实验设计
化学
生物信息学
效力
乙状窦函数
实验数据
计算生物学
生物系统
计算机科学
机器学习
生物化学
统计
体外
数学
生物
人工神经网络
基因
作者
Marco Kunzelmann,Anja Wittmann,Verena Nold,Beate Presser,Jasmin Schreiber,Tanja Gehrig,Sabine Sadlers,Reinhard Scholz,Johannes Solzin,Alexander Berger,Karoline Eppler
标识
DOI:10.1016/j.jpba.2023.115584
摘要
For biotherapeutic analytics, robust and reliable potency assays are required. Design of experiment (DoE) approaches are used to investigate the impact of multiple assay parameters. Currently, specific assay features (e.g., half effective concentration) are modelled independently from each other. A joint interpretation of several assay features is thus difficult. In our functional DoE approach, we use the functional relationship of the assay features to describe the sigmoidal dose-response curve. With the composed functional form, the direct impact of assay parameters on the dose-response curve shape was modelled. Moreover, a multivariate desirability was defined and used for assay optimization. We believe that functional modelling contributes to understanding the joint influence of assay parameters and helps to design robust biotherapeutic analytics.
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