Quantitative CPP Evaluation from Risk Assessment Using Integrated Process Modeling.

Daniel Borchert,Diego A Suarez-Zuluaga,Thomas Zahel,Christoph Herwig,Yvonne E Thomassen

doi:10.3390/bioengineering6040114

Daniel Borchert, Diego A Suarez-Zuluaga + Show 3 more

Open Access

https://doi.org/10.3390/bioengineering6040114

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Journal: Bioengineering	Publication Date: Dec 13, 2019
Citations: 5	License type: CC BY 4.0

Affiliation: TU Wien, Intravacc (Netherlands)

Abstract

Risk assessments (RAs) are frequently conducted to assess the potential effect of process parameters (PPs) on product quality attributes (e.g., a critical quality attribute (CQA)). To evaluate the PPs criticality the risk priority number (RPN) for each PP is often calculated. This number is generated by the multiplication of three factors: severity, occurrence, and detectability. This mathematical operation may result in some potential errors due to the multiplication of ordinal scaled values and the assumption that the factors contribute equally to the PPs criticality. To avoid these misinterpretations and to assess the out of specification (OOS) probability of the drug substance, we present a novel and straightforward mathematical algorithm. This algorithm quantitatively describes the PPs effect on each CQA assessed within the RA. The transcription of severity and occurrence to model effect sizes and parameters distribution are the key elements of the herein developed approach. This approach can be applied to any conventional RA within the biopharmaceutical industry. We demonstrate that severity and occurrence contribute differently to the PP criticality and compare these results with the RPN number. Detectability is used in a final step to precisely sort the contribution of each factor. To illustrate, we show the misinterpretation risk of the PP critically by using the conventional RPN approach.

Highlights

Risk assessment (RA) is a famous tool for risk evaluation, within a biopharmaceutical process, to evaluate the likely reason for not meeting drug substance (DS) specification
A case study was presented where the method was applied to data obtained from an RA performed for a vaccine production process
To evaluate the simulation based on a real RA, we considered a risk assessment carried out for a vaccine production process provided by a leading research and development (R&D) institute for vaccine research

Summary

Introduction

Risk assessment (RA) is a famous tool for risk evaluation, within a biopharmaceutical process, to evaluate the likely reason for not meeting drug substance (DS) specification. The main focus of the RA is to identify potential critical process parameters (pCPPs) affecting a particular critical quality attribute (CQA). The identified CPPs can be further assessed using a design of experiment (DoE) approach. Considering the International Conference of Harmonization (ICH) Q11 guideline, a CQA is defined as “A physical, chemical, biological or microbial property or characteristic that should be within an appropriate limit range or distribution to ensure the desired product quality” [1]. The ICH Q9 Guideline addresses risk management, and suggests conducting a RA for each CQA individually. Due to its systematic approach for risk management, this guideline lays the basis for the quality by design approach [4,5,6], which has been implemented in the biopharmaceutical industry for years

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