P312: AI-Enabled Multi-Framework Regulatory and Quality Gap Detection for Pharmaceutical Operations Across the Product Lifecycle
Poster Presenter
Harsh Shah
Founder and CEO
Clinplex United States
Objectives
To validate an AI-enabled methodology that detects regulatory and quality gaps across lifecycle documents and uses cross-document analysis to infer potential operational and execution risk relevant to compliance oversight, audit-readiness, and quality system performance.
Method
A validated AI methodology was applied across pharmaceutical lifecycle documents using FDA 21 CFR, ICH Q7/Q9/Q10, EU GMP, WHO GMP, and an ICH Q9-aligned risk classification approach. Document classes included SOPs, regulatory submission packages including CMC documentation, quality records, and preclinical documents. Validation used SME panel review, concordance analysis, and reproducibility testing across document classes.
Results
To validate an AI-enabled methodology that detects regulatory and quality gaps across lifecycle documents and uses cross-document analysis to infer potential operational and execution risk relevant to compliance oversight, audit-readiness, and quality system performance.
Conclusion
AI-enabled multi-framework gap analysis identified regulatory and quality deficiencies that single-framework manual review can miss, including documented content gaps, cross-document linkages, and cascading control weaknesses across the drug development pipeline. Validation across SOPs, regulatory submission packages including CMC documentation, quality records (CAPAs, deviations, and audit documentation), and preclinical documents demonstrated reproducible gap detection and risk classification with strong concordance to SME review. These findings indicate that multi-framework AI review can expose latent compliance risk earlier, connect upstream document deficiencies to downstream quality signals, and support more risk-based prioritization of remediation activities. The results have implications for lifecycle-wide quality oversight, Pre-Approval Inspection (PAI) readiness, continuous regulatory monitoring, and risk-based remediation planning in pharmaceutical operations.
