Boost dynamic protocols for producing mammalian biopharmaceuticals with intensified DoE—a practical guide to analyses with OLS and hybrid modeling

IF 2.5 Q3 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Frontiers in chemical engineering Pub Date : 2023-01-04 DOI:10.3389/fceng.2022.1044245
V. Nold, L. Junghans, B. Bayer, L. Bisgen, M. Duerkop, R. Drerup, B. Presser, T. Schwab, E. Bluhmki, S. Wieschalka, B. Knapp
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引用次数: 2

Abstract

Introduction: For the implementation of robust bioprocesses, understanding of temporal cell behavior with respect to relevant inputs is crucial. Intensified Design of Experiments (iDoE) is an efficient tool to assess the joint influence of input parameters by including intra-experimental changes. Methods: We applied iDoE to the production phase of a monoclonal antibody in a mammalian bioprocess. The multidimensional design space spanned by temperature, dissolved oxygen (DO), timing of change, and growth category was investigated in 12 cultivations. We built ordinary least squares (OLS) and hybrid models (HM) on the iDoE-data, validated them with classical DoE (cDoE)-derived data, and used the models as in silico representation for process optimization. Results: If the complexity of interactions between changing setpoints of inputs is sufficiently captured during planning and modeling, iDoE proved to be valid for characterizing the mammalian biopharmaceutical production phase. For local behavior and flexible composition of optimization goals, OLS regressions can easily be implemented. To predict global and interconnected dynamics while incorporating mass balances, HM holds potential. Discussion: iDoE will boost protocols that optimize inputs for different bioprocess phases. The described key aspects of OLS- and HM-based analyses of iDoE-data shall guide future applications during manufacturing.
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增强动态协议生产哺乳动物生物制药强化doe -一个实用指南,分析与OLS和混合建模
为了实现健壮的生物过程,了解时间细胞的行为与相关输入是至关重要的。强化实验设计(iDoE)是一种评估输入参数联合影响的有效工具,它包含了实验内部的变化。方法:我们将iDoE应用于哺乳动物生物过程中单克隆抗体的生产阶段。研究了温度、溶解氧(DO)、变化时间和生长类别所跨越的多维设计空间。在idoe数据上建立了普通最小二乘(OLS)和混合模型(HM),并用经典DoE (cDoE)衍生数据进行验证,并将模型作为工艺优化的计算机表示。结果:如果在规划和建模过程中充分捕捉到输入的变化设定值之间相互作用的复杂性,则iDoE被证明是有效的,可用于表征哺乳动物生物制药生产阶段。对于局部行为和优化目标的灵活组合,OLS回归可以很容易地实现。在结合质量平衡的同时预测全球和相互关联的动态,HM具有潜力。讨论:iDoE将促进优化不同生物过程阶段投入的协议。所描述的基于OLS和hm的idoe数据分析的关键方面将指导未来在制造过程中的应用。
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CiteScore
3.50
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0.00%
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0
审稿时长
13 weeks
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