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Annual Review of Pharmacology and Toxicology

Volume 58, 2018

Precision Medicine Is Not Just Genomics: The Right Dose for Every Patient

Abstract

Genomics has helped to initiate the era of precision medicine, with some drugs now prescribed on the basis of molecular genetic tests that indicate which patients are likely to respond or should not receive a drug because of a high risk of adverse effects. However, for precision medicine to realize its potential, the patient's history, environment, and lifestyle must also be taken into account. Improving precision medicine requires a better understanding of the underlying reasons for the variability in drug response so as to better identify which drug or combination of drugs is likely to be most effective for an individual patient, along with consideration of the optimal dose or doses for that patient. Greater individualization of dose will be an important means to achieve more precise medicine and mitigate significant variability in drug response. Achieving this will require changes in how drugs are developed, approved, prescribed, monitored, and paid for. Each of these factors is discussed in this review.

Keyword(s): precision medicine, variability, dose individualization
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2018-01-06
2024-04-19
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Literature Cited

  1. Stone A. 1.  2016. Precision medicine: health care tailored to you. The White House Blog Feb. 25. https://obamawhitehouse.archives.gov/blog/2016/02/25/precision-medicine-health-care-tailored-you
  2. Collins FS, Varmus H. 2.  2015. A new initiative on precision medicine. N. Engl. J. Med. 372:793–95 [Google Scholar]
  3. Ashley EA. 3.  2016. Towards precision medicine. Nat. Rev. Genet. 17:507–22 [Google Scholar]
  4. Slamon DJ, Leyland-Jones B, Shak S, Fuchs H, Paton V. 4.  et al. 2001. Use of chemotherapy plus a monoclonal antibody against HER2 for metastatic breast cancer that overexpresses HER2. N. Engl. J. Med. 344:783–92 [Google Scholar]
  5. Hughes TP, Kaeda J, Branford S, Rudzki Z, Hochhaus A. 5.  et al. 2003. Frequency of major molecular responses to imatinib or interferon alfa plus cytarabine in newly diagnosed chronic myeloid leukemia. N. Engl. J. Med. 417:949–54 [Google Scholar]
  6. Flaherty KT, Puzanov I, Kim KB, Ribas A, McArthur GA. 6.  et al. 2010. Inhibition of mutated, activated BRAF in metastatic melanoma. N. Engl. J. Med. 363:809–19 [Google Scholar]
  7. Gadgeel SM, Gandhi L, Riely GJ, Chiappori AA, West HL. 7.  et al. 2014. Safety and activity of alectinib against systemic disease and brain metastases in patients with crizotinib-resistant ALK-rearranged non-small-cell lung cancer (AF-002JG): results from the dose-finding portion of a phase 1/2 study. Lancet Oncol 15:1119–28 [Google Scholar]
  8. Gloyn AL, Pearson ER, Antcliff JF, Proks P, Bruining GJ. 8.  et al. 2004. Activating mutations in the gene encoding the ATP-sensitive potassium-channel subunit Kir6.2 and permanent neonatal diabetes. N. Engl. J. Med. 350:1838–49 [Google Scholar]
  9. Proks P, Arnold AL, Bruining J, Girard C, Flanagan SE. 9.  et al. 2006. A heterozygous activating mutation in the sulphonylurea receptor SUR1 (ABCC8) causes neonatal diabetes. Hum. Mol. Genet. 15:1793–800 [Google Scholar]
  10. Accurso FJ, Rowe SM, Clancy JP, Boyle MP, Dunitz JM. 10.  et al. 2010. Effect of VX-770 in persons with cystic fibrosis and the G551D-CFTR mutation. N. Engl. J. Med. 363:1991–2003 [Google Scholar]
