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Christopher Love and Colin Fogarty. Xie, Yucen,M.B.A.Massachusetts Institute of Technology. Sloan School of Management. Massachusetts Institute of Technology. Department of Chemical Engineering. Leaders for Global Operations Program. Sloan School of Management Massachusetts Institute of Technology. Department of Chemical Engineering Leaders for Global Operations Program 2019-10-11T22:24:17Z 2019-10-11T22:24:17Z 2019 2019 2019 https://hdl.handle.net/1721.1/122575 1119388527 Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2019, In conjunction with the Leaders for Global Operations Program at MIT Thesis: S.M., Massachusetts Institute of Technology, Department of Chemical Engineering, 2019, In conjunction with the Leaders for Global Operations Program at MIT Cataloged from PDF version of thesis. Includes bibliographical references (pages 99-105). A critical component of the biopharmaceutical development cycle is the selection of the cell line that will become the Master Cell Bank for product manufacturing for clinical and commercial use. This cell line selection process is resource-intensive, requiring several months, involving hundreds of cell cultures and corresponding assays, and is largely conducted on a per-experiment basis. Ultimately, a single cell line that can yield product of consistently high quality and titers is selected. In this thesis, we aggregated historical, pre-clinical program data to create analytic tools. We deployed machine learning algorithms to produce insights and provide predictive power for cell line selection in future experiments. Our models reduced prediction errors by 38 - 90% for bioreactor end-point titer and product quality metrics. These interpretable and robust models lead to better knowledge of key attributes affecting titer and product quality as well. Our models are currently deployed as a web-based tool, and pilot studies prove we can generate massively parallel in silico predictions with high accuracy. Ultimately, our project can lead to more productive and higher quality cell lines and reduced development cycle times. Utilizing a modular algorithmic framework, our novel application of machine learning not only delivers efficiency and differentiation in the cell line selection process, but also promotes a scalable and transferable digital platform for analogous applications throughout the biopharmaceutical industry. by Yucen Xie. M.B.A. S.M. M.B.A. Massachusetts Institute of Technology, Sloan School of Management S.M. Massachusetts Institute of Technology, Department of Chemical Engineering 114 pages eng Massachusetts Institute of Technology MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 Sloan School of Management. Chemical Engineering. Leaders for Global Operations Program. Data-driven predictive modeling for cell line selection in biopharmaceutical production Thesis application/pdf 2019-10-11T22:24:16Z Publication Master Sloan ChemEng unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="81f8b998-a0d0-4559-87c7-4119a9a88435"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Data-driven predictive modeling for cell line selection in biopharmaceutical production</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <PublicationDate>2019</PublicationDate> <Authors> <Author> <DisplayName>Xie, Yucen,M.B.A.Massachusetts Institute of Technology.</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Sloan School of Management.</Keyword> <Keyword>Chemical Engineering.</Keyword> <Keyword>Leaders for Global Operations Program.</Keyword> <Abstract>A critical component of the biopharmaceutical development cycle is the selection of the cell line that will become the Master Cell Bank for product manufacturing for clinical and commercial use. This cell line selection process is resource-intensive, requiring several months, involving hundreds of cell cultures and corresponding assays, and is largely conducted on a per-experiment basis. Ultimately, a single cell line that can yield product of consistently high quality and titers is selected. In this thesis, we aggregated historical, pre-clinical program data to create analytic tools. We deployed machine learning algorithms to produce insights and provide predictive power for cell line selection in future experiments. Our models reduced prediction errors by 38 - 90% for bioreactor end-point titer and product quality metrics. These interpretable and robust models lead to better knowledge of key attributes affecting titer and product quality as well. Our models are currently deployed as a web-based tool, and pilot studies prove we can generate massively parallel in silico predictions with high accuracy. Ultimately, our project can lead to more productive and higher quality cell lines and reduced development cycle times. Utilizing a modular algorithmic framework, our novel application of machine learning not only delivers efficiency and differentiation in the cell line selection process, but also promotes a scalable and transferable digital platform for analogous applications throughout the biopharmaceutical industry.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>