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The Impact of Model-Misspecification on Model Based Personalised Dosing

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Abstract

Model Based Personalised Dosing (MBPD) requires a population pharmacokinetic (PK) or pharmacodynamic model to determine the optimal dose of medication for an individual. Often several models are published, and the decision of which model is implemented in MBPD may have a large impact on its clinical utility. As quoted by Box, “all models are wrong, the practical question is how wrong can they be and still be useful”. Voriconzole, a triazole antifungal and the example used in this manuscript, currently has nine population PK models published. To assess the impact of model-misspecification on MBPD, five structurally mis-specified models for voriconazole were developed. Intensive plasma concentrations were simulated for 100 virtual subjects. The dose adjustments required to reach a target exposure were determined by using the empirical Bayes estimates of the PK parameters under each of the mis-specified models. The predicted plasma concentrations and the probability of clinical outcomes, upon following the dose recommendations, were determined. Models that did not contain non-linear clearance performed poorly, with a median dose recommendation 155–310 mg higher than appropriate doses, when only one plasma concentration was available. Removal of body weight and CYP2C9 genotype as covariates had no clinically significant impact on outcomes. In summary, the removal of important structural components, such as non-linear clearance in the case of voriconazole, had a large impact on the clinical utility of MBPD. The removal of patient covariates, even highly influential covariates such as CYP2C9 genotype for voriconazole, had no clinical impact.

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References

  1. Sheiner LB, Rosenberg B, Melmon KL. Modelling of individual pharmacokinetics for computer-aided drug dosage. Comput Biomed Res. 1972;5(5):411–59.

    Article  CAS  PubMed  Google Scholar 

  2. Bonate P. Pharmacokinetic-pharmacodynamic modeling and simulation. New York: Springer; 2005.

    Google Scholar 

  3. Han PY, Kirkpatrick CM, Green B. Informative study designs to identify true parameter-covariate relationships. J Pharmacokinet Pharmacodyn. 2009;36(2):147–63.

    Article  PubMed  Google Scholar 

  4. Walsh TJ, Pappas P, Winston DJ, Lazarus HM, Petersen F, Raffalli J, et al. Voriconazole compared with liposomal amphotericin B for empirical antifungal therapy in patients with neutropenia and persistent fever. N Engl J Med. 2002;346(4):225–34.

    Article  CAS  PubMed  Google Scholar 

  5. Hicheri Y, Cook G, Cordonnier C. Antifungal prophylaxis in haematology patients: the role of voriconazole. Clin Microbiol Infect. 2012;18 Suppl 2:1–15.

    Article  CAS  PubMed  Google Scholar 

  6. Dolton MJ, Ray JE, Chen SC, Ng K, Pont LG, McLachlan AJ. Multicenter study of voriconazole pharmacokinetics and therapeutic drug monitoring. Antimicrob Agents Chemother. 2012;56(9):4793–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Pascual A, Csajka C, Buclin T, Bolay S, Bille J, Calandra T, et al. Challenging recommended oral and intravenous voriconazole doses for improved efficacy and safety: population pharmacokinetics-based analysis of adult patients with invasive fungal infections. Clin Infect Dis. 2012;55(3):381–90.

    Article  CAS  PubMed  Google Scholar 

  8. Hamada Y, Seto Y, Yago K, Kuroyama M. Investigation and threshold of optimum blood concentration of voriconazole: a descriptive statistical meta-analysis. J Infect Chemother. 2012;18(4):501–7.

    Article  CAS  PubMed  Google Scholar 

  9. Miyakis S, van Hal SJ, Ray J, Marriott D. Voriconazole concentrations and outcome of invasive fungal infections. Clin Microbiol Infect. 2010;16(7):927–33.

    Article  CAS  PubMed  Google Scholar 

  10. Pascual A, Calandra T, Bolay S, Buclin T, Bille J, Marchetti O. Voriconazole therapeutic drug monitoring in patients with invasive mycoses improves efficacy and safety outcomes. Clin Infect Dis. 2008;46(2):201–11.

    Article  CAS  PubMed  Google Scholar 

  11. Hope WW, Billaud EM, Lestner J, Denning DW. Therapeutic drug monitoring for triazoles. Curr Opin Infect Dis. 2008;21(6):580–6.

    Article  CAS  PubMed  Google Scholar 

  12. Brüggemann RJM, Donnelly JP, Aarnoutse RE, Warris A, Blijlevens NMA, Mouton JW, et al. Therapeutic drug monitoring of voriconazole. Ther Drug Monit. 2008;30(4):403–11.

    PubMed  Google Scholar 

  13. Wang T, Chen S, Sun J, Cai J, Cheng X, Dong H, et al. Identification of factors influencing the pharmacokinetics of voriconazole and the optimization of dosage regimens based on Monte Carlo simulation in patients with invasive fungal infections. J Antimicrob Chemother. 2014; 69(2):463−70.

  14. Karlsson MO, Lutsar I, Milligan PA. Population pharmacokinetic analysis of voriconazole plasma concentration data from pediatric studies. Antimicrob Agents Chemother. 2009;53(3):935–44.

