BACKGROUND: Tacrolimus (Tac) is a potent immunosuppressant with considerable toxicity. Tac pharmacokinetics varies between individuals and thus complicates its use in preventing rejection after kidney transplantation. This variability might be caused by genetic polymorphisms in metabolizing enzymes.METHODS: We used TaqMan analyses to evaluate the impact of a newly discovered CYP3A4 (cytochrome P450, family 3, subfamily A, polypeptide 4) single-nucleotide polymorphism (SNP) (rs35599367C>T; CYP3A4*22) on Tac pharmacokinetics in 185 renal transplant recipients who participated in an international randomized controlled clinical trial (fixed-dose, concentration-controlled study).RESULTS: The overall mean daily-dose requirement to reach the same predose Tac blood concentration was 33% lower for carriers of the T variant allele than for rs35599367CC patients (95% CI, −46% to −20%; P = 0.018). When combined with the *3 genotype of the CYP3A5 (cytochrome P450, family 3, subfamily A, polypeptide 5) gene, the rs35599367C>T SNP was also associated with a risk of supratherapeutic Tac concentrations (>15 μg/L) during the first 3 days after surgery, with an odds ratio of 8.7 for carriers of the CYP3A4 T allele plus CYP3A5*3/*3 (P = 0.027) and 4.2 for the CYP3A4 CC homozygotes plus CYP3A5*3/*3 (P = 0.002), compared with CYP3A4 CC homozygotes having 1 or 2 CYP3A5*1 alleles. The overall increase in the Tac dose-adjusted trough blood concentration was +179% for carriers of the CYP3A4 T allele with CYP3A5*3/*3 (P < 0.001), +101% for CYP3A4 CC homozygotes with CYP3A5*3/*3 (P < 0.001), and +64% for CYP3A4 T allele carriers with CYP3A5*1 (P = 0.020),compared with CYP3A4 CC homozygotes with CYP3A5*1.CONCLUSIONS: The CYP3A4 rs35599367C>T polymorphism is associated with a significantly altered Tac metabolism and therefore increases the risk of supratherapeutic Tac concentrations early after transplantation. Analysis of this CYP3A4*22 SNP may help in identifying patients at risk of Tac overexposure.

Aims: The aims of this study were to describe the pharmacokinetics of tacrolimus immediately after kidney transplantation, and to develop a clinical tool for selecting the best starting dose for each patient.Methods: Data on tacrolimus exposure were collected for the first 3 months following renal transplantation. A population pharmacokinetic analysis was conducted using nonlinear mixed‐effects modelling. Demographic, clinical and genetic parameters were evaluated as covariates.Results: A total of 4527 tacrolimus blood samples collected from 337 kidney transplant recipients were available. Data were best described using a two‐compartment model. The mean absorption rate was 3.6 h⁻¹, clearance was 23.0 l h^–1 (39% interindividual variability, IIV), central volume of distribution was 692 l (49% IIV) and the peripheral volume of distribution 5340 l (53% IIV). Interoccasion variability was added to clearance (14%). Higher body surface area (BSA), lower serum creatinine, younger age, higher albumin and lower haematocrit levels were identified as covariates enhancing tacrolimus clearance. Cytochrome P450 (CYP) 3A5 expressers had a significantly higher tacrolimus clearance (160%), whereas CYP3A4*22 carriers had a significantly lower clearance (80%). From these significant covariates, age, BSA, CYP3A4 and CYP3A5 genotype were incorporated in a second model to individualize the tacrolimus starting dose: urn:x-wiley:03065251:media:bcp13838:bcp13838-math-0001 Both models were successfully internally and externally validated. A clinical trial was simulated to demonstrate the added value of the starting dose model.Conclusions: For a good prediction of tacrolimus pharmacokinetics, age, BSA, CYP3A4 and CYP3A5 genotype are important covariates. These covariates explained 30% of the variability in CL/F. The model proved effective in calculating the optimal tacrolimus dose based on these parameters and can be used to individualize the tacrolimus dose in the early period after transplantation.

Purpose: Paclitaxel is used for the treatment of several solid tumors and displays a high interindividual variation in exposure and toxicity. Neurotoxicity is one of the most prominent side effects of paclitaxel. This study explores potential predictive pharmacokinetic and pharmacogenetic determinants for the onset and severity of neurotoxicity. Experimental Design: In an exploratory cohort of patients (n = 261) treated with paclitaxel, neurotoxicity incidence, and severity, pharmacokinetic parameters and pharmacogenetic variants were determined. Paclitaxel plasma concentrations were measured by high-performance liquid chromatography or liquid chromatography/tandem mass spectrometry, and individual pharmacokinetic parameters were estimated from previously developed population pharmacokinetic models by nonlinear mixed effects modeling. Genetic variants of paclitaxel pharmacokinetics tested were CYP3A4*22, CYP2C8*3, CYP2C8*4, and ABCB1 3435 C>T. The association between CYP3A4*22 and neurotoxicity observed in the exploratory cohort was validated in an independent patient cohort (n = 239). Results: Exposure to paclitaxel (_logAUC) was correlated with severity of neurotoxicity (P < 0.00001). Female CYP3A4*22 carriers were at increased risk of developing neurotoxicity (P = 0.043) in the exploratory cohort. CYP3A4*22 carrier status itself was not associated with pharmacokinetic parameters (CL, AUC, C_max, or T_>0.05) of paclitaxel in males or females. Other genetic variants displayed no association with neurotoxicity. In the subsequent independent validation cohort, CYP3A4*22 carriers were at risk of developing grade 3 neurotoxicity (OR = 19.1; P = 0.001). Conclusions: Paclitaxel exposure showed a relationship with the severity of paclitaxel-induced neurotoxicity. In this study, female CYP3A4*22 carriers had increased risk of developing severe neurotoxicity during paclitaxel therapy. These observations may guide future individualization of paclitaxel treatment. Clin Cancer Res; 19(12); 3316–24. ©2013 AACR.