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An examination of process models and model risk frameworks for pharmaceutical manufacturing
Thomas F O'Connor1, Sharmista Chatterjee1, Johnny Lam2
1Food and Drug Administration, Center for Drug Evaluation and Research, Silver Spring, MD 20993, United States.
Process models enhance pharmaceutical manufacturing, with AI accelerating development. This work reviews risk frameworks for validating and maintaining these models throughout the product lifecycle.
Area of Science:
- Pharmaceutical Manufacturing
- Process Modeling
- Industry 4.0
Background:
- Process models are increasingly vital for pharmaceutical manufacturing design and control.
- Industry 4.0 and Artificial Intelligence (AI) offer enhanced data and predictive capabilities.
- Existing applications show process model benefits in areas like lyophilization and chromatography.
Purpose of the Study:
- To discuss risk-based frameworks for process model validation and lifecycle maintenance.
- To facilitate the adoption of process models in pharmaceutical manufacturing.
- To illustrate the application of model risk frameworks through case studies.
Main Methods:
- Review of existing risk-based frameworks for process model validation.
- Discussion of lifecycle maintenance considerations for process models.
- Application of hypothetical case studies to demonstrate framework implications.
Main Results:
- Identified relevant regulatory documents addressing risk for process models.
- Presented a discussion on applying risk frameworks to model validation and maintenance.
- Illustrated practical implications through hypothetical pharmaceutical manufacturing scenarios.
Conclusions:
- Risk-based frameworks are crucial for effective process model validation and lifecycle management.
- Implementing these frameworks can aid the adoption of advanced process models in pharmaceutical manufacturing.
- The discussed approaches support robust quality and risk management in drug production.
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