Artificial Intelligence in Regulatory Affairs: Current Applications, Regulatory Challenges, and Future Perspectives in the Pharmaceutical Industry

fairoosa Thaikkat; Afra P; Hanna Asaraf

Artificial Intelligence in Regulatory Affairs: Current Applications, Regulatory Challenges, and Future Perspectives in the Pharmaceutical Industry

Author(s)	Ms. fairoosa Thaikkat, Ms. Afra P, Ms. Hanna Asaraf
Country	India
Abstract	Artificial intelligence (AI) is progressively reshaping pharmaceutical regulatory affairs, extending its influence from drug discovery into dossier preparation, electronic submission management, regulatory intelligence, pharmacovigilance, clinical trial operations, and Chemistry, Manufacturing, and Controls (CMC) documentation. This narrative review synthesizes current peer-reviewed literature and official guidance issued by the United States Food and Drug Administration (FDA), the European Medicines Agency (EMA), the UK Medicines and Healthcare products Regulatory Agency (MHRA), Health Canada, Japan's Pharmaceuticals and Medical Devices Agency (PMDA), China's National Medical Products Administration (NMPA), the International Council for Harmonization (ICH), and the World Health Organization (WHO) to characterise the present state, regulatory challenges, and future trajectory of AI adoption in regulatory science. AI-enabled natural language processing (NLP) and large language models (LLMs) are increasingly used to automate Common Technical Document (CTD) authoring, structure electronic Common Technical Document (eCTD) submissions, monitor global regulatory intelligence, and accelerate pharmacovigilance signal detection, while machine learning supports predictive quality-by-design (QbD) frameworks in CMC development. Regulatory agencies have responded with an expanding but fragmented body of guidance, including the FDA's 2025 draft guidance on AI-based regulatory decision-making, the EMA's 2024 reflection paper on AI across the medicinal product lifecycle, and jointly issued Good Machine Learning Practice principles. Despite substantial efficiency gains, critical challenges persist regarding model explainability, algorithmic bias, data provenance, validation of "black-box" architectures, cross-jurisdictional harmonisation, and safeguarding sensitive clinical and manufacturing data against cybersecurity threats. This review critically evaluates the adequacy of existing frameworks, identifies gaps in explainability standards, workforce readiness, and global regulatory convergence, and offers a comparative analysis of national and regional approaches through structured tables. It concludes that realising AI's transformative potential in regulatory affairs will require harmonised, risk-based, and lifecycle-oriented governance, sustained human oversight, and closer collaboration between regulators, industry, and academia. The review provides a scholarly reference point for regulatory professionals, postgraduate researchers, and policymakers navigating the evolving intersection of AI and pharmaceutical regulation
Keywords	Keywords: Artificial intelligence; Regulatory affairs; Machine learning; Pharmacovigilance; eCTD; Regulatory intelligence; Quality by design; Drug regulation
Field	Medical / Pharmacy
Published In	Volume 8, Issue 5, September-October 2026
Published On	2026-09-27

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Artificial Intelligence in Regulatory Affairs: Current Applications, Regulatory Challenges, and Future Perspectives in the Pharmaceutical Industry

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