  11. Baynes RD, Gansert J. 11.  2009. KRAS mutational status as a predictor of epidermal growth factor receptor inhibitor efficacy in colorectal cancer. Am. J. Ther. 16:6554–61 [Google Scholar]
  12. Stekler J, Maenza J, Stevens C, Holte S, Malhotra U. 12.  et al. 2006. Abacavir hypersensitivity reaction in primary HIV infection. AIDS 20:1269–74 [Google Scholar]
  13. Spear BB, Heath-Chiozzi M, Huff J. 13.  2001. Clinical application of pharmacogenetics. Trends Mol. Med. 7:201–4 [Google Scholar]
  14. Schork N. 14.  2015. Time for one-person trials. Nature 520:609–11 [Google Scholar]
  15. Lonergan M, Senn SJ, McNamee C, Daly A, Sutton R. 15.  et al. 2017. Defining drug response for stratified medicine. Drug Disc. Today. 22:173–79 [Google Scholar]
  16. Nayak RR, Turnbaugh PJ. 16.  2016. Mirror, mirror on the wall: Which microbiomes will help heal them all?. BMC Med 14:72 [Google Scholar]
  17. Relling MV, Klein TE. 17.  2011. CPIC: Clinical Pharmacogenetics Implementation Consortium of the Pharmacogenetics Research Network. Clin. Pharmacol. Ther. 89:464–67 [Google Scholar]
  18. Caudle K, Klein TE, Hoffman JM, Muller DJ, Whirl-Carrillo M. 18.  et al. 2014. Incorporation of pharmacogenetics into routine clinical practice: the Clinical Pharmacogenetics Implementation Consortium guideline development process. Curr. Drug Metab. 15:209–17 [Google Scholar]
  19. Relling MV, Evans WE. 19.  2015. Pharmacogenetics in the clinic. Nature 526:343–50 [Google Scholar]
  20. Huang S-M, Zhao H, Lee J-I, Reynolds K, Zhang L. 20.  et al. 2010. Therapeutic protein-drug interactions and implications for drug development. Clin. Pharmacol. Ther. 89:481–84 [Google Scholar]
  21. Levy G. 21.  1994. Pharmacologic target-mediated drug disposition. Clin. Pharmacol. Ther. 56:248–52 [Google Scholar]
  22. Mager DE, Jusko W. 22.  2001. General pharmacokinetic model for drugs exhibiting target-mediated drug disposition. J. Pharmacokinet. Pharmacodyn. 28:507–32 [Google Scholar]
  23. Han K, Jin J, Maia M, Lowe J, Sersch MA, Allison DE. 23.  2014. Lower exposure and faster clearance of bevacizumab in gastric cancer and the impact of patient variables: analysis of individual data from AVAGAST phase III trial. AAPS J 16:1056–63 [Google Scholar]
  24. Oude Munnink TH, Henstra MJ, Segerink LI, Movig KL, Brummelhuis-Visser P. 24.  2015. Therapeutic drug monitoring of monoclonal antibodies in inflammatory and malignant disease: translating TNF-α experience to oncology. Clin. Pharmacol. Ther. 99:419–30 [Google Scholar]
  25. An G. 25.  2017. Small-molecule compounds exhibiting target-mediated drug disposition (TMDD): a minireview. J. Clin. Pharmacol. 57:2137–50 [Google Scholar]
  26. Levy G. 26.  1998. Predicting effective drug concentration for individual patients: determinants of pharmacodynamic variability. Clin. Pharmacokinet. 34:323–33 [Google Scholar]
  27. Hamberg A-K, Dahl M-L, Barban M, Scordo MG, Wadelius M. 27.  et al. 2007. A PKPD model for predicting the impact of age, Cyp2C9, and VKORC1 genotype on individualization of warfarin therapy. Clin. Pharmacol. Ther. 81:529–38 [Google Scholar]
  28. 28. Int. Warfarin Pharmacogenet. Consort. 2009. Estimation of the warfarin dose with clinical and pharmacogenetic data. N. Engl. J. Med. 360:753–64 [Google Scholar]
  29. Avery PJ, Jorgensen A, Hamberg AK, Wadelius M, Pirmohamed M, Kamali F. 29.  2011. A proposal for an individualized pharmacogenetics-based warfarin initiation dose regimen for patients commencing anticoagulation therapy. Clin. Pharmacol. Ther. 90:701–6 [Google Scholar]
  30. Man CBL, Kwan P, Baum L, Yu E, Lau KM. 30.  et al. 2007. Association between HLA-B*1502 allele and antiepileptic drug-induced cutaneous reactions in Han Chinese. Epilepsia 48:1015–18 [Google Scholar]
