Specification-driven acceptance criteria for validation of biopharmaceutical processes.

Area of Science:

• Pharmaceutical Manufacturing
• Process Validation
• Data Science Applications

Background:

• Intermediate acceptance criteria are crucial for pharmaceutical process validation and control strategies.
• Current guidelines emphasize using process knowledge but lack clarity on deriving these criteria.
• Specification limits exist for drug substance/product but not for intermediate process steps.

Purpose of the Study:

• To present a data science methodology for defining intermediate acceptance criteria.
• To operationalize guideline recommendations (ICH Q6B, 1999) for acceptance criteria derivation.
• To address the challenge of setting acceptance criteria for intermediate process steps.

Main Methods:

• Utilized an integrated process model approach.
• Leveraged manufacturing and experimental data from small-scale studies.
• Derived intermediate acceptance criteria based on pre-defined out-of-specification probabilities.
• Incorporated manufacturing variability in process parameters.

Main Results:

• The proposed methodology provides a sound basis for defining intermediate acceptance criteria.
• Compared the data science approach to the conventional +/- 3 standard deviations (3SD) method.
• Demonstrated the superiority of the presented methodology over conventional approaches.

Conclusions:

• The data science methodology offers a robust framework for setting intermediate acceptance criteria.
• This approach provides a solid line of reasoning for audits and regulatory submissions.
• Successfully puts guideline recommendations into practice for pharmaceutical process control.