Assessing drug development risk using big data and machine learning
Vergetis, V. Skaltsas, D. Gorgoulis, Vassilis G Tsirigos, A.
Vergetis, V.
Skaltsas, D.
Gorgoulis, Vassilis G
Tsirigos, A.
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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.
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Date
2021
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Article
Citation
Vergetis V, Skaltsas D, Gorgoulis VG, Tsirigos A. Assessing Drug Development Risk Using Big Data and Machine Learning. Cancer Res. 2021;81(4):816-9.