RESUMO
Biologics manufacturing is capital and consumable intensive with need for advanced inventory planning to account for supply chain constraints. Early-stage process design and technology transfer are often challenging due to limited information on process variability regarding bioreactor titer, process yield, and product quality. Monte Carlo (MC) methods offer a stochastic modeling approach for process optimization where probabilities of occurrence for process inputs are incorporated into a deterministic model to simulate more likely scenarios for process outputs. In this study, we explore MC simulation-based design of a monoclonal antibody downstream manufacturing process. We demonstrate that this probabilistic approach offers more representative outcomes over the conventional worst-case approach where the theoretical minimum and maximum values of each process parameter are used without consideration for their probability of occurrence. Our work demonstrates case studies on more practically sizing unit operations to improve consumable utilization, thereby reducing manufacturing costs. We also used MC simulations to minimize process cadence by constraining the number of cycles per unit operation to fit facility preferences. By factoring in process uncertainty, we have implemented MC simulation-based facility fit analyses to efficiently plan for inventory when accounting for process constraints during technology transfer from lab-scale to clinical or commercial manufacturing.
