Abstract
In bioprocess engineering the Qualtiy by Design (QbD) initiative encourages the use of models to define design spaces. However, clear guidelines on how models for QbD are validated are still missing. In this review we provide a comprehensive overview of the validation methods, mathematical approaches, and metrics currently applied in bioprocess modeling. The methods cover analytics for data used for modeling, model training and selection, measures for predictiveness, and model uncertainties. We point out the general issues in model validation and calibration for different types of models and put this into the context of existing health authority recommendations. This review provides a starting point for developing a guide for model validation approaches. There is no one-fits-all approach, but this review should help to identify the best fitting validation method, or combination of methods, for the specific task and the type of bioprocess model that is being developed.
Highlights
During the last few years, the biopharmaceutical industry has aimed at developing biopharmaceutical products and the corresponding process in a quality by design (QbD)
Well designed measurements (e.g., in a design of experiments (DoE) workflow) of key process parameters (KPPs) and critical process parameters (CPPs) are necessary, as well as a corresponding process model that describes the dependencies between critical quality attributes (CQAs) and the process parameters
The validation methods currently used in bioprocess modeling are described
Summary
During the last few years, the biopharmaceutical industry has aimed at developing biopharmaceutical products and the corresponding process in a quality by design (QbD). The quality of the models, e.g., in terms of predictability and interpretability has to be evaluated early on This ensures that later, in the context of commercialization, the models are well validated to make decisions about the models, such as the classification of parameters into KPP or CPP, or the definition of ranges. No clear recommendations have been outlined for model validation and a straightforward and comprehensive workflow is difficult to define One reason for this might be the diversity of mathematics (e.g., statistical, mechanistical, hybrid, etc.) and the different nature of the underlying data (scale differences, batch versus perfusion mode, sample size differences, and many more).
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