1. Assessing Drug Development Risk Using Big Data and Machine Learning
- Author
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Aristotelis Tsirigos, Vassilis G. Gorgoulis, Dimitrios Skaltsas, and Vangelis Vergetis
- Subjects
Big Data ,Male ,0301 basic medicine ,Drug ,Cancer Research ,Drug-Related Side Effects and Adverse Reactions ,media_common.quotation_subject ,Big data ,Antineoplastic Agents ,Machine learning ,computer.software_genre ,Risk Assessment ,Machine Learning ,03 medical and health sciences ,Drug Delivery Systems ,0302 clinical medicine ,Drug Development ,Neoplasms ,Inherent risk ,Humans ,Productivity ,media_common ,business.industry ,Clinical trial ,030104 developmental biology ,Oncology ,Drug development ,030220 oncology & carcinogenesis ,Female ,Patient Safety ,Artificial intelligence ,business ,computer - 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.
- Published
- 2021