Translational research at Vanderbilt has been driven by the promise of personalized and precision medicine for more than a decade.

Our vision for precision medicine has catalyzed the development of internationally recognized programs that have enabled genomic discovery1–7 and clinical implementation, 8–13 and has led to our active participation in national networks14–17 that are deciphering the interactions of the human phenome and genome. The Vanderbilt Drug Repurposing Program, supported with infrastructure funding by the CTSA award No. UL1TR000445 from the National Center for Advancing Translational Sciences, is one way in which we are actively exploring how this new knowledge can be translated intoimprovements in human health in a shorter time frame.

The Vanderbilt Institute for Clinical and Translational Research (VICTR) has served as the key enabling group in this infrastructure and its substantive support, project management, and leadership over the last 10 years was essential in the development and deployment of all shared resources. The flagship program is BioVU, a large repository of de-identified DNA that Vanderbilt has established from discarded patient blood samples that are collected during routine clinical testing and is a centralized resource for investigating genotype-phenotype associations that has enabled large-scale innovative research. Biospecimens within BioVU are matched with corresponding clinical and demographic data derived from the Synthetic Derivative, our de-identified research database and is overseen by a comprehensive governance structure with participant consent since January 25, 2015.1,18 As of April 2018, BioVU contains >245k unique samples and there are 39 billion genotypes in BioVU available for reuse.

Building off this strong and unique infrastructure, and with substantial VICTR support, Dr. Josh Denny introduced the phenome wide association study (PheWAS) as a systematic and efficient approach to elucidate novel disease-variant associations and pleiotropy using BioVU.20 In contrast to traditional methods that can be used to identify genesassociated with specific diseases, PheWAS can be used to identify the diseases that are associated with a specific gene/genetic variant or other analyte.20,21 It is the comprehensive and diverse nature of the diagnostic information in electronic health records that enables PheWAS. The Vanderbilt Drug Repurposing program has extended these methods to focus on drug development and drug repurposing with the goal to accelerate and advance the availability of targeted therapies for precision indications.

We are strongly motivated by the observation that the past few decades have witnessed an alarming decline in the rate of introduction of new therapies, and much of this has been attributed to that fact that existing in vitro and preclinical models are extremely poor predictors of human biology.22 We envision a process by which new drugs have more targeted and precise indications, and older drugs can be repurposed to novel therapeutic applications more readily.

Key features of the program include:

  • Attainment of precision in and congruence between indication, population, and primary endpoint (i.e., no umbrella diseases or crude endpoints)
  • Commercialization as a priority but human impact stands firmly as our first concern
    Seeking multiple sources of sponsorship and engaging in clever reformulation to support optimized therapeutic delivery and commercialization
  • Focus on generic medicines and/or therapies with strong safety profiles
  • Prioritization of unmet medical need with no constraints around specific disease area or prevalence

In its entirety, the Vanderbilt Drug Repurposing Program intends to alter paradigms. Review our publications for more information.


