Multiobjective evolutionary optimization in antibody purification process design
Multiobjective evolutionary optimization in antibody purification process design
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DOI：
10.1016/j.bej.2014.08.016
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发表时间：
2014-10-15
影响因子：
3.9
通讯作者：
Farid, Suzanne S.
中科院分区：
文献类型：
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作者：
Allmendinger, Richard;Simaria, Ana S.;Farid, Suzanne S.
为了帮助设计更具成本效益、更稳健和更灵活的单抗生产下游工艺，提出了一个由进化多目标优化算法(EMOA)和生物制造工艺经济学模型相连接的框架。对EMOA进行了调整，以发现层析纯化步骤和柱大小调整策略的序列，这些步骤和柱大小调整策略在包括每克商品成本(COG/g)、COG/g稳定性和杂质去除能力在内的多个目标方面提供了最佳的权衡。该框架考虑到的其他复杂性包括不确定性和制约因素。该框架在与工业相关的案例研究中得到了验证，这些案例研究在上下游加工列车比率、年需求量和杂质负荷方面存在差异。该框架获得的结果使用了一系列可视化工具，并表明不确定性对性能的影响是不确定性水平和被优化目标的函数，并且不确定性可能导致原本最优的过程变得次优。发现的最佳净化工艺优于工业标准，例如，COG/g节省高达10%。提供了根据被优化的目标和存在的杂质水平来选择最佳提纯工艺的指南。(C)爱思唯尔出版的2014年。
To contribute towards designing more cost-efficient, robust and flexible downstream processes for the manufacture of monoclonal antibodies (mAbs), a framework consisting of an evolutionary multiobjective optimization algorithm (EMOA) linked to a biomanufacturing process economics model is presented. The EMOA is tuned to discover sequences of chromatographic purification steps and column sizing strategies that provide the best trade-off with respect to multiple objectives including cost of goods per gram (COG/g), robustness in COG/g, and impurity removal capabilities. Additional complexities accounted for by the framework include uncertainties and constraints. The framework is validated on industrially relevant case studies varying in upstream and downstream processing train ratios, annual demands, and impurity loads. Results obtained by the framework are presented using a range of visualization tools, and indicate that the performance impact of uncertainty is a function of both the level of uncertainty and the objective being optimized, and that uncertainty can cause otherwise optimal processes to become suboptimal. The optimal purification processes discovered outperform the industrial standard with, e.g. savings in COG/g of up to 10%. Guidelines are provided for choosing an optimal purification process as a function of the objectives being optimized and impurity levels present. (C) 2014 Published by Elsevier B.V.