Boram Ohk, Mi-Ri Gwon, Bo Kyung Kim, Sook Jin Seong, Woo Youl Kang, Hae Won Lee, Seungil Cho and Young-Ran Yoon
Department of Biomedical Science, BK21 Plus KNU Bio-Medical Convergence Program for Creative Talent, Cell and Matrix Research Institute and Clinical Trial Center, Kyungpook National University Graduate School and Hospital, Daegu 41944, Republic of Korea
Objectives:
Tacrolimus has a narrow therapeutic range while it shows large inter-individual variability(IIV) in its pharmacokinetics(PK) [1]. Several studies have been conducted to find out the factors contributing to IIV in Tacrolimus PK, and many studies have consistently shown that the CYP3A5 polymorphism is a significant covariate in tacrolimus clearance [2-4]. Here, we aimed to deepen the understanding of the IIV of Tacrolimus PK using population pharmacokinetic modeling by taking into account the CYP3A5 polymorphism and the metabolites identified from untargeted metabolic profiles [5].
Methods:
Data were obtained from clinical trial, involving 29 healthy subjects. At screening, CYP3A5 genotype of subjects was determined by polymerase chain reaction-restriction fragment length polymorphism assay. The number of subjects with CYP3A5 *1/*1, *1/*3, and *3/*3 were 3, 11, and 15, respectively. For CYP3A5 polymorphism, subjects was divided into two groups (*1/*1 or *1/*3, *3/*3) for analysis. For untargeted metabolic analysis, urine samples were collected over a 24-hours period before pre-dose and after post-dose, respectively. All subjects received 0.075 mg/kg of oral tacrolimus as a single dose in fasting condition. Sequential blood samples (6 ml per sample) were collected just before (0 h) and at 0.5, 1, 1.5, 2, 2.5, 3, 4, 6, 8, 12, 24, 48, and 72 h after oral drug administration. Tacrolimus blood concentrations were determined by ultra-performance of the liquid chromatography-tandem mass spectrometry. A total of 406 tacrolimus concentrations were used to develop population PK model. The final PK model was validated using bootstrap and visual predictive check.
Results:
The mean age of a total of 29 subjects were 23.4 years old. The mean height, weight and BMI were 174.7 cm, 67.1 kg, and 21.9 kg/m2, respectively. A 2-compartment model with first-order absorption after a lag time provided the best fit from healthy subjects. Estimates of the population PK parameter were as follows; CLCYP3A5*3/*3, 9.4 L/h; Vc, 16 L; Vp, 361 L/h; Ka, 0.53 h-1; ALAG, 0.39 h-1; Q, 26 L/h. CYP3A5*1 and hydroxycotinine were found to be significant covariates for the CL of tacrolimus(CL/F=9.4×(2.03,if CYP3A5*1)×?(hydroxycotinine/10.95)?^1.65). Median values of the parameter estimates and their 95% CIs from bootstrapping were very similar to the population mean estimates from the final model The visual predictive check (VPC) was performed and the result exhibited the acceptable predictive performance of the final model.
Conclusions:
In this study, we performed a population PK modeling to investigate that the endogenous metabolites could provide additional information to genetic data for explaining variability of the tacrolimus PK. As a result, we confirmed that CYP3A5 genotype explained a part of variability in tacrolimus CL/F, and hydroxycotinine intensity additionally account for some of variability, which was unexplained by CYP3A5 genotype. This result shows that integrating pharmacogenomics and pharmacometabolomics into PK could provide the valuable information in explaining the variability of PK parameters and predicting individual PK parameters.
References:
[1] Wallemacq P, Armstrong VW, Brunet M, et al. Opportunities to optimize tacrolimus therapy in solid organ transplantation: report of the European consensus conference. Ther Drug Monit. 2009;31(2):139–152.
[2] Woillard JB, de Winter BC, Kamar N, Marquet P, Rostaing L, Rousseau A. Population pharmacokinetic model and Bayesian estimator for two tacrolimus formulations–twice daily Prograf® and once daily Advagraf®. British journal of clinical pharmacology. 2011;71(3):391-402.
[3] Zuo X-c, Ng CM, Barrett JS, Luo A-j, Zhang B-k, Deng C-h, et al. Effects of CYP3A4 and CYP3A5 polymorphisms on tacrolimus pharmacokinetics in Chinese adult renal transplant recipients: a population pharmacokinetic analysis. Pharmacogenetics and genomics. 2013;23(5):251-61.
[4] Musuamba FT, Mourad M, Haufroid V, Demeyer M, Capron A, Delattre IK, et al. A simultaneous d-optimal designed study for population pharmacokinetic analyses of mycophenolic acid and tacrolimus early after renal transplantation. The Journal of Clinical Pharmacology. 2012;52(12):1833-43.
[5] Phapale P, Kim SD, Lee H, Lim M, Kale D, Kim YL, et al. An integrative approach for identifying a metabolic phenotype predictive of individualized pharmacokinetics of tacrolimus. Clinical Pharmacology & Therapeutics. 2010;87(4):426-36.
Reference: PAGE 28 (2019) Abstr 9091 [www.page-meeting.org/?abstract=9091]
Poster: Drug/Disease Modelling - Other Topics