1932

Abstract

Model-informed precision dosing (MIPD) has become synonymous with modern approaches for individualizing drug therapy, in which the characteristics of each patient are considered as opposed to applying a one-size-fits-all alternative. This review provides a brief account of the current knowledge, practices, and opinions on MIPD while defining an achievable vision for MIPD in clinical care based on available evidence. We begin with a historical perspective on variability in dose requirements and then discuss technical aspects of MIPD, including the need for clinical decision support tools, practical validation, and implementation of MIPD in health care. We also discuss novel ways to characterize patient variability beyond the common perceptions of genetic control. Finally, we address current debates on MIPD from the perspectives of the new drug development, health economics, and drug regulations.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-pharmtox-033020-113257
2021-01-06
2024-12-04
Loading full text...

Full text loading...

/deliver/fulltext/pharmtox/61/1/annurev-pharmtox-033020-113257.html?itemId=/content/journals/10.1146/annurev-pharmtox-033020-113257&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    WHO (World Health Organ.). 2017. Medication Without Harm—WHO Global Patient Safety Challenge Geneva: WHO https://www.who.int/patientsafety/medication-safety/en/
    [Google Scholar]
  2. 2. 
    Polasek TM, Kirkpatrick CMJ, Rostami-Hodjegan A 2019. Precision dosing to avoid adverse drug reactions. Ther. Adv. Drug Saf. 10: https://doi.org/10.1177/2042098619894147
    [Crossref] [Google Scholar]
  3. 3. 
    Gonzalez D, Rao GG, Bailey SC, Brouwer KLR, Cao Y et al. 2017. Precision dosing: public health need, proposed framework, and anticipated impact. Clin. Transl. Sci. 10:443–54
    [Google Scholar]
  4. 4. 
    Polasek TM, Shakib S, Rostami-Hodjegan A 2018. Precision dosing in clinical medicine: present and future. Expert Rev. Clin. Pharmacol. 11:743–46
    [Google Scholar]
  5. 5. 
    Vinks AA, Emoto C, Fukuda T 2015. Modeling and simulation in pediatric drug therapy: application of pharmacometrics to define the right dose for children. Clin. Pharmacol. Ther. 98:298–308
    [Google Scholar]
  6. 6. 
    Sheiner LB. 1969. Computer-aided long-term anticoagulation therapy. Comput. Biomed. Res. 2:507–18
    [Google Scholar]
  7. 7. 
    Jelliffe RW. 1969. Administration of digoxin. Dis. Chest 56:56–60
    [Google Scholar]
  8. 8. 
    Wright DFB, Martin JH, Cremers S 2019. Spotlight commentary: Model-informed precision dosing must demonstrate improved patient outcomes. Br. J. Clin. Pharmacol. 85:2238–40
    [Google Scholar]
  9. 9. 
    Polasek TM, Rayner CR, Peck RW, Rowland A, Kimko H, Rostami-Hodjegan A 2019. Toward dynamic prescribing information: codevelopment of companion model-informed precision dosing tools in drug development. Clin. Pharmacol. Drug Dev. 8:418–25
    [Google Scholar]
  10. 10. 
    Darwich AS, Ogungbenro K, Vinks AA, Powell JR, Reny J-L et al. 2017. Why has model-informed precision dosing not yet become common clinical reality? Lessons from the past and a roadmap for the future. Clin. Pharmacol. Ther. 101:646–56Highlights the state of the art and future direction of MIPD based on an expert meeting held in 2016.
    [Google Scholar]
  11. 11. 
    Polasek TM, Rostami-Hodjegan A, Yim D-S, Jamei M, Lee H et al. 2019. What does it take to make model-informed precision dosing common practice? Report from the 1st Asian Symposium on Precision Dosing. AAPS J 21:17
    [Google Scholar]
  12. 12. 
    FDA (US Food Drug Adm.). 2019. Precision dosing: defining the need and approaches to deliver individualized drug dosing in the real-world setting FDA Precision Dosing Workshop Silver Spring, MD: Aug. 12. https://www.fda.gov/drugs/precision-dosing-defining-need-and-approaches-deliver-individualized-drug-dosing-real-world-setting
    [Google Scholar]
  13. 13. 
