The support of human genetic evidence for approved drug indications

Matthew Nelson and colleagues investigate how well genetic evidence for disease susceptibility predicts drug mechanisms. They find a correlation between gene products that are successful drug targets and genetic loci associated with the disease treated by the drug and predict that selecting genetica...

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Published in:	Nature genetics Vol. 47; no. 8; pp. 856 - 860
Main Authors:	Nelson, Matthew R, Tipney, Hannah, Painter, Jeffery L, Shen, Judong, Nicoletti, Paola, Shen, Yufeng, Floratos, Aris, Sham, Pak Chung, Li, Mulin Jun, Wang, Junwen, Cardon, Lon R, Whittaker, John C, Sanseau, Philippe
Format:	Journal Article
Language:	English
Published:	New York Nature Publishing Group US 01.08.2015 Nature Publishing Group
Subjects:	631/154/1435 631/208/205 Agriculture analysis Animal Genetics and Genomics Biomedicine Cancer Research Chromosome Mapping Databases, Genetic - statistics & numerical data Drug Approval - statistics & numerical data Drug interactions Drug metabolism Drugs Gene Function Genes Genetic aspects Genetic Association Studies - statistics & numerical data Genetic Predisposition to Disease - genetics Genetic research Genetics Genetics, Medical - methods Genetics, Medical - statistics & numerical data Genome-Wide Association Study - statistics & numerical data Human Genetics Humans Innovations Linkage Disequilibrium Medical Subject Headings - utilization Molecular targeted therapy Molecular Targeted Therapy - statistics & numerical data Pharmacology Polymorphism, Single Nucleotide
ISSN:	1061-4036, 1546-1718
Online Access:	Get full text
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Abstract	Matthew Nelson and colleagues investigate how well genetic evidence for disease susceptibility predicts drug mechanisms. They find a correlation between gene products that are successful drug targets and genetic loci associated with the disease treated by the drug and predict that selecting genetically supported targets could increase the success rate of drugs in clinical development. 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.
AbstractList	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. Matthew Nelson and colleagues investigate how well genetic evidence for disease susceptibility predicts drug mechanisms. They find a correlation between gene products that are successful drug targets and genetic loci associated with the disease treated by the drug and predict that selecting genetically supported targets could increase the success rate of drugs in clinical development. 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.
Audience	Academic
Author	Sham, Pak Chung Shen, Judong Cardon, Lon R Floratos, Aris Tipney, Hannah Nicoletti, Paola Nelson, Matthew R Wang, Junwen Whittaker, John C Li, Mulin Jun Painter, Jeffery L Sanseau, Philippe Shen, Yufeng
Author_xml	– sequence: 1 givenname: Matthew R surname: Nelson fullname: Nelson, Matthew R email: matthew.r.nelson@gsk.com organization: Quantitative Sciences, GlaxoSmithKline – sequence: 2 givenname: Hannah surname: Tipney fullname: Tipney, Hannah organization: Quantitative Sciences – sequence: 3 givenname: Jeffery L surname: Painter fullname: Painter, Jeffery L organization: Quantitative Sciences, GlaxoSmithKline – sequence: 4 givenname: Judong surname: Shen fullname: Shen, Judong organization: Quantitative Sciences, GlaxoSmithKline – sequence: 5 givenname: Paola surname: Nicoletti fullname: Nicoletti, Paola organization: Department of Systems Biology, Columbia University – sequence: 6 givenname: Yufeng surname: Shen fullname: Shen, Yufeng organization: Department of Systems Biology, Columbia University, Department of Biomedical Informatics, Columbia University – sequence: 7 givenname: Aris surname: Floratos fullname: Floratos, Aris organization: Department of Systems Biology, Columbia University, Department of Biomedical Informatics, Columbia University – sequence: 8 givenname: Pak Chung surname: Sham fullname: Sham, Pak Chung organization: State Key Laboratory of Brain and Cognitive Sciences, University of Hong Kong, Centre for Genomics Sciences, Li Ka Shing (LKS) Faculty of Medicine, University of Hong Kong – sequence: 9 givenname: Mulin Jun surname: Li fullname: Li, Mulin Jun organization: Centre for Genomics Sciences, Li Ka Shing (LKS) Faculty of Medicine, University of Hong Kong, Department of Biochemistry, LKS Faculty of Medicine, University of Hong Kong – sequence: 10 givenname: Junwen surname: Wang fullname: Wang, Junwen organization: Centre for Genomics Sciences, Li Ka Shing (LKS) Faculty of Medicine, University of Hong Kong, Department of Biochemistry, LKS Faculty of Medicine, University of Hong Kong – sequence: 11 givenname: Lon R surname: Cardon fullname: Cardon, Lon R organization: Alternative Discovery and Development, GlaxoSmithKline, Upper Merion – sequence: 12 givenname: John C surname: Whittaker fullname: Whittaker, John C organization: Quantitative Sciences – sequence: 13 givenname: Philippe surname: Sanseau fullname: Sanseau, Philippe organization: Quantitative Sciences
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/26121088$$D View this record in MEDLINE/PubMed
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Snippet	Matthew Nelson and colleagues investigate how well genetic evidence for disease susceptibility predicts drug mechanisms. They find a correlation between gene... Over a quarter of drugs that enter clinical development fail because they are ineffective. Growing insight into genes that influence human disease may affect...
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SubjectTerms	631/154/1435 631/208/205 Agriculture analysis Animal Genetics and Genomics Biomedicine Cancer Research Chromosome Mapping Databases, Genetic - statistics & numerical data Drug Approval - statistics & numerical data Drug interactions Drug metabolism Drugs Gene Function Genes Genetic aspects Genetic Association Studies - statistics & numerical data Genetic Predisposition to Disease - genetics Genetic research Genetics Genetics, Medical - methods Genetics, Medical - statistics & numerical data Genome-Wide Association Study - statistics & numerical data Human Genetics Humans Innovations Linkage Disequilibrium Medical Subject Headings - utilization Molecular targeted therapy Molecular Targeted Therapy - statistics & numerical data Pharmacology Polymorphism, Single Nucleotide
Title	The support of human genetic evidence for approved drug indications
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