Application of a simple unstructured kinetic and cost of goods models to support T‐cell therapy manufacture

• Published in: Biotechnology progress 2021-11, Vol.37 (6), p.e3205-n/a
• Main Authors: Shariatzadeh, Maryam, Lopes, Adriana G., Glen, Katie E., Sinclair, Andrew, Thomas, Rob J.
• Format: Article
• Language: English
• Subjects: Bioreactors; Cell Count; Cell culture; Cell Culture Techniques - methods; Cell growth; Cell Proliferation; Cell therapy; Cell- and Tissue-Based Therapy; Cells, Cultured; Consumption; cost analysis; Costs and Cost Analysis; Differential equations; Glucose; Glucose metabolism; Growth rate; Humans; Kinetics; Manufacturing; Manufacturing industry; Minimum cost; modeling framework; Optimization; process optimization; Process parameters; Risk analysis; Risk management; scalable T‐cell manufacturing; T-Lymphocytes - cytology; T‐cell processing
• ISSN: 8756-7938; 1520-6033
• DOI: 10.1002/btpr.3205

Manufacturing of cell therapy products requires sufficient understanding of the cell culture variables and associated mechanisms for adequate control and risk analysis. The aim of this study was to apply an unstructured ordinary differential equation‐based model for prediction of T‐cell bioprocess outcomes as a function of process input parameters. A series of models were developed to represent the growth of T‐cells as a function of time, culture volumes, cell densities, and glucose concentration using data from the Ambr®15 stirred bioreactor system. The models were sufficiently representative of the process to predict the glucose and volume provision required to maintain cell growth rate and quantitatively defined the relationship between glucose concentration, cell growth rate, and glucose utilization rate. The models demonstrated that although glucose is a limiting factor in batch supplied medium, a delivery rate of glucose at significantly less than the maximal specific consumption rate (0.05 mg 1 × 106 cell h−1) will adequately sustain cell growth due to a lower glucose Monod constant determining glucose consumption rate relative to the glucose Monod constant determining cell growth rate. The resultant volume and exchange requirements were used as inputs to an operational BioSolve cost model to suggest a cost‐effective T‐cell manufacturing process with minimum cost of goods per million cells produced and optimal volumetric productivity in a manufacturing settings. These findings highlight the potential of a simple unstructured model of T‐cell growth in a stirred tank system to provide a framework for control and optimization of bioprocesses for manufacture.