Chapter 7: AI for Regulatory and Biosynthetic Analysis
Synopsis
Author
Mrs. Pavani Avula,
Assistant Professor, Department of Pharmacology, Sir C.R. Reddy College of Pharmaceutical Sciences, Eluru, Andhra Pradesh, India
Abstract
Artificial Intelligence transforms regulatory intelligence by employing Natural Language Processing (NLP) to continuously scan, interpret, and summarize updates from major health authorities like the EU HMPC, WHO, and ICH. These algorithmic engines track shifting guidelines and predict future regulatory trends, allowing manufacturers to adapt their strategies preemptively to avoid market withdrawal. Simultaneously, AI automates the generation of complex compliance documentation, ensuring data integrity and accelerating market authorization through intelligent authoring tools. On the scientific front, computational biology delves into the molecular engineering of plant metabolism to enhance production efficiency. Machine learning models simulate dynamic biosynthetic pathways, such as the Shikimate and Acetate pathways, to understand the flow of carbon toward high-value secondary metabolites. Metabolic flux analysis and kinetic modeling identify rate-limiting enzymes and regulatory control points, guiding metabolic engineering strategies that remove bottlenecks. This dual application of AI ensures that herbal products are not only scientifically optimized for maximum potency but also rigorously compliant with the evolving legal standards of the global market, fostering a robust and sustainable industry.
Keywords: Regulatory Intelligence, Horizon Scanning, Automated Compliance (eCTD), Metabolic Engineering, Flux Balance Analysis (FBA), Natural Language Generation (NLG)
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