  31. 31. SEARCH Collab. Group. 2008. SLCO1B1 variants and statin-induced myopathy — a genomewide study. N. Engl. J. Med. 359:789–99 [Google Scholar]
  32. Flood-Page P, Swenson C, Faiferman I, Matthews J, Williams M. 32.  et al. 2007. A study to evaluate safety and efficacy of mepolizumab in patients with moderate asthma. Am. J. Respir. Crit. Care 176:1062–71 [Google Scholar]
  33. Bel EH, Wenzel SE, Thompson PJ, Prazma CM, Keene O. 33.  et al. 2014. Oral glucocorticoid sparing effect of mepolizumab in eosinophilic asthma. N. Engl. J. Med. 371:1189–97 [Google Scholar]
  34. Ortega HG, Liu MC, Pavord ID, Brusselle GG, FitzGerald M. 34.  et al. 2014. Mepolizumab treatment in patients with severe eosinophilic asthma. N. Engl. J. Med. 371:1198–207 [Google Scholar]
  35. Kola I, Bell J. 35.  2011. A call to reform the taxonomy of human disease. Nat. Rev. Drug Discov. 10:641–42 [Google Scholar]
  36. 36. Commit. Framew. Dev. New Taxon. Dis. 2011. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease Washington, DC: Natl. Acad. Press
  37. Chan AC, Behrens TW. 37.  2013. Personalizing medicine for autoimmune and inflammatory diseases. Nat. Immunol. 14:106–9 [Google Scholar]
  38. Slavin RG, Ferioli C, Tannenbaum SJ, Martin C, Blogg M, Lowe PJ. 38.  2009. Asthma symptom re-emergence after omalizumab withdrawal correlates well with increasing IgE and decreasing pharmacokinetic concentrations. J. Allergy Clin. Immunol. 123:107–13 [Google Scholar]
  39. 39. Xolair® [package insert]. Silver Spring, MD: FDA 2007. http://www.accessdata.fda.gov/drugsatfda_docs/label/2007/103976s5102lbl.pdf
  40. Salloway S, Sperling R, Fox NC, Blennow K, Klunk W. 40.  et al. 2014. Two phase 3 trials of bapineuzumab in mild-to-moderate Alzheimer's disease. N. Engl. J. Med. 370:322–33 [Google Scholar]
  41. Peck RW. 41.  2016. The right dose for every patient: a key step for precision medicine. Nat. Rev. Drug Discov. 15:145–46 [Google Scholar]
  42. Sacks LV, Shamsuddin HH, Yasinskaya YI, Bouri K, Lanthier ML, Sherman RE. 42.  2014. Scientific and regulatory reasons for delay and denial of FDA approval of initial applications for new drugs, 2000–2012. JAMA 311:378–84 [Google Scholar]
  43. 43. Eur. Med. Agency. 2015. Report from dose-finding workshop Eur. Med. Agency Rep. EMA/117491/2015 London: http://www.ema.europa.eu/docs/en_GB/document_library/Report/2015/04/WC500185864.pdf
  44. Hyman DM, Puzanov I, Subbiah V, Faris JE, Chau I. 44.  et al. 2015. Vemurafenib in multiple nonmelanoma cancers with BRAF V600 mutations. N. Engl. J. Med. 373:726–36 [Google Scholar]
  45. Ge D, Fellay J, Thompson AJ, Simon JS, Shianna KV. 45.  et al. Genetic variation in IL28B predicts hepatitis C treatment-induced viral clearance. Nature 461:399–401 [Google Scholar]
  46. Townsend MJ, Arron JR. 46.  2016. Reducing the risk of failure: biomarker-guided trial design. Nat. Rev. Drug Discov. 15:517–18 [Google Scholar]
  47. Wang J, Song P, Schrieber S, Liu Q, Xu Q. 47.  et al. 2014. Exposure-response relationship of T-DM1: insight into dose optimization for patients with Her2-positive metastatic breast cancer. Clin. Pharmacol. Ther. 95:558–64 [Google Scholar]
  48. Mould D. 48.  2012. Models for disease progression: new approaches and uses. Clin. Pharmacol. Ther. 92:125–31 [Google Scholar]
  49. Holford N. 49.  2014. Clinical pharmacology = disease progression + drug action. Br. J. Clin. Pharmacol. 79:18–27 [Google Scholar]
  50. Latourelle J, Beste M, Hadzi T, Hayete B, Miller R. 50.  et al. 2016. Identification of clinical and genetic predictors of Parkinson's disease progression via Bayesian machine learning Presented at World Parkinson's Congr., Poster 1312 Portland, OR:
