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Application of simplex-based experimental optimization to challenging bioprocess development problems: Case studies in downstream processing

机译：基于单纯形的实验优化在挑战性生物工艺开发问题中的应用：下游加工中的案例研究

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The identification of feasible operating conditions during the early stages of bioprocess development is implemented frequently through High Throughput (HT) studies. These typically employ techniques based on regression analysis, such as Design of Experiments. In this work, an alternative approach, based on a previously developed variant of the Simplex algorithm, is compared to the conventional regression-based method for three experimental systems involving polishing chromatography and protein refolding. This Simplex algorithm variant was found to be more effective in identifying superior operating conditions, and in fact it reached the global optimum in most cases involving multiple optima. By contrast, the regression-based method often failed to reach the global optimum, and in many cases reached poor operating conditions. The Simplex-based method is further shown to be robust in dealing with noisy experimental data, and requires fewer experiments than regression-based methods to reach favorable operating conditions. The Simplex-variant also lends itself to the use of HT analytical methods, when they are available, which can assist in avoiding analytical bottlenecks. It is suggested that this Simplex-variant is ideally suited to rapid optimization in early-phase process development. (c) 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:404-419, 2016
机译：在生物过程开发的早期阶段，通过高通量（HT）研究经常确定可行的操作条件。这些通常采用基于回归分析的技术，例如实验设计。在这项工作中，将基于先前开发的Simplex算法变体的另一种方法与涉及抛光色谱法和蛋白质重折叠的三个实验系统的基于常规回归的方法进行了比较。发现此Simplex算法变体在识别优越的运行条件时更有效，实际上，在涉及多重优化的大多数情况下，它已达到全局最优。相比之下，基于回归的方法通常无法达到全局最优值，并且在许多情况下达到较差的运行条件。基于单纯形的方法还被证明在处理嘈杂的实验数据方面具有鲁棒性，并且与基于回归的方法相比，需要较少的实验才能达到良好的操作条件。 Simplex变量也适用于HT分析方法（如果可用），可以帮助避免分析瓶颈。建议该单纯形变量非常适合于早期过程开发中的快速优化。 （c）2016美国化学工程师学会生物技术学会。 Prog。，32：404-419，2016

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