    Polasek TM, Tucker GT, Sorich MJ, Wiese MD, Mohan T et al. 2018. Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation. Br. J. Clin. Pharmacol. 84:462–76
    [Google Scholar]
  14. 14. 
    Rowland A, Ruanglertboon W, van Dyk M, Wijayakumara D, Wood LS et al. 2019. Plasma extracellular nanovesicle (exosome)-derived biomarkers for drug metabolism pathways: a novel approach to characterize variability in drug exposure. Br. J. Clin. Pharmacol. 85:216–26
    [Google Scholar]
  15. 15. 
    Rostami-Hodjegan A, Achour B, Rothman JE 2019. Methods and apparatus for quantifying protein abundance in tissues via cell free ribonucleic acids in liquid biopsy WIPO Patent WO2019191297
    [Google Scholar]
  16. 16. 
    van der Graaf PH. 2019. Pharmacometrics and/or systems pharmacology. CPT Pharmacomet. Syst. Pharmacol. 8:331–32
    [Google Scholar]
  17. 17. 
    Zhang J, Zhou F, Qi H, Ni H, Hu Q et al. 2019. Randomized study of individualized pharmacokinetically-guided dosing of paclitaxel compared with body-surface area dosing in Chinese patients with advanced non-small cell lung cancer. Br. J. Clin. Pharmacol. 85:2292–301
    [Google Scholar]
  18. 18. 
    Smith FE, Rawlins MD. 1973. Variability in Human Drug Response London: Butterworths
    [Google Scholar]
  19. 19. 
    Richens A. 1974. Book review. Variability in Human Drug Response. Proc. R. Soc. Med. 67:520–21
    [Google Scholar]
  20. 20. 
    Rogoff B. 1954. Rheumatoid arthritis; the need for individualized therapy. Mo. Med. 51:1001–2
    [Google Scholar]
  21. 21. 
    Koos EL. 1947. What society demands of the nurse. Am. J. Nurs. 47:306–7
    [Google Scholar]
  22. 22. 
    Dausset J. 1981. The major histocompatibility complex in man. Science 213:1469–74
    [Google Scholar]
  23. 23. 
    Langreth R, Waldholz M. 1999. New era of personalized medicine: targeting drugs for each unique genetic profile. Oncologist 4:426–27
    [Google Scholar]
  24. 24. 
    Trusheim MR, Berndt ER, Douglas FL 2007. Stratified medicine: strategic and economic implications of combining drugs and clinical biomarkers. Nat. Rev. Drug Discov. 6:287–93
    [Google Scholar]
  25. 25. 
    Wasi P. 1997. Human genomics: implications for health. Southeast Asian J. Trop. Med. Publ. Health 28:Suppl. 219–24
    [Google Scholar]
  26. 26. 
    NRC (Natl. Res. Counc.). 2011. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease Washington, DC: National Academies Press
    [Google Scholar]
  27. 27. 
    Turnbull C, Scott RH, Thomas E, Jones L, Murugaesu N et al. 2018. The 100,000 Genomes Project: bringing whole genome sequencing to the NHS. BMJ 361:k1687
    [Google Scholar]
  28. 28. 
    Klepstad P, Fladvad T, Skorpen F, Bjordal K, Caraceni A et al. 2011. Influence from genetic variability on opioid use for cancer pain: a European genetic association study of 2294 cancer pain patients. Pain 152:1139–45
    [Google Scholar]
  29. 29. 
    Gaikwad T, Ghosh K, Avery P, Kamali F, Shetty S 2018. Warfarin dose model for the prediction of stable maintenance dose in Indian patients. Clin. Appl. Thromb. Hemost. 24:353–59
    [Google Scholar]
  30. 30. 
    Sousa-Pinto B, Pinto-Ramos J, Correia C, Gonçalves-Costa G, Gomes L et al. 2015. Pharmacogenetics of abacavir hypersensitivity: a systematic review and meta-analysis of the association with HLA-B*57:01. J. Allergy Clin. Immunol. 136:1092–94.e3
    [Google Scholar]
  31. 31. 
    Wald NJ, Law MR. 2003. A strategy to reduce cardiovascular disease by more than 80%. BMJ 326:1419
    [Google Scholar]
  32. 32. 
