One challenge pharmaceutical companies face is moving promising biotherapeutic products into the clinic as fast as safely possible.
Devoting the correct amount of time and resources to products in early stages can streamline late-stage and commercial development activities. Early Design of Experiments (DOE) screening studies can select cell lines that have the most robust manufacturability profile, perhaps negating the need for a cell line switch. As the project progresses, process characterization studies can be very labor intensive and are usually delayed until a product has proven itself worthy of investment through success in early clinical trials.
Definitive screening designs have the potential to be a onestop shop for screening important parameters and optimizing settings with low resource costs. Advanced machinge learning methods have revolutionized the analysis of large data sets where the data can be divided into portions for training, testing and validation. During bioprocessing development, the datasets are usually small and until now have not been suitable for portioning the data into different training and validation sets. The frationally weighted bootstrapping method uses the same data set for training and validation. This autovalidation technique has been employed to successfully de-risk model testing experiments that complete the DOE lifecycle.