A novel random forest integrative approach based on endogenous CYP3A4 phenotype for predicting tacrolimus concentrations and dosages in Chinese renal transplant patients

Abstract

Personalized treatment with tacrolimus has remained a challenge. The present study aimed to evaluate the potential of an integrative approach to predict individual tacrolimus concentrations and dosages based on endogenous CYP3A4 phenotype, CYP3A5 genotype and clinical variables. A random forest (RF) algorithm which incorporated an endogenous CYP3A4 phenotype (assessed by urinary ratio of 6β-hydroxycortisol and 6β-hydroxycortisone to cortisol and cortisone), CYP3A5*3 genotype and other clinical determinants of tacrolimus disposition was performed in 182 medically stable renal transplant recipients. The results suggested that endogenous CYP3A4 phenotype was the most important determinant of tacrolimus concentrations and dose requirements. RF models provided high goodness of fit (R2 ) with .92 and .95 for the prediction of tacrolimus trough concentrations and dosages, respectively, as well as high predictability (Q2 ) with 0.63 and 0.70, respectively. Significant correlations existed between experimental and predictive data. In summary, endogenous CYP3A4 phenotype is a critical biomarker for the determination of tacrolimus disposition. This predictive RF approach based on CYP3A4 biomarker with the combination of CYP3A5*3 genotype and other clinical variables can be used for predicting tacrolimus concentrations and dosages, which may serve as a useful tool in individualized tacrolimus dosing.