    Glasziou P, Irwig L, Aronson JK 2008. Evidence-Based Medical Monitoring: From Principles to Practice Oxford, UK: Blackwell Publishing/BMJ Books
    [Google Scholar]
  33. 33. 
    Aronson JK, Ferner RE. 2017. Biomarkers—a general review. Curr. Protoc. Pharmacol. 76:9.23.1–9.23.17
    [Google Scholar]
  34. 34. 
    FDA (US Food Drug Adm.). 2019. CFR - Code of Federal Regulations Title 21. US Food and Drug Administration https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=201.56
    [Google Scholar]
  35. 35. 
    Zimdahl Kahlin A, Helander S, Skoglund K, Söderkvist P, Mårtensson L-G, Appell ML 2019. Comprehensive study of thiopurine methyltransferase genotype, phenotype, and genotype-phenotype discrepancies in Sweden. Biochem. Pharmacol. 164:263–72
    [Google Scholar]
  36. 36. 
    Aarons L. 1991. Population pharmacokinetics: theory and practice. Br. J. Clin. Pharmacol. 32:669–70
    [Google Scholar]
  37. 37. 
    Holford NH, Buclin T. 2012. Safe and effective variability—a criterion for dose individualization. Ther. Drug Monit. 34:565–68
    [Google Scholar]
  38. 38. 
    Wicha SG, Kees MG, Solms A, Minichmayr IK, Kratzer A, Kloft C 2015. TDMx: a novel web-based open-access support tool for optimising antimicrobial dosing regimens in clinical routine. Int. J. Antimicrob. Agents 45:442–44
    [Google Scholar]
  39. 39. 
    Ince I, de Wildt SN, Tibboel D, Danhof M, Knibbe CA 2009. Tailor-made drug treatment for children: creation of an infrastructure for data-sharing and population PK-PD modeling. Drug Discov. Today 14:316–20
    [Google Scholar]
  40. 40. 
    Sheiner LB, Beal S, Rosenberg B, Marathe VV 1979. Forecasting individual pharmacokinetics. Clin. Pharmacol. Ther. 26:294–305
    [Google Scholar]
  41. 41. 
    Duffull SB, Begg EJ, Robinson BA, Deely JJ 1997. A sequential Bayesian algorithm for dose individualisation of carboplatin. Cancer Chemother. Pharmacol. 39:317–26
    [Google Scholar]
  42. 42. 
    Sheiner LB, Rosenberg B, Melmon KL 1972. Modelling of individual pharmacokinetics for computer-aided drug dosage. Comput. Biomed. Res. 5:411–59
    [Google Scholar]
  43. 43. 
    Thomson AH, Whiting B. 1992. Bayesian parameter estimation and population pharmacokinetics. Clin. Pharmacokinet. 22:447–67
    [Google Scholar]
  44. 44. 
    Staatz CE, Tett SE. 2011. Maximum a posteriori Bayesian estimation of mycophenolic acid area under the concentration-time curve: Is this clinically useful for dosage prediction yet. ? Clin. Pharmacokinet. 50:759–72
    [Google Scholar]
  45. 45. 
    Turner RB, Kojiro K, Shephard EA, Won R, Chang E et al. 2018. Review and validation of Bayesian dose-optimizing software and equations for calculation of the vancomycin area under the curve in critically ill patients. Pharmacotherapy 38:1174–83
    [Google Scholar]
  46. 46. 
    Kirkpatrick CM, Duffull SB, Begg EJ 1999. Pharmacokinetics of gentamicin in 957 patients with varying renal function dosed once daily. Br. J. Clin. Pharmacol. 47:637–43
    [Google Scholar]
  47. 47. 
    Neely MN, Kato L, Youn G, Kraler L, Bayard D et al. 2018. Prospective trial on the use of trough concentration versus area under the curve to determine therapeutic vancomycin dosing. Antimicrob. Agents Chemother. 62:e02042–17
    [Google Scholar]
  48. 48. 
    Neely M, Margol A, Fu X, van Guilder M, Bayard D et al. 2015. Achieving target voriconazole concentrations more accurately in children and adolescents. Antimicrob. Agents Chemother. 59:3090–97
    [Google Scholar]
  49. 49. 
