The AAPS Journal

, 21:17 | Cite as

What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing

  • Thomas M. PolasekEmail author
  • Amin Rostami-Hodjegan
  • Dong-Seok Yim
  • Masoud Jamei
  • Howard Lee
  • Holly Kimko
  • Jae Kyoung Kim
  • Phuong Thi Thu Nguyen
  • Adam S. Darwich
  • Jae-Gook Shin
Meeting Report


Model-informed precision dosing (MIPD) is modeling and simulation in healthcare to predict the drug dose for a given patient based on their individual characteristics that is most likely to improve efficacy and/or lower toxicity in comparison to traditional dosing. This paper describes the background and status of MIPD and the activities at the 1st Asian Symposium of Precision Dosing. The theme of the meeting was the question, “What does it take to make MIPD common practice?” Formal presentations highlighted the distinction between genetic and non-genetic sources of variability in drug exposure and response, the use of modeling and simulation as decision support tools, and the facilitators to MIPD implementation. A panel discussion addressed the types of models used for MIPD, how the pharmaceutical industry views MIPD, ways to upscale MIPD beyond academic hospital centers, and the essential role of healthcare professional education as a way to progress. The meeting concluded with an ongoing commitment to use MIPD to improve patient care.



Finance from Inje University and Certara was provided to support the symposium. The authors are grateful to Ms. (Emma) Si Yeon Nam from the Pharmacogenomics Research Center at Inje University for her efforts in organizing the symposium, and to all the attendees who made the day very informative and enjoyable.

Compliance with Ethical Standards

Conflict of Interest

Thomas M. Polasek, Amin Rostami-Hodjegan, and Masoud Jamei are employees of Certara. Certara makes modeling and simulation software, including one type of PBPK platform (Simcyp®), which is used by the pharmaceutical industry for drug development. All other authors declare that they have no conflicts of interest.


This article reflects the views of the authors and should not be construed to represent their organizations’ views or policies.


