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Physiologically-based pharmacokinetic modeling-guided rational combination of tacrolimus and voriconazole in patients with different CYP3A5 and CYP2C19 alleles #465
Abstract
The drug-drug interactions (DDIs) between tacrolimus and voriconazole are highly variable among individuals. We aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the DDIs in people with different CYP3A5 and CYP2C19 alleles. First, pharmacokinetic data of humans receiving tacrolimus with or without voriconazole from the literature were used to construct and validate the PBPK model. Thereafter, we developed a model incorporating the metabolism of voriconazole mediated by CYP2C19 and the inhibitory effect of voriconazole on CYP3A4/5. Finally, the model was used to evaluate the dose adjustment of tacrolimus in people with different CYP3A5 and CYP2C19 alleles. When tacrolimus was administered alone (3 mg PO, single dose), the predicted AUC0-∞ of tacrolimus in CYP3A5 nonexpressers (19.22) was 3.5-fold higher than that in expressers (5.48). Following voriconazole (200 mg PO, bid) administration in human with different CYP2C19 genotypes, the AUC0-∞ of tacrolimus increased by 5.1- to 8.3-fold in CYP3A5 expressers and by 5.3- to 10.2-fold in CYP3A5 nonexpressers. The lower the gene expression level of CYP2C19 in the population, the higher the exposure to tacrolimus. When tacrolimus was combined with voriconazole (200 mg, bid; 400 mg, bid, on Day 1), the final model simulations suggested that the dose regimen of tacrolimus should be regulated to 0.15 mg/kg/day (qd) in CYP3A5 expressers with different CYP2C19 genotypes. For CYP3A5 nonexpressers, the dosing schedule of tacrolimus should be modified to 0.05 mg/kg/24 h for patients with 2C19 EM, 0.05 mg/kg/48 h for 2C19 IM and 0.05 mg/kg/72 h for 2C19 PM. In conclusion, a PBPK model with CYP3A5 and CYP2C19 polymorphisms was successfully established, providing more insights regarding the DDIs between tacrolimus and voriconazole to guide the clinical use of tacrolimus.
https://pubmed.ncbi.nlm.nih.gov/36931438/ Toxicol Appl Pharmacol. 2023 May 1;466:116475. Fei Gong, Huihui Hu, Ying Ouyang, Zheng-Zheng Liao, Ying Kong, Jin-Fang Hu, Hua He, Ying Zhou
Abstract The drug-drug interactions (DDIs) between tacrolimus and voriconazole are highly variable among individuals. We aimed to develop a physiologically based pharmacokinetic (PBPK) model to predict the DDIs in people with different CYP3A5 and CYP2C19 alleles. First, pharmacokinetic data of humans receiving tacrolimus with or without voriconazole from the literature were used to construct and validate the PBPK model. Thereafter, we developed a model incorporating the metabolism of voriconazole mediated by CYP2C19 and the inhibitory effect of voriconazole on CYP3A4/5. Finally, the model was used to evaluate the dose adjustment of tacrolimus in people with different CYP3A5 and CYP2C19 alleles. When tacrolimus was administered alone (3 mg PO, single dose), the predicted AUC0-∞ of tacrolimus in CYP3A5 nonexpressers (19.22) was 3.5-fold higher than that in expressers (5.48). Following voriconazole (200 mg PO, bid) administration in human with different CYP2C19 genotypes, the AUC0-∞ of tacrolimus increased by 5.1- to 8.3-fold in CYP3A5 expressers and by 5.3- to 10.2-fold in CYP3A5 nonexpressers. The lower the gene expression level of CYP2C19 in the population, the higher the exposure to tacrolimus. When tacrolimus was combined with voriconazole (200 mg, bid; 400 mg, bid, on Day 1), the final model simulations suggested that the dose regimen of tacrolimus should be regulated to 0.15 mg/kg/day (qd) in CYP3A5 expressers with different CYP2C19 genotypes. For CYP3A5 nonexpressers, the dosing schedule of tacrolimus should be modified to 0.05 mg/kg/24 h for patients with 2C19 EM, 0.05 mg/kg/48 h for 2C19 IM and 0.05 mg/kg/72 h for 2C19 PM. In conclusion, a PBPK model with CYP3A5 and CYP2C19 polymorphisms was successfully established, providing more insights regarding the DDIs between tacrolimus and voriconazole to guide the clinical use of tacrolimus.
Keywords: CYP2C19 polymorphisms; CYP3A5 polymorphisms; Drug-drug interactions; PBPK modeling; Tacrolimus; Voriconazole.
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