Identification of novel therapeutics for complex diseases from genome-wide association data

Mani P Grover, Sara Ballouz, Kaavya A Mohanasundaram, Richard A George, Craig D H Sherman, Tamsyn M Crowley, Merridee A Wouters, Mani P Grover, Sara Ballouz, Kaavya A Mohanasundaram, Richard A George, Craig D H Sherman, Tamsyn M Crowley, Merridee A Wouters

Abstract

Background: Human genome sequencing has enabled the association of phenotypes with genetic loci, but our ability to effectively translate this data to the clinic has not kept pace. Over the past 60 years, pharmaceutical companies have successfully demonstrated the safety and efficacy of over 1,200 novel therapeutic drugs via costly clinical studies. While this process must continue, better use can be made of the existing valuable data. In silico tools such as candidate gene prediction systems allow rapid identification of disease genes by identifying the most probable candidate genes linked to genetic markers of the disease or phenotype under investigation. Integration of drug-target data with candidate gene prediction systems can identify novel phenotypes which may benefit from current therapeutics. Such a drug repositioning tool can save valuable time and money spent on preclinical studies and phase I clinical trials.

Methods: We previously used Gentrepid (http://www.gentrepid.org) as a platform to predict 1,497 candidate genes for the seven complex diseases considered in the Wellcome Trust Case-Control Consortium genome-wide association study; namely Type 2 Diabetes, Bipolar Disorder, Crohn's Disease, Hypertension, Type 1 Diabetes, Coronary Artery Disease and Rheumatoid Arthritis. Here, we adopted a simple approach to integrate drug data from three publicly available drug databases: the Therapeutic Target Database, the Pharmacogenomics Knowledgebase and DrugBank; with candidate gene predictions from Gentrepid at the systems level.

Results: Using the publicly available drug databases as sources of drug-target association data, we identified a total of 428 candidate genes as novel therapeutic targets for the seven phenotypes of interest, and 2,130 drugs feasible for repositioning against the predicted novel targets.

Conclusions: By integrating genetic, bioinformatic and drug data, we have demonstrated that currently available drugs may be repositioned as novel therapeutics for the seven diseases studied here, quickly taking advantage of prior work in pharmaceutics to translate ground-breaking results in genetics to clinical treatments.

Figures

Figure 1
Figure 1
Workflow. The complete workflow designed to predict novel therapeutic targets and identify novel therapeutics. We used Gentrepid as a platform for candidate gene prediction and DrugBank, TTD and PharmGKB as drug repositories.
Figure 2
Figure 2
Coverage of the human genome by targets annotated in the three drug databases. The Venn diagram shows that gene targets annotated in drug databases comprise 8% of the entire human genome. It also describes the percentage of the genome covered by each database individually and upon pairwise comparison.
Figure 3
Figure 3
Comparison of human drug targets from three drug databases. Comparison of three drug databases to identify unique and common human drug targets extracted from DrugBank, TTD and PharmGKB. DrugBank has the highest number of unique human targets followed by PharmGKB and TTD.
Figure 4
Figure 4
Comparison of coverage of drugs in three drug databases. Comparison of drug coverage of three drug databases to identify unique and common drugs. DrugBank has the highest number of unique drugs followed by TTD and PharmGKB.
Figure 5
Figure 5
Predicted therapeutic targets from three source databases. The Venn diagram represents the identified 30% of 1,497 candidates are potential therapeutic targets for all the seven diseases. 17% of the targets were unique to one of the three drug databases (DrugBank), 1-2% of targets were found in at least two databases (PharmGKB, TTD) and only 1.6% of targets are common to all the three drug databases.
Figure 6
Figure 6
Predicted therapeutic targets for each of the seven phenotypes. Abbreviations - T2D - Type 2 Diabetes; BD - Bipolar Disorder; CD - Crohn's Disease; HT - Hypertension; T1D - Type 1 Diabetes; CAD - Coronary Artery Disease; RA - Rheumatoid Arthritis. For each phenotype, the contributions of each of the three drug databases are shown in primary colours on the left, and the set of total unique targets is shown in green on the right. The cross-hatched portion of the bar shows targets replicated by the system which are already targeted by therapeutics for that phenotype. The solid portions of the bars are novel predictions, which may potentially be utilized in repositioning.
Figure 7
Figure 7
FDA-approved and clinical therapeutic targets. Abbreviation - T2D - Type 2 Diabetes; Comparison of Gentrepid predicted targets for Type 2 diabetes targeted by FDA-approved and clinical trial drugs with targets obtained from the TTD database for Type 2 Diabetes. Three predicted therapeutic targets (HSD11B1, PPARA, NR3C1) targeted by drugs currently in clinical trials for T2D. In addition, PPARA is also targeted by FDA-approved drugs.

