Bioprocess feeding optimization through in silico dynamic experiments and hybrid digital models—a proof of concept - Barberi, Gianmarco; Giacopuzzi, Christian; Facco, Pierantonio (Frontiers in Chemical Engineering, 2024)
The development of cell cultures to produce monoclonal antibodies is a multi-step time-consuming and labor-intensive procedure, which usually lasts several years and requires heavy investments by biopharmaceutical companies. One key aspect for process optimization is improving the feeding strategy. This step is typically performed though Design of Experiments (DoE) during process development, in such a way as to find the optimal combinations of factors which maximize the productivity of the cell cultures. However, Design of Experiments is not suitable for time-varying factors profiles, because it requires a large number of experimental runs, which can last several weeks and cost tens of thousands of dollars. In this work we suggest a methodology to perform the feeding schedule optimization of mammalian cell cultures by virtualizing part of the experimental campaign on a hybrid digital model of the process, which accelerates the experimentation and reduces the experimental burden. The proposed methodology couples Design of Dynamic Experiments (DoDE) with a hybrid semi-parametric digital model. In particular, DoDE is used to design optimal experiments with time-varying factors’ profiles whose experimental data are then utilized to train the hybrid model which is able to identify the optimal time profiles of glucose and glutamine maximizing the antibody titer in the culture despite the limited number of experiments performed on the process. As a proof of concept, the proposed methodology is applied on a simulated process to produce monoclonal antibodies at 1-L shake flask scale, and the results are compared with an experimental campaign based on DoDE and Response Surface Modelling. The hybrid digital model requires an extremely limited number of experiments (i.e., 9) to be accurately trained and results a promising solution to perform in-silico experimental campaigns. The proposed optimization strategy provides a 34.9% increase in the antibody titer with respect to the training data and a 2.8% higher antibody titer than the optimal results of two DoDE-based experimental campaigns comprising different numbers of experiments (i.e., 9 and 31), achieving a high antibody titer (3222.8 mg/L), very close to the real process optimum (3228.8 mg/L).