The aim of this study was to develop a limited sampling strategy to estimate the area under the concentration-time curve (AUC) of a modified-release, once-daily formulation of tacrolimus (Advagraf, Japanese trade name Graceptor) with Japanese renal transplant patients. Among the 43 enrolled patients, 23 patients continued to take Graceptor for 1 year. A total of 66 profiles on day 28 and day 365 from the 43 patients were randomly divided into a training group (N = 33) and a validation group (N = 33) without any overlap. The prediction formula for the AUC 0-24 using the single C 12h time point gave the highest correlation with the observed AUC 0-24 (r2 = 0.9057). When 2 sampling times were used, C 0h-C 12h were the best time points for the estimation of the AUC 0-24 (AUC 0-24 = 26.8 + 8.0C 0h + 17.8C 12h, r2 = 0.9221, P < 0.0001). There was no significant difference in the prediction error for the prediction formulas with the C 0h-C 12h combination between CYP3A5 genotypes. The % mean prediction error, % mean absolute error, and % root mean squared prediction error of the prediction formula using C 0h-C 12h were 0.1%, 7.6%, and 8.8%, respectively. In a hospital setting, a limited sampling strategy using C 0h-C 12h would be applicable to estimating the AUC 0-24 of tacrolimus once daily.

While CYP3A5 polymorphisms are used to predict the initial dosage of tacrolimus therapy, the predictive capability of genetic information for dosing at early stage post-renal transplantation is unknown. We investigated the influence of polymorphisms over time. An initial oral dose of modified-release once-daily tacrolimus formulation (0.20 mg/kg) was administered to 50 Japanese renal transplant patients every 24 h. Stepwise multiple linear regression analysis for tacrolimus dosing was performed each week to determine the effect of patient clinical characteristics. The dose-adjusted trough concentration was approximately 70% higher for patients with the CYP3A5*3/*3 than patients with the CYP3A5*1 allele before the second pre-transplantation tacrolimus dose (0.97 (0.78–1.17) vs. 0.59 (0.45–0.87) ng/mL/mg; p < 0.001). The contribution of genetic factors (CYP3A5*1 or *3) for tacrolimus dosing showed increased variation from Day 14 to Day 28 after transplantation: 7.2%, 18.4% and 19.5% on Days 14, 21 and 28, respectively. The influence of CYP3A5 polymorphisms on the tacrolimus maintenance dosage became evident after Day 14 post-transplantation, although the tacrolimus dosage was determined based only on patient body weight for the first three days after surgery. Tacrolimus dosage starting with the initial administration should be individualized using the CYP3A5 genotype information.

While tacrolimus and everolimus have common metabolic pathways through CYP3A4/5, tacrolimus is metabolized solely by CYP3A4 in recipients with the CYP3A5*3/*3. The purpose of this study was to evaluate how the area under the blood concentration-time curves (AUC) of tacrolimus could be predicted based on CYP3A5 genotype and the AUC of everolimus in renal transplant patients taking both drugs. The dose-adjusted AUC (AUC/D) of tacrolimus and everolimus were calculated at one month and one year after transplantation. Significant correlations between the AUC/D of tacrolimus and everolimus were found for patients with the CYP3A5*1 allele or CYP3A5*3/*3 at both one month and one year. At both stages, the determination coefficients were higher and the slopes of regression equations were larger for patients with CYP3A5*3/*3 compared to the CYP3A5*1 allele. A good correlation between single doses of tacrolimus and everolimus was found for CYP3A5*3/*3 patients at 1 year after transplantation (r = 0.794, p < 0.001). The variability of the AUC_0–24/D of tacrolimus for each CYP3A5 genotype could be predicted based on the AUC_0–12/D of everolimus. Clinicians may be able to comprehensively carry out the dose adjustments of tacrolimus and everolimus based on relationship with AUCs of both drugs in each CYP3A5 genotype.

The purpose of this study was to evaluate the effects of NR1I2 (7635G>A and 8055C>T) and ABCB1 (1236C>T, 2677G>T/A, and 3435C>T) genetic polymorphisms on everolimus pharmacokinetics in 98 Japanese renal transplant patients. On day 15 after everolimus administration, blood samples were collected just prior to and 1, 2, 3, 4, 6, 9, and 12 h after administration. The dose-adjusted area under the blood concentration–time curve (AUC_0-12) of everolimus was significantly lower in patients with the NR1I2 8055C/C genotype than in those with other genotypes (p = 0.022) and was significantly higher in male patients than female patients (p = 0.045). Significant correlations between the dose-adjusted AUC_0-12 of everolimus and age (p = 0.001), aspartate transaminase (p = 0.001), and alanine transaminase (p = 0.005) were found. In multivariate analysis, aging (p = 0.008) and higher alanine transaminase levels (p = 0.032) were independently predictive of a higher dose-adjusted everolimus AUC_0-12. Aging and hepatic dysfunction in patients may need to be considered when evaluating dose reductions in everolimus. In renal transplant patients, management using everolimus blood concentrations after administration may be more important than analysis of NR1I2 8055C>T polymorphism before administration.

448 Background: We investigated the impact of genetic polymorphisms and pharmacokinetics of axitinib on adverse events, objective responses, and survival in patients with metastatic renal cell carcinoma (mRCC). Methods: In total, 53 patients with mRCC treated with axitinib were analyzed. Patient was classified as favorable (n = 5), intermediate (n = 36), and poor (n = 12) using the MSKCC risk classification system. High-performance liquid chromatography was used to measure serum axitinib levels. AUC_0–12 (ng∙h/mL) was calculated using various serum levels at 0 to 12 h (C₀–C₁₂) following administration on day 7 of treatment. The genetic polymorphisms related to the drug pharmacokinetics, including SLCO1B1, SLCO1B3, SLCO2B1, ABCB1, ABCG2, CYP2C19, CYP3A5, and UGT1A1, were analyzed using PCR-RFLP analysis. Results: The most frequently reported ≥ G3 adverse events were hypertension (43.3%), proteinuria (30.2%), and anorexia (20.1%). The axitinib trough levels (C₀) were significantly correlated with AUC_0–12 of axitinib. Patients with axitinib C₀ ≥ 10 ng/mL had a significantly higher rate of hand-foot syndrome ≥ G2 and hypothyroidism ≥ G2 than those with axitinib C₀ < 10 ng/mL (p = 0.013, p = 0.005). The overall survival in patients with axitinib C₀ ≥ 5 ng/mL was significantly better than that in those with axitinib C₀ < 5 ng/mL (p = 0.022). The mean C₀ and AUC_0–12 values in patients with a poor metabolizer (*6/*6, *6/*28, and *28/*28) of UGT1A1 polymorphism were significantly higher than those in patients with an extensive metabolizer (p = 0.045, p = 0.035, respectively). The mean AUC_0–12 value in patients with the SLCO1B1 *15 was significantly higher than that in those without the SLCO1B1 *15 (p = 0.038). Conclusions: Serum axitinib levels were associated with adverse events and overall survival of patients with mRCC. The genetic polymorphisms in UGT1A1 and SLCO1B1 may affect serum axitinib levels in patients with mRCC. Pharmacokinetics and genetic polymorphisms play important roles in the outcomes of patients with mRCC treated with axitinib.