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Bioengineering 2017, 4(4), 86; https://doi.org/10.3390/bioengineering4040086

Integrated Process Modeling—A Process Validation Life Cycle Companion

1
Exputec GmbH, Mariahilferstraße 147, 1150 Vienna, Austria
2
Versartis Inc., 4200 Bohannon Drive, Suite 250, Menlo Park, CA 94025, USA
3
Boehringer Ingelheim RCV GmbH & Co KG, Doktor-Boehringer-Gasse 5-11, 1120 Vienna, Austria
*
Author to whom correspondence should be addressed.
Academic Editor: Liang Luo
Received: 9 September 2017 / Revised: 9 October 2017 / Accepted: 12 October 2017 / Published: 17 October 2017
(This article belongs to the Special Issue Hybrid Modelling and Multi-Parametric Control of Bioprocesses)
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Abstract

During the regulatory requested process validation of pharmaceutical manufacturing processes, companies aim to identify, control, and continuously monitor process variation and its impact on critical quality attributes (CQAs) of the final product. It is difficult to directly connect the impact of single process parameters (PPs) to final product CQAs, especially in biopharmaceutical process development and production, where multiple unit operations are stacked together and interact with each other. Therefore, we want to present the application of Monte Carlo (MC) simulation using an integrated process model (IPM) that enables estimation of process capability even in early stages of process validation. Once the IPM is established, its capability in risk and criticality assessment is furthermore demonstrated. IPMs can be used to enable holistic production control strategies that take interactions of process parameters of multiple unit operations into account. Moreover, IPMs can be trained with development data, refined with qualification runs, and maintained with routine manufacturing data which underlines the lifecycle concept. These applications will be shown by means of a process characterization study recently conducted at a world-leading contract manufacturing organization (CMO). The new IPM methodology therefore allows anticipation of out of specification (OOS) events, identify critical process parameters, and take risk-based decisions on counteractions that increase process robustness and decrease the likelihood of OOS events. View Full-Text
Keywords: process validation; process characterization study; holistic process model; predict out of specification events; Monte Carlo simulation; biopharmaceutical manufacturing process validation; process characterization study; holistic process model; predict out of specification events; Monte Carlo simulation; biopharmaceutical manufacturing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Zahel, T.; Hauer, S.; Mueller, E.M.; Murphy, P.; Abad, S.; Vasilieva, E.; Maurer, D.; Brocard, C.; Reinisch, D.; Sagmeister, P.; Herwig, C. Integrated Process Modeling—A Process Validation Life Cycle Companion. Bioengineering 2017, 4, 86.

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