Decisional tool to assess current and future process robustness in an antibody purification facility

Abstract

Increases in cell culture titers in existing facilities have prompted efforts to identify strategies that alleviate purification bottlenecks while controlling costs. This article describes the application of a database-driven dynamic simulation tool to identify optimal purification sizing strategies and visualize their robustness to future titer increases. The tool harnessed the benefits of MySQL to capture the process, business, and risk features of multiple purification options and better manage the large datasets required for uncertainty analysis and optimization. The database was linked to a discrete-event simulation engine so as to model the dynamic features of biopharmaceutical manufacture and impact of resource constraints. For a given titer, the tool performed brute force optimization so as to identify optimal purification sizing strategies that minimized the batch material cost while maintaining the schedule. The tool was applied to industrial case studies based on a platform monoclonal antibody purification process in a multisuite clinical scale manufacturing facility. The case studies assessed the robustness of optimal strategies to batch-to-batch titer variability and extended this to assess the long-term fit of the platform process as titers increase from 1 to 10 g/L, given a range of equipment sizes available to enable scale intensification efforts. Novel visualization plots consisting of multiple Pareto frontiers with tie-lines connecting the position of optimal configurations over a given titer range were constructed. These enabled rapid identification of robust purification configurations given titer fluctuations and the facility limit that the purification suites could handle in terms of the maximum titer and hence harvest load.

Improved virus purification processes for vaccines and gene therapy

“…However, the ability of simulation software to accurately predict unit operation scale‐up and perform bioprocess optimization is limited (Shanklin et al, ). Still, several papers describe process economics for different mAbs (Farid, ; Stonier et al, ), although no data are available in the literature for viral vaccines with the exception of a very recent study of VLP process economics (Chuan et al, ). In this study, Monte Carlo simulation was employed to identify the variance in the unit production cost (UPC); through bioprocess simulation data it was shown that a vaccine could be manufactured at 1 cent (USD) per dose using bioreactor sizes of 300 and 500L.…”

Integration of stochastic simulation with multivariate analysis: Short‐term facility fit prediction

“…A database‐driven discrete‐event simulation tool for modeling the logistics and process economics of antibody processes was used as the core evaluation engine for this study. It was adapted to perform Monte Carlo simulations and handle the larger datasets required so as to mimic the impact of process fluctuations on the key outputs.…”

“…A database‐driven discrete‐event simulation tool was built to model the logistics and economics of alternative purification strategies. The overall structure along with the key input and output parameters is shown in Stonier et al The benefits of a database‐driven approach using MySQL (MySQL AB, Uppsala, Sweden) were exploited to capture the process, business, and risk features of multiple biopharmaceutical purification sequences in a multiproduct facility and better manage the large datasets required for multiple processes, uncertainty analysis, and optimization. The dynamic features of bioprocesses and impact of resource constraints, including suite availability, were captured through linkage to a discrete‐event simulation engine, Extend (Imagine That!…”

“…Long‐term facility fit issues are generally concerned with the future proofing of manufacturing facilities and platform processes . Any challenges highlighted can be addressed over time with the development of new technologies and large capital projects.…”

“…This article builds on the flexible database‐driven simulation platform described in Stonier et al that captures the mass balances, equipment sizing, dynamic resource allocation, and process economics of purification sequences in monoclonal antibody manufacturing processes. Previous work by the authors discusses the use of the tool to perform a small number of simulation runs to generate sufficient data to assess the impact of a single parameter change. This article describes the extension of the tool to mimic the stochastic nature of industrial batch processes when transferred to large‐scale facilities and to identify the potential root causes of short‐term facility fit issues.…”