Dzhem Farandzha, Veneta Dimitrova, Ivanka Atanasova, Lubomir Spassov, Dimiter Terziivanov
Sofia University St. Kliment Ohridski
Objectives: Immunosuppression after liver-transplantation is of key importance for the survival of both the patient and the transplant. Tacrolimus has become the preferred immunosuppressant in liver transplant recipients due to its superior role in improving patient and graft survival in addition to the lower number of acute and steroid-resistant rejections as compared with cyclosporine [1]. Although its extensive inter- and intra-individual pharmacokinetic variability as well as the changing dose requirements with time are well documented, the literature on the distribution patterns of tacrolimus concentrations in liver-transplant infants is still quite scarce for obvious reasons [2]. The primary concern of this research was to conduct a pilot population pharmacokinetic study with tacrolimus in liver-transplant infants in the early post-transplantation period that could later be further developed with data from new patients.
Methods: The study involved 5 infants, median age 8 months (ranging from 2 months to 11 months), who underwent orthotopic liver transplantations between November 2010 and April 2015 at Lozenetz Hospital, Sofia. All patients received oral tacrolimus (Prograf®) twice daily at 8 am and 8 pm respectively in the intensive care unit (ICU) during the early post-transplantation period (ICU stay ranging from 30.5 to 44.5 days, median 41 days). Therapeutic drug monitoring (TDM) by trough level measurement was used to tailor the doses to fit in the therapeutic range. A total of 65 trough tacrolimus whole-blood concentrations were analyzed using Origin 9.0 and a p-value of ≤ 0.05 was considered as statistically significant. The pharmacokinetic (PK) population modeling was performed using Pmetrics [3]. The concentrations/time kinetics was best described by a one-compartmental absorption model with first order elimination. The absorption profile of tacrolimus was shaped by the first order absorption rate constant, Ka, lag-time (Tlag) and bioavailability (Fa) terms.
Results: The statistical distribution pattern of trough tacrolimus concentrations at the end of each dosing interval was characterized by non-Gaussian distribution pattern skewed to the right. The Q-Q plot confirmed the non-normal distribution pattern and revealed subgroups of trough tacrolimus concentrations deviating from linearity. The estimates of tacrolimus pharmacokinetic model parameters were presented as medians. Median absorption rate (Ka) and elimination rate (Ke) constants were 8.46 h-1 and 0.04 h-1 respectively. Volume of distribution (VD) was 0.09 L/kg. Lag time (Tlag) was 0.41 h. Bioavailability (Fa) was 0.38. Due to the small number of patients external validation on Pmetrics could not be performed. Internal validation was performed using the normalized prediction distribution errors (NPDE) method of Mentré and Escolano instead [4]. Plotting observed versus individual predicted tacrolimus concentrations yielded a correlation coefficient (r) of 0.81 with a p-value of <0.001.
Conclusion: The search through the current available literature did not yield results that could be compared to the findings presented here. More data from similar patients will enable us to perform external validation in addition to the analyses presented here, which will significantly improve the reliability of the predicted tacrolimus concentrations. The inclusion of additional covariates such as hematocrit, steroid dose and time after transplantation to the model could also potentially improve the predictions. Pharmacometric tools such as Pmetrics could then successfully be used to build pharmacokinetic models for specific groups of patients that could later be implemented in computer-based dose individualization strategies.
References:
[1] Haddad EM, McAlister VC, Renouf E, Malthaner R, Kjaer MS, Gluud LL. Cyclosporin versus tacrolimus for liver transplanted patients. Cochrane Database Syst Rev. 2006;(4):CD005161.
[2] Saint-Marcoux F, Woillard JB, Jurado C, Marquet P. Lessons from routine dose adjustment of tacrolimus in renal transplant patients based on global exposure. Ther Drug Monit. 2013 Jun;35(3):322-7. doi: 10.1097/FTD.0b013e318285e779.
[3] Neely MN, van Guilder MG, Yamada WM, Schumitzky A, Jelliffe RW. Accurate Detection of Outliers and Subpopulations With Pmetrics, a Nonparametric and Parametric Pharmacometric Modeling and Simulation Package for R. Therapeutic Drug Monitoring. 2012; 34(4): 467-476.
[4] Mentré F, Escolano S. Prediction discrepancies for the evaluation of nonlinear mixed-effects models. J Pharmacokinet Pharmacodyn 2006; 33:345-367.
[5] Passey C, Birnbaum AK, Brundage RC et al. Dosing equation for tacrolimus using genetic variants and clinical factors. Br J Clin Pharmacol. 2011;72(6):948-957
[6] Dimitrova V, Farandzha D, Atanasova I et al. Cyclosporine and creatinine blood levels monitoring in pediatric and adult liver transplant patients. Annual of Sofia University “St. Kliment Ohridski”. 2017; vol. 2:171-180.
Reference: PAGE 27 (2018) Abstr 8565 [www.page-meeting.org/?abstract=8565]
Poster: Drug/Disease Modelling - Paediatrics