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Quantitative CPP Evaluation from Risk Assessment Using Integrated Process Modeling.

Daniel Borchert1, Thomas Zahel1, Yvonne E Thomassen2

  • 1Exputec GmbH, Mariahilferstraße 88a/1/9, 1070 Vienna, Austria.

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|December 19, 2019
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Summary
This summary is machine-generated.

This study introduces a new algorithm to improve risk assessments (RAs) in biopharmaceutical manufacturing. It more accurately evaluates process parameters (PPs) and their impact on critical quality attributes (CQAs), avoiding errors from the traditional risk priority number (RPN) method.

Keywords:
OOS estimationintegrated process modelingoccurrence contributionpotential critical process parameter (pCPP) assessmentprocess knowledgeseverity contributionsimulation based on risk assessment

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Area of Science:

  • Biopharmaceutical Manufacturing
  • Risk Assessment Methodologies
  • Process Analytical Technology (PAT)

Background:

  • Risk assessments (RAs) are crucial for evaluating process parameters (PPs) and their impact on critical quality attributes (CQAs) in drug development.
  • The conventional risk priority number (RPN) method, calculated by multiplying severity, occurrence, and detectability, has potential mathematical and interpretational limitations.
  • These limitations can lead to misinterpretations of PP criticality and inaccurate predictions of out-of-specification (OOS) probabilities.

Purpose of the Study:

  • To present a novel mathematical algorithm for quantitatively assessing the effect of PPs on CQAs within RAs.
  • To provide a more accurate method for evaluating PP criticality and predicting OOS probabilities.
  • To address the limitations of the traditional RPN approach in biopharmaceutical risk assessment.

Main Methods:

  • Developed a new algorithm that quantifies the effect of PPs on CQAs.
  • Transformed severity and occurrence factors into model effect sizes and parameter distributions.
  • Incorporated detectability in a final step to refine the contribution sorting of each factor.

Main Results:

  • The novel algorithm quantitatively describes the effect of PPs on each CQA.
  • Demonstrated that severity and occurrence contribute differently to PP criticality, unlike the RPN method's assumption of equal contribution.
  • Highlighted the risk of misinterpreting PP criticality using the conventional RPN approach.

Conclusions:

  • The new algorithm offers a more accurate and straightforward approach to risk assessment in the biopharmaceutical industry.
  • This method improves the evaluation of PP criticality and the assessment of OOS probabilities.
  • The findings underscore the need to move beyond the traditional RPN calculation for more reliable risk management.