On-line reoptimization of mammalian fed-batch culture using a nonlinear model predictive controller

Abstract

Fed-batch culture enables high productivity by maintaining low substrate concentrations in the early stage of the culture to suppress the accumulation of by-products that are harmful to cell growth. Therefore, they are widely used in the production of biopharmaceuticals by mammalian cells. However, there exists a trade-off in the design of the fed-batch process: early feeding results in the accumulation of harmful by-products, whereas late feeding results in a shortage of substrates needed for cell growth and synthesis of the desired product. To manage this trade-off and maximize the product yield, model-based optimization of the feeding trajectory has been reported in several studies. A significant drawback of this off-line optimization approach is the mismatch between the predictions made using the model and the actual process states, called the process-model mismatch (PMM). If the PMM is large, the off-line optimized feeding trajectory is no longer optimal for the actual process, resulting in lower product yields. Mammalian cell culture models typically contain dozens of unknown parameters that must be estimated prior to optimization. Sufficient parameter estimation is often unachievable owing to the nonlinear nature of these models. We believe that reoptimizing the feeding trajectory in real time using a nonlinear model predictive controller (NLMPC) is an effective solution to this PMM. Although NLMPC is a model-based feedback controller widely utilised in mammalian fed-batch culture, only a few studies have applied it to on-line reoptimization, and it remains unclear whether NLMPC with a standard kinetic model can effectively compensate for a large PMM. In this study, we demonstrated the reoptimization of the feeding trajectory with a NLMPC using two previously reported standard monoclonal antibody (mAb) production models. In both models, NLMPC successfully suppressed the reduction in mAb yield caused by the intentional introduction of PMM.