  1. 1.
    World Health Organization. Medication Without Harm - Global Patient Safety Challenge on Medication Safety. Geneva, Switzerland; 2017.Google Scholar
  2. 2.
    Peck RW. The right dose for every patient: a key step for precision medicine. Nat Rev Drug Discov. 2016;15(3):145–6.CrossRefGoogle Scholar
  3. 3.
    Polasek TM, Shakib S, Rostami-Hodjegan A. Precision dosing in clinical medicine: present and future. Expert Rev Clin Pharmacol. 2018;11(8):743–6.CrossRefGoogle Scholar
  4. 4.
    Snyder BD, Polasek TM, Doogue MP. Drug interactions: principles and practice. Aust Prescr. 2012;35(3):85–8.CrossRefGoogle Scholar
  5. 5.
    Rowland A, van Dyk M, Hopkins AM, Mounzer M, Polasek TM, Rostami-Hodjegan A, et al. Physiologically-based pharmacokinetic modelling to identify physiological and molecular characteristics driving variability in drug exposure. Clin Pharmacol Ther. 2018;104(6):1219–28.CrossRefGoogle Scholar
  6. 6.
    Martin MA, Klein TE, Dong BJ, Pirmohamed M, Haas DW, Kroetz DL, et al. Clinical pharmacogenetics implementation consortium guidelines for HLA-B genotype and abacavir dosing. Clin Pharmacol Ther. 2012;91(4):734–8.CrossRefGoogle Scholar
  7. 7.
    Johnson JA, Caudle KE, Gong L, Whirl-Carrillo M, Stein CM, Scott SA, et al. Clinical pharmacogenetics implementation consortium (CPIC) guideline for pharmacogenetics-guided warfarin dosing: 2017 update. Clin Pharmacol Ther. 2017;102(3):397–404.Google Scholar
  8. 8.
    Hicks JK, Bishop JR, Sangkuhl K, Muller DJ, Ji Y, Leckband SG, et al. Clinical pharmacogenetics implementation consortium (CPIC) guideline for CYP2D6 and CYP2C19 genotypes and dosing of selective serotonin reuptake inhibitors. Clin Pharmacol Ther. 2015;98(2):127–34.CrossRefGoogle Scholar
  9. 9.
    van den Anker J, Reed MD, Allegaert K, Kearns GL. Developmental changes in pharmacokinetics and pharmacodynamics. J Clin Pharmacol. 2018;58(Suppl 10):S10–25.CrossRefGoogle Scholar
  10. 10.
    Salem F, Abduljalil K, Kamiyama Y, Rostami-Hodjegan A. Considering age variation when coining drugs as high versus low hepatic extraction ratio. Drug Metab Dispos. 2016;44(7):1099–102.CrossRefGoogle Scholar
  11. 11.
    Charles B. Population pharmacokinetics: an overview. Aust Prescr. 2014;37(6):210–3.CrossRefGoogle Scholar
  12. 12.
    Rowland M, Lesko LJ, Rostami-Hodjegan A. Physiologically based pharmacokinetics is impacting drug development and regulatory decision making. CPT Pharmacometrics Syst Pharmacol. 2015;4(6):313–5.CrossRefGoogle Scholar
  13. 13.
    Snowden TJ, van der Graaf PH, Tindall MJ. Model reduction in mathematical pharmacology : integration, reduction and linking of PBPK and systems biology models. J Pharmacokinet Pharmacodyn. 2018;45(4):537–55.CrossRefGoogle Scholar
  14. 14.
    Geerts H, Gieschke R, Peck R. Use of quantitative clinical pharmacology to improve early clinical development success in neurodegenerative diseases. Expert Rev Clin Pharmacol. 2018;11(8):789–95.CrossRefGoogle Scholar
  15. 15.
    Allerheiligen SR. Impact of modeling and simulation: myth or fact? Clin Pharmacol Ther. 2014;96(4):413–5.CrossRefGoogle Scholar
  16. 16.
    Milligan PA, Brown MJ, Marchant B, Martin SW, van der Graaf PH, Benson N, et al. Model-based drug development: a rational approach to efficiently accelerate drug development. Clin Pharmacol Ther. 2013;93(6):502–14.Google Scholar
  17. 17.
    Gottlieb S. How FDA plans to help consumers capitalize on advances in science 2017 [Available from:]. Accessed 16 Aug 2018
  18. 18.
    Jamei M. Recent advances in development and application of physiologically-based pharmacokinetic (PBPK) models: a transition from academic curiosity to regulatory acceptance. Curr Pharmacol Rep. 2016;2:161–9.CrossRefGoogle Scholar
  19. 19.
    Polasek TM, Rayner CR, Peck RW, Rowland A, Kimko H, Rostami-Hodjegan A. Toward dynamic prescribing information: codevelopment of companion model-informed precision dosing tools in drug development. Clin Pharmacol Drug Dev. 2018.
  20. 20.
