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Harnessing machine learning for development of microbiome therapeutics

McCoubrey, LE; Elbadawi, M; Orlu, M; Gaisford, S; Basit, AW; (2021) Harnessing machine learning for development of microbiome therapeutics. Gut Microbes , 13 (1) pp. 1-20. 10.1080/19490976.2021.1872323. Green open access

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Abstract

The last twenty years of seminal microbiome research has uncovered microbiota's intrinsic relationship with human health. Studies elucidating the relationship between an unbalanced microbiome and disease are currently published daily. As such, microbiome big data have become a reality that provide a mine of information for the development of new therapeutics. Machine learning (ML), a branch of artificial intelligence, offers powerful techniques for big data analysis and prediction-making, that are out of reach of human intellect alone. This review will explore how ML can be applied for the development of microbiome-targeted therapeutics. A background on ML will be given, followed by a guide on where to find reliable microbiome big data. Existing applications and opportunities will be discussed, including the use of ML to discover, design, and characterize microbiome therapeutics. The use of ML to optimize advanced processes, such as 3D printing and in silico prediction of drug-microbiome interactions, will also be highlighted. Finally, barriers to adoption of ML in academic and industrial settings will be examined, concluded by a future outlook for the field.

Type: Article
Title: Harnessing machine learning for development of microbiome therapeutics
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1080/19490976.2021.1872323
Publisher version: https://doi.org/10.1080/19490976.2021.1872323
Language: English
Additional information: © 2021 The Author(s). Published with license by Taylor & Francis Group, LLC. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Keywords: COVID-19, artificial intelligence, clinical translation, colonic drug delivery, drug product development, machine learning, microbial therapeutics, microbiome, personalized medicines, pharmaceutical sciences
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > UCL School of Pharmacy > Pharmaceutics
URI: https://discovery.ucl.ac.uk/id/eprint/10120864
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