Advertisement

Roadmap to Drug Development Enabled by Pharmacogenetics

  • James P. BishopEmail author
  • Sonal B. Halburnt
  • Patrick A. Akkari
  • Scott Sundseth
  • Iris Grossman
Chapter
Part of the Advances in Predictive, Preventive and Personalised Medicine book series (APPPM, volume 9)

Abstract

The primary goal of the pharmaceutical industry is to develop safe and effective medications. As the industry matures and the existing arsenal of marketed therapeutics grows, novel drugs must exhibit greater efficacy and safety to achieve registration and favorable reimbursement. Furthermore, gaining market-share has become extremely competitive, in terms of both meaningful clinical effects and tolerated safety profiles. As a result, the pharmaceutical industry has experienced a steady decline in productivity in recent decades. However, the achievement of regulatory approvals for targeted therapeutics may reverse this drop in productivity. The convergence of high-throughput genetic analysis technologies and the exponentially expanding biological and genomic knowledgebase have provided many clear examples that genetic variation can affect both disease risk and drug response. Therefore, evaluation of genetic variation in clinical trial populations should be considered essential and routine from the earliest phases of drug development. Pharmacogenetics (PGx) in particular has gained considerable attention from drug developers, regulators and payers over the past decade as a means to achieving safer, efficacious and more cost-effective drugs. While PGx science has great potential to impact positively the success of developing a new medicine, the integration of PGx into the decision making processes of the drug development pipeline has been difficult. The goal of this chapter is to describe the principles and requirements of an efficient and valuable PGx strategy that makes use of every opportunity during the course of developing innovative medicines. This strategy combines a proven methodology with rigorous genetic science to create a “Pipeline Pharmacogenetic Program”.

Keywords

Novel drugs Companion diagnostics Pipeline pharmacogenetics Clinical trials Drug development Project management methodology 

