Decisional Tools for Supply Chain Economics of Cell and Gene Therapy Products.
Doctoral thesis (Eng.D), UCL (University College London).
Gene therapy products have tremendous therapeutic potential for indications such as cancer and even curative potential for some genetic diseases. Most of today’s gene therapy products are viral vector-based, typically relying on plasmid DNA supply for their production, and many are autologous ex vivo applications (e.g. chimeric antigen receptor T-cell therapy – CAR T), hence the supply chain of these products is highly complex. Given the relative infancy of the sector, there is a strong drive towards adopting technologies that minimise costs and supply chain complexity. This thesis aims to explore these avenues by developing and applying advanced decisional tools that analyse the gene therapy supply chain systematically whilst capturing multiple stakeholder perspectives.
The decisional tools employed in this thesis included bioprocess economics models tailored to autologous CAR T-cell therapy and viral vector products. From the cost perspective, models were built to compute manufacturing costs, namely cost of goods (COG) and fixed capital investment (FCI), and coupled with brute force optimisation to identify optimal manufacturing strategies. In addition, a cost of drug development model and a cash flow model were built to evaluate the impact of process changes at different stages in the drug development pathway and evaluate the profitability of different manufacturing strategies.
The case studies presented in this thesis explored the autologous supply chains and automation, a range of viral vector manufacturing flowsheets and viral vector process changes. In particular, the autologous supply chain case study provides a feasibility analysis of the optimal number of sites for the decentralised enterprise models and gives new insights into the feasibility of bedside models and impact of quality control (QC) automation. For example, for autologous CAR T cell therapy commercial manufacture, the tool predicted that bedside models such as “GMP-in-a-box” can be more profitable than the regional model for low demand scenarios and identified the critical demand where the regional model starts to outperform bedside manufacture.
The viral vector manufacture case study offers the first thorough analysis of the COG associated with a range of flowsheets employing different cell culture technologies for multiple gene therapy product type and process performance scenarios. For lentiviral vector manufacture, it was found that suspension culture or adherent cell culture using fixed bed technology can offer cost savings in the order of 95% when compared to traditional manufacturing approaches in multi-layer vessels. Moreover, suspension cell culture was found to be more suitable for supplying large indications due to its high scalability potential.
The process change case study offers a detailed evaluation of the switch from transient transfection to a stable producer cell line for viral vector manufacture by capturing the impact on key financial outputs for both drug development and commercial manufacture, in the case of four topical gene therapy product types. The analysis highlighted that the optimal time to switch was most sensitive to the pDNA requirement and unit cost, the expected delay to market and the titre differences. For example, for products associated with a low pDNA requirement (e.g. CAR T and AAV), switching to stable cell lines post-approval was found to be more attractive than switching early if delays to market were incurred.
This thesis provides an account of how the advanced decisional tools employed can help decision-makers create optimal manufacturing strategies so as to maximise patient accessibility and provides a methodology for building decisional tools for emerging products.
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