Assessing Drug Development Risk Using Big Data and Machine Learning.

Journal: Cancer research
Published Date: Dec 22, 2020

Abstract

Identifying new drug targets and developing safe and effective drugs is both challenging and risky. Furthermore, characterizing drug development risk, the probability that a drug will eventually receive regulatory approval, has been notoriously hard given the complexities of drug biology and clinical trials. This inherent risk is often misunderstood and mischaracterized, leading to inefficient allocation of resources and, as a result, an overall reduction in R&D productivity. Here we argue that the recent resurgence of Machine Learning in combination with the availability of data can provide a more accurate and unbiased estimate of drug development risk.

Authors

  • Vangelis Vergetis
    Intelligencia Inc., New York, New York.
  • Dimitrios Skaltsas
    Intelligencia Inc., New York, New York.
  • Vassilis G Gorgoulis
    Biomedical Research Foundation of the Academy of Athens, 4 Soranou Ephessiou Str., Athens GR-11527, Greece; Molecular Carcinogenesis Group, Department of Histology and Embryology, School of Medicine, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece; Division of Cancer Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, Manchester Cancer Research Centre, NIHR Manchester Biomedical Research Centre, University of Manchester, Manchester M20 4GJ, UK; Center for New Biotechnologies and Precision Medicine, Medical School, National and Kapodistrian University of Athens, 75 Mikras Asias Str, Athens GR-11527, Greece. Electronic address: vgorg@med.uoa.gr.
  • Aristotelis Tsirigos
    Department of Pathology, NYU School of Medicine, New York, NY 10016, USA; Laura and Isaac Perlmutter Cancer Center, NYU School of Medicine, New York, NY 10016, USA; Applied Bioinformatics Laboratories, NYU School of Medicine, New York, NY 10016, USA. Electronic address: aristotelis.tsirigos@nyulangone.org.

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