The following are the key considerations under, for example, the Good Manufacturing Practices for human medicinal products.
1. Regulatory Expectations
Regulatory authorities like the EMA, US FDA, MHRA, and others recognize ML’s potential.
- GMP Compliance: Any ML system impacting product quality must adhere to GMP principles.
- Validation: Systems must be validated according to the applicable sections in 21 CFR 210 and 211 per the Computerized Drug Processing; CGMP Applicability to Hardware and Software (CPG 7132a.11).
- Traceability & Documentation: Every decision and model update must be traceable.
- Risk-Based Approach: The application of ML should align with ICH Q9 (Quality Risk Management).
2.?? Key Challenges in GMP Compliance
- Model Interpretability: Regulators demand explainability for decisions affecting patient safety.
- #DataQuality: Data used for ML training must comply with the data quality principles (accuracy, conformity, validity, consistency, reliability, and completeness).
- Continuous Learning: Traditional validation assumes static systems, while ML models may evolve. This requires careful revalidation strategies.
- Change Control: A well-defined change management process must be implemented if the model retrains dynamically.
3.?? Practical ML Applications in GMP
ML can be used in regulated manufacturing environments, including:
- Predictive Maintenance: Prevent equipment failures to ensure production continuity.
- Anomaly Detection: Identifying deviations in manufacturing processes.
- Process Optimization: Enhancing efficiency while maintaining compliance.
- Quality Control: Automated defect detection in pharmaceutical manufacturing.
4. ? Validation & Lifecycle Management
- Follow ICH Q8, Q9, and Q10 for quality and risk-based lifecycle approaches (https://www.ich.org/page/quality-guidelines).
- Apply ISO/IEC 25012, Data quality model, for data quality validation.
- Follow ML System Lifecycle.
- Document model training, validation, deployment, and monitoring processes.