    Musuamba FT, Manolis E, Holford N, Cheung S, Friberg LE, Ogungbenro K, et al. Advanced methods for dose and regimen finding during drug development: summary of the EMA/EFPIA workshop on dose finding (London 4-5 December 2014). CPT Pharmacometrics Syst Pharmacol. 2017;6(7):418–29.Google Scholar
  21. 21.
    Yellepeddi V, Rower J, Liu X, Kumar S, Rashid J, Sherwin CMT. State-of-the-art review on physiologically based pharmacokinetic modeling in pediatric drug development. Clin Pharmacokinet. 2018.
  22. 22.
    Darwich AS, Ogungbenro K, Vinks AA, Powell JR, Reny JL, Marsousi N, et al. 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. 2017;101(5):646–56.Google Scholar
  23. 23.
    Abdel-Rahman SM, Casey KL, Garg U, Dalal J. Intravenous busulfan dose individualization - impact of modeling approach on dose recommendation. Pediatr Transplant. 2016;20(3):443–8.CrossRefGoogle Scholar
  24. 24.
    Størset E, Asberg A, Skauby M, Neely M, Bergan S, Bremer S, et al. Improved tacrolimus target concentration achievement using computerized dosing in renal transplant recipients-a prospective, randomized study. Transplantation. 2015;99(10):2158–66.CrossRefGoogle Scholar
  25. 25.
    Tängdén T, Ramos Martin V, Felton TW, Nielsen EI, Marchand S, Bruggemann RJ, et al. The role of infection models and PK/PD modelling for optimising care of critically ill patients with severe infections. Intensive Care Med. 2017;43(7):1021–32.CrossRefGoogle Scholar
  26. 26.
    Roberts JA, Abdul-Aziz MH, Lipman J, Mouton JW, Vinks AA, Felton TW, et al. Individualised antibiotic dosing for patients who are critically ill: challenges and potential solutions. Lancet Infect Dis. 2014;14(6):498–509.Google Scholar
  27. 27.
    Duong JK, Kroonen M, Kumar SS, Heerspink HL, Kirkpatrick CM, Graham GG, et al. A dosing algorithm for metformin based on the relationships between exposure and renal clearance of metformin in patients with varying degrees of kidney function. Eur J Clin Pharmacol. 2017;73(8):981–90.CrossRefGoogle Scholar
  28. 28.
    Marsousi N, Desmeules JA, Rudaz S, Daali Y. Usefulness of PBPK modeling in incorporation of clinical conditions in personalized medicine. J Pharm Sci. 2017;106(9):2380–91.CrossRefGoogle Scholar
  29. 29.
    Hennig S, Holthouse F, Staatz CE. Comparing dosage adjustment methods for once-daily tobramycin in paediatric and adolescent patients with cystic fibrosis. Clin Pharmacokinet. 2015;54(4):409–21.CrossRefGoogle Scholar
  30. 30.
    Tillotson G. PK-PD compass, a novel computerized decision support system. Lancet Infect Dis. 2017;17(9):908.CrossRefGoogle Scholar
  31. 31.
    Frymoyer A, Stockmann C, Hersh AL, Goswami S, Keizer RJ. Individualized empiric vancomycin dosing in neonates using a model-based approach. J Pediatric Infect Dis Soc. 2017.
  32. 32.
    Janssen EJ, Valitalo PA, Allegaert K, de Cock RF, Simons SH, Sherwin CM, et al. Towards rational dosing algorithms for vancomycin in neonates and infants based on population pharmacokinetic modeling. Antimicrob Agents Chemother. 2016;60(2):1013–21.CrossRefGoogle Scholar
  33. 33.
    Smits A, De Cock RF, Allegaert K, Vanhaesebrouck S, Danhof M, Knibbe CA. Prospective evaluation of a model-based dosing regimen for amikacin in preterm and term neonates in clinical practice. Antimicrob Agents Chemother. 2015;59(10):6344–51.CrossRefGoogle Scholar
  34. 34.
    Ogungbenro K, Patel A, Duncombe R, Nuttall R, Clark J, Lorigan P. Dose rationalization of pembrolizumab and nivolumab using pharmacokinetic modeling and simulation and cost analysis. Clin Pharmacol Ther. 2018;103(4):582–90.CrossRefGoogle Scholar
  35. 35.
    Patel N, Wisniowska B, Jamei M, Polak S. Real patient and its virtual twin: application of quantitative systems toxicology modelling in the cardiac safety assessment of citalopram. AAPS J. 2017;20(1):6–15.CrossRefGoogle Scholar
  36. 36.
    Polasek TM, Tucker GT, Sorich MJ, Wiese MD, Mohan T, Rostami-Hodjegan A, et al. Prediction of olanzapine exposure in individual patients using physiologically based pharmacokinetic modelling and simulation. Br J Clin Pharmacol. 2018;84(3):462–76.Google Scholar
  37. 37.