    Woillard JB, de Winter BC, Kamar N, Marquet P, Rostaing L, Rousseau A 2011. Population pharmacokinetic model and Bayesian estimator for two tacrolimus formulations—twice daily Prograf and once daily Advagraf. Br. J. Clin. Pharmacol. 71:391–402
    [Google Scholar]
  50. 50. 
    Zhao W, Elie V, Baudouin V, Bensman A, Andre JL et al. 2010. Population pharmacokinetics and Bayesian estimator of mycophenolic acid in children with idiopathic nephrotic syndrome. Br. J. Clin. Pharmacol. 69:358–66
    [Google Scholar]
  51. 51. 
    Joerger M, von Pawel J, Kraff S, Fischer JR, Eberhardt W et al. 2016. Open-label, randomized study of individualized, pharmacokinetically (PK)-guided dosing of paclitaxel combined with carboplatin or cisplatin in patients with advanced non-small-cell lung cancer (NSCLC). Ann. Oncol. 27:1895–902
    [Google Scholar]
  52. 52. 
    Darwich AS, Ogungbenro K, Hatley OJ, Rostami-Hodjegan A 2017. Role of pharmacokinetic modeling and simulation in precision dosing of anticancer drugs. Transl. Cancer Res. 6:S1512–29Reviews the practices and potential of MIPD in oncology.
    [Google Scholar]
  53. 53. 
    Wright DF, Duffull SB. 2011. Development of a Bayesian forecasting method for warfarin dose individualization. Pharm. Res. 28:1100–11
    [Google Scholar]
  54. 54. 
    Wright DF, Duffull SB. 2013. A Bayesian dose-individualization method for warfarin. Clin. Pharmacokinet. 52:59–68
    [Google Scholar]
  55. 55. 
    Standing JF. 2017. Understanding and applying pharmacometric modelling and simulation in clinical practice and research. Br. J. Clin. Pharmacol. 83:247–54
    [Google Scholar]
  56. 56. 
    Keizer RJ, Ter Heine R, Frymoyer A, Lesko LJ, Mangat R, Goswami S 2018. Model-informed precision dosing at the bedside: scientific challenges and opportunities. CPT Pharmacomet. Syst. Pharmacol. 7:785–87
    [Google Scholar]
  57. 57. 
    FDA (US Food Drug Adm.). 2020. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD) Discus. Pap., FDA Washington, DC: https://www.fda.gov/media/122535/download
    [Google Scholar]
  58. 58. 
    Castellan AC, Tod M, Gueyffier F, Audars M, Cambriels F et al. 2013. Quantitative prediction of the impact of drug interactions and genetic polymorphisms on cytochrome P450 2C9 substrate exposure. Clin. Pharmacokinet. 52:199–209
    [Google Scholar]
  59. 59. 
    Lamba J, Hebert JM, Schuetz EG, Klein TE, Altman RB 2012. PharmGKB summary: very important pharmacogene information for CYP3A5. Pharmacogenet. Genom. 22:555–58
    [Google Scholar]
  60. 60. 
    Mariappan TT, Shen H, Marathe P 2017. Endogenous biomarkers to assess drug-drug interactions by drug transporters and enzymes. Curr. Drug Metab. 18:757–68
    [Google Scholar]
  61. 61. 
    Rodrigues D, Rowland A. 2019. From endogenous compounds as biomarkers to plasma-derived nanovesicles as liquid biopsy; has the Golden Age of translational pharmacokinetics-absorption, distribution, metabolism, excretion-drug-drug interaction science finally arrived. ? Clin. Pharmacol. Ther. 105:1407–20
    [Google Scholar]
  62. 62. 
    Diczfalusy U, Nylen H, Elander P, Bertilsson L 2011. 4β-Hydroxycholesterol, an endogenous marker of CYP3A4/5 activity in humans. Br. J. Clin. Pharmacol. 71:183–89
    [Google Scholar]
  63. 63. 
    Li Y, Elashoff D, Oh M, Sinha U, St John MA et al. 2006. Serum circulating human mRNA profiling and its utility for oral cancer detection. J. Clin. Oncol. 24:1754–60
    [Google Scholar]
  64. 64. 
