Workflow for Criticality Assessment Applied in Biopharmaceutical Process Validation Stage 1.

Thomas Zahel,Patrick Sagmeister,Cécile Brocard,Daniel Maurer,Daniela Reinisch,Lukas Marschall,Christoph Herwig,Patrick Murphy,Thomas Natschläger,Eric Mueller,Elena Vasilieva,Sandra Abad

doi:10.3390/bioengineering4040085

Abstract

Identification of critical process parameters that impact product quality is a central task during regulatory requested process validation. Commonly, this is done via design of experiments and identification of parameters significantly impacting product quality (rejection of the null hypothesis that the effect equals 0). However, parameters which show a large uncertainty and might result in an undesirable product quality limit critical to the product, may be missed. This might occur during the evaluation of experiments since residual/un-modelled variance in the experiments is larger than expected a priori. Estimation of such a risk is the task of the presented novel retrospective power analysis permutation test. This is evaluated using a data set for two unit operations established during characterization of a biopharmaceutical process in industry. The results show that, for one unit operation, the observed variance in the experiments is much larger than expected a priori, resulting in low power levels for all non-significant parameters. Moreover, we present a workflow of how to mitigate the risk associated with overlooked parameter effects. This enables a statistically sound identification of critical process parameters. The developed workflow will substantially support industry in delivering constant product quality, reduce process variance and increase patient safety.

Highlights

Process validation of pharmaceutical processes aims to demonstrate the capability of the process to constantly deliver high product quality [1,2]
Experiments performed in biotechnological studies might contain data that violate the statistical assumptions of parametric tests
Nonparametric approaches bear potential and we want to present a novel permutation test to assess the power of individual design of experiments (DoEs) factors in a multivariate regression model

Summary

Introduction

Process validation of pharmaceutical processes aims to demonstrate the capability of the process to constantly deliver high product quality [1,2]. The aim of process validation stage 1 is to identify a robust process design that enables the ability to constantly deliver product quality. It is key to identify critical process parameters (CPPs) that are likely to create risk to critical quality attributes (CQAs) and set up control strategies for these CQAs. Thereby it is possible to reduce out-of-specification (OOS) events, recalls, and risk to the patient. At process validation stage 1, it is of the highest priority not to overlook a CPP in the design of the process, which as a consequence might not be controlled properly

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Results

Conclusion