    Rowland A, Ruanglertboon W, van Dyk M, Wijayakumara D, Wood LS, Meech R, et al. Plasma extracellular nanovesicle (exosome)-derived biomarkers for drug metabolism pathways: a novel approach to characterize variability in drug exposure. Br J Clin Pharmacol. 2018.
  38. 38.
    Rostami-Hodjegan A. Reverse translation in PBPK and QSP: going backwards in order to go forward with confidence. Clin Pharmacol Ther. 2018;103(2):224–32.CrossRefGoogle Scholar
  39. 39.
    Jorga K, Chavanne C, Frey N, Lave T, Lukacova V, Parrott N, et al. Bottom-up meets top-down: complementary physiologically based pharmacokinetic and population pharmacokinetic modeling for regulatory approval of a dosing algorithm of valganciclovir in very young children. Clin Pharmacol Ther. 2016;100(6):761–9.Google Scholar
  40. 40.
    Food and Drug Administration. Physiologically based pharmacokinetic analyses - format and content. Guidance for Industry. Rockville, USA; 2016.Google Scholar
  41. 41.
    European Medicines Agency. Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. London, UK; 2016.Google Scholar
  42. 42.
    Zhou W, Johnson TN, Bui KH, Cheung SYA, Li J, Xu H, et al. Predictive performance of physiologically based pharmacokinetic (PBPK) modeling of drugs extensively metabolized by major cytochrome P450s in children. Clin Pharmacol Ther. 2018;104(1):188–200.Google Scholar
  43. 43.
    Yee KL, Li M, Cabalu T, Sahasrabudhe V, Lin J, Zhao P, et al. Evaluation of model-based prediction of pharmacokinetics in the renal impairment population. J Clin Pharmacol. 2018;58(3):364–76.Google Scholar
  44. 44.
    Ke AB, Greupink R, Abduljalil K. Drug dosing in pregnant women: challenges and opportunities in using physiologically based pharmacokinetic modeling and simulations. CPT Pharmacometrics Syst Pharmacol. 2018;7(2):103–10.CrossRefGoogle Scholar
  45. 45.
    Relling MV, Klein TE. CPIC: clinical pharmacogenetics implementation consortium of the pharmacogenomics research network. Clin Pharmacol Ther. 2011;89(3):464–7.CrossRefGoogle Scholar
  46. 46.
    Swen JJ, Nijenhuis M, de Boer A, Grandia L, Maitland-van der Zee AH, Mulder H, et al. Pharmacogenetics: from bench to byte-an update of guidelines. Clin Pharmacol Ther. 2011;89(5):662–73.CrossRefGoogle Scholar
  47. 47.
    Klein ME, Parvez MM, Shin JG. Clinical implementation of pharmacogenomics for personalized precision medicine: barriers and solutions. J Pharm Sci. 2017;106(9):2368–79.CrossRefGoogle Scholar
  48. 48.
    Peck RW. Precision medicine is not just genomics: the right dose for every patient. Annu Rev Pharmacol Toxicol. 2018;58:105–22.CrossRefGoogle Scholar
  49. 49.
    Hauser AS, Chavali S, Masuho I, Jahn LJ, Martemyanov KA, Gloriam DE, et al. Pharmacogenomics of GPCR drug targets. Cell. 2017;172(1–2):41–54 e19.PubMedGoogle Scholar
  50. 50.
    Zhou M, Kim JK, Eng GW, Forger DB, Virshup DM. A period2 phosphoswitch regulates and temperature compensates circadian period. Mol Cell. 2015;60(1):77–88.CrossRefGoogle Scholar
  51. 51.
    Kim JK, Forger DB, Marconi M, Wood D, Doran A, Wager T, et al. Modeling and validating chronic pharmacological manipulation of circadian rhythms. CPT Pharmacometrics Syst Pharmacol. 2013;2:e57.Google Scholar
  52. 52.
    Keijzer H, Snitselaar MA, Smits MG, Spruyt K, Zee PC, Ehrhart F, et al. Precision medicine in circadian rhythm sleep-wake disorders: current state and future perspectives. Pers Med. 2017;14(2):171–82.Google Scholar
  53. 53.
    Mould DR, D'Haens G, Upton RN. Clinical decision support tools: the evolution of a revolution. Clin Pharmacol Ther. 2016;99(4):405–18.CrossRefGoogle Scholar
  54. 54.
    Abdel-Rahman SM, Breitkreutz ML, Bi C, Matzuka BJ, Dalal J, Casey KL, et al. Design and testing of an EHR-integrated, busulfan pharmacokinetic decision support tool for the point-of-care clinician. Front Pharmacol. 2016;7:65.CrossRefGoogle Scholar
  55. 55.
    Barrett JS. Paediatric models in motion: requirements for model-based decision support at the bedside. Br J Clin Pharmacol. 2015;79(1):85–96.CrossRefGoogle Scholar
  56. 56.
