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

Abstract

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.

Influence of FMO3 and CYP3A4 Polymorphisms on the Pharmacokinetics of Teneligliptin in Humans

“…Several studies have suggested the conflicting roles of CYP3A4 *1G in CYP3A4 activity. CYP3A4 *1G polymorphism enhances the disposition of CYP3A4 substrates, including atorvastatin ( Gao et al, 2008 ), cyclosporin A ( El-Shair et al, 2019 ), and tacrolimus ( Tamashiro et al, 2017 ; Tang et al, 2019 ). In contrast, CYP3A4 *1G polymorphism is reported to decrease the disposition of fentanyl ( Yuan et al, 2015 ).…”

CYP3A4andCYP3A5Expression is Regulated by CYP3A4*1Gin CRISPR/Cas9-Edited HepG2 Cells

“…Genetic variation in CYP3A4, such as the single nucleotide polymorphism (SNP) CYP3A4*22 (rs35599367, G>A) in intron 6, which affects its transcript splicing, decreases CYP3A4 expression (Wang and Sadee, 2016), resulting in improved pharmacokinetics and clinically effective therapy (Wang et al, 2011Mulder et al, 2021. The intronic genetic variation in CYP3A5, CYP3A5*3 (rs776746, A>G), leads to decreased normal CYP3A5 expression through alternative splicing (Kuehl et al, 2001) and is associated with tacrolimus dosage and pharmacokinetics (Dorr et al, 2017;Tang et al, 2019). Recently these two SNPs have become biomarkers for clinical individualized medications.…”

A Prediction Model for Tacrolimus Daily Dose in Kidney Transplant Recipients With Machine Learning and Deep Learning Techniques

“…With the development of machine learning and deep learning techniques, more studies applied these techniques to enhance the model expression of the complicated relationship between individual factors and medication dose. Tang et al first applied multiple machine learning algorithms to establish a stable dosing prediction model of tacrolimus in Chinese KTRs, and proved the method was superior to the MLR ( 20 , 21 ). In addition to the algorithms used in Tang's research, some algorithms with more sophisticated principles were developed, such as eXtreme Gradient Boosting (XGBoost), light gradient boosting machine (LightGBM), Categorical Boosting (CatBoost), random forest (RF), and TabNet (a deep learning technique), which were highly recognized in the algorithm competitions ( 22 – 26 ).…”