Modeling, simulation and machine learning techniques nowadays allow for a data-driven analysis of complex processes. These computational modeling techniques can be used to summarize process characteristics from experimental data and offer a framework to also include process knowledge from historical data. The development of biopharmaceutical purification processes can benefit from applying these methods to ensure an efficient and robust DSP design. The application of mechanistic models for the description of chromatography processes has been shown in various examples and case studies. Model-based techniques allow for analyzing experimental results and help to improve process development by guiding experimental design. Based on model predictions optimal conditions for protein purification can be chosen. The success of model-based process development depends on the prediction accuracy of in silico simulations. Factors which influence this accuracy are for instance: general model assumptions, quality of the data used for calibrating the model, and model parameter estimation results. We will therefore discuss phase-appropriate modeling strategies which take into account these factors with respect to their influence on the required prediction accuracy for specific DSP process development stages. For instance at early stage development limited data sets are available. While it remains challenging to establish high accuracy semi-quantitative model predictions have shown to be satisfying in order to guide process development and optimize protein purification processes. We will highlight applications of this approach for case studies with in-silico generated data and experimental data from multivariate studies. In particular we will discuss statistical and computational methods that were applied to evaluate the model prediction quality. Finally we will also address challenges and gaps which need to be addressed for a successful implementation of data-based technologies as a tool in purification process development.

Chromatography modeling in purification development: Assessing accuracy of predictions to select phase-appropriate modeling strategies

255th National Meeting (2018)

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