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      Leveraging Population‐Based Clinical Quantitative Phenotyping for Drug Repositioning

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          Abstract

          Computational drug repositioning methods can scalably nominate approved drugs for new diseases, with reduced risk of unforeseen side effects. The majority of methods eschew individual‐level phenotypes despite the promise of biomarker‐driven repositioning. In this study, we propose a framework for discovering serendipitous interactions between drugs and routine clinical phenotypes in cross‐sectional observational studies. Key to our strategy is the use of a healthy and nondiabetic population derived from the National Health and Nutrition Examination Survey, mitigating risk for confounding by indication. We combine complementary diagnostic phenotypes (fasting glucose and glucose response) and associate them with prescription drug usage. We then sought confirmation of phenotype‐drug associations in unidentifiable member claims data from the Aetna Insurance company using a retrospective self‐controlled case analysis approach. We identify bupropion as a plausible glucose lowering agent, suggesting that surveying otherwise healthy individuals in cross‐sectional studies can discover new drug repositioning hypotheses that have applicability to longitudinal clinical practice.

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          Most cited references27

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age

            Cathie Sudlow and colleagues describe the UK Biobank, a large population-based prospective study, established to allow investigation of the genetic and non-genetic determinants of the diseases of middle and old age.
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              The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease.

              To pursue a systematic approach to the discovery of functional connections among diseases, genetic perturbation, and drug action, we have created the first installment of a reference collection of gene-expression profiles from cultured human cells treated with bioactive small molecules, together with pattern-matching software to mine these data. We demonstrate that this "Connectivity Map" resource can be used to find connections among small molecules sharing a mechanism of action, chemicals and physiological processes, and diseases and drugs. These results indicate the feasibility of the approach and suggest the value of a large-scale community Connectivity Map project.
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                Author and article information

                Contributors
                Chirag_Patel@hms.harvard.edu
                Journal
                CPT Pharmacometrics Syst Pharmacol
                CPT Pharmacometrics Syst Pharmacol
                10.1002/(ISSN)2163-8306
                PSP4
                CPT: Pharmacometrics & Systems Pharmacology
                John Wiley and Sons Inc. (Hoboken )
                2163-8306
                24 January 2018
                February 2018
                : 7
                : 2 ( doiID: 10.1002/psp4.v7.2 )
                : 124-129
                Affiliations
                [ 1 ] Department of Biomedical Informatics Harvard Medical School Boston Massachusetts
                Author notes
                [*] [* ]Correspondence: C. J. Patel ( Chirag_Patel@ 123456hms.harvard.edu )
                Article
                PSP412258
                10.1002/psp4.12258
                5824113
                28941007
                52707a88-e8e9-404d-9a12-fdb967ba86a7
                © 2018 The Authors CPT: Pharmacometrics & Systems Pharmacology published by Wiley Periodicals, Inc. on behalf of American Society for Clinical Pharmacology and Therapeutics

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 26 April 2017
                : 07 September 2017
                : 19 September 2017
                Page count
                Figures: 1, Tables: 3, Pages: 6, Words: 4638
                Funding
                Funded by: National Institutes of Health (NIH)
                Funded by: National Human Genome Research Institute
                Award ID: T32HG002295‐13
                Funded by: National Institute of Environmental Health Sciences
                Award ID: R00 ES023504
                Award ID: R21 ES025052
                Funded by: National Science Foundation Big Data Spoke
                Award ID: 1636870
                Funded by: Agilent Technologies
                Categories
                Original Article
                Original Article
                Custom metadata
                2.0
                psp412258
                February 2018
                Converter:WILEY_ML3GV2_TO_NLMPMC version:version=5.3.2.2 mode:remove_FC converted:23.02.2018

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