Data intelligence for process perf ormance prediction in biologics manufacturing
Data intelligence for process perf ormance prediction in biologics manufacturing
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DOI：
10.1016/j.compchemeng.2021.107226
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发表时间：
2021-01-18
影响因子：
4.3
通讯作者：
Dikicioglu, Duygu
中科院分区：
文献类型：
--
作者：
Gangadharan, Nishanthi;Sewell, David;Dikicioglu, Duygu
Despite the availability of large amount of data in bioprocess databases, little has been done for its retrospective analysis for process improvement. Historic bioprocess data is multivariate time-series, and due to its inherent nature, is incompatible with a variety of statistical methods employed in data analysis resulting in the lack of a tailored methodology. We present here an integrative framework of knowledge discovery tailored for handling historical bioprocess datasets. The pipeline successfully predicts process performance at harvest from an early time point, and robustly identifies the most relevant process parameters to model process performance. We present the utility of this pipeline on biologics manufacturing data from upstream bioprocess development for antibody production by mammalian cells. The proposed multi-model system that employs machine learning can predict performance at harvest after two weeks of operation with satisfactory accuracy employing data generated as early as on the sixth day of the culture. (c) 2021 Elsevier Ltd. All rights reserved.
