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Discovery of new senolytics using machine learning

Vanessa Smer-Barreto, Andrea Quintanilla, Richard J. R. Elliot, John C. Dawson, Jiugeng Sun, View ORCID ProfileNeil O. Carragher, View ORCID ProfileJuan Carlos Acosta, View ORCID ProfileDiego A. Oyarzún
doi: https://doi.org/10.1101/2022.04.26.489505
Vanessa Smer-Barreto
1Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
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  • For correspondence: vanessa.smerbarreto@ed.ac.uk juan.acosta@unican.es d.oyarzun@ed.ac.uk
Andrea Quintanilla
2Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain
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Richard J. R. Elliot
1Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
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John C. Dawson
1Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
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Jiugeng Sun
3School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK
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Neil O. Carragher
1Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
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Juan Carlos Acosta
1Cancer Research UK Edinburgh Centre, MRC Institute of Genetics and Cancer, University of Edinburgh, Crewe Road, Edinburgh, EH4 2XR, UK
2Instituto de Biomedicina y Biotecnología de Cantabria (IBBTEC), CSIC-Universidad de Cantabria-SODERCAN. C/ Albert Einstein 22, Santander, 39011, Spain
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  • For correspondence: vanessa.smerbarreto@ed.ac.uk juan.acosta@unican.es d.oyarzun@ed.ac.uk
Diego A. Oyarzún
3School of Informatics, University of Edinburgh, 10 Crichton St, Edinburgh, EH8 9AB, UK
4School of Biological Sciences, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK
5The Alan Turing Institute, 96 Euston Road, London, NW1 2DB, UK
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  • For correspondence: vanessa.smerbarreto@ed.ac.uk juan.acosta@unican.es d.oyarzun@ed.ac.uk
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Abstract

Cellular senescence is a stress response characterised by a permanent cell cycle arrest and a proinflammatory secretome. In addition to its tumour suppressor role, senescence is involved in ageing and promotes many disease processes such as cancer, type 2 diabetes, osteoarthritis, and SARS-CoV-2 infection. There is a growing interest in therapies based on targeted elimination of senescent cells, yet so far only a few such senolytics are known, partly due to the poor grasp of the molecular mechanisms that control the senescence survival programme. Here we report a highly effective machine learning pipeline for the discovery of senolytic compounds. Using solely published data, we trained machine learning algorithms to classify compounds according to their senolytic action. Models were trained on as few as 58 known senolytics against a background of FDA-approved compounds or in late-stage clinical development (2,523 in total). We computationally screened various chemical libraries and singled out top candidates for validation in human lung fibroblasts (IMR90) and lung adenocarcinoma (A549) cell lines. This led to the discovery of three novel senolytics: ginkgetin, oleandrin and periplocin, with potency comparable to current senolytics and a several hundred-fold reduction in experimental screening costs. Our work demonstrates that machine learning can take maximum advantage of existing drug screening data, paving the way for new open science approaches to drug discovery for senescence-associated diseases.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 27, 2022.
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Discovery of new senolytics using machine learning
Vanessa Smer-Barreto, Andrea Quintanilla, Richard J. R. Elliot, John C. Dawson, Jiugeng Sun, Neil O. Carragher, Juan Carlos Acosta, Diego A. Oyarzún
bioRxiv 2022.04.26.489505; doi: https://doi.org/10.1101/2022.04.26.489505
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Discovery of new senolytics using machine learning
Vanessa Smer-Barreto, Andrea Quintanilla, Richard J. R. Elliot, John C. Dawson, Jiugeng Sun, Neil O. Carragher, Juan Carlos Acosta, Diego A. Oyarzún
bioRxiv 2022.04.26.489505; doi: https://doi.org/10.1101/2022.04.26.489505

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