An Overview of AI-Driven Pharmaceutical GMP: Regulatory Challenges, Quality Risk Management and Future Perspectives

Good Manufacturing Practice (GMP) plays an important role in assuring safety, quality and effectiveness of pharmaceutical products. It provides the standards of manufacture, quality assurance, documentation, equipment management and regulatory compliance. Rapid technological developments have introduced the promising technology of Artificial Intelligence (AI) in pharmaceutical manufacturing and quality management. Machine learning, deep learning, computer vision and predictive analytics are some of the technologies which can be used to monitor processes in real time, conduct automated quality inspections, perform predictive maintenance and detect any deviations in the manufacturing process. Integration of AI into the field of GMP in pharmaceuticals could provide benefits in the form of better understanding of process, identification of quality risks, consistent quality of products and better data analysis. AI can help in QA/QC functions by detecting anomalous patterns, analysis of manufacturing documents and deviation investigations. However, implementation of AI in GMP environments raises a number of issues such as integrity of data, model validation, algorithm transparency, cybersecurity, reliability of the system, and compliance with regulation. Proper validation, performance monitoring, change control documentation, and human supervision are required to guarantee the reliability and applicability of the AI systems. In this review, a general idea of AI technology is introduced and described with regard to pharmaceutical GMP. The basic technologies used in AI and its applications in manufacturing and quality assurance are described. Moreover, the possible advantages, quality risk management, regulatory issues, and future prospects of this approach are reviewed.