    Patino WD, Mian OY, Kang JG, Matoba S, Bartlett LD et al. 2005. Circulating transcriptome reveals markers of atherosclerosis. PNAS 102:3423–28
    [Google Scholar]
  65. 65. 
    Kumar S, Sinha N, Gerth KA, Rahman MA, Yallapu MM, Midde NM 2017. Specific packaging and circulation of cytochromes P450, especially 2E1 isozyme, in human plasma exosomes and their implications in cellular communications. Biochem. Biophys. Res. Commun. 491:675–80
    [Google Scholar]
  66. 66. 
    Conde-Vancells J, Rodriguez-Suarez E, Embade N, Gil D, Matthiesen R et al. 2008. Characterization and comprehensive proteome profiling of exosomes secreted by hepatocytes. J. Proteome Res. 7:5157–66
    [Google Scholar]
  67. 67. 
    Gotanda K, Hirota T, Saito J, Fukae M, Egashira Y et al. 2016. Circulating intestine-derived exosomal miR-328 in plasma, a possible biomarker for estimating BCRP function in the human intestines. Sci. Rep. 6:32299
    [Google Scholar]
  68. 68. 
    Console L, Scalise M, Tonazzi A, Giangregorio N, Indiveri C 2018. Characterization of exosomal SLC22A5 (OCTN2) carnitine transporter. Sci. Rep. 8:3758
    [Google Scholar]
  69. 69. 
    Wang X, Xu C, Hua Y, Sun L, Cheng K et al. 2016. Exosomes play an important role in the process of psoralen reverse multidrug resistance of breast cancer. J. Exp. Clin. Cancer Res. 35:186
    [Google Scholar]
  70. 70. 
    Polasek TM, Rostami-Hodjegan A. 2020. Virtual twins: understanding the data required for model-informed precision dosing. Clin. Pharmacol. Ther. 107:742–45
    [Google Scholar]
  71. 71. 
    Davies EC, Green CF, Mottram DR, Rowe PH, Pirmohamed M 2010. Emergency re-admissions to hospital due to adverse drug reactions within 1 year of the index admission. Br. J. Clin. Pharmacol. 70:749–55
    [Google Scholar]
  72. 72. 
    Repp KL, Hayes C 3rd, Woods TM, Allen KB, Kennedy K, Borkon MA 2012. Drug-related problems and hospital admissions in cardiac transplant recipients. Ann. Pharmacother. 46:1299–307
    [Google Scholar]
  73. 73. 
    Howard RL, Avery AJ, Slavenburg S, Royal S, Pipe G et al. 2007. Which drugs cause preventable admissions to hospital? A systematic review. Br. J. Clin. Pharmacol. 63:136–47
    [Google Scholar]
  74. 74. 
    Gasche Y, Daali Y, Fathi M, Chiappe A, Cottini S et al. 2004. Codeine intoxication associated with ultrarapid CYP2D6 metabolism. N. Engl. J. Med. 351:2827–31
    [Google Scholar]
  75. 75. 
    Eikelboom JW, Quinlan DJ, Hirsh J, Connolly SJ, Weitz JI 2017. Laboratory monitoring of non-vitamin K antagonist oral anticoagulant use in patients with atrial fibrillation: a review. JAMA Cardiol 2:566–74
    [Google Scholar]
  76. 76. 
    Reny J-L, Fontana P, Hochholzer W, Neumann FJ, Ten Berg J et al. 2016. Vascular risk levels affect the predictive value of platelet reactivity for the occurrence of MACE in patients on clopidogrel. Systematic review and meta-analysis of individual patient data. Thromb. Haemost. 115:844–55
    [Google Scholar]
  77. 77. 
    Storelli F, Samer C, Reny J-L, Desmeules J, Daali Y 2018. Complex drug–drug–gene–disease interactions involving cytochromes P450: systematic review of published case reports and clinical perspectives. Clin. Pharmacokinet. 57:1267–93
    [Google Scholar]
  78. 78. 
    Clarkson J, Dean J, Ward J, Komashie A, Bashford T 2018. A systems approach to healthcare: from thinking to practice. Future Healthc. J. 5:151–55
    [Google Scholar]
  79. 79. 