  51. Samtani MN, Farnum M, Lobanov V, Yang E, Raghavan N. 51.  et al. 2012. Improved model for disease progression in patients from the Alzheimer's disease neuroimaging initiative. J. Clin. Pharmacol. 52:629–44 [Google Scholar]
  52. Delor I, Charoin J-E, Gieschke R, Retout S, Jacqumin P. 52.  2013. Modeling Alzheimer's disease progression using disease onset time and disease trajectory concepts applied to CDS-SOB scores from ADNI. CPT Pharmacomet. Syst. Pharmacol. 2:e78 [Google Scholar]
  53. Turner RM, Park BK, Pirmohamed M. 53.  2015. Parsing interindividual drug variability: an emerging role for systems pharmacology. WIREs Syst. Biol. Med. 7:221–41 [Google Scholar]
  54. Lewis AK, Paterson T, Leong CC, Defranoux N, Holgate ST, Stokes CL. 54.  2001. The roles of cells and mediators in a computer model of asthma. Int. Arch. Allergy Immunol. 124:282–86 [Google Scholar]
  55. Nakashima K, Narukawa M, Kanazu Y, Takeuchi M. 55.  2011. Differences between Japan and the United States in dosages of drugs recently approved in Japan. J. Clin. Pharmacol. 51:549–60 [Google Scholar]
  56. Ramamoorthy A, Pacanowski MA, Bull J, Zhang L. 56.  2015. Racial/ethnic differences in drug disposition and response: review of recently approved drugs. Clin. Pharmacol. Ther. 97:263–73 [Google Scholar]
  57. Warner TD, Nylander S, Whatling C. 57.  2011. Anti-platelet therapy: cyclo-oxygenase inhibition and the use of aspirin with particular regard to dual anti-platelet therapy. Br. J. Clin. Pharmacol. 72:619–33 [Google Scholar]
  58. Meyer UA. 58.  1997. Molecular mechanisms of genetic polymorphisms of drug metabolism. Annu. Rev. Pharmacol. Toxicol. 37:269–96 [Google Scholar]
  59. Woodcock J, Lesko LJ. 59.  2009. Pharmacogenetics—tailoring treatment for the outliers. N. Engl. J. Med. 360:811–13 [Google Scholar]
  60. Wang YH, Trucksis M, McElwee JJ, Wong PH, Maciolek C. 60.  et al. 2012. UGT2B17 genetic polymorphisms dramatically affect the pharmacokinetics of MK-7246 in healthy subjects in a first-in-human study. Clin. Pharmacol. Ther. 92:96–102 [Google Scholar]
  61. Fowler S, Kletzl H, Finel M, Manevski N, Schmid P. 61.  et al. 2015. A UGT2B10 splicing polymorphism common in African populations may greatly increase drug exposure. Clin. Pharmacol. Ther. 352:358–67 [Google Scholar]
  62. Mullard A. 62.  2014. Learning from exceptional drug responders. Nat. Rev. Drug Discov. 13:401–2 [Google Scholar]
  63. Jones AG, Shields BM, Hyde CJ, Henley WE, Hattersley AT. 63.  2014. Identifying good responders to glucose lowering therapy in type 2 diabetes: implications for stratified medicine. PLOS ONE 9:e111235 [Google Scholar]
  64. Blaschke TJ, Osterberg L, Vrijens B, Urquhart J. 64.  2012. Adherence to medications: insights arising from studies on the unreliable link between prescribed and actual drug dosing histories. Annu. Rev. Pharmacol. Toxicol. 52:275–301 [Google Scholar]
  65. Vrijens B, Urquhart J. 65.  2014. Methods for measuring, enhancing, and accounting for medication adherence in clinical trials. Clin. Pharmacol. Ther. 95:617–26 [Google Scholar]
  66. Savic RM, Barrail-Tran A, Duval X, Nembot G, Panhard X. 66.  et al. 2012. Effect of adherence as measured by MEMS, ritonavir boosting, and Cyp3A5 genotype on atazanavir pharmacokinetics in treatment-naïve HIV-infected patients. Clin. Pharmacol. Ther. 92:575–83 [Google Scholar]
  67. Assawasuwannakit P, Braund R, Duffull SB. 67.  2016. A framework for quantifying the influence of adherence and dose individualization. Clin. Pharmacol. Ther. 99:452–59 [Google Scholar]