    Kimko H, Pinheiro J. Model-based clinical drug development in the past, present and future: a commentary. Br J Clin Pharmacol. 2015;79(1):108–16.CrossRefGoogle Scholar
  57. 57.
    Rostami-Hodjegan A. Physiologically based pharmacokinetics joined with in vitro-in vivo extrapolation of ADME: a marriage under the arch of systems pharmacology. Clin Pharmacol Ther. 2012;92(1):50–61.CrossRefGoogle Scholar
  58. 58.
    Korprasertthaworn P, Polasek TM, Sorich MJ, McLachlan AJ, Miners JO, Tucker GT, et al. In vitro characterization of the human liver microsomal kinetics and reaction phenotyping of olanzapine metabolism. Drug Metab Dispos. 2015;43(11):1806–14.CrossRefGoogle Scholar
  59. 59.
    Alsultan A, Peloquin CA. Therapeutic drug monitoring in the treatment of tuberculosis: an update. Drugs. 2014;74(8):839–54.CrossRefGoogle Scholar
  60. 60.
    Horita Y, Alsultan A, Kwara A, Antwi S, Enimil A, Ortsin A, et al. Evaluation of the adequacy of WHO revised dosages of the first-line antituberculosis drugs in children with tuberculosis using population pharmacokinetic modeling and simulations. Antimicrob Agents Chemother. 2018;62(9):e00008–18.CrossRefGoogle Scholar
  61. 61.
    Cordes H, Thiel C, Aschmann HE, Baier V, Blank LM, Kuepfer L. A physiologically based pharmacokinetic model of isoniazid and its application in individualizing tuberculosis chemotherapy. Antimicrob Agents Chemother. 2016;60(10):6134–45.CrossRefGoogle Scholar
  62. 62.
    Parvez MM, Kaisar N, Shin HJ, Jae Lee Y, Shin JG. Comprehensive substrate characterization of 22 antituberculosis drugs for multiple solute carrier (SLC) uptake transporters in vitro. Antimicrob Agents Chemother. 2018;62(9):e00512–8.CrossRefGoogle Scholar
  63. 63.
    Parvez MM, Kaisar N, Shin HJ, Jung JA, Shin JG. Inhibitory interaction potential of 22 antituberculosis drugs on organic anion and cation transporters of the SLC22A family. Antimicrob Agents Chemother. 2016;60(11):e00512–8.CrossRefGoogle Scholar
  64. 64.
    Nguyen PTT, Parvez MM, Kim MJ, Ho Lee J, Ahn S, Ghim JL, et al. Development of a physiologically based pharmacokinetic model of ethionamide in the pediatric population by integrating flavin-containing monooxygenase 3 maturational changes over time. J Clin Pharmacol. 2018;58(10):1347–60.Google Scholar
  65. 65.
    Neely M. Scalpels not hammers: the way forward for precision drug prescription. Clin Pharmacol Ther. 2017;101(3):368–72.CrossRefGoogle Scholar
  66. 66.
    Euteneuer JC, Kamatkar S, Fukuda T, Vinks AA, Akinbi HT. Suggestions for model-informed precision dosing to optimize neonatal drug therapy. J Clin Pharmacol. 2018.
  67. 67.
    Neely M, Bayard D, Desai A, Kovanda L, Edginton A. Pharmacometric modeling and simulation is essential to pediatric clinical pharmacology. J Clin Pharmacol. 2018;58(Suppl 10):S73–85.CrossRefGoogle Scholar
  68. 68.
    Wright DF, Stamp LK, Merriman TR, Barclay ML, Duffull SB, Holford NH. The population pharmacokinetics of allopurinol and oxypurinol in patients with gout. Eur J Clin Pharmacol. 2013;69(7):1411–21.CrossRefGoogle Scholar
  69. 69.
    Gonzalez D, Rao GG, Bailey SC, Brouwer KLR, Cao Y, Crona DJ, et al. Precision dosing: public health need, proposed framework, and anticipated impact. Clin Transl Sci. 2017;10(6):443–54.Google Scholar
  70. 70.
    Vinks AA, Emoto C, Fukuda T. Modeling and simulation in pediatric drug therapy: application of pharmacometrics to define the right dose for children. Clin Pharmacol Ther. 2015;98(3):298–308.CrossRefGoogle Scholar
  71. 71.
    Le J, Bradley JS. Optimizing antibiotic drug therapy in pediatrics: current state and future needs. J Clin Pharmacol. 2018;58(Suppl 10):S108–S22.CrossRefGoogle Scholar
  72. 72.
    Mandema JW, Gibbs M, Boyd RA, Wada DR, Pfister M. Model-based meta-analysis for comparative efficacy and safety: application in drug development and beyond. Clin Pharmacol Ther. 2011;90(6):766–9.CrossRefGoogle Scholar
  73. 73.
    Mould DR. Model-based meta-analysis: an important tool for making quantitative decisions during drug development. Clin Pharmacol Ther. 2012;92(3):283–6.CrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2019

