Presentation Information
[4DSP-10-KL]Enhancing Process Development Efficiency in Therapeutic Antibody Purification through Model-assisted Process Simulation
○Chyi-Shin Chen1 (1. Chugai Pharmaceutical (Japan))
Keywords:
process simulation,therapeutic antibodies,chromatography,downstream process development
In therapeutic antibody manufacturing, process development for chromatography polishing steps to remove impurities requires substantial time and resources. This is particularly pronounced for antibodies exhibiting unconventional structures and physicochemical properties, which are a result of advanced engineering technologies. To enhance development efficiency, we applied mechanistic models through process simulation to predict product quality and yield for specific polishing steps.
Two case studies will be presented. The first focuses on early-stage process optimization of a monoclonal antibody containing high levels of high molecular weight species (HMWS) in mixed-mode chromatography. The optimal conditions with a robust operating range to achieve HMWS <1.0% and yield >80% were identified from in silico Design of Experiments (DoE) results and confirmed by experimental validation. The second study focuses on an engineered antibody featuring a more complex molecular format that exhibits strong hydrophobicity in anion exchange chromatography. Both size and charge related product variants were modeled using pH-dependent equations. The validated models were applied to in silico process characterization to assess process parameters using a univariate approach. The analysis of each parameter's main effect supported risk ranking, helping to classify them as potential critical process parameter (CPP) candidates. This simulation-driven parameter prioritization step, performed prior to DoE, optimized the experimental design and reduced the required number of experiments by 17%.
Overall, the adoption of model-assisted process simulations in the case studies demonstrates the potential to improve downstream process development efficiency for antibodies through science-based mechanistic modeling approaches.
Two case studies will be presented. The first focuses on early-stage process optimization of a monoclonal antibody containing high levels of high molecular weight species (HMWS) in mixed-mode chromatography. The optimal conditions with a robust operating range to achieve HMWS <1.0% and yield >80% were identified from in silico Design of Experiments (DoE) results and confirmed by experimental validation. The second study focuses on an engineered antibody featuring a more complex molecular format that exhibits strong hydrophobicity in anion exchange chromatography. Both size and charge related product variants were modeled using pH-dependent equations. The validated models were applied to in silico process characterization to assess process parameters using a univariate approach. The analysis of each parameter's main effect supported risk ranking, helping to classify them as potential critical process parameter (CPP) candidates. This simulation-driven parameter prioritization step, performed prior to DoE, optimized the experimental design and reduced the required number of experiments by 17%.
Overall, the adoption of model-assisted process simulations in the case studies demonstrates the potential to improve downstream process development efficiency for antibodies through science-based mechanistic modeling approaches.
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