Volume 8 ; Issue 2 ; in Month : July-Dec (2025) Article No : 170
Megala M, Saraswathy T, Priya Dharsini R, et al. T

Abstract

Background: Regulatory Affairs (RA) has long served as the backbone of the Pharmaceutical Industry ensuring compliance with safety, efficacy and quality standards through dossier preparation, drug approvals and post marketing surveillance. However, increasing submission complexity, large regulatory datasets and growing demand for rapid approvals pose major challenges. At the same time, Artificial Intelligence (AI) is emerging as a transformative force across Pharmaceutical Sciences with applications in drug discovery, predictive toxicology, Pharmacovigilance and analytical method development. Its integration into regulatory affairs practice marks a paradigm shift from documentation based processes to predictive, data driven models. Objective: This review explores the convergence of AI, advanced pharmaceutical analytics and regulatory affairs practice. It highlights current applications, benefits and limitations while outlining future opportunities to advance towards a smart, technology enabled regulatory ecosystem. Methods: A narrative review of peer reviewed literature, regulatory guidelines and industry reports was conducted, focusing on AI applications in stability indicating method development, degradation profiling, In-silico toxicology, dossier preparation, pharmacovigilance, Chemistry Manufacturing and Controls (CMC) and regulatory intelligence. Results: AI technologies such as machine learning, deep learning, natural language processing and generative AI are reshaping modern Pharmaceutical Analytics by enabling predictive degradation modeling, optimizing UHPLC/UPLC methods and supporting sustainable green chemistry initiatives. Within regulatory affairs practice, AI facilitates eCTD (Electronic Common Technical Document) dossier preparation, enhances pharmacovigilance through automated signal detection and strengthens global regulatory intelligence. These innovations accelerate approvals, improve data quality, reduce costs and enhance patient safety. However, challenges remain, including regulatory acceptance of AI driven outcomes, ensuring algorithm transparency, maintaining data integrity and the absence of globally harmonized standards across regulatory agencies. Conclusion: AI driven regulatory affairs practice offers the potential to shift drug development and approval towards proactive, real- time and patient centered decision making. Future directions include regulatory sandboxes, incorporation of AI into ICH guidelines, adoption of digital twins, blockchain and personalized medicine approvals. A balanced approach embracing innovation while addressing ethical, legal and global challenges will be critical to fully realize AI’s potential in reshaping Pharmaceutical Innovation.

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