A Technical Framework for Predicting Regulatory Deficiencies in Pharmaceutical Chemistry, Manufacturing, and Controls (CMC) Submissions Using Machine Learning

Regulatory review of Chemistry, Manufacturing, and Controls (CMC) information can reveal issues with stability, impurities, specifications, manufacturing controls, analytical methods, or process validation. These problems may lead to extra review cycles and approval delays. This report introduces a conceptual machine-learning framework to support the identification of potential CMC deficiency risks prior to submission. The framework brings together document ingestion, natural language processing, a regulatory knowledge base, predictive classification, risk categorization, and recommendation generation. It outlines possible methods and provides an example use case to show how the system could work. No regulatory submission data were analyzed, no model was trained, and no predictive results were measured; therefore, all examples are hypothetical and not validated. Future work needs to focus on developing models with appropriate data, comparing results with expert reviewers, validating externally and prospectively, and handling confidentiality, cybersecurity, explainability, and regulatory acceptance.