The support of human genetic evidence for approved drug indications

• 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; Pharmacology, Experimental; Polymorphism, Single Nucleotide
• ISSN: 1061-4036; 1546-1718
• DOI: 10.1038/ng.3314

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.