Assessing drug development risk using big data and machine learning
AffiliationIntelligencia Inc., New York, New York.
MetadataShow full item record
AbstractIdentifying 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.
CitationVergetis V, Skaltsas D, Gorgoulis VG, Tsirigos A. Assessing Drug Development Risk Using Big Data and Machine Learning. Cancer Res. 2021;81(4):816-9.
- Automating Construction of Machine Learning Models With Clinical Big Data: Proposal Rationale and Methods.
- Authors: Luo G, Stone BL, Johnson MD, Tarczy-Hornoch P, Wilcox AB, Mooney SD, Sheng X, Haug PJ, Nkoy FL
- Issue date: 2017 Aug 29
- [Development of antituberculous drugs: current status and future prospects].
- Authors: Tomioka H, Namba K
- Issue date: 2006 Dec
- The future of Cochrane Neonatal.
- Authors: Soll RF, Ovelman C, McGuire W
- Issue date: 2020 Nov
- Use of big data in drug development for precision medicine: an update.
- Authors: Qian T, Zhu S, Hoshida Y
- Issue date: 2019
- Authors: Bloom BR, Atun R, Cohen T, Dye C, Fraser H, Gomez GB, Knight G, Murray M, Nardell E, Rubin E, Salomon J, Vassall A, Volchenkov G, White R, Wilson D, Yadav P, Holmes KK, Bertozzi S, Bloom BR, Jha P
- Issue date: 2017 Nov 3