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.

Keywords: Monte Carlo simulation; biopharmaceutical manufacturing; holistic process model; predict out of specification events; process characterization study; process validation.

Figure 1

Exemplary plot for dependency of specific clearance (here of process-related impurity 2) against impurity loading density of process-related impurity 2 of DoE runs (blue) and large scale (LS) runs (red). Yellow error bars indicate the mean model prediction error. Normalization has been performed by division of the maximal value for each axis.

Figure 2

Schematic description of the integrated process model using a Monte Carlo approach: 1000 simulations are performed, each having a different set of process parameters (indicated as distribution on the x-axis of (A,B)) and initial specific CQA concentration $({\mathrm{c}}_{\mathrm{CQA},\mathrm{init}})$. Multiple linear regression models describe the relationship between the ${\mathrm{c}}_{\mathrm{CQA}}$ of the pool of unit operation u (B) and the PP of this unit operation as well as the pool concentration of the previous unit operation u − 1 (A). Thereby, models from multiple unit operations (A,B) are connected to predict the CQA distribution in the drug substance (C). Since 1000 simulations are performed, the CQA values form a distribution after each unit operation. The higher the model uncertainty, indicated by blue shaded area around the regression line, the wider the resulting CQA distribution. This ultimately propagates until drug substance, where the chance of out of specification events can be assessed.

Figure 3

Comparison of simulated (top) product-related impurity 1 distribution and observed (bottom) product-related impurity 1 from LS after each column step. Normalization was performed by dividing by the maximum observed ${\mathrm{c}}_{\mathrm{CQA}}$.

Figure 4

Comparison of simulated (top) product-related impurity 2 distribution and observed (bottom) product-related impurity 2 from LS after each column step. Normalization was performed by dividing by the maximum observed ${ \mathrm{c}}_{\mathrm{CQA}}$.

Figure 5

Comparison of simulated (top) process-related impurity 1 distribution and observed (bottom) process-related impurity 1 from LS after each column step. For chromatography column 3 pool, no process-related impurity 1 value was observed above LoQ, therefore, no histogram bar is plotted for the observed values at chromatography column 3 pool. Normalization was performed by dividing by the maximum observed ${\mathrm{c}}_{\mathrm{CQA}}$.

Figure 6

Comparison of simulated (top) process-related impurity 2 distribution and observed (bottom) process-related impurity 2 from LS after each column step. Normalization was performed by dividing by the maximum observed ${\mathrm{c}}_{\mathrm{CQA}}$.

Figure 7

Estimated OOS event for product-related impurity 2 at drug substance as a function of change in set-point (A) and variance (B) of all PPs as well as a function of increased specific impurity concentration after primary recovery (C). Deviations in set-point of pH and salt concentration in wash of chromatography column 1 impact severely on OOS chance, which is not the case when variance in PPs increases by up to 50%. A change of specific product-related impurity 2 concentration at the primary recovery level will also increase OOS chances.