Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities

Khuat, TT; Bassett, R; Otte, E; Grevis-James, A; Gabrys, B

Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities

Khuat, TT Bassett, R Otte, E Grevis-James, A Gabrys, B

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Publisher:: PERGAMON-ELSEVIER SCIENCE LTD
Publication Type:: Journal Article
Citation:: Computers and Chemical Engineering, 2024, 182
Issue Date:: 2024-03-01

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Field	Value	Language
dc.contributor.author	Khuat, TT
dc.contributor.author	Bassett, R
dc.contributor.author	Otte, E
dc.contributor.author	Grevis-James, A
dc.contributor.author	Gabrys, B https://orcid.org/0000-0002-0790-2846
dc.date.accessioned	2024-11-01T08:24:46Z
dc.date.available	2024-11-01T08:24:46Z
dc.date.issued	2024-03-01
dc.identifier.citation	Computers and Chemical Engineering, 2024, 182
dc.identifier.issn	0098-1354
dc.identifier.issn	1873-4375
dc.identifier.uri	http://hdl.handle.net/10453/181663
dc.description.abstract	While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biologics, hindering the enormous potential for bioprocesses automation from their development to manufacturing. However, the adoption of ML-based models instead of conventional multivariate data analysis methods is significantly increasing due to the accumulation of large-scale production data. This trend is primarily driven by the real-time monitoring of process variables and quality attributes of biopharmaceutical products through the implementation of advanced process analytical technologies. Given the complexity and multidimensionality of a bioproduct design, bioprocess development, and product manufacturing data, ML-based approaches are increasingly being employed to achieve accurate, flexible, and high-performing predictive models to address the problems of analytics, monitoring, and control within the biopharma field. This paper aims to provide a comprehensive review of the current applications of ML solutions in the design, monitoring, control, and optimisation of upstream, downstream, and product formulation processes of monoclonal antibodies. Finally, this paper thoroughly discusses the main challenges related to the bioprocesses themselves, process data, and the use of machine learning models in monoclonal antibody process development and manufacturing. Moreover, it offers further insights into the adoption of innovative machine learning methods and novel trends in the development of new digital biopharma solutions.
dc.language	English
dc.publisher	PERGAMON-ELSEVIER SCIENCE LTD
dc.relation	http://purl.org/au-research/grants/arc/IH210100051
dc.relation.ispartof	Computers and Chemical Engineering
dc.relation.isbasedon	10.1016/j.compchemeng.2024.108585
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0904 Chemical Engineering, 0913 Mechanical Engineering
dc.subject.classification	Chemical Engineering
dc.subject.classification	4004 Chemical engineering
dc.title	Applications of machine learning in antibody discovery, process development, manufacturing and formulation: Current trends, challenges, and opportunities
dc.type	Journal Article
utslib.citation.volume	182
utslib.for	0904 Chemical Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Health Technologies (CHT)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Data Science Institute (DSI)
pubs.organisational-group	University of Technology Sydney/Strength - DSI - Data Science Institute
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-11-01T08:24:41Z
pubs.publication-status	Published
pubs.volume	182

Abstract:

While machine learning (ML) has made significant contributions to the biopharmaceutical field, its applications are still in the early stages in terms of providing direct support for quality-by-design based development and manufacturing of biologics, hindering the enormous potential for bioprocesses automation from their development to manufacturing. However, the adoption of ML-based models instead of conventional multivariate data analysis methods is significantly increasing due to the accumulation of large-scale production data. This trend is primarily driven by the real-time monitoring of process variables and quality attributes of biopharmaceutical products through the implementation of advanced process analytical technologies. Given the complexity and multidimensionality of a bioproduct design, bioprocess development, and product manufacturing data, ML-based approaches are increasingly being employed to achieve accurate, flexible, and high-performing predictive models to address the problems of analytics, monitoring, and control within the biopharma field. This paper aims to provide a comprehensive review of the current applications of ML solutions in the design, monitoring, control, and optimisation of upstream, downstream, and product formulation processes of monoclonal antibodies. Finally, this paper thoroughly discusses the main challenges related to the bioprocesses themselves, process data, and the use of machine learning models in monoclonal antibody process development and manufacturing. Moreover, it offers further insights into the adoption of innovative machine learning methods and novel trends in the development of new digital biopharma solutions.

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http://hdl.handle.net/10453/181663