    Essen A, Lindblad S. 2013. Innovation as emergence in healthcare: unpacking change from within. Soc. Sci. Med. 93:203–11
    [Google Scholar]
  80. 80. 
    Zhang C, Zhang C, Grandits T, Härenstam KP, Hauge JB, Meijer S 2018. A systematic literature review of simulation models for non-technical skill training in healthcare logistics. Adv. Simul. 3:15
    [Google Scholar]
  81. 81. 
    Vinks AA, Peck RW, Neely M, Mould DR 2020. Development and implementation of electronic health record–integrated model-informed clinical decision support tools for the precision dosing of drugs. Clin. Pharmacol. Ther. 107:129–35
    [Google Scholar]
  82. 82. 
    Neely M. 2017. Scalpels not hammers: the way forward for precision drug prescription. Clin. Pharmacol. Ther. 101:368–72
    [Google Scholar]
  83. 83. 
    Murphy EV. 2014. Clinical decision support: effectiveness in improving quality processes and clinical outcomes and factors that may influence success. Yale J. Biol. Med. 87:187–97
    [Google Scholar]
  84. 84. 
    Roshanov PS, Fernandes N, Wilczynski JM, Hemens BJ, You JJ et al. 2013. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ 346:f657
    [Google Scholar]
  85. 85. 
    Ruano G, Robinson S, Holford T, Mehendru R, Baker S et al. 2019. Results of the CYP-GUIDES randomized controlled trial: total cohort and primary endpoints. Contemp. Clin. Trials 89:105910
    [Google Scholar]
  86. 86. 
    Coleman JJ, van der Sijs H, Haefeli WE, Slight SP, McDowell SE et al. 2013. On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop. BMC Med. Inf. Decis. Mak. 13:111
    [Google Scholar]
  87. 87. 
    Orszag PR, Ellis P. 2007. The challenge of rising health care costs—a view from the Congressional Budget Office. N. Engl. J. Med. 357:1793–95
    [Google Scholar]
  88. 88. 
    Nicholson S, Pauly MV, Wu AY, Murray JF, Teutsch SM, Berger ML 2008. Getting real performance out of pay-for-performance. Milbank Q 86:435–57
    [Google Scholar]
  89. 89. 
    Lalonde RL, Kowalski KG, Hutmacher MM, Ewy W, Nichols DJ et al. 2007. Model-based drug development. Clin. Pharmacol. Ther. 82:21–32
    [Google Scholar]
  90. 90. 
    FDA (US Food Drug Adm.). 2016. Guidance for Industry: Clinical Pharmacology Section of Labeling for Human Prescription Drug and Biological ProductsContent and Format Washington, DC: FDA https://www.fda.gov/media/74346/download
    [Google Scholar]
  91. 91. 
    Jadhav PR, Cook J, Sinha V, Zhao P, Rostami-Hodjegan A et al. 2015. A proposal for scientific framework enabling specific population drug dosing recommendations. J. Clin. Pharmacol. 55:1073–78
    [Google Scholar]
  92. 92. 
    Liu T, Ghafoori P, Gobburu JV 2017. Allometry is a reasonable choice in pediatric drug development. J. Clin. Pharmacol. 57:469–75
    [Google Scholar]
  93. 93. 
    SGG-TS (Strateg. Gov. Groups–Transl. Saf.). 2017. Newsletter - January 2017 https://www.sgg-ts.org/
    [Google Scholar]
  94. 94. 
    FDA (US Food Drug Adm.). 2019. Draft Guidance for Industry: Enhancing the Diversity of Clinical Trial PopulationsEligibility Criteria, Enrollment Practices, and Trial Designs Washington, DC: FDA https://www.fda.gov/media/127712/download
    [Google Scholar]
  95. 95. 
    Lewis PJ, Dornan T, Taylor D, Tully MP, Wass V, Ashcroft DM 2009. Prevalence, incidence and nature of prescribing errors in hospital inpatients: a systematic review. Drug Saf 32:379–89
    [Google Scholar]
  96. 96. 
    Roughead EE, Semple SJ, Rosenfeld E 2016. The extent of medication errors and adverse drug reactions throughout the patient journey in acute care in Australia. Int. J. Evid. Based Healthc. 14:113–22
    [Google Scholar]
  97. 97. 
