Integrated Process Modeling—A Process Validation Life Cycle Companion

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.

Quantitative CPP Evaluation from Risk Assessment Using Integrated Process Modeling

“…The integrated process aims to consider the entire process, where all assessed unit operations are connected consecutively. Zahel et al [23] described the applied method and used a Monte Carlo [22] approach to include the error propagation within the unit operation concatenation. For our approach, we use just the knowledge from the conducted RA, and we never use experimental data.…”

“…Zahel et al [23] recently introduced the methodology of IPM into the biopharmaceutical industry and used manufacturing data to simulate the OOS by using the IPM approach. For our simulation, no experimental data are needed, and the herein developed IPM simulation uses all the information available within the RA.…”

“…Use the output of the previous unit operation as input for the following one (Section 2.5). We assume that the starting data input for each unit operation is the output of the previous one [23]. The unit operation and its PP are connected based on the assumed process format.…”

“…The holistic numerical simulation of all unit operations until DS, evaluated within the RA, is considered as one cycle. To ensure reproducibility of the IPM, a Monte Carlo is conducted where 1000 cycles were simulated [22,23]. The concatenation workflow is shown in Figure 4, using the linearization of the S and the operation condition estimation based on the O value, the process can be described mathematically as:…”

“…The holistic numerical simulation of all unit operations until DS, evaluated within the RA, is considered as one cycle. To ensure reproducibility of the IPM, a Monte Carlo is conducted where 1000 cycles were simulated [22,23].…”