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

English version

Autoři: Emily A. King ^aff001; J. Wade Davis ^aff001; Jacob F. Degner ^aff001
Působiště autorů: Department of Computational Genomics, AbbVie, North Chicago, Illinois, United States of America ^aff001
Vyšlo v časopise: 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. PLoS Genet 15(12): e32767. doi:10.1371/journal.pgen.1008489
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008489

Souhrn

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.

Klíčová slova:

Drug discovery – Drug research and development – Gene mapping – Genetic linkage – Genetics of disease – Genome-wide association studies – Human genetics – Catalogs

Zdroje

1. Schuhmacher A, Gassmann O, Hinder M. Changing R&D models in research-based pharmaceutical companies. Journal of Translational Medicine. 2016;14(1):105. doi: 10.1186/s12967-016-0838-4 27118048

2. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, et al. How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nature Reviews Drug Discovery. 2010;9(3):203. doi: 10.1038/nrd3078 20168317

3. Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, et al. The support of human genetic evidence for approved drug indications. Nature Genetics. 2015;47(8):856. doi: 10.1038/ng.3314 26121088

4. Hurle MR, Nelson MR, Agarwal P, Cardon LR. Trial watch: Impact of genetically supported target selection on R&D productivity; 2016.

5. Plenge RM, Scolnick EM, Altshuler D. Validating therapeutic targets through human genetics. Nature Reviews Drug Discovery. 2013;12(8):581–594. doi: 10.1038/nrd4051 23868113

6. Cohen J, Pertsemlidis A, Kotowski IK, Graham R, Garcia CK, Hobbs HH. Low LDL cholesterol in individuals of African descent resulting from frequent nonsense mutations in PCSK9. Nature Genetics. 2005;37(2):161. doi: 10.1038/ng1509 15654334

7. Abifadel M, Varret M, Rabès JP, Allard D, Ouguerram K, Devillers M, et al. Mutations in PCSK9 cause autosomal dominant hypercholesterolemia. Nature Genetics. 2003;34(2):154. doi: 10.1038/ng1161 12730697

8. Kotowski IK, Pertsemlidis A, Luke A, Cooper RS, Vega GL, Cohen JC, et al. A spectrum of PCSK9 alleles contributes to plasma levels of low-density lipoprotein cholesterol. The American Journal of Human Genetics. 2006;78(3):410–422. doi: 10.1086/500615 16465619

9. Cohen JC, Boerwinkle E, Mosley TH Jr, Hobbs HH. Sequence variations in PCSK9, low LDL, and protection against coronary heart disease. New England Journal of Medicine. 2006;354(12):1264–1272. doi: 10.1056/NEJMoa054013 16554528

10. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Research. 2017;45(D1):D896–D901. doi: 10.1093/nar/gkw1133 27899670

11. Welter D, MacArthur J, Morales J, Burdett T, Hall P, Junkins H, et al. The NHGRI GWAS Catalog, a curated resource of SNP-trait associations. Nucleic Acids Research. 2013;42(D1):D1001–D1006. doi: 10.1093/nar/gkt1229 24316577

12. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Research. 2016;45(D1):D896–D901. doi: 10.1093/nar/gkw1133 27899670

13. GTEx Consortium, et al. The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans. Science. 2015;348(6235):648–660. doi: 10.1126/science.1262110 25954001

14. Informa’s Pharmaprojects;. https://pharmaintelligence.informa.com/products-and-services/data-and-analysis/pharmaprojects.

15. McKusick-Nathans Institute of Genetic Medicine JHUB. Online Mendelian Inheritance in Man, OMIM®;. https://omim.org/.

16. Cao C, Moult J. GWAS and drug targets. BMC Genomics. 2014;15(4):S5. doi: 10.1186/1471-2164-15-S4-S5 25057111

17. Petrovski S, Wang Q, Heinzen EL, Allen AS, Goldstein DB. Genic intolerance to functional variation and the interpretation of personal genomes. PLoS genetics. 2013;9(8):e1003709. doi: 10.1371/journal.pgen.1003709 23990802

18. Hay M, Thomas DW, Craighead JL, Economides C, Rosenthal J. Clinical development success rates for investigational drugs. Nature Biotechnology. 2014;32(1):40–51. doi: 10.1038/nbt.2786 24406927

19. Shih HP, Zhang X, Aronov AM. Drug discovery effectiveness from the standpoint of therapeutic mechanisms and indications. Nature Reviews Drug Discovery. 2018;17(1):19. doi: 10.1038/nrd.2017.194 29075002

20. Gallagher MD, Chen-Plotkin AS. The post-GWAS Era: from association to function. The American Journal of Human Genetics. 2018;102(5):717–730. doi: 10.1016/j.ajhg.2018.04.002 29727686

21. Cingolani P, Platts A, Coon M, Nguyen T, Wang L, Land SJ, et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly. 2012;6(2):80–92. doi: 10.4161/fly.19695 22728672

22. Cook D, Brown D, Alexander R, March R, Morgan P, Satterthwaite G, et al. Lessons learned from the fate of AstraZeneca’s drug pipeline: a five-dimensional framework. Nature Reviews Drug Discovery. 2014;13(6):419. doi: 10.1038/nrd4309 24833294

23. Nguyen PA, Born DA, Deaton AM, Nioi P, Ward LD. Phenotypes associated with genes encoding drug targets are predictive of clinical trial side effects. Nature communications. 2019;10(1):1579. doi: 10.1038/s41467-019-09407-3 30952858

24. Yao J, Hurle MR, Nelson MR, Agarwal P. Predicting clinically promising therapeutic hypotheses using tensor factorization. bioRxiv. 2018; p. 272740.

25. Gorzelany JA, de Souza MP. Protein replacement therapies for rare diseases: A breeze for regulatory approval? Science translational medicine. 2013;5(178):178fs10–178fs10. doi: 10.1126/scitranslmed.3005007 23536010

26. Chang W, Cheng J, Allaire J, Xie Y, McPherson J. shiny: Web Application Framework for R; 2018. Available from: https://CRAN.R-project.org/package=shiny.

27. Consortium GP, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. doi: 10.1038/nature15393

28. Sheffield NC, Thurman RE, Song L, Safi A, Stamatoyannopoulos JA, Lenhard B, et al. Patterns of regulatory activity across diverse human cell types predict tissue identity, transcription factor binding, and long-range interactions. Genome Research. 2013;23(5):777–788. doi: 10.1101/gr.152140.112 23482648

29. Resnik P, et al. Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res(JAIR). 1999;11 : 95–130. doi: 10.1613/jair.514

30. Lin D, et al. An information-theoretic definition of similarity. In: ICML. vol. 98. Citeseer; 1998. p. 296–304.

31. Greene D, Richardson S, Turro E. ontologyX: a suite of R packages for working with ontological data. Bioinformatics. 2017;33(7):1104–1106. doi: 10.1093/bioinformatics/btw763 28062448

32. Aragon TJ. epitools: Epidemiology Tools; 2017. Available from: https://CRAN.R-project.org/package=epitools.

33. Vehtari A, Gabry J, Yao Y, Gelman A. loo: Efficient leave-one-out cross-validation and WAIC for Bayesian models; 2018. Available from: https://CRAN.R-project.org/package=loo.

34. Watanabe S. Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. Journal of Machine Learning Research. 2010;11(Dec):3571–3594.

35. Stan Development Team. RStan: the R interface to Stan; 2018. Available from: http://mc-stan.org/.