Konstantinidis Spyridon, Titchener-Hooker Nigel, Velayudhan Ajoy
The Advanced Centre for Biochemical Engineering, Department of Biochemical Engineering, University College London, London, UK.
Biotechnol J. 2017 Aug;12(8). doi: 10.1002/biot.201700174. Epub 2017 Jul 19.
Bioprocess development studies often involve the investigation of numerical and categorical inputs via the adoption of Design of Experiments (DoE) techniques. An attractive alternative is the deployment of a grid compatible Simplex variant which has been shown to yield optima rapidly and consistently. In this work, the method is combined with dummy variables and it is deployed in three case studies wherein spaces are comprised of both categorical and numerical inputs, a situation intractable by traditional Simplex methods. The first study employs in silico data and lays out the dummy variable methodology. The latter two employ experimental data from chromatography based studies performed with the filter-plate and miniature column High Throughput (HT) techniques. The solute of interest in the former case study was a monoclonal antibody whereas the latter dealt with the separation of a binary system of model proteins. The implemented approach prevented the stranding of the Simplex method at local optima, due to the arbitrary handling of the categorical inputs, and allowed for the concurrent optimization of numerical and categorical, multilevel and/or dichotomous, inputs. The deployment of the Simplex method, combined with dummy variables, was therefore entirely successful in identifying and characterizing global optima in all three case studies. The Simplex-based method was further shown to be of equivalent efficiency to a DoE-based approach, represented here by D-Optimal designs. Such an approach failed, however, to both capture trends and identify optima, and led to poor operating conditions. It is suggested that the Simplex-variant is suited to development activities involving numerical and categorical inputs in early bioprocess development.
生物工艺开发研究通常涉及通过采用实验设计（DoE）技术来研究数值和分类输入。一种有吸引力的替代方法是部署一种与网格兼容的单纯形变体，该变体已被证明能够快速且一致地产生最优解。在这项工作中，该方法与虚拟变量相结合，并应用于三个案例研究中，其中空间由分类和数值输入组成，这是传统单纯形方法难以处理的情况。第一个研究使用计算机模拟数据并阐述了虚拟变量方法。后两个研究使用基于过滤板和微型柱高通量（HT）技术的色谱研究的实验数据。前一个案例研究中感兴趣的溶质是一种单克隆抗体，而后者处理的是模型蛋白二元系统的分离。所实施的方法避免了单纯形方法因对分类输入的随意处理而被困在局部最优解，并且允许同时优化数值和分类、多级和/或二分法输入。因此，在所有三个案例研究中，结合虚拟变量的单纯形方法在识别和表征全局最优解方面完全成功。基于单纯形的方法还被证明与基于DoE的方法（在此以D - 最优设计表示）具有同等效率。然而，这样的方法既无法捕捉趋势也无法识别最优解，并且导致操作条件不佳。建议这种单纯形变体适用于早期生物工艺开发中涉及数值和分类输入的开发活动。
