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Abstract Objective Random effects are often neglected when defining the control strategy for a biopharmaceutical process. In this article, we present a case study that highlights the importance of considering the variance introduced by random effects in the calculation of proven acceptable ranges (PAR), which form the basis of the control strategy. Methods Linear mixed models were used to model relations between process parameters and critical quality attributes in a set of unit operations that comprises a typical biopharmaceutical manufacturing process. Fitting such models yields estimates of fixed and random effect sizes as well as random and residual variance components. To form PARs, tolerance intervals specific to mixed models were applied that incorporate the random effect contribution to variance. Results We compared standardized fixed and random effect sizes for each unit operation and CQA. The results show that the investigated random effect is not only significant but in some unit operations even larger than the average fixed effect. A comparison between ordinary least squares and mixed models tolerance intervals shows that neglecting the contribution of the random effect can result in PARs that are too optimistic. Conclusions Uncontrollable effects such as week-to-week variability play a major role in process variability and can be modelled as a random effect. Following a workflow such as the one suggested in this article, random effects can be incorporated into a statistically sound control strategy, leading to lowered out of specification results and reduced patient risk.

Intermediate acceptance criteria are the foundation for developing control strategies in process validation stage 1 in the pharmaceutical industry. At drug substance or product level such intermediate acceptance criteria for quality are available and referred to as specification limits. However, it often remains a challenge to define acceptance criteria for intermediate process steps. Available guidelines underpin the importance of intermediate acceptance criteria, because they are an integral part for setting up a control strategy for the manufacturing process. The guidelines recommend to base the definition of acceptance criteria on the entirety of process knowledge. Nevertheless, the guidelines remain unclear on how to derive such limits. Within this contribution we aim to present a sound data science methodology for the definition of intermediate acceptance criteria by putting the guidelines recommendations into practice (ICH Q6B, 1999). By using an integrated process model approach, we leverage manufacturing data and experimental data from small scale to derive intermediate acceptance criteria. The novelty of this approach is that the acceptance criteria are based on pre-defined out-of-specification probabilities, while also considering manufacturing variability in process parameters. In a case study we compare this methodology to a conventional +/- 3 standard deviations (3SD) approach and demonstrate that the presented methodology is superior to conventional approaches and provides a solid line of reasoning for justifying them in audits and regulatory submission.

Statistical experimental designs such as factorial, optimal, or definitive screening designs represent the state of the art in biopharmaceutical process characterization. However, such methods alone do not leverage the fact that processes operate as a mutual interplay of multiple steps. Instead, they aim to investigate only one process step at a time. Here, we want to develop a new experimental design method that seeks to gain information about final product quality, placing the right type of run at the right unit operation. This is done by minimizing the simulated out-of-specification rate of an integrated process model comprised of a chain of regression models that map process parameters to critical quality attributes for each unit operation. Unit operation models are connected by passing their response to the next unit operation model as a load parameter, as is done in real-world manufacturing processes. The proposed holistic DoE (hDoE) method is benchmarked against standard process characterization approaches in a set of in silico simulation studies where data are generated by different ground truth processes to illustrate the validity over a range of scenarios. Results show that the hDoE approach leads to a >50% decrease in experiments, even for simple cases, and, at the same time, achieves the main goal of process development, validation, and manufacturing to consistently deliver product quality.

Integrated or holistic process models may serve as the engine of a digital asset in a multistep-process digital twin. Concatenated individual-unit operation models are effective at propagating errors over an entire process, but are nonetheless limited in certain aspects of recent applications that prevent their deployment as a plausible digital asset, particularly regarding bioprocess development requirements. Sequential critical quality attribute tests along the process chain that form output–input (i.e., pool-to-load) relationships, are impacted by nonaligned design spaces at different scales and by simulation distribution challenges. Limited development experiments also inhibit the exploration of the overall design space, particularly regarding the propagation of extreme noncontrolled parameter values. In this contribution, bioprocess requirements are used as the framework to improve integrated process models by introducing a simplified data model for multiunit operation processes, increasing statistical robustness, adding a new simulation flow for scale-dependent variables, and describing a novel algorithm for extrapolation in a data-driven environment. Lastly, architectural and procedural requirements for a deployed digital twin are described, and a real-time workflow is proposed, thus providing a final framework for a digital asset in bioprocessing along the full product life cycle.

Maximizing the value of each available data point in bioprocess development is essential in order to reduce the time-to-market, lower the number of expensive wet-lab experiments, and maximize process understanding. Advanced in silico methods are increasingly being investigated to accomplish these goals. Within this contribution, we propose a novel integrated process model procedure to maximize the use of development data to optimize the Stage 1 process validation work flow. We generate an integrated process model based on available data and apply two innovative Monte Carlo simulation-based parameter sensitivity analysis linearization techniques to automate two quality by design activities: determining risk assessment severity rankings and establishing preliminary control strategies for critical process parameters. These procedures are assessed in a case study for proof of concept on a candidate monoclonal antibody bioprocess after process development, but prior to process characterization. The evaluation was successful in returning results that were used to support Stage I process validation milestones and demonstrated the potential to reduce the investigated parameters by up to 24% in process characterization, while simultaneously setting up a strategy for iterative updates of risk assessments and process controls throughout the process life-cycle to ensure a robust and efficient drug supply.

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.

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.