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. 2014 Feb 12;3(2):e100.
doi: 10.1038/psp.2013.78.

Effect of CYP3A4*22, CYP3A5*3, and CYP3A Combined Genotypes on Cyclosporine, Everolimus, and Tacrolimus Pharmacokinetics in Renal Transplantation

D J A R Moes  1 , J J Swen  1 , J den Hartigh  2 , T van der Straaten  2 , J J Homan van der Heide  3 , J S Sanders  4 , F J Bemelman  3 , J W de Fijter  5 , H J Guchelaar  2
Affiliations

Effect of CYP3A4*22, CYP3A5*3, and CYP3A Combined Genotypes on Cyclosporine, Everolimus, and Tacrolimus Pharmacokinetics in Renal Transplantation

D J A R Moes et al. CPT Pharmacometrics Syst Pharmacol. .

Abstract

Cyclosporine, everolimus, and tacrolimus are the cornerstone of immunosuppressive therapy in renal transplantation. These drugs are characterized by narrow therapeutic windows, highly variable pharmacokinetics (PK), and metabolism by CYP3A enzymes. Recently, the decreased activity allele, CYP3A4*22, was described as a potential predictive marker for CYP3A4 activity. This study investigated the effect of CYP3A4*22, CYP3A5*3, and CYP3A combined genotypes on cyclosporine, everolimus, and tacrolimus PK in renal transplant patients. CYP3A4*22 carriers showed a significant lower clearance for cyclosporine (-15%), and a trend was observed for everolimus (-7%) and tacrolimus (-16%). Patients carrying at least one CYP3A5*1 allele had 1.5-fold higher tacrolimus clearance compared with noncarriers; however, CYP3A5*3 appeared to be nonpredictive for everolimus and cyclosporine. CYP3A combined genotype did not significantly improve prediction of clearance compared with CYP3A5*3 or CYP3A4*22 alone. These data suggest that dose individualization of cyclosporine, everolimus, or tacrolimus therapy based on CYP3A4*22 is not indicated.CPT: Pharmacometrics Systems Pharmacology (2014); 3, e100; doi:10.1038/psp.2013.78; published online 12 February 2014.

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Figures

Figure 1
Figure 1
Schematic representation of the linear two-compartment model with first-order absorption and elimination of cyclosporine, including the transit compartment to describe the absorption phase.
Figure 2
Figure 2
Box plots representing the average cyclosporine, everolimus, and tacrolimus apparent clearance (l/h) of the different genotype groups with error bars and the number of patients in each group. CYP3A4 (*1/*1 = CYP3A4*22 noncarriers, *1/*22 or *22/*22 = CYP3A4*22 carriers, NG = not genotyped), CYP3A5 (*1/*3 or *1/*1 = CYP3A5*1 carriers, *3/*3 = CYP3A5*1 noncarriers, NG = not genotyped), and CYP3A cluster: (C1: CYP3A5*3/*3 and CYP3A4*22/*22 or CYP3A4*1/*22, C2: CYP3A5*3/*3 and CYP3A4*1/*1, C3: CYP3A5*1/*1 or CYP3A5*1/*3 and CYP3A4*22/*22 or CYP3A4*1/*22, and C4: CYP3A5*1/*1 or CYP3A5*1/*3 and CYP3A4*1/*1, NG = not genotyped). *P < 0.01. Apparent clearance was calculated using the base model.
Figure 3
Figure 3
Prediction-corrected visual predictive checks with 80% prediction interval of cyclosporine, everolimus, and tacrolimus. The observed concentrations are shown as solid circles. The solid lines with open circles represent the observation intervals. The solid lines represent the prediction interval. The shaded areas around the prediction intervals represent the 95% confidence interval around each of the prediction interval.
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