title: The support of human genetic evidence for approved drug indications.
abstract: Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect how drug targets and indications are selected. However, there is little guidance about how much weight should be given to genetic evidence in making these key decisions. To answer this question, we investigated how well the current archive of genetic evidence predicts drug mechanisms. We found that, among well-studied indications, the proportion of drug mechanisms with direct genetic support increases significantly across the drug development pipeline, from 2.0% at the preclinical stage to 8.2% among mechanisms for approved drugs, and varies dramatically among disease areas. We estimate that selecting genetically supported targets could double the success rate in clinical development. Therefore, using the growing wealth of human genetic data to select the best targets and indications should have a measurable impact on the successful development of new drugs.
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, North Carolina, USA.
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Nelson
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Stevenage, UK.
authors.forename: Hannah
authors.initials: H
authors.lastname: Tipney
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, North Carolina, USA.
authors.forename: Jeffery L
authors.initials: JL
authors.lastname: Painter
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Research Triangle Park, North Carolina, USA.
authors.forename: Judong
authors.initials: J
authors.lastname: Shen
authors.affiliation: Department of Systems Biology, Columbia University, New York, New York, USA.
authors.forename: Paola
authors.initials: P
authors.lastname: Nicoletti
authors.affiliation: 1] Department of Systems Biology, Columbia University, New York, New York, USA. [2] Department of Biomedical Informatics, Columbia University, New York, New York, USA.
authors.forename: Yufeng
authors.initials: Y
authors.lastname: Shen
authors.affiliation: 1] Department of Systems Biology, Columbia University, New York, New York, USA. [2] Department of Biomedical Informatics, Columbia University, New York, New York, USA.
authors.forename: Aris
authors.initials: A
authors.lastname: Floratos
authors.affiliation: 1] State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Hong Kong, SAR, China. [2] Centre for Genomics Sciences, Li Ka Shing (LKS) Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China.
authors.forename: Pak Chung
authors.initials: PC
authors.lastname: Sham
authors.affiliation: 1] Centre for Genomics Sciences, Li Ka Shing (LKS) Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China. [2] Department of Biochemistry, LKS Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China.
authors.forename: Mulin Jun
authors.initials: MJ
authors.lastname: Li
authors.affiliation: 1] Centre for Genomics Sciences, Li Ka Shing (LKS) Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China. [2] Department of Biochemistry, LKS Faculty of Medicine, University of Hong Kong, Hong Kong SAR, China.
authors.forename: Junwen
authors.initials: J
authors.lastname: Wang
authors.affiliation: Alternative Discovery and Development, GlaxoSmithKline, Upper Merion, Pennsylvania, USA.
authors.forename: Lon R
authors.initials: LR
authors.lastname: Cardon
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Stevenage, UK.
authors.forename: John C
authors.initials: JC
authors.lastname: Whittaker
authors.affiliation: Quantitative Sciences, GlaxoSmithKline, Stevenage, UK.
authors.forename: Philippe
authors.initials: P
authors.lastname: Sanseau
mesh_terms: D002874:Chromosome Mapping; D030541:Databases, Genetic / Q000706:statistics & numerical data; D017277:Drug Approval / Q000706:statistics & numerical data*; D056726:Genetic Association Studies / Q000706:statistics & numerical data; D020022:Genetic Predisposition to Disease / Q000235:genetics*; D005826:Genetics, Medical / Q000379:methods / Q000706:statistics & numerical data; D055106:Genome-Wide Association Study / Q000706:statistics & numerical data*; D006801:Humans; D015810:Linkage Disequilibrium; D046650:Medical Subject Headings / Q000706:statistics & numerical data; D058990:Molecular Targeted Therapy / Q000706:statistics & numerical data; D020641:Polymorphism, Single Nucleotide