Application of a simple unstructured kinetic and cost of goods models to support T-cell therapy manufacture.
Application of a simple unstructured kinetic and cost of goods models to support T-cell therapy manufacture.
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应用简单的非结构化动力学和商品成本模型来支持 T 细胞疗法的生产。
DOI：
10.1002/btpr.3205
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发表时间：
2021
影响因子：
2.9
通讯作者：
Shariatzadeh M
中科院分区：
文献类型：
--
作者：
Shariatzadeh M
细胞治疗产品的生产需要充分了解细胞培养变量和相关机制，以进行充分的控制和风险分析。本研究的目的是应用基于非结构化常微分方程的模型预测T细胞生物过程结果作为过程输入参数的函数。使用Ambr®15搅拌生物反应器系统的数据，开发了一系列模型，以代表T细胞生长与时间、培养体积、细胞密度和葡萄糖浓度的函数关系。该模型充分代表了该过程，以预测维持细胞生长速率所需的葡萄糖和体积供应，并定量定义了葡萄糖浓度、细胞生长速率和葡萄糖利用率之间的关系。该模型表明，虽然葡萄糖是批量供应培养基中的限制因素，但由于葡萄糖Monod常数决定葡萄糖消耗速率相对于葡萄糖Monod常数决定细胞生长速率较低，因此显著低于最大比消耗速率（0.05 mg 1 × 106 cell h−1）的葡萄糖输送速率将足以维持细胞生长。由此产生的体积和交换要求被用作操作BioSolve成本模型的输入，以建议具有成本效益的T细胞制造工艺，该工艺具有生产每百万个细胞的最低商品成本和制造环境中的最佳体积生产率。这些发现突出了搅拌罐系统中T细胞生长的简单非结构化模型的潜力，为控制和优化制造生物过程提供了框架。
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 × 106cell 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.