    Assiri GA, Shebl NA, Mahmoud MA, Aloudah N, Grant E et al. 2018. What is the epidemiology of medication errors, error-related adverse events and risk factors for errors in adults managed in community care contexts? A systematic review of the international literature. BMJ Open 8:e019101
    [Google Scholar]
  98. 98. 
    Schachter M. 2009. The epidemiology of medication errors: how many, how serious. ? Br. J. Clin. Pharmacol. 67:621–23
    [Google Scholar]
  99. 99. 
    Walley T, Haycox A. 1997. Pharmacoeconomics: basic concepts and terminology. Br. J. Clin. Pharmacol. 43:343–48
    [Google Scholar]
  100. 100. 
    Fischer KE, Heisser T, Stargardt T 2016. Health benefit assessment of pharmaceuticals: an international comparison of decisions from Germany, England, Scotland and Australia. Health Policy 120:1115–22
    [Google Scholar]
  101. 101. 
    Beaulieu-Jones BK, Finlayson SG, Yuan W, Altman RB, Kohane IS et al. 2020. Examining the use of real-world evidence in the regulatory process. Clin. Pharmacol. Ther. 107:843–52
    [Google Scholar]
  102. 102. 
    Gavan SP, Thompson AJ, Payne K 2018. The economic case for precision medicine. Expert Rev. Precis. Med. Drug Dev. 3:1–9
    [Google Scholar]
  103. 103. 
    Hoebert JM, van Dijk L, Mantel-Teeuwisse AK, Leufkens HG, Laing RO 2013. National medicines policies—a review of the evolution and development processes. J. Pharm. Policy Pract. 6:5
    [Google Scholar]
  104. 104. 
    Honig P, Lalonde R. 2010. The economics of drug development: a grim reality and a role for clinical pharmacology. Clin. Pharmacol. Ther. 87:247–51
    [Google Scholar]
  105. 105. 
    Giacomini KM, Yee SW, Ratain MJ, Weinshilboum RM, Kamatani N, Nakamura Y 2012. Pharmacogenomics and patient care: One size does not fit all. Sci. Transl. Med. 4:153ps18
    [Google Scholar]
  106. 106. 
    Marcotte LM, Schuttner L, Liao JM 2020. Measuring low-value care: learning from the US experience measuring quality. BMJ Qual. Saf. 29:154–56
    [Google Scholar]
  107. 107. 
    Elwyn G, Frosch D, Thomson R, Joseph-Williams N, Lloyd A et al. 2012. Shared decision making: a model for clinical practice. J. Gen. Intern. Med. 27:1361–67
    [Google Scholar]
  108. 108. 
    Hilmer SN, McLachlan AJ, Le Couteur DG 2007. Clinical pharmacology in the geriatric patient. Fundam. Clin. Pharmacol. 21:217–30
    [Google Scholar]
  109. 109. 
    McLachlan AJ, Hilmer SN, Le Couteur DG 2009. Variability in response to medicines in older people: phenotypic and genotypic factors. Clin. Pharmacol. Ther. 85:431–33
    [Google Scholar]
  110. 110. 
    Hilmer SN, Gnjidic D, Abernethy DR 2012. Pharmacoepidemiology in the postmarketing assessment of the safety and efficacy of drugs in older adults. J. Gerontol. A Biol. Sci. Med. Sci. 67:181–88
    [Google Scholar]
  111. 111. 
    FDA (US Food Drug Adm.). 1997. Rituxan. 1.14.2.3. Final labeling text. Washington, DC: FDA https://www.accessdata.fda.gov/drugsatfda_docs/label/2010/103705s5311lbl.pdf
    [Google Scholar]
  112. 112. 
    Cavallo J. 2018. Has the promise of precision medicine been oversold. ? The ASCO Post Oct. 25. https://ascopost.com/issues/october-25-2018/has-the-promise-of-precision-medicine-been-oversold/
    [Google Scholar]
  113. 113. 
    FDA (US Food Drug Adm.). 2020. Prescription drug user fee amendments. US Food and Drug Administration https://www.fda.gov/industry/fda-user-fee-programs/prescription-drug-user-fee-amendments
    [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-033020-113257
Loading
/content/journals/10.1146/annurev-pharmtox-033020-113257
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error