Incorporation of Gene-Environment Interaction Terms Improved the Predictive Accuracy of Tacrolimus Stable Dose Algorithms in Chinese Adult Renal Transplant Recipients.

The narrow therapeutic window of tacrolimus necessitates daily monitoring and predictive algorithms based on genetic and nongenetic factors. In this study, we constructed predictive algorithms for tacrolimus stable dose in a retrospective cohort of 1045 Chinese renal transplant recipients. All patients were genotyped for CYP3A4 20230T>C (rs2242480), CYP3A4 T>C (rs4646437), CYP3A5*3 6898A>G (rs776746), ABCB1 129T>C (rs3213619); ABCB1 c.1236C>T (rs01128503), ABCB1 c.2677G>T/A (rs2032582) and ABCB1 c.3435C>T (rs1045642) polymorphisms, and the effects of gene-gene and gene-environment interactions on the predictive accuracy of algorithm were evaluated. In wild-type CYP3A4 rs2242480 (TT) carriers, patients who took calcium channel blockers had lower tacrolimus stable doses than those without the concomitant medications (P < 1 × 10-4 ). In contrast, there was no significant difference in mutant type patients. Similarly, the tacrolimus stable doses in wild-type CYP3A5 rs776746 carriers who had hypertension were higher than those without hypertension (P = 4.10 × 10-3 ). More importantly, dose-predictive algorithms with interaction terms showed higher accuracy and better performance than those without interaction terms. Our finding suggested that wild-type CYP3A4 rs2242480 (TT) carriers should be more cautious to take tacrolimus when they are coadministrated with calcium channel blockers, and CYP3A5 rs776746 (AA) carriers may need higher tacrolimus dosage when they are in combination with hypertension.