Authors and Affiliations

  • Thomas M. Polasek
    • 1
    • 2
    Email author return OK on get
  • Amin Rostami-Hodjegan
    • 1
    • 3
  • Dong-Seok Yim
    • 4
  • Masoud Jamei
    • 1
  • Howard Lee
    • 5
    • 6
  • Holly Kimko
    • 7
  • Jae Kyoung Kim
    • 8
  • Phuong Thi Thu Nguyen
    • 9
    • 10
  • Adam S. Darwich
    • 3
  • Jae-Gook Shin
    • 9
  1. 1.CertaraPrincetonUSA
  2. 2.Centre for Medicines Use and SafetyMonash UniversityMelbourneAustralia
  3. 3.Centre for Applied Pharmacokinetic ResearchUniversity of ManchesterManchesterUK
  4. 4.Department of Pharmacology, College of MedicineThe Catholic University of KoreaSeoulSouth Korea
  5. 5.Department of Clinical Pharmacology and TherapeuticsSeoul National University College of Medicine and HospitalSeoulSouth Korea
  6. 6.Department of Transdisciplinary Studies, Graduate School of Convergence Science and TechnologySeoul National UniversitySeoulSouth Korea
  7. 7.Janssen Research and DevelopmentLower Gwynedd TownshipUSA
  8. 8.Korea Advanced Institute of Advanced TechnologyDaejeonSouth Korea
  9. 9.Department of Pharmacology and Clinical Pharmacology, Pharmacogenomics Research CenterInje University College of MedicineBusanRepublic of Korea
  10. 10.Faculty of PharmacyHaiphong University of Medicine and PharmacyHaiphongVietnam

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