Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval

Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association ar...

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Veröffentlicht in:	PLoS genetics Jg. 15; H. 12; S. e1008489
Hauptverfasser:	King, Emily A., Davis, J. Wade, Degner, Jacob F.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Public Library of Science 01.12.2019 Public Library of Science (PLoS)
Schlagworte:	Biology and Life Sciences Cardiovascular disease Clinical trials Computer and Information Sciences Databases, Genetic Datasets Drug Approval - statistics & numerical data Drug development Drugs Estimates Genes Genetic Predisposition to Disease Genome-Wide Association Study Genomics Genomics - methods Humans Mathematical models Medicine and Health Sciences Methods Models, Statistical Mutation Pharmaceutical industry Pharmacogenomic Variants Phenotype Precision Medicine Quantitative Trait Loci Research and Analysis Methods Statistical analysis Success Therapeutic targets United States > US North Chicago Illinois Illinois
ISSN:	1553-7404, 1553-7390, 1553-7404
Online-Zugang:	Volltext
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Abstract	Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. Taking advantage of recent clinical development advances and rapid growth in GWAS datasets, we extend the original work using updated data, test whether genetic evidence predicts future successes and introduce statistical models adjusting for target and indication-level properties. Our work confirms drugs with genetically supported targets were more likely to be successful in Phases II and III. When causal genes are clear (Mendelian traits and GWAS associations linked to coding variants), we find the use of human genetic evidence increases approval by greater than two-fold, and, for Mendelian associations, the positive association holds prospectively. Our findings suggest investments into genomics and genetics are likely to be beneficial to companies deploying this strategy.
AbstractList	Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. Taking advantage of recent clinical development advances and rapid growth in GWAS datasets, we extend the original work using updated data, test whether genetic evidence predicts future successes and introduce statistical models adjusting for target and indication-level properties. Our work confirms drugs with genetically supported targets were more likely to be successful in Phases II and III. When causal genes are clear (Mendelian traits and GWAS associations linked to coding variants), we find the use of human genetic evidence increases approval by greater than two-fold, and, for Mendelian associations, the positive association holds prospectively. Our findings suggest investments into genomics and genetics are likely to be beneficial to companies deploying this strategy. Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. Taking advantage of recent clinical development advances and rapid growth in GWAS datasets, we extend the original work using updated data, test whether genetic evidence predicts future successes and introduce statistical models adjusting for target and indication-level properties. Our work confirms drugs with genetically supported targets were more likely to be successful in Phases II and III. When causal genes are clear (Mendelian traits and GWAS associations linked to coding variants), we find the use of human genetic evidence increases approval by greater than two-fold, and, for Mendelian associations, the positive association holds prospectively. Our findings suggest investments into genomics and genetics are likely to be beneficial to companies deploying this strategy.Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. Taking advantage of recent clinical development advances and rapid growth in GWAS datasets, we extend the original work using updated data, test whether genetic evidence predicts future successes and introduce statistical models adjusting for target and indication-level properties. Our work confirms drugs with genetically supported targets were more likely to be successful in Phases II and III. When causal genes are clear (Mendelian traits and GWAS associations linked to coding variants), we find the use of human genetic evidence increases approval by greater than two-fold, and, for Mendelian associations, the positive association holds prospectively. Our findings suggest investments into genomics and genetics are likely to be beneficial to companies deploying this strategy. Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing historical pipeline data, Nelson et al. 2015 (Nat. Genet.) concluded pipeline drug targets with human genetic evidence of disease association are twice as likely to lead to approved drugs. Taking advantage of recent clinical development advances and rapid growth in GWAS datasets, we extend the original work using updated data, test whether genetic evidence predicts future successes and introduce statistical models adjusting for target and indication-level properties. Our work confirms drugs with genetically supported targets were more likely to be successful in Phases II and III. When causal genes are clear (Mendelian traits and GWAS associations linked to coding variants), we find the use of human genetic evidence increases approval by greater than two-fold, and, for Mendelian associations, the positive association holds prospectively. Our findings suggest investments into genomics and genetics are likely to be beneficial to companies deploying this strategy. The growth of human genetics resources has the potential to help us develop better drugs. By looking at whether and how historical drug approvals could have been predicted from our current knowledge of human genetics, we can validate this approach and assess which types of genetic evidence are most likely to be useful in guiding drug discovery. Validation is important because we are often uncertain about the biological mechanisms behind genetic variants linked to disease. Most associated variants do not occur within protein-coding regions of the genome, and it is difficult to tell which of many nearby genes is contributing to disease risk. In this paper, we confirm previous correlations between genetic evidence and historical drug approvals. We find genetic evidence from severe genetic disorders and from genetic variants that alter protein sequence is more strongly associated with historical approvals. We offer statistical approaches for prioritizing new drug candidates based on whether their mechanisms are supported by human genetic evidence.
Author	Davis, J. Wade Degner, Jacob F. King, Emily A.
AuthorAffiliation	Department of Computational Genomics, AbbVie, North Chicago, Illinois, United States of America Regeneron Genetics Center, UNITED STATES
AuthorAffiliation_xml	– name: Department of Computational Genomics, AbbVie, North Chicago, Illinois, United States of America – name: Regeneron Genetics Center, UNITED STATES
Author_xml	– sequence: 1 givenname: Emily A. orcidid: 0000-0003-1877-8673 surname: King fullname: King, Emily A. – sequence: 2 givenname: J. Wade surname: Davis fullname: Davis, J. Wade – sequence: 3 givenname: Jacob F. surname: Degner fullname: Degner, Jacob F.
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/31830040$$D View this record in MEDLINE/PubMed
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Snippet	Despite strong vetting for disease activity, only 10% of candidate new molecular entities in early stage clinical trials are eventually approved. Analyzing...
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SubjectTerms	Biology and Life Sciences Cardiovascular disease Clinical trials Computer and Information Sciences Databases, Genetic Datasets Drug Approval - statistics & numerical data Drug development Drugs Estimates Genes Genetic Predisposition to Disease Genome-Wide Association Study Genomics Genomics - methods Humans Mathematical models Medicine and Health Sciences Methods Models, Statistical Mutation Pharmaceutical industry Pharmacogenomic Variants Phenotype Precision Medicine Quantitative Trait Loci Research and Analysis Methods Statistical analysis Success Therapeutic targets
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Title	Are drug targets with genetic support twice as likely to be approved? Revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval
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