References

  1. 1.
    Spear BB, Heath-Chiozzi M, Huff J (2001) Clinical application of pharmacogenetics. Trends Mol Med 7(5):201–204PubMedCrossRefGoogle Scholar
  2. 2.
    Personalized Medicine Coalition (2014) http://www.personalizedmedicinecoalition.org/. Accessed 7 Feb 2014
  3. 3.
    European Commission (2013) Use of ‘-omics’ technologies in the development of personalised medicine. http://ec.europa.eu/health/files/latest_news/2013-10_personalised_medicine_en.pdf. Accessed 20 Feb 2014
  4. 4.
    Food and Drug Administration (2013) Table of pharmacogenomic biomarkers in drug labels. http://www.fda.gov/drugs/scienceresearch/researchareas/pharmacogenetics/ucm083378.htm. Accessed 3 Oct 2013
  5. 5.
    Food and Drug Administration (2013) Guidance for industry clinical pharmacogenomics: premarket evaluation in early-phase clinical studies and recommendations for labeling. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM337169.pdf. Accessed 20 Feb 2014
  6. 6.
    Haga SB, O’Daniel JM, Tindall GM, Lipkus IR, Agans R (2012) Survey of US public attitudes toward pharmacogenetic testing. Pharmacogenomics J 12:197–204PubMedCentralPubMedCrossRefGoogle Scholar
  7. 7.
    Battelle Technology Partnership Practice (2012) The economic and functional impact of genetic and genomic clinical laboratory testing in the United States. http://www.labresultsforlife.org/news/battelle_impact_report.pdf. Accessed 20 Feb 2014
  8. 8.
    Saukko P (2013) State of play in direct-to-consumer genetic testing for lifestyle-related diseases: market, marketing content, user experiences and regulation. Proc Nutr Soc 72(1):53–60PubMedCentralPubMedCrossRefGoogle Scholar
  9. 9.
    Wetterstrand KA (2014) DNA sequencing costs: data from the NHGRI Genome Sequencing Program (GSP). www.genome.gov/sequencingcosts. Accessed 6 Feb 2014
  10. 10.
    Kulkarni S, Ma P, Furstenthal L, Evers M (2013) McKinsey & Company: personalized medicine—The path forward. http://www.mckinsey.com/Search.aspx?q=personalized%20medicine%20the%20path%20forward. Accessed 6 Nov 2013
  11. 11.
    Wester K, Jonsson AK, Spigset O, Druid H, Hagg S (2007) Incidence of fatal adverse drug reactions: a population based study. Br J Clin Pharmacol 65(4):573–579PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Lazarou J, Pomeranz BH, Corey PN (1998) Incidence of adverse drug reactions in hospitalized patients: a meta-analysis of prospective studies. JAMA 279(15):1200–1205PubMedCrossRefGoogle Scholar
  13. 13.
    Akkari PA, Swanson TW, Crenshaw DG, Grossman I, Sundseth S, Burns DK, Roses AD (2009) Pipeline pharmacogenetics: a novel approach to integrating pharmaco-genetics into drug development. Curr Pharm Des 15(32):3754–3763PubMedCrossRefGoogle Scholar
  14. 14.
    Swanson TW, Akkari PA, Arbuckle JB, Grossman I, Sundseth S, Roses AD (2013) Methodology to enable integration of genomic knowledge into drug development. In: Vizirianakis IS (ed) Handbook of personalized medicine: advances in nanotechnology, drug delivery and therapy. CRC Press, Boca Raton. doi:10.4032/9789814411202Google Scholar
  15. 15.
    Ma Q, Lu AY (2011) Pharmacogenetics, pharmacogenomics, and individualized medicine. Pharmacol Rev 63(2):437–459PubMedCrossRefGoogle Scholar
  16. 16.
    Hughes AR, Brothers CH, Mosteller M, Spreen WR, Burns DK (2009) Genetic association studies to detect adverse drug reactions: abacavir hypersensitivity as an example. Pharmacogenomics 10(2):225–233. doi:10.2217/14622416.10.2.225PubMedCrossRefGoogle Scholar
  17. 17.
    Hughes S, Hughes A, Brothers C, Spreen W, Horborn D, CNA106030 Study Team (2008) PREDICT-1 (CNA106030): the first powered, prospective trial of pharmacogenetic screening to reduce drug adverse events. Pharm Stat 7(2):121–129PubMedCrossRefGoogle Scholar
  18. 18.
    Mega JL, Close SL, Wiviott SD, Shen L, Walker JR, Simon T, Antman EM, Braunwald E, Sabatine MS (2010) Genetic variants in ABCB1 and CYP2C19 and cardiovascular outcomes after treatment with clopidogrel and prasugrel in the TRITON-TIMI 38 trial: a pharmacogenetic analysis. Lancet 376(9749):1312–1319PubMedCentralPubMedCrossRefGoogle Scholar