  1. Roden DM, Pulley JM, Basford MA, Bernard GR, Clayton EW, Balser JR, Masys DR. Development of a large-scale de-identified DNA biobank to enable personalized medicine. Clin Pharmacol Ther. 2008;84(3):362-369 PMCID: PMC3763939. doi:10.1038/clpt.2008.89. PMCID: PMC3763939
  2. Rosenbloom ST, Madison JL, Brothers KB, Bowton EA, Clayton EW, Malin BA, Roden DM, Pulley J. Ethical and practical challenges to studying patients who opt out of large-scale biorepository research. J Am Med Inform Assoc. 2013;20(e2):e221-225 PMCID: PMC3861935. doi:10.1136/amiajnl-2013-001937. PMCID: PMC3861935
  3. Pulley J, Clayton E, Bernard GR, Roden DM, Masys DR. Principles of human subjects protections applied in an opt-out, de-identified biobank. Clin Transl Sci. 2010;3(1):42-48 PMCID: PMC3075971. doi:10.1111/j.1752-8062.2010.00175.x. PMCID: PMC3075971
  4. Pulley JM, Brace MM, Bernard GR, Masys DR. Attitudes and perceptions of patients towards methods of establishing a DNA biobank. Cell Tissue Bank. 2008;9(1):55-65. doi:10.1007/s10561-007-9051-2. PMID: 17960495
  5. Bowton E, Field JR, Wang S, Schildcrout JS, Van Driest SL, Delaney JT, Cowan J, Weeke P, Mosley JD, Wells QS, Karnes JH, Shaffer C, Peterson JF, Denny JC, Roden DM, Pulley JM. Biobanks and electronic medical records: enabling cost-effective research. Sci Transl Med. 2014;6(234):234cm3 PMCID: PMC4226414. doi:10.1126/scitranslmed.3008604. PMCID: PMC4226414
  6. Bowton EA, Collier SP, Wang X, Sutcliffe CB, Van Driest SL, Couch LJ, Herrera M, Jerome RN, Slebos RJC, Alborn WE, Liebler DC, McNaughton CD, Mernaugh RL, Wells QS, Brown NJ, Roden DM, Pulley JM. Phenotype-Driven Plasma Biobanking Strategies and Methods. Journal of Personalized Medicine. 2015;5(2):140-152. doi:10.3390/jpm5020140. PMCID: PMC4493492
  7. Brothers KB, Westbrook MJ, Wright MF, Myers JA, Morrison DR, Madison JL, Pulley JM, Clayton EW. Patient awareness and approval for an opt-out genomic biorepository. Per Med. 2013;10(4):PMCID: PMC3882901. doi:10.2217/pme.13.34. PMCID: PMC3882901
  8. Pulley JM, Denny JC, Peterson JF, Bernard GR, Vnencak-Jones CL, Ramirez AH, Delaney JT, Bowton E, Brothers K, Johnson K, Crawford DC, Schildcrout J, Masys DR, Dilks HH, Wilke RA, Clayton EW, Shultz E, Laposata M, McPherson J, Jirjis JN, Roden DM. Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin Pharmacol Ther. 2012;92(1):87-95. doi:10.1038/clpt.2011.371. PMCID: PMC3581305
  9. Delaney JT, Ramirez AH, Bowton E, Pulley JM, Basford MA, Schildcrout JS, Shi Y, Zink R, Oetjens M, Xu H, Cleator JH, Jahangir E, Ritchie MD, Masys DR, Roden DM, Crawford DC, Denny JC. Predicting clopidogrel response using DNA samples linked to an electronic health record. Clin Pharmacol Ther. 2012;91(2):257-263. doi:10.1038/clpt.2011.221. PMCID: PMC3621954
  10. Karnes JH, Van Driest S, Bowton EA, Weeke PE, Mosley JD, Peterson JF, Denny JC, Roden DM. Using systems approaches to address challenges for clinical implementation of pharmacogenomics. Wiley Interdiscip Rev Syst Biol Med. 2014;6(2):125-135. doi:10.1002/wsbm.1255. PMCID: PMC3944797
  11. Peterson JF, Bowton E, Field JR, Beller M, Mitchell J, Schildcrout J, Gregg W, Johnson K, Jirjis JN, Roden DM, Pulley JM, Denny JC. Electronic health record design and implementation for pharmacogenomics: a local perspective. Genet Med. 2013;15(10):833-841 PMCID: PMC3925979. doi:10.1038/gim.2013.109. PMCID: PMC3925979
