Smart process development: Application of machine-learning and integrated process modeling for inclusion body purification processes.

The development of a biopharmaceutical production process usually occurs sequentially, and tedious optimization of each individual unit operation is very time-consuming. Here, the conditions established as optimal for one-step serve as input for the following step. Yet, this strategy does not consider potential interactions between a priori distant process steps and therefore cannot guarantee for optimal overall process performance. To overcome these limitations, we established a smart approach to develop and utilize integrated process models using machine learning techniques and genetic algorithms. We evaluated the application of the data-driven models to explore potential efficiency increases and compared them to a conventional development approach for one of our development products. First, we developed a data-driven integrated process model using gradient boosting machines and Gaussian processes as machine learning techniques and a genetic algorithm as recommendation engine for two downstream unit operations, namely solubilization and refolding. Through projection of the results into our large-scale facility, we predicted a twofold increase in productivity. Second, we extended the model to a three-step model by including the capture chromatography. Here, depending on the selected baseline-process chosen for comparison, we obtained between 50% and 100% increase in productivity. These data show the successful application of machine learning techniques and optimization algorithms for downstream process development. Finally, our results highlight the importance of considering integrated process models for the whole process chain, including all unit operations.

Host cell protein analysis in therapeutic protein bioprocessing - methods and applications.

The analysis of host cell proteins (HCPs) is one of the most important analytical requirements during bioprocess development of therapeutic moieties. In this review, we focus on the comparison of different methods for the analysis of HCPs and how cell lines, fermentation conditions, and unit operations influence HCP distribution during the process chain. Current guidelines typically require reduction of HCPs to the ppm level, depending on the intended use, the route of administration of the product, and the production system. A range of immunospecific and non-specific methods are available that have been globally accepted by regulatory bodies. Immunospecific methods, such as ELISA, are simple to use in routine analysis and can quantify low levels of HCPs when specific antibodies are available. Non-specific methods are more complex; however, they provide a holistic view of the HCP profile and qualitative information of the composition of HCP in the sample. Different methods for the comparison of bioprocessing strategies during scale-up and purification development are compared herein. The methods include immunospecific methods, such as ELISA, western blot, and threshold, and non-specific methods, such as 2D-DIGE and 2D-HPLC combined with MS.