  19. 19.
    Roberts JD, Wells GA, Le May MR, Labinaz M, Glover C, Froeschl M, Dick A, Marquis JF, O’Brien E, Goncalves S, Druce I, Stewart A, Gollob MH, So DY (2012) Point-of-care genetic testing for personalisation of antiplatelet treatment (RAPID GENE): a prospective, randomised, proof-of-concept trial. Lancet 379(9827):1705–1711PubMedCrossRefGoogle Scholar
  20. 20.
    Food and Drug Administration (2011) Clinical pharmacology and biopharmaceutics review(s). http://www.accessdata.fda.gov/drugsatfda_docs/nda/2011/202570Orig1s000ClinPharmR.pdf. Accessed 7 Feb 2014
  21. 21.
    Franc MA, Warner AW, Cohen N, Shaw PM, Groenen P, Snapir A (2011) Current practices for DNA sample collection and storage in the pharmaceutical industry, and potential areas for hamonization: perspective of the I-PWG. Clin Pharm Ther 89:546–553CrossRefGoogle Scholar
  22. 22.
    Warner AW, Bhathena A, Gilardi S, Mohr D, Leong D, Bienfait KL, Sarang J, Duprey S, Franc MA, Nelsen A, Snapir A (2011) Challenges in obtaining adequate genetic sample sets in clinical trials: the perspective of the industry pharmacogenomics working group. Clin Pharmacol Ther 89(4):529–536PubMedCrossRefGoogle Scholar
  23. 23.
    Franc MA, Cohen N, Warner AW, Shaw PM, Groenen P, Snapir A (2011) Industry pharmacogenomics working group. Coding of DNA samples and data in the pharmaceutical industry: current practices and future directions-perspective of the I-PWG. Clin Pharmacol Ther 89(4):537–545PubMedCrossRefGoogle Scholar
  24. 24.
    Pharmacogenetics Working Group (2002) Elements of informed consent for pharmacogenetic research; perspective of the pharmacogenetics working group. Pharmacogenomics J 2:284–292. doi:10.1038/sj.tpj.6500131CrossRefGoogle Scholar
  25. 25.
    Ricci DS, Broderick ED, Tchelet A, Hong F, Mayevsky S, Mohr DM, Schaffer ME, Warner AW, Hakkulinen P, Snapir A (2011) Global requirements for DNA sample collections: results of a survey of 204 ethics committees in 40 countries. Clin Pharm Ther 89:554–561CrossRefGoogle Scholar
  26. 26.
    Hakonarson H (2013) Ask the experts: pharmacogenomics and genome-wide association studies. Pharmacogenomics 14(4):365–368PubMedCrossRefGoogle Scholar
  27. 27.
    Ni X, Zhang W, Huang RS (2013) Pharmacogenomics discovery and implementation in genome-wide association studies era. Wiley Interdiscip Rev Syst Biol Med 5(1):1–9PubMedCentralPubMedCrossRefGoogle Scholar
  28. 28.
    Barrett PM, Topol EJ (2013) Pharm-econogenomics: a new appraisal. Clin Chem 59(4):592–594PubMedCrossRefGoogle Scholar
  29. 29.
    Arnaout R, Buck TP, Roulette P, Sukhatme VP (2013) Predicting the cost and pace of pharmacogenomic advances: an evidence-based study. Clin Chem 59(4):649–657PubMedCrossRefGoogle Scholar
  30. 30.
    Voight BF, Kang HM, Ding J, Palmer CD, Sidore C, Chines PS, Burtt NP, Fuchsberger C, Li Y, Erdmann J, Frayling TM, Heid IM, Jackson AU, Johnson T, Kilpeläinen TO, Lindgren CM, Morris AP, Prokopenko I, Randall JC, Saxena R, Soranzo N, Speliotes EK, Teslovich TM, Wheeler E, Maguire J, Parkin M, Potter S, Rayner NW, Robertson N, Stirrups K, Winckler W, Sanna S, Mulas A, Nagaraja R, Cucca F, Barroso I, Deloukas P, Loos RJ, Kathiresan S, Munroe PB, Newton-Cheh C, Pfeufer A, Samani NJ, Schunkert H, Hirschhorn JN, Altshuler D, McCarthy MI, Abecasis GR, Boehnke M (2012) The metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet 8(8):e1002793PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Johnson JA, Burkley BM, Langaee TY, Clare-Salzler MJ, Klein TE, Altman RB (2012) Implementing personalized medicine: development of a cost-effective customized pharmacogenetics genotyping array. Clin Pharmacol Ther 92(4):437–439PubMedCentralPubMedCrossRefGoogle Scholar
  32. 32.
    Illumina Inc. Omni Array Family http://www.illumina.com/applications/genotyping/omni_family.ilmn. Accessed 7 Feb 2014
  33. 33.
