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Real world big data for clinical research and drug development

Singh, G; Schulthess, D; Hughes, N; Vannieuwenhuyse, B; Kalra, D; (2017) Real world big data for clinical research and drug development. Drug Discovery Today 10.1016/j.drudis.2017.12.002. (In press). Green open access

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The objective of this paper is to identify the extent to which real world data (RWD) is being utilized, or could be utilized, at scale in drug development. Through screening peer-reviewed literature, we have cited specific examples where RWD can be used for biomarker discovery or validation, gaining a new understanding of a disease or disease associations, discovering new markers for patient stratification and targeted therapies, new markers for identifying persons with a disease, and pharmacovigilance. None of the papers meeting our criteria was specifically geared toward new novel targets or indications in the biopharmaceutical sector; the majority were focused on the area of public health, often sponsored by universities, insurance providers or in combination with public health bodies such as national insurers. The field is still in an early phase of practical application, and is being harnessed broadly where it serves the most direct need in public health applications in early, rare and novel disease incidents. However, these exemplars provide a valuable contribution to insights on the use of RWD to create novel, faster and less invasive approaches to advance disease understanding and biomarker discovery. We believe that pharma needs to invest in making better use of EHRs and the need for more precompetitive collaboration to grow the scale of this 'big denominator' capability, especially given the needs of precision medicine research.

Type: Article
Title: Real world big data for clinical research and drug development
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.drudis.2017.12.002
Publisher version: http://doi.org/10.1016/j.drudis.2017.12.002
Language: English
Additional information: Copyright 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
URI: http://discovery.ucl.ac.uk/id/eprint/10043159
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