What Does it Take to Make Model-Informed Precision Dosing Common Practice? Report from the 1st Asian Symposium on Precision Dosing
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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.World Health Organization. Medication Without Harm - Global Patient Safety Challenge on Medication Safety. Geneva, Switzerland; 2017.Google Scholar
- 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
- 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.Gottlieb S. How FDA plans to help consumers capitalize on advances in science 2017 [Available from: https://blogs.fda.gov/fdavoice/index.php/2017/07/how-fda-plans-to-help-consumers-capitalize-on-advances-in-science]. Accessed 16 Aug 2018
- 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. https://doi.org/10.1002/cpdd.638.
- 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.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. https://doi.org/10.1007/s40262-018-0677-y.
- 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
- 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.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
- 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. https://doi.org/10.1007/s40262-018-0677-y.
- 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.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. https://doi.org/10.1111/bcp.13793.
- 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.Food and Drug Administration. Physiologically based pharmacokinetic analyses - format and content. Guidance for Industry. Rockville, USA; 2016.Google Scholar
- 41.European Medicines Agency. Guideline on the qualification and reporting of physiologically based pharmacokinetic (PBPK) modelling and simulation. London, UK; 2016.Google Scholar
- 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.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
- 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.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
- 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
- 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
- 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. https://doi.org/10.1002/jcph.1315.
- 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