    Urban TJ (2013) Whole-genome sequencing in pharmacogenetics. Pharmacogenomics 14(4):345–348PubMedCrossRefGoogle Scholar
  34. 34.
    Goldstein DB, Allen A, Keebler J, Margulies EH, Petrou S, Petrovski S, Sunyaev S (2013) Sequencing studies in human genetics: design and interpretation. Nat Rev Genet 14(7):460–470PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Kiezun A, Garimella K, Do R, Stitziel NO, Neale BM, McLaren PJ, Gupta N, Sklar P, Sullivan PF, Moran JL, Hultman CM, Lichtenstein P, Magnusson P, Lehner T, Shugart YY, Price AL, de Bakker PI, Purcell SM, Sunyaev SR (2012) Exome sequencing and the genetic basis of complex traits. Nat Genet 44(6):623–630PubMedCentralPubMedCrossRefGoogle Scholar
  36. 36.
    Fromer M, Moran JL, Chambert K, Banks E, Bergen SE, Ruderfer DM, Handsaker RE, McCarroll SA, O’Donovan MC, Owen MJ, Kirov G, Sullivan PF, Hultman CM, Sklar P, Purcell SM (2012) Discovery and statistical genotyping of copy-number variation from whole-exome sequencing depth. Am J Hum Genet 91(4):597–607PubMedCentralPubMedCrossRefGoogle Scholar
  37. 37.
    Wang C, Krishnakumar S, Wilhelmy J, Babrzadeh F, Stepanyan L, Su LF, Levinson D, Fernandez-Viña MA, Davis RW, Davis MM, Mindrinos M (2012) High-throughput, high-fidelity HLA genotyping with deep sequencing. Proc Natl Acad Sci U S A 109(22):8676–8681PubMedCentralPubMedCrossRefGoogle Scholar
  38. 38.
    Mohamed S, Syed BA (2013) Commercial prospects for genomic sequencing technologies. Nat Rev Drug Discov 12(5):341–342PubMedCrossRefGoogle Scholar
  39. 39.
    Roses AD, Saunders AM, Lutz MW, Zhang N, Hariri AR, Asin KE, Crenshaw DG, Budur K, Burns DK, Brannan SK (2014) New applications of disease genetics and pharmacogenetics to drug development. Curr Opin Pharmacol 14:81–89PubMedCrossRefGoogle Scholar
  40. 40.
    Altman RB (2013) Personal genomic measurements: the opportunity for information integration. Clin Pharmacol Ther 93(1):21–23PubMedCentralPubMedCrossRefGoogle Scholar
  41. 41.
    Berger B, Peng J, Singh M (2013) Computational solutions for omics data. Nat Rev Genet 14(5):333–346PubMedCentralPubMedCrossRefGoogle Scholar
  42. 42.
    Flynn AA (2011) Pharmacogenetics: practices and opportunities for study design and data analysis. Drug Discov Today 16(19–20):862–866PubMedCrossRefGoogle Scholar
  43. 43.
    Flannick J, Korn JM, Fontanillas P, Grant GB, Banks E, Depristo MA, Altshuler D (2012) Efficiency and power as a function of sequence coverage, SNP array density, and imputation. PLoS Comput Biol 8(7):e1002604PubMedCentralPubMedCrossRefGoogle Scholar
  44. 44.
    Lee SH, Wray NR (2013) Novel genetic analysis for case-control genome-wide association studies: quantification of power and genomic prediction accuracy. PLoS ONE 8(8):e71494PubMedCentralPubMedCrossRefGoogle Scholar
  45. 45.
    Wray NR, Yang J, Hayes BJ, Price AL, Goddard ME, Visscher PM (2013) Pitfalls of predicting complex traits from SNPs. Nat Rev Genet 14(7):507–515PubMedCentralPubMedCrossRefGoogle Scholar
  46. 46.
    Solovieff N, Cotsapas C, Lee PH, Purcell SM, Smoller JW (2013) Pleiotropy in complex traits: challenges and strategies. Nat Rev Genet 14(7):483–495PubMedCentralPubMedCrossRefGoogle Scholar
  47. 47.
    Howie B, Fuchsberger C, Stephens M, Marchini J, Abecasis GR (2012) Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat Genet 44(8):955–959PubMedCentralPubMedCrossRefGoogle Scholar
  48. 48.
    Food and Drug Administration (2012) Draft guidance for industry: enrichment strategies for clinical trials to support approval of human drugs and biological products. http://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM332181.pdf. Accessed 7 Feb 2014
  49. 49.
    Simon R, Roychowdhury S (2013) Implementing personalized cancer genomics in clinical trials. Nat Rev Drug Discov 12(5):358–369PubMedCrossRefGoogle Scholar
  50. 50.
    Gurwitz D, McLeod HL (2013) Genome-wide studies in pharmacogenomics: harnessing the power of extreme phenotypes. Pharmacogenomics 14(4):337–339PubMedCrossRefGoogle Scholar