    Article  CAS  PubMed  Google Scholar 

  15. Hope WW. Population pharmacokinetics of voriconazole in adults. Antimicrob Agents Chemother. 2012;56(1):526–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Dolton MJ, Mikus G, Weiss J, Ray JE, McLachlan AJ. Understanding variability with voriconazole using a population pharmacokinetic approach: implications for optimal dosing. J Antimicrob Chemother. 2014; 69(6):1633–41.

  17. Han K, Capitano B, Bies R, Potoski BA, Husain S, Gilbert S, et al. Bioavailability and population pharmacokinetics of voriconazole in lung transplant recipients. Antimicrob Agents Chemother. 2010;54(10):4424–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Friberg LE, Ravva P, Karlsson MO, Liu P. Integrated population pharmacokinetic analysis of voriconazole in children, adolescents, and adults. Antimicrob Agents Chemother. 2012;56(6):3032–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Nomura K, Fujimoto Y, Kanbayashi Y, Ikawa K, Taniwaki M. Pharmacokinetic-pharmacodynamic analysis of voriconazole in Japanese patients with hematological malignancies. Eur J Clin Microbiol Infect Dis. 2008;27(11):1141–3.

    Article  CAS  PubMed  Google Scholar 

  20. Han K, Bies R, Johnson H, Capitano B, Venkataramanan R. Population pharmacokinetic evaluation with external validation and Bayesian estimator of voriconazole in liver transplant recipients. Clin Pharmacokinet. 2011;50(3):201–14.

    Article  CAS  PubMed  Google Scholar 

  21. McDougall DA, Martin J, Playford EG, Green B. Determination of a suitable voriconazole pharmacokinetic model for personalised dosing. J Pharmacokinet Pharmacodyn. 2016; 43(2):165–77.

  22. Lindbom L, Pihlgren P, Jonsson EN. PsN-Toolkit—a collection of computer intensive statistical methods for non-linear mixed effect modeling using NONMEM. Comput Methods Prog Biomed. 2005;79(3):241–57.

    Article  Google Scholar 

  23. Wang G, Lei H-P, Li Z, Tan Z-R, Guo D, Fan L, et al. The CYP2C19 ultra-rapid metabolizer genotype influences the pharmacokinetics of voriconazole in healthy male volunteers. Eur J Clin Pharmacol. 2009;65(3):281–5.

    Article  CAS  PubMed  Google Scholar 

  24. Wedlund PJ. The CYP2C19 enzyme polymorphism. Pharmacology. 2000;61(3):174–83.

    Article  CAS  PubMed  Google Scholar 

  25. Janmahasatian S, Duffull SB, Ash S, Ward LC, Byrne NM, Green B. Quantification of lean bodyweight. Clin Pharmacokinet. 2005;44(10):1051–65.

    Article  PubMed  Google Scholar 

  26. Al-Sallami H, Goulding A, Taylor R, Grant A, Williams S, Duffull S. A semi-mechanistic model for estimating fat free mass in children. Population Analysis Group Europe; Athens 2011.

  27. Gilbert DN. The Sanford guide to antimicrobial therapy. Sperryville: Antimicrobial Therapy, Inc; 2011. p. 220.

    Google Scholar 

  28. S B, LB S, A B, RJ B. NONMEM user’s guides. (1989–2009). Ellicott City, MD, USA: Icon Development Solutions; 2009.

  29. R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2014.

    Google Scholar 

  30. Troke PF, Hockey HP, Hope WW. Observational study of the clinical efficacy of voriconazole and its relationship to plasma concentrations in patients. Antimicrob Agents Chemother. 2011;55(10):4782–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Antifungal agents: breakpoint tables for interpretation of MICs. European Committee on Antimicrobial Susceptibility Testing, 2015 2015-11-16. Report No.: Contract No.: 8.0.

  32. Box G, NR D. Emperical model-building and response surgaces. New York, NY: Wiley; 1987.

  33. Dolton MJ, McLachlan AJ. Voriconazole pharmacokinetics and exposure-response relationships: assessing the links between exposure, efficacy and toxicity. Int J Antimicrob Agents. 2014;44(3):183–93.

    Article  CAS  PubMed  Google Scholar 

  34. Theuretzbacher U, Ihle F, Derendorf H. Pharmacokinetic/pharmacodynamic profile of voriconazole. Clin Pharmacokinet. 2006;45(7):649–63.

    Article  CAS  PubMed  Google Scholar 

  35. Geist MJP, Egerer G, Burhenne J, Mikus G. Safety of voriconazole in a patient with CYP2C9*2/CYP2C9*2 genotype. Antimicrob Agents Chemother. 2006;50(9):3227–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Weiss J, Ten Hoevel MM, Burhenne J, Walter-Sack I, Hoffmann MM, Rengelshausen J, et al. CYP2C19 genotype is a major factor contributing to the highly variable pharmacokinetics of voriconazole. J Clin Pharmacol. 2009;49(2):196–204.

    Article  CAS  PubMed  Google Scholar 

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ACKNOWLEDGMENTS

D.A.J.M. is supported by an Australian Postgraduate Award.

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Correspondence to David A. J. McDougall.

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McDougall, D.A.J., Martin, J., Playford, E.G. et al. The Impact of Model-Misspecification on Model Based Personalised Dosing. AAPS J 18, 1244–1253 (2016). https://doi.org/10.1208/s12248-016-9943-9

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