https://pubmed.ncbi.nlm.nih.gov/36808892/
Helena Leonie Hanae Loer, Denise Feick, Simeon Rüdesheim 2 , Dominik Selzer, Matthias Schwab, Donato Teutonico, Sebastian Frechen, Maaike van der Lee, Dirk Jan A R Moes, Jesse J Swen, Thorsten Lehr
CPT Pharmacometrics Syst Pharmacol. 2023 Feb 19. doi: 10.1002/psp4.12946
Abstract
The immunosuppressant and narrow therapeutic index drug tacrolimus is metabolized mainly via cytochrome P450 (CYP) 3A4 and CYP3A5. For its pharmacokinetics (PK), a high inter- and intra-individual variability can be observed. Underlying causes include the effect of food intake on tacrolimus absorption as well as genetic polymorphism in the CYP3A5 gene. Furthermore, tacrolimus is highly susceptible to drug-drug interactions (DDIs), acting as a victim drug when co-administered with CYP3A perpetrators. This work describes the development of a whole-body physiologically based pharmacokinetic (PBPK) model for tacrolimus as well as its application for investigation and prediction of (i) the impact of food intake on tacrolimus PK (food-drug interactions, FDIs) and (ii) drug-drug(-gene) interactions (DD(G)Is) involving the CYP3A perpetrator drugs voriconazole, itraconazole, and rifampicin. The model was built in PK-Sim® Version 10 using a total of 37 whole blood concentration-time profiles of tacrolimus (training and test) compiled from 911 healthy individuals covering the administration of tacrolimus as intravenous infusions as well as immediate-release and extended-release capsules. Metabolism was incorporated via CYP3A4 and CYP3A5, with varying activities implemented for different CYP3A5 genotypes and study populations. The good predictive model performance is demonstrated for the examined food effect studies with 6/6 predicted FDI AUClast and 6/6 predicted FDI Cmax ratios within the limits proposed by Guest et al. In addition, 7/7 predicted DD(G)I AUClast and 6/7 predicted DD(G)I Cmax ratios were within the Guest-limits. Potential applications of the final model include model-informed drug discovery and development or the support of model-informed precision dosing.
https://pubmed.ncbi.nlm.nih.gov/36808892/ Helena Leonie Hanae Loer, Denise Feick, Simeon Rüdesheim 2 , Dominik Selzer, Matthias Schwab, Donato Teutonico