Decisional tools for cost-effective bioprocess design for cell therapies and patient-specific drug discovery tools

A specific challenge to the translation of cell therapies and stem-cell derived products is the ability to develop and manufacture such products in a cost-effective, scalable and robust manner. To this end, this thesis investigates the creation and application of a set of computational tools designed to aid bioprocess design decisions for cell therapy and stem-cell derived research products. The decision-support tools comprise advanced bioprocess economics models with databases tailored to cellular products. These are linked to Monte Carlo simulation for uncertainty analysis and techniques to identify optimal bioprocess designs that include brute-force search algorithms, an evolutionary algorithm, and multi-attribute decision making analysis. A trio of industrially-relevant case studies is presented within this thesis, along with an additional study included in the appendices of this work, in order to demonstrate the applicability of the decisional tools to bioprocess design for different cell therapies (allogeneic, human embryonic stem cell-derived retinal pigment epithelial (RPE) cells for macular degeneration, allogeneic CAR-T cells for oncology) and induced pluripotent stem cells (iPSCs) for drug discovery applications. Questions tackled included manual versus automated production, costeffective inflection points of planar vs microcarrier-based bioprocess strategies, and the identification optimal process technologies for an allogeneic CAR-T cell therapy based on both qualitative and quantitative attributes. The analyses highlighted key bioprocess economic drivers and process bottlenecks. Furthermore, the Monte Carlo simulation technique was used in order to capture the effects of the inherent uncertainty associated with cell therapy bioprocessing on manufacturing costs and process throughputs. Future process improvements required to create financially feasible bioprocesses were also identified. This thesis presents the application of a series of decisional tools to bioprocess design problems and demonstrates how they can facilitate informed decisions regarding cost-effective process design in the cell therapy sector.