References

    1. Ashburn TT, Thor KB. Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;7:673–683. doi: 10.1038/nrd1468.
    1. Pharmaceutical Research and Manufacturers of America. PhRMA annual membership survey. Washington, DC; 2013.
    1. Lary J, Daniel K, Erickson J, Roberts H, Moore C. The return of thalidomide: can birth defects be prevented? Drug Saf. 1999;7:161–169. doi: 10.2165/00002018-199921030-00002.
    1. Chong CR, Sullivan DJ. New uses for old drugs. Nature. 2007;7:645–646. doi: 10.1038/448645a.
    1. Emig D, Ivliev A, Pustovalova O, Lancashire L, Bureeva S, Nikolsky Y, Bessarabova M. Drug target prediction and repositioning using an integrated network-based approach. PLoS One. 2013;7:e60618. doi: 10.1371/journal.pone.0060618.
    1. Cooper RS. Gene-Environment interactions and the etiology of common complex disease. Ann Intern Med. 2003;7:437–440. doi: 10.7326/0003-4819-139-5_Part_2-200309021-00011.
    1. Altshuler D, Daly MJ, Lander ES. Genetic mapping in human disease. Science. 2008;7:881–888. doi: 10.1126/science.1156409.
    1. Sanseau P, Agarwal P, Barnes MR, Pastinen T, Richards JB, Cardon LR, Mooser V. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 2012;7:317–320. doi: 10.1038/nbt.2151.
    1. Ballouz S, Liu J, Oti M, Gaeta B, Fatkin D, Bahlo M, Wouters M. Analysis of genome-wide association study data using the protein knowledge base. BMC Genet. 2011;7:98.
    1. Cantor RM, Lange K, Sinsheimer JS. Prioritizing GWAS results: a review of statistical methods and recommendations for their application. Am J Hum Genet. 2010;7:6–22. doi: 10.1016/j.ajhg.2009.11.017.
    1. Wellcome Trust Case Control Consortium. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007;7:661–678. doi: 10.1038/nature05911.
    1. Turner FS, Clutterbuck DR, Semple CAM. POCUS: mining genomic sequence annotation to predict disease genes. Genome Biol. 2003;7:75. doi: 10.1186/gb-2003-4-11-r75.
    1. Oti M, Ballouz S, Wouters MA. Web tools for the prioritization of candidate disease genes. Methods Mol Biol. 2011;7:189–206. doi: 10.1007/978-1-61779-176-5_12.
    1. Moreau Y, Tranchevent L-C. Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet. 2012;7:523–536. doi: 10.1038/nrg3253.
    1. Teber E, Liu J, Ballouz S, Fatkin D, Wouters M. Comparison of automated candidate gene prediction systems using genes implicated in type 2 diabetes by genome-wide association studies. BMC Bioinformatics. 2009;7(Suppl 1):S69. doi: 10.1186/1471-2105-10-S1-S69.
    1. Ballouz S, Liu JY, George RA, Bains N, Liu A, Oti M, Gaeta B, Fatkin D, Wouters MA. Gentrepid V2. 0: a web server for candidate disease gene prediction. BMC Bioinformatics. 2013;7:249. doi: 10.1186/1471-2105-14-249.
    1. Ballouz S, Liu JY, Oti M, Gaeta B, Fatkin D, Bahlo M, Wouters MA (2013) Candidate disease gene prediction using Gentrepid: application to a genome-wide association study on coronary artery disease. Mol Genet Genomic Med.
    1. Knox C, Law V, Jewison T, Liu P, Ly S, Frolkis A, Pon A, Banco K, Mak C, Neveu V. DrugBank 3.0: a comprehensive resource for 'omics' research on drugs. Nucleic Acids Res. 2011;7(Suppl 1):D1035–D1041.
    1. Hernandez-Boussard T, Whirl-Carrillo M, Hebert JM, Gong L, Owen R, Gong M, Gor W, Liu F, Truong C, Whaley R. The pharmacogenetics and pharmacogenomics knowledge base: accentuating the knowledge. Nucleic Acids Res. 2008;7(Suppl 1):D913–D918.
    1. Zhu F, Shi Z, Qin C, Tao L, Liu X, Xu F, Zhang L, Song Y, Liu X, Zhang J. Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery. Nucleic Acids Res. 2012;7:D1128–D1136. doi: 10.1093/nar/gkr797.
    1. George RA, Liu JY, Feng LL, Bryson-Richardson RJ, Fatkin D, Wouters MA. Analysis of protein sequence and interaction data for candidate disease gene prediction. Nucleic Acids Res. 2006;7:e130–e130. doi: 10.1093/nar/gkl707.
    1. Badano JL, Katsanis N. Beyond Mendel: an evolving view of human genetic disease transmission. Nat Rev Genet. 2002;7:779–789.
    1. van Driel MA, Cuelenaere K, Kemmeren PPCW, Leunissen JAM, Brunner HG, Vriend G. GeneSeeker: extraction and integration of human disease-related information from web-based genetic databases. Nucleic Acids Res. 2005;7(Suppl 2):W758–W761.
    1. Chen J, Bardes EE, Aronow BJ, Jegga AG. ToppGene suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 2009;7(Suppl 2):W305–W311.
    1. Tranchevent LC, Barriot R, Yu S, Van Vooren S, Van Loo P, Coessens B, De Moor B, Aerts S, Moreau Y. ENDEAVOUR update: a web resource for gene prioritization in multiple species. Nucleic Acids Res. 2008;7(Suppl 2):W377–W384.
    1. Jimenez-Sanchez G, Childs B, Valle D. Human disease genes. Nature. 2001;7:853–855. doi: 10.1038/35057050.
    1. Reimand J, Arak T, Vilo J. g: Profiler--a web server for functional interpretation of gene lists (2011 update) Nucleic Acids Res. 2011;7(Suppl 2):W307–W315.
    1. Hajduk PJ, Huth JR, Tse C. Predicting protein druggability. Drug Discov Today. 2005;7:1675–1682. doi: 10.1016/S1359-6446(05)03624-X.
    1. Overington JP, Al-Lazikani B, Hopkins AL. How many drug targets are there? Nat Rev Drug Discov. 2006;7:993–996. doi: 10.1038/nrd2199.
    1. Hopkins AL, Groom CR. The druggable genome. Nat Rev Drug Discov. 2002;7:727–730. doi: 10.1038/nrd892.
    1. Sakharkar MK, Sakharkar KR, Pervaiz S. Druggability of human disease genes. Int J Biochem Cell Biol. 2007;7:1156–1164. doi: 10.1016/j.biocel.2007.02.018.
    1. Zambrowicz BP, Sands AT. Knockouts model the 100 best-selling drugs--will they model the next 100? Nat Rev Drug Discov. 2003;7:38–51. doi: 10.1038/nrd987.
    1. Stockwell B. The Quest for the Cure: The Science, Stories and Struggles Behind the Next Generation of Medicines. Columbia University Press; 2011.
    1. Wu CC, D'Argenio D, Asgharzadeh S, Triche T. TARGETgene: a tool for identification of potential therapeutic targets in cancer. PLoS One. 2012;7:e43305. doi: 10.1371/journal.pone.0043305.
    1. Kaimal V, Sardana D, Bardes EE, Gudivada RC, Chen J, Jegga AG. Integrative systems biology approaches to identify and prioritize disease and drug candidate genes. Methods Mol Biol. 2011;7:241–259. doi: 10.1007/978-1-61737-954-3_16.
    1. Dudley JT, Deshpande T, Butte AJ. Exploiting drug-disease relationships for computational drug repositioning. Brief Bioinform. 2011;7:303–311. doi: 10.1093/bib/bbr013.
    1. Cavalla D. Predictive methods in drug repurposing: gold mine or just a bigger haystack? Drug Discov Today. 2012.

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