In the rapidly evolving field of pharmaceutical bioprocess development, the application of transfer learning techniques presents a transformative opportunity to enhance model performance and streamline process development activities. This study investigates the efficacy of transfer learning in leveraging historical process data to inform the development of models for new products. We demonstrate that transfer learning significantly improves the predictive accuracy of process models when applied to process data of unseen products. Our results indicate that utilizing knowledge from previously established processes allows for a more efficient calibration of models, reducing the number of required experiments while maintaining comparable performance to traditional Design of Experiments (DoE) approaches.
This research highlights the potential of transfer learning as a powerful tool in bioprocess development, paving the way for more agile and informed decision-making in the pharmaceutical industry.
