PurposeTo identify patient characteristics that influence tacrolimus individual dose requirement in kidney transplant recipients.MethodsData on forty-four 12-h pharmacokinetic profiles from 29 patients and trough concentrations in 44 patients measured during the first 70 days after transplantation (1,546 tacrolimus whole blood concentrations) were analyzed. Population pharmacokinetic modeling was performed using NONMEM 7.2®.ResultsStandardization of tacrolimus whole blood concentrations to a hematocrit value of 45 % improved the model fit significantly (p < 0.001). Fat-free mass was the best body size metric to predict tacrolimus clearance and volume of distribution. Bioavailability was 49 % lower in expressers of cytochrome P450 3A5 (CYP3A5) than in CYP3A5 nonexpressers. Younger females (<40 years) showed a 35 % lower bioavailability than younger males. Bioavailability increased with age for both males and females towards a common value at age >55 years that was 47 % higher than the male value at age <40 years. Bioavailability was highest immediately after transplantation, decreasing steeply thereafter to reach its nadir at day 5, following which it increased during the next 55 days towards an asymptotic value that was 28 % higher than that on day 5.ConclusionsHematocrit predicts variability in tacrolimus whole blood concentrations but is not expected to influence unbound (therapeutically active) concentrations. Fat-free mass, CYP3A5 genotype, sex, age and time after transplant influence the tacrolimus individual dose requirement. Because hematocrit is highly variable in kidney transplant patients and increases substantially after kidney transplantation, hematocrit is a key factor in the interpretation of tacrolimus whole blood concentrations.Electronic supplementary materialThe online version of this article (doi:10.1007/s00228-013-1584-7) contains supplementary material, which is available to authorized users.
AimsThe aim was to develop a theory-based population pharmacokinetic model of tacrolimus in adult kidney transplant recipients and to externally evaluate this model and two previous empirical models.MethodsData were obtained from 242 patients with 3100 tacrolimus whole blood concentrations. External evaluation was performed by examining model predictive performance using Bayesian forecasting.ResultsPharmacokinetic disposition parameters were estimated based on tacrolimus plasma concentrations, predicted from whole blood concentrations, haematocrit and literature values for tacrolimus binding to red blood cells. Disposition parameters were allometrically scaled to fat free mass. Tacrolimus whole blood clearance/bioavailability standardized to haematocrit of 45% and fat free mass of 60 kg was estimated to be 16.1 l h−1 [95% CI 12.6, 18.0 l h−1]. Tacrolimus clearance was 30% higher (95% CI 13, 46%) and bioavailability 18% lower (95% CI 2, 29%) in CYP3A5 expressers compared with non-expressers. An Emax model described decreasing tacrolimus bioavailability with increasing prednisolone dose. The theory-based model was superior to the empirical models during external evaluation displaying a median prediction error of −1.2% (95% CI −3.0, 0.1%). Based on simulation, Bayesian forecasting led to 65% (95% CI 62, 68%) of patients achieving a tacrolimus average steady-state concentration within a suggested acceptable range.ConclusionA theory-based population pharmacokinetic model was superior to two empirical models for prediction of tacrolimus concentrations and seemed suitable for Bayesian prediction of tacrolimus doses early after kidney transplantation.
PurposeTacrolimus (Tac) and cyclosporine (CsA) are mainly metabolized by CYP3A4 and CYP3A5. Several studies have demonstrated an association between the CYP3A5 genotype and Tac dose requirements. Recently, CYP3A4, PPARA, and POR gene variants have been shown to influence CYP3A metabolism. The present study investigated potential associations between CYP3A5*3, CYP3A4*22, PPARA c.209-1003G>A and c.208 + 3819A>G, and POR*28 alleles and dose-adjusted concentrations (C/D) of Tac and CsA in 177 renal transplant patients early post-transplant.MethodsAll patients (n = 177) were genotyped for CYP3A4*22, CYP3A5*3, POR*28, PPARA c.209-1003G>A, and PPARA c.208 + 3819A>G using real-time polymerase chain reaction (PCR) and melting curve analysis with allele-specific hybridization probes or PCR restriction fragment length polymorphisms (RFLP) methods. Drug concentrations and administered doses were retrospectively collected from patient charts at Oslo University Hospital, Rikshospitalet, Norway. One steady-state concentration was collected for each patient.ResultsWe confirmed a significant impact of the CYP3A5*3 allele on Tac exposure. Patients with POR*28 and PPARA variant alleles demonstrated 15 % lower (P = 0.04) and 19 % higher (P = 0.01) Tac C0/D respectively. CsA C2/D was 53 % higher among CYP3A4*22 carriers (P = 0.03).ConclusionThe results support the use of pre-transplant CYP3A5 genotyping to improve initial dosing of Tac, and suggest that Tac dosing may be further individualized by additional POR and PPARA genotyping. Furthermore, initial CsA dosing may be improved by pre-transplant CYP3A4*22 determination.Electronic supplementary materialThe online version of this article (doi:10.1007/s00228-014-1656-3) contains supplementary material, which is available to authorized users
Background Early after renal transplantation it is often challenging to achieve and maintain tacrolimus concentrations within the target range. Computerized dose individualization utilizing population pharmacokinetic models may be helpful. The objective of this study was to prospectively evaluate the target concentration achievement of tacrolimus using computerized dosing compared with conventional dosing performed by experienced transplant physicians. Methods A single-center, prospective study was conducted. Renal transplant recipients were randomized to receive either computerized or conventional tacrolimus dosing during the first eight weeks post-transplant. The median proportion of tacrolimus trough concentrations within the target range was compared between the groups. Standard risk (target 3-7 μg/L) and high-risk (8-12 μg/L) recipients were analyzed separately. Results Eighty renal transplant recipients were randomized, and seventy-eight were included in the analysis (Computerized dosing (n=39): 32 standard risk/7 high-risk, Conventional dosing (n=39): 35 standard risk/4 high-risk). A total of 1711 tacrolimus whole blood concentrations were evaluated. The proportion of concentrations per patient within the target range was significantly higher with computerized dosing than with conventional dosing, both in standard risk patients (medians 90% [95% confidence interval (CI) 84-95%] vs. 78% [95% CI 76-82%], respectively, p<0.001) and in high-risk patients (medians 77% [95% CI: 71-80%] vs. 59% [95% CI: 40-74%], respectively, p=0.04). Conclusions Computerized dose individualization improves target concentration achievement of tacrolimus after renal transplantation. The computer software is applicable as a clinical dosing tool to optimize tacrolimus exposure and may potentially improve long-term outcome.
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