Gangadharan, N;
Sewell, D;
Turner, R;
Field, R;
Cheeks, M;
Oliver, SG;
Slater, NKH;
(2021)
Data intelligence for process performance prediction in biologics manufacturing.
Computers & Chemical Engineering
, 146
, Article 107226. 10.1016/j.compchemeng.2021.107226.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | Data intelligence for process performance prediction in biologics manufacturing |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.compchemeng.2021.107226 |
| Publisher version: | https://doi.org/10.1016/j.compchemeng.2021.107226 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Biologics manufacturing, Culture performance prediction, Data mining, Time-series analysis, Machine learning, Two-dimensional modelling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Biochemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10119588 |
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