Model-Based Dynamic Optimization of Monoclonal Antibodies Production in Semibatch Operation—Use of Reformulation Techniques

Boost dynamic protocols for producing mammalian biopharmaceuticals with intensified DoE—a practical guide to analyses with OLS and hybrid modeling

Introduction: For the implementation of robust bioprocesses, understanding of temporal cell behavior with respect to relevant inputs is crucial. Intensified Design of Experiments (iDoE) is an efficient tool to assess the joint influence of input parameters by including intra-experimental changes.

Methods: We applied iDoE to the production phase of a monoclonal antibody in a mammalian bioprocess. The multidimensional design space spanned by temperature, dissolved oxygen (DO), timing of change, and growth category was investigated in 12 cultivations. We built ordinary least squares (OLS) and hybrid models (HM) on the iDoE-data, validated them with classical DoE (cDoE)-derived data, and used the models as in silico representation for process optimization.

Results: If the complexity of interactions between changing setpoints of inputs is sufficiently captured during planning and modeling, iDoE proved to be valid for characterizing the mammalian biopharmaceutical production phase. For local behavior and flexible composition of optimization goals, OLS regressions can easily be implemented. To predict global and interconnected dynamics while incorporating mass balances, HM holds potential.

Discussion: iDoE will boost protocols that optimize inputs for different bioprocess phases. The described key aspects of OLS- and HM-based analyses of iDoE-data shall guide future applications during manufacturing.

Population balance modelling captures host cell protein dynamics in CHO cell cultures

Monoclonal antibodies (mAbs) have been extensively studied for their wide therapeutic and research applications. Increases in mAb titre has been achieved mainly by cell culture media/feed improvement and cell line engineering to increase cell density and specific mAb productivity. However, this improvement has shifted the bottleneck to downstream purification steps. The higher accumulation of the main cell-derived impurities, host cell proteins (HCPs), in the supernatant can negatively affect product integrity and immunogenicity in addition to increasing the cost of capture and polishing steps. Mathematical modelling of bioprocess dynamics is a valuable tool to improve industrial production at fast rate and low cost. Herein, a single stage volume-based population balance model (PBM) has been built to capture Chinese hamster ovary (CHO) cell behaviour in fed-batch bioreactors. Using cell volume as the internal variable, the model captures the dynamics of mAb and HCP accumulation extracellularly under physiological and mild hypothermic culture conditions. Model-based analysis and orthogonal measurements of lactate dehydrogenase activity and double-stranded DNA concentration in the supernatant show that a significant proportion of HCPs found in the extracellular matrix is secreted by viable cells. The PBM then served as a platform for generating operating strategies that optimise antibody titre and increase cost-efficiency while minimising impurity levels.

A hybrid mechanistic-empirical model for in silico mammalian cell bioprocess simulation

In cell culture processes cell growth and metabolism drive changes in the chemical environment of the culture. These environmental changes elicit reactor control actions, cell growth response, and are sensed by cell signaling pathways that influence metabolism. The interplay of these forces shapes the culture dynamics through different stages of cell cultivation and the outcome greatly affects process productivity, product quality, and robustness. Developing a systems model that describes the interactions of those major players in the cell culture system can lead to better process understanding and enhance process robustness. Here we report the construction of a hybrid mechanistic-empirical bioprocess model which integrates a mechanistic metabolic model with subcomponent models for cell growth, signaling regulation, and the bioreactor environment for in silico exploration of process scenarios. Model parameters were optimized by fitting to a dataset of cell culture manufacturing process which exhibits variability in metabolism and productivity. The model fitting process was broken into multiple steps to mitigate the substantial numerical challenges related to the first-principles model components. The optimized model captured the dynamics of metabolism and the variability of the process runs with different kinetic profiles and productivity. The variability of the process was attributed in part to the metabolic state of cell inoculum. The model was then used to identify potential mitigation strategies to reduce process variability by altering the initial process conditions as well as to explore the effect of changing CO₂ removal capacity in different bioreactor scales on process performance. By incorporating a mechanistic model of cell metabolism and appropriately fitting it to a large dataset, the hybrid model can describe the different metabolic phases in culture and the variability in manufacturing runs. This approach of employing a hybrid model has the potential to greatly facilitate process development and reactor scaling.

Dynamic Optimization of a Fed-Batch Nosiheptide Reactor

Nosiheptide is a sulfur-containing peptide antibiotic, showing exceptional activity against critical pathogens such as methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE) with livestock applications that can be synthesized via fed-batch fermentation. A simplified mechanistic fed-batch fermentation model for nosiheptide production considers temperature- and pH-dependence of biomass growth, substrate consumption, nosiheptide production and oxygen mass transfer into the broth. Herein, we perform dynamic simulation over a broad range of possible feeding policies to understand and visualize the region of attainable reactor performances. We then formulate a dynamic optimization problem for maximization of nosiheptide production for different constraints of batch duration and operability limits. A direct method for dynamic optimization (simultaneous strategy) is performed in each case to compute the optimal control trajectories. Orthogonal polynomials on finite elements are used to approximate the control and state trajectories allowing the continuous problem to be converted to a nonlinear program (NLP). The resultant large-scale NLP is solved using IPOPT. Optimal operation requires feedrate to be manipulated in such a way that the inhibitory mechanism of the substrate can be avoided, with significant nosiheptide yield improvement realized.