  51. 51.
    Wittkowski KM, Sonakya V, Song T, Seybold MP, Keddache M, Durner M (2013) From single-SNP to wide-locus: genome-wide association studies identifying functionally related genes and intragenic regions in small sample studies. Pharmacogenomics 14(4):391–401PubMedCentralPubMedCrossRefGoogle Scholar
  52. 52.
    Motsinger-Reif A (2013) Interview: a discussion on genome-wide associations in pharmacogenomics. Pharmacogenomics 14(4):361–363PubMedCrossRefGoogle Scholar
  53. 53.
    Roses AD1, Lutz MW, Amrine-Madsen H, Saunders AM, Crenshaw DG, Sundseth SS, Huentelman MJ, Welsh-Bohmer KA, Reiman EM (2010) A TOMM40 variable-length polymorphism predicts the age of late-onset alzheimer’s disease. Pharmacogenomics J 10(5):375–384PubMedCentralPubMedCrossRefGoogle Scholar
  54. 54.
    Perez MV, Ashley EA (2010) Taming rare variation with known biology in long QT syndrome. Circ Cardiovasc Genet 6(3):227–229CrossRefGoogle Scholar
  55. 55.
    Liao W, Tsai F (2013) Personalized medicine: a paradigm shift in healthcare. Biomedicine 3(2):66–72CrossRefGoogle Scholar
  56. 56.
    Manolio TA (2013) Bringing genome-wide association findings into clinical use. Nat Rev Genet 14(8):549–558PubMedCrossRefGoogle Scholar
  57. 57.
    Manolio TA, Chisholm RL, Ozenberger B, Roden DM, Williams MS, Wilson R, Bick D, Bottinger EP, Brilliant MH, Eng C, Frazer KA, Korf B, Ledbetter DH, Lupski JR, Marsh C, Mrazek D, Murray MF, O’Donnell PH, Rader DJ, Relling MV, Shuldiner AR, Valle D, Weinshilboum R, Green ED, Ginsburg GS (2013) Implementing genomic medicine in the clinic: the future is here. Genet Med 15(4):258–267PubMedCentralPubMedCrossRefGoogle Scholar
  58. 58.
    Food and Drug Administration (2013) Paving the way for personalized medicine—FDA’s role in a new era of medical product development. http://www.fda.gov/downloads/scienceresearch/specialtopics/personalizedmedicine/ucm372421.pdf. Accessed 20 Feb 2014
  59. 59.
    Project Management Institute (2013) What is a Project. In: A guide to project management body of knowledge (PMBOK® Guide)—Fifth Edition. Project Management Institute Inc. (Newton Square, PA, USA). p3. ISDN: 978-1-935589-67-9Google Scholar
  60. 60.
    Anton RF (2008) Genetic basis for predicting response to naltrexone in the treatment of alcohol dependence. Pharmacogenomics 9(6):655–658PubMedCrossRefGoogle Scholar
  61. 61.
    Wong CJ, Witcher J, Mallinckrodt C, Dean RA, Anton RF, Chen Y, Fijal BA, Ouyang H, Dharia S, Sundseth SS, Schuh KJ, Kinon BJ (2013) A phase 2, placebo-controlled study of the opioid receptor antagonist LY2196044 for the treatment of alcohol dependence. Alcohol Clin Exp Res 38(2):522–520Google Scholar
  62. 62.
    Hutchison KE, Wooden A, Swift RM, Smolen A, McGeary J, Adler L, Paris L (2003) Olanzapine reduces craving for alcohol: a DRD4 VNTR polymorphism by pharmacotherapy interaction. Neuropsychopharmacology 28(10):1882–1888PubMedCrossRefGoogle Scholar
  63. 63.
    Tidey JW, Monti PM, Rohsenow DJ, Gwaltney CJ, Miranda R Jr, McGeary JE, MacKillop J, Swift RM, Abrams DB, Shiffman S, Paty JA (2007) Moderators of naltrexone’s effects on drinking, urge, and alcohol effects in non-treatment-seeking heavy drinkers in the natural environment. Alcohol Clin Exp Res 32(1):58–66PubMedCentralPubMedCrossRefGoogle Scholar
  64. 64.
    Food and Drug Administration (2011) Improving the prevention, diagnosis, and treatment of rare and neglected diseases. http://www.fda.gov/downloads/ScienceResearch/SpecialTopics/CriticalPathInitiative/UCM265525.pdf. Accessed 20 Feb 2014

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • James P. Bishop
    • 1
    Email author
  • Sonal B. Halburnt
    • 2
  • Patrick A. Akkari
    • 2
  • Scott Sundseth
    • 2
  • Iris Grossman
    • 3
    • 4
  1. 1.Teva PharmaceuticalsFrazerUSA
  2. 2.Cabernet PharmaceuticalsDurhamUSA
  3. 3.IsraGene Ltd.YakirIsrael
  4. 4.Personalized Medicine & PharmacogenomicsTeva Pharmaceutical IndustriesNetanyaIsrael

Personalised recommendations