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Process performance evaluation in pharmaceutical manufacturing operations

corresponding

DIRK J. SPIELVOGEL*, HOSSAM FAROUK, EIKE J. BERGNER
*Corresponding author
F. Hoffmann-La Roche, Ltd., Grenzacherstrasse, Basel

Abstract

Process performance (PP) is an established practice in many industries. It continuously contributes to higher levels of product performance and economic optimization. In pharmaceutical manufacturing operations PP is a natural extension of continued process verification (CPV), the 3rd stage of the validation life cycle, and of increasing health authority focus and importance to deliver highest possible quality products. This perspective aims to provide insight into the various aspects of PP, with particular focus on options for handling both normally and non-normally distributed manufacturing quality data. Ultimately, through PP and the combination of both process stability and process capability analysis pharmaceutical manufacturing operations ensure reliable delivery of safe products to patients.


INTRODUCTION

In the 2009 EU Guideline “Good Manufacturing Practice for Medicinal Products for Human and Veterinary Use” (1) it is stated, “Pharmaceutical companies should plan and execute a system for the monitoring of process performance and product quality to ensure a state of control is maintained. An effective monitoring system provides assurance of the continued capability of processes and controls to meet product quality and to identify areas for continual improvement.” Similar content is expressed in the 2011 FDA Guidance for Industry “Process Validation: General Principles and Practices” and other relevant guidelines and standards (2).

Although the term “process performance” is sometimes used synonymously with process capability, it is important to distinguish between process stability, process capability and process performance. Process stability describes the statistical behavior of a process in respect to control limits, which are set according to statistical rules. Process capability describes the probability that a process will deliver out of specification (OOS) events. A manufacturing process can be stable and/or capabl ...




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