  68. Reddy VP, Kozielska M, de Greef R, Vermeulen A, Proost JH. 68.  2013. Modelling and simulation of placebo effect: application to drug development in schizophrenia. J. Pharmacokinet. Pharmacodyn. 40:377–88 [Google Scholar]
  69. Papakostas GI, Østergaard SD, Iovieno NJ. 69.  2015. The nature of placebo response in clinical studies of major depressive disorder. Clin. Psychiatry 76:456–66 [Google Scholar]
  70. Cragg JJ, Warner FM, Finnerup NB, Jensen MP, Mercier C. 70.  et al. 2016. Meta-analysis of placebo responses in central neuropathic pain: impact of subject, study, and pain characteristics. Pain 157:530–40 [Google Scholar]
  71. Cremers S, Guha N, Shine B. 71.  2016. Therapeutic drug monitoring in the era of precision medicine: opportunities. Br. J. Clin. Pharmacol. 82:900–2 [Google Scholar]
  72. Mould DR. 72.  2016. Why therapeutic drug monitoring is needed for antibodies and how do we implement this?. Clin. Pharmacol. Ther. 99:351–54 [Google Scholar]
  73. Bastida C, Ruíz V, Pascal M, Yagüe J, Sanmartí R, Soy D. 73.  2016. Is there potential for therapeutic drug monitoring of biologic agents in rheumatoid arthritis?. Br. J. Clin. Pharmacol. 83:962–75 [Google Scholar]
  74. Grieve AP. 74.  2016. Response adaptive clinical trials: case studies in the medical literature. Pharm. Stat. 16:64–86 [Google Scholar]
  75. Barker AD, Sigman CC, Kelloff GJ, Hylton NM, Berry D, Esserman LJ. 75.  2009. I-SPY2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin. Pharmacol. Ther. 86:97–100 [Google Scholar]
  76. Park JW, Liu MC, Yee D, Yau C, van ’t Veer LJ. 76.  et al. 2016. Adaptive randomization of neratinib in early breast cancer. N. Engl. J. Med. 375:11–22 [Google Scholar]
  77. Rugo HS, Olopade OI, DeMichele A, Yau C, van ’t Veer LJ. 77.  et al. 2016. Adaptive randomization of veliparib–carboplatin treatment in breast cancer. N. Engl. J. Med. 375:23–34 [Google Scholar]
  78. Willyard C. 78.  2013. Basket studies will hold intricate data for cancer drug approvals. Nat. Med. 19:655 [Google Scholar]
  79. Redig AJ, Janne PA. 79.  2015. Basket trials and the evolution of clinical trial design in an era of genomic medicine. J. Clin. Oncol. 33:975–77 [Google Scholar]
  80. Poh A. 80.  2016. Treating tumors by molecular profile, not type. Cancer Discov 6:688 [Google Scholar]
  81. Lopez-Chavez A, Thomas A, Rajan A, Raffeld M, Morrow B. 81.  et al. 2015. Molecular profiling and targeted therapy for advanced thoracic malignancies: a biomarker-derived, multiarm, multihistology phase II basket trial. J. Clin. Oncol. 33:1000–7 [Google Scholar]
  82. Trippa L, Alexander BM. 82.  2017. Bayesian baskets: a novel design for biomarker-based clinical trials. J. Clin. Oncol. 35:681–87 [Google Scholar]
  83. Sanathanan LP, Peck CC. 83.  1991. The randomized concentration-controlled trial: an evaluation of its sample size efficiency. Control. Clin. Trials. 12:780–94 [Google Scholar]
  84. Levy G, Ebling WF, Forrest A. 84.  1994. Concentration- or effect-controlled clinical trials with sparse data. Clin. Pharmacol. Ther. 56:1–8 [Google Scholar]
  85. Johnston A, Holt DW. 85.  1995. Concentration-controlled trials: What does the future hold?. Clin. Pharmacokinet. 28:93–99 [Google Scholar]
  86. Kraiczi H, Jang T, Ludden T, Peck CC. 86.  2003. Randomized concentration-controlled trials: motivations, use, and limitations. Clin. Pharmacol. Ther. 74:203–14 [Google Scholar]
  87. Grahnen A, Karlsson M. 87.  2001. Concentration-controlled or effect-controlled trials. Clin. Pharmacokinet. 40:317–25 [Google Scholar]
