. Retrieved from https://www.ema.europa.eu/en/news/reflection-paper-use-artificial-intelligence-lifecycle- medicines. .

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 European Medicines Agency. (2021, August 16). Artificial intelligence in medicine regulation [News release]. Retrieved from https://www.ema.europa.eu/en/news/artificial-intelligence-medicine- regulation.  Finch, R. (2019, September 24). Regulatory Intelligence: How AI will change regulatory operations. Pharma IQ. https://www.pharma-iq.com/business-development/articles/regulatory- intelligence-how-ai-will-change-regulatory-operations  Liu, Z., Roberts, R. A., Lal-Nag, M., Chen, X., Huang, R., & Tong, W. (2021). AI-based language models powering drug discovery and development. Drug Discovery Today, 26(11), 2593–2607. https://doi.org/10.1016/j.drudis.2021.06.009

PART: II Post Market Approval plan for Anti- Viral Drug in a Pandemic

Introduction to Post-Market Approval Plan  XYZ-2024 Antiviral Post-Approval Plan Overview - The XYZ-2024 Antiviral Post-Approval Plan ensures continued compliance, risk management, and therapeutic efficacy post-regulatory approval, emphasizing patient safety and public health. It aligns with regulatory guidelines and best practices (FDA, 2019).

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Key Components of the Post-Market Approval Plan  Key Components for XYZ- 2024 Antiviral Post- Approval Plan - Pharmacovigilance and Risk Management: Continuous monitoring and risk evaluation ensure prompt management of adverse events, prioritizing patient safety (FDA, 2016). - Real-world Data Collection and Analysis: Utilizing real-world evidence complements clinical data, offering insights into the drug's real-world performance (EMA, 2020).

Post-Market Requirements and Lifecycle Management  Compliance and Lifecycle Management for XYZ-2024 Antiviral -Compliance with Post-Market Requirements: Fulfilling post-approval commitments ensures regulatory compliance and effective risk management (FDA, 2019). - Post-Approval Studies and Lifecycle Management: These studies enhance safety profiles and expand indications, driving continued therapeutic advancements (FDA, 2021).

Development Timeline and Pre- Clinical Requirements  Development Timeline and Pre-Clinical Strategy - Estimated Development Timeline: Factors impacting drug development timelines and the influence of accelerated approval pathways during public health crises (FDA, 2020). - Pre-Clinical Requirements and Regulatory Strategy: Rigorous preclinical studies establish safety profiles and mechanisms of action (Woodcock & LaVange, 2017).

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Clinical Trial Design and Intellectual Property Considerations  Clinical Trials and IP Considerations for XYZ-2024 Antiviral  - Clinical Trial Design: Adaptive trial designs expedite data collection and decision-making for the XYZ-2024 Antiviral (Berry et al., 2015).  - Intellectual Property Issues: Securing early patents and conducting freedom-to-operate analyses protect drug innovation and navigate complex patent landscapes (WIPO, 2016).

References  FDA. (2016). Good pharmacovigilance practices and pharmacoepidemiologic assessment. Retrieved from https://www.fda.gov/media/88838/download  EMA. (2020). Real-world evidence in medicine development. Retrieved from https://www.ema.europa.eu/en/documents/presentation/presentation-real-world-evidence- medicine-development_en.pdf  FDA. (2019). Post-approval commitments. Retrieved from https://www.fda.gov/drugs/postmarket- drug-safety-information-patients-and-providers/postapproval-commitments  FDA. (2018). Drug safety communications. Retrieved from https://www.fda.gov/drugs/drug-safety- and-availability/drug-safety-communications  EMA. (2019). Pharmacovigilance Risk Assessment Committee (PRAC). Retrieved from https://www.ema.europa.eu/en/committees/pharmacovigilance-risk-assessment-committee-prac  FDA. (2021). Post-approval research. Retrieved from https://www.fda.gov/science-research/science- and-research-special-topics/post-approval-research  EMA. (2018). Guideline on good pharmacovigilance practices (GVP): Module VII - Periodic safety update report (Rev 1). Retrieved from https://www.ema.europa.eu/en/documents/regulatory- procedural-guideline/guideline-good-pharmacovigilance-practices-gvp-module-vii-periodic-safety- update-report-rev-1_en.pdf  Berry, S. M., et al. (2015). Bayesian adaptive methods for clinical trials. CRC Press.  Drummond, M. F., et al. (2015). Methods for the economic evaluation of health care programmes. Oxford University Press.  FDA. (2018). Guidance for industry: E9 statistical principles for clinical trials. Retrieved from https://www.fda.gov/media/71342/download

 EMA. (2021). Guideline on good pharmacovigilance practices (GVP): Module XVI - Risk minimisation measures. Retrieved from https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/draft- guideline-good-pharmacovigilance-practices-gvp-module-xvi-risk-minimisation- measures-selection_en-3.pdf  FDA. (2019). Nonclinical safety evaluation of reformulated drug products and products intended for administration by an alternate route. Retrieved from https://www.fda.gov/regulatory-information/search-fda-guidance-documents/ nonclinical-safety-evaluation-reformulated-drug-products-and-products-intended- administration-alternate  FDA. (2020). Emergency use authorisation of medical products and related authorities. Retrieved from https://www.fda.gov/media/97321/download  Garrison, L. P., et al. (2018). Performance-based risk-sharing arrangements: Good practices for design, implementation, and evaluation. Value in Health, 21(4), 477- 484.  ICH. (1998). ICH harmonised tripartite guideline: E10 choice of control group and related issues in clinical trials. Retrieved from https://database.ich.org/sites/default/files/E10_Guideline.pdf  WIPO. (2016). Patent Landscape Report on Antiviral Drugs for the Treatment of Hepatitis C Infection. Retrieved from

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Final Assignment Presentation Summary RGA 6463 Kaustubh Panda, Latesh Yogesh Chaudhari

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