  12. Schildcrout JS, Denny JC, Bowton E, Gregg W, Pulley JM, Basford MA, Cowan JD, Xu H, Ramirez AH, Crawford DC, Ritchie MD, Peterson JF, Masys DR, Wilke RA, Roden DM. Optimizing drug outcomes through pharmacogenetics: a case for preemptive genotyping. Clin Pharmacol Ther. 2012;92(2):235-242 PMC3785311. doi:10.1038/clpt.2012.66. PMCID: PMC3785311
  13. Van Driest S, Shi Y, Bowton E, Schildcrout J, Peterson J, Pulley J, Denny J, Roden D. Clinically actionable genotypes among 10,000 patients with preemptive pharmacogenomic testing. Clin Pharmacol Ther. 2014;95(4):423-431. doi:10.1038/clpt.2013.229. PMCID: PMC3961508
  14. McCarty CA, Chisholm RL, Chute CG, Kullo IJ, Jarvik GP, Larson EB, Li R, Masys DR, Ritchie MD, Roden DM, Struewing JP, Wolf WA, eMERGE Team. The eMERGE Network: a consortium of biorepositories linked to electronic medical records data for conducting genomic studies. BMC Med Genomics. 2011;4:13. doi:10.1186/1755-8794-4-13. PMCID: PMC3038887
  15. Zuvich RL, Armstrong LL, Bielinski SJ, Bradford Y, Carlson CS, Crawford DC, Crenshaw AT, de Andrade M, Doheny KF, Haines JL, Hayes MG, Jarvik GP, Jiang L, Kullo IJ, Li R, Ling H, Manolio TA, Matsumoto ME, McCarty CA, McDavid AN, Mirel DB, Olson LM, Paschall JE, Pugh EW, Rasmussen LV, Rasmussen-Torvik LJ, Turner SD, Wilke RA, Ritchie MD. Pitfalls of merging GWAS data: lessons learned in the eMERGE network and quality control procedures to maintain high data quality. Genet Epidemiol. 2011;35(8):887-898. doi:10.1002/gepi.20639. PMCID: PMC3592376
  16. Lemke AA, Wu JT, Waudby C, Pulley J, Somkin CP, Trinidad SB. Community engagement in biobanking: Experiences from the eMERGE Network. Genomics Soc Policy. 2010;6(3):35-52. PMCID: PMC3434453
  17. Kho AN, Pacheco JA, Peissig PL, Rasmussen L, Newton KM, Weston N, Crane PK, Pathak J, Chute CG, Bielinski SJ, Kullo IJ, Li R, Manolio TA, Chisholm RL, Denny JC. Electronic medical records for genetic research: results of the eMERGE consortium. Sci Transl Med. 2011;3(79):79re1. doi:10.1126/scitranslmed.3001807. PMCID: PMC3690272
  18. National Institutes of Health. NIH Genomic Data Sharing Policy. August 2014. http://grants.nih.gov/grants/guide/notice-files/NOT-OD-14-124.html.
  19. Mailman MD, Feolo M, Jin Y, Kimura M, Tryka K, Bagoutdinov R, Hao L, Kiang A, Paschall J, Phan L, Popova N, Pretel S, Ziyabari L, Shao Y, Wang ZY, Sirotkin K, Ward M, Kholodov M, Zbicz K, Beck J, Kimelman M, Shevelev S, Preuss D, Yaschenko E, Graeff A, Ostell J, Sherry ST. The NCBI dbGaP database of genotypes and phenotypes. Nat Genet. 2007;39(10):1181-1186. doi:10.1038/ng1007-1181. PMCID: PMC2031016
  20. Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, Wang D, Masys DR, Roden DM, Crawford DC. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 2010;26(9):1205-1210 PMCID: PMC2859132. doi:10.1093/bioinformatics/btq126. PMCID: PMC2859132
  21. Denny JC, Bastarache L, Ritchie MD, Carroll RJ, Zink R, Mosley JD, Field JR, Pulley JM, Ramirez AH, Bowton E, Basford MA, Carrell DS, Peissig PL, Kho AN, Pacheco JA, Rasmussen LV, Crosslin DR, Crane PK, Pathak J, Bielinski SJ, Pendergrass SA, Xu H, Hindorff LA, Li R, Manolio TA, Chute CG, Chisholm RL, Larson EB, Jarvik GP, Brilliant MH, McCarty CA, Kullo IJ, Haines JL, Crawford DC, Masys DR, Roden DM. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat Biotechnol. 2013;31(12):1102-1110. doi:10.1038/nbt.2749. PMCID: PMC3969265
  22. Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nat Rev Drug Discov. 2012;11(3):191-200. doi:10.1038/nrd3681.