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A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events

View ORCID ProfileNeel S. Madhukar, Kaitlyn Gayvert, Coryandar Gilvary, View ORCID ProfileOlivier Elemento
doi: https://doi.org/10.1101/288332
Neel S. Madhukar
1HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
2Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA
3Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA
4Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
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  • ORCID record for Neel S. Madhukar
Kaitlyn Gayvert
1HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
2Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA
3Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA
4Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
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Coryandar Gilvary
1HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
2Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA
3Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA
4Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
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Olivier Elemento
1HRH Prince Alwaleed Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine, Dept. of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10065, USA
2Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY 10065, USA
3Sandra and Edward Meyer Cancer Center, Weill Cornell Medicine, New York, NY 10065, USA
4Tri-Institutional Training Program in Computational Biology and Medicine, New York, NY 10065, USA
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  • ORCID record for Olivier Elemento
  • For correspondence: ole2001@med.cornell.edu
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ABSTRACT

One of the main causes for failure in the drug development pipeline or withdrawal post approval is the unexpected occurrence of severe drug adverse events. Even though such events should be detected by in vitro, in vivo, and human trials, they continue to unexpectedly arise at different stages of drug development causing costly clinical trial failures and market withdrawal. Inspired by the “moneyball” approach used in baseball to integrate diverse features to predict player success, we hypothesized that a similar approach could leverage existing adverse event and tissue-specific toxicity data to learn how to predict adverse events. We introduce MAESTER, a data-driven machine learning approach that integrates information on a compound’s structure, targets, and phenotypic effects with tissue-wide genomic profiling and our toxic target database to predict the probability of a compound presenting with different types of tissue-specific adverse events. When tested on 6 different types of adverse events MAESTER maintains a high accuracy, sensitivity, and specificity across both the training data and new test sets. Additionally, MAESTER scores could flag a number of drugs that were approved, but later withdrawn due to unknown adverse events – highlighting its potential to identify events missed by traditional methods. MAESTER can also be used to identify toxic targets for each tissue type. Overall MAESTER provides a broadly applicable framework to identify toxic targets and predict specific adverse events and can accelerate the drug development pipeline and drive the design of new safer compounds.

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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 March 24, 2018.
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A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events
Neel S. Madhukar, Kaitlyn Gayvert, Coryandar Gilvary, Olivier Elemento
bioRxiv 288332; doi: https://doi.org/10.1101/288332
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A Machine Learning Approach Predicts Tissue-Specific Drug Adverse Events
Neel S. Madhukar, Kaitlyn Gayvert, Coryandar Gilvary, Olivier Elemento
bioRxiv 288332; doi: https://doi.org/10.1101/288332

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