Monte Carlo simulation enabled facility fit: A case study in commercial monoclonal antibody purification

The manufacturing process of biological pharmaceuticals demands significant capital investment and consumable resources. For the downstream process, step yield variations can accumulate and significantly influence the overall process output, especially in commercial production where optimal utilization of all chromatography columns and filters without material waste is a priority.

In this case study, we developed a digital tool to track material balance through an example downstream process train including multiple chromatography steps, two normal flow filtration steps, one tangential flow filtration step and one viral inactivation step with intermediate holding, sampling, and conditioning between unit operations. Relevant facility capacity parameters such as column sizes, column load density and filter throughput are constrained against the process demand. A VBA (Visual Basic for Application) module was embedded into the tool to enable Monte Carlo Simulation. Through the iterations of Monte Carlo simulation, we were able to convert process parameters, step yields and yield variations to the probability and amount of product processed/wasted due to facility constraints, giving us practical assessment of facility fit and process efficiency while excluding the extreme scenarios that would otherwise drive decision-making in a traditional worst-case (minimum-maximum) approach. This tool and workflow can assist facility fit, identify bottlenecks, and propose paths for improvement to maximize return on investment. In tandem with other incremental process modifications to improve process output, the tool may serve as a co-pilot to continuously monitor facility fit and process efficiency as the step yields and yield variations change as a result of such process modifications.

In this presentation, we aim to highlight the practical application of Monte Carlo Simulation as an effective strategy for managing step yield variation in commercial scale monoclonal antibody production.