Enhancing Regulatory Response Efficiency: The Role of AI in Optimizing RTQ Submissions

The increasing complexity of regulatory submissions in Chemistry, Manufacturing, and Controls (CMC) has led to a surge in Regulatory Technical Queries (RTQs) from health authorities. Addressing these queries promptly and accurately is crucial for ensuring timely drug approvals. Traditional RTQ response processes often involve labor-intensive document searches, data validation, and expert consultations, leading to delays and inconsistencies. Artificial Intelligence (AI) presents a transformative solution by automating and optimizing RTQ management. This paper explores AI-driven approaches, including Natural Language Processing (NLP) for query interpretation, automated data extraction, predictive analytics for query trends, and expert systems for knowledge management. By leveraging AI, pharmaceutical companies can enhance response quality, reduce turnaround time, and improve compliance with regulatory expectations. Additionally, case studies highlight successful AI implementations that have streamlined RTQ responses and accelerated regulatory decision-making. The integration of AI into CMC regulatory processes marks a paradigm shift, offering significant efficiency gains while maintaining data integrity and regulatory compliance.