Misener Ruth, Allenby Mark C, Fuentes-Garí María, Gupta Karan, Wiggins Thomas, Panoskaltsis Nicki, Pistikopoulos Efstratios N, Mantalaris Athanasios
Dept. of Computing Imperial College London South Kensington London SW7 2AZ U.K.
Dept. of Haematology Imperial College London Harrow London HA1 3UJ U. K.
AIChE J. 2018 Aug;64(8):3011-3022. doi: 10.1002/aic.16042. Epub 2017 Dec 7.
As breakthrough cellular therapy discoveries are translated into reliable, commercializable applications, effective stem cell biomanufacturing requires systematically developing and optimizing bioprocess design and operation. This article proposes a rigorous computational framework for stem cell biomanufacturing under uncertainty. Our mathematical tool kit incorporates: high-fidelity modeling, single variate and multivariate sensitivity analysis, global topological superstructure optimization, and robust optimization. The advantages of the proposed bioprocess optimization framework using, as a case study, a dual hollow fiber bioreactor producing red blood cells from progenitor cells were quantitatively demonstrated. The optimization phase reduces the cost by a factor of 4, and the price of insuring process performance against uncertainty is approximately 15% over the nominal optimal solution. Mathematical modeling and optimization can guide decision making; the possible commercial impact of this cellular therapy using the disruptive technology paradigm was quantitatively evaluated.
随着突破性的细胞治疗发现转化为可靠的、可商业化的应用，有效的干细胞生物制造需要系统地开发和优化生物工艺设计与操作。本文提出了一个用于不确定性条件下干细胞生物制造的严格计算框架。我们的数学工具包包括：高保真建模、单变量和多变量敏感性分析、全局拓扑超结构优化以及鲁棒优化。以一个使用双中空纤维生物反应器从祖细胞生产红细胞的案例研究为例，定量展示了所提出的生物工艺优化框架的优势。优化阶段将成本降低了四倍，并且针对不确定性确保工艺性能的成本比标称最优解高出约15%。数学建模和优化可以指导决策；使用颠覆性技术范式的这种细胞治疗的潜在商业影响得到了定量评估。