  88. Chae YK, Pan A, Davis AA, Raparia K, Mohindra NA. 88.  et al. 2016. Biomarkers for PD-1/PD-L1 blockade therapy in non–small-cell lung cancer: Is PD-L1 expression a good marker for patient selection?. Clin. Lung Cancer 17:350–61 [Google Scholar]
  89. Abdel-Rahman O. 89.  2016. Correlation between PD-L1 expression and outcome of NSCLC patients treated with anti-PD-1/PD-L1 agents: a meta-analysis. Crit. Rev. Oncol. 101:75–85 [Google Scholar]
  90. Chow LQM, Mehra R, Haddad RI, Mahipal A, Weiss J. 90.  et al. 2016. Biomarkers and response to pembrolizumab (pembro) in recurrent/metastatic head and neck squamous cell carcinoma. J. Clin. Oncol. 34:suppl.6010 [Google Scholar]
  91. Blank CU, Haanen JB, Ribas A, Schumacher TN. 91.  2016. Cancer immunology: the “cancer immunogram.”. Science 352:658–60 [Google Scholar]
  92. Hugo W, Zaretsky JM, Sun L, Song C, Homet Moreno B. 92.  et al. 2016. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165:35–44 [Google Scholar]
  93. Schalper KA, Kaftan E, Herbst RS. 93.  2016. Predictive biomarkers for PD-1 axis therapies: the hidden treasure or a call for research. Cancer Res 22:2102–4 [Google Scholar]
  94. Eichler H-G, Baird LG, Barker R, Bloechl-Daum B, Borlum-Kristensen F. 94.  et al. 2015. From adaptive licensing to adaptive pathways: delivering a flexible life-span approach to bring new drugs to patients. Clin. Pharmacol. Ther. 97:234–36 [Google Scholar]
  95. Breckenridge A, Eichler H-G, Jarow J. 95.  2016. Precision medicine and the changing role of regulatory agencies. Nat. Rev. Drug Discov. 15:805–6 [Google Scholar]
  96. Chen Z, Liew D, Kwan P. 96.  2014. Effects of a HLA-B*15:02 screening policy on antiepileptic drug use and severe skin reactions. Neurology 83:2077–84 [Google Scholar]
  97. Manolio TA, Abramowicz M, Al-Mulla F, Anderson W, Balling R. 97.  et al. 2015. Global implementation of genomic medicine: We are not alone. Sci. Transl. Med. 7:290ps13 [Google Scholar]
  98. Shuldiner AR, Relling MV, Peterson JF, Hicks JK, Freimuth RR. 98.  et al. 2013. The Pharmacogenomics Research Network Translational Pharmacogenetics Program: overcoming challenges of real-world implementation. Clin. Pharmacol. Ther. 94:207–10 [Google Scholar]
  99. Pulley JM, Denny JC, Peterson JF, Bernard GR, Vnencak-Jones CL. 99.  et al. 2012. Operational implementation of prospective genotyping for personalized medicine: the design of the Vanderbilt PREDICT project. Clin. Pharmacol. Ther. 92:87–95 [Google Scholar]
  100. O'Donnell PH, Danahey K, Jacobs M, Wadhwa NR, Yuen S. 100.  et al. 2014. Adoption of a clinical pharmacogenomics implementation program during outpatient care—initial results of the University of Chicago “1,200 Patients Project.”. Am. J. Med. Genet. C Semin. Med. Genet. 166C:68–75 [Google Scholar]
  101. Johnson JA, Weitzl KW. 101.  2016. Advancing pharmacogenomics as a component of precision medicine: Who, where and who?. Clin. Pharmacol. Ther. 99:15456 [Google Scholar]
  102. Mould DR, D'Haens G, Upton RN. 102.  2016. Clinical decision support tools: the evolution of a revolution. Clin. Pharmacol. Ther. 99:405–18 [Google Scholar]
  103. Powell JR. 103.  2015. Are new oral anticoagulant dosing recommendations optimal for all patients?. JAMA 313:1013–14 [Google Scholar]
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Precision Medicine Is Not Just Genomics: The Right Dose for Every Patient
Annual Review of Pharmacology and Toxicology 58, 105 (2018); https://doi.org/10.1146/annurev-pharmtox-010617-052446
/content/journals/10.1146/annurev-pharmtox-010617-052446
/content/journals/10.1146/annurev-pharmtox-010617-052446
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