Frontiers in Chemical Engineering (Oct 2024)
Bioprocess feeding optimization through in silico dynamic experiments and hybrid digital models—a proof of concept
Abstract
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 investment by biopharmaceutical companies. One key aspect of 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 identify the optimal combinations of factors which maximize the productivity of the cell cultures. However, DoE is not suitable for time-varying factor profiles because it requires a large number of experimental runs which can last several weeks and cost tens of thousands of dollars. We here suggest a methodology to optimize the feeding schedule of mammalian cell cultures by virtualizing part of the experimental campaign on a hybrid digital model of the process to accelerate experimentation and reduce 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 factor profiles, whose experimental data are then utilized to train the hybrid model. This will identify the optimal time profiles of glucose and glutamine for 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 a 1-L shake flask scale, and the results are compared with an experimental campaign based on DoDE and response surface modeling. The hybrid digital model requires an extremely limited number of experiments (nine) to be accurately trained, resulting in a promising solution for performing 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 (3,222.8 mg/L) —very close to the real process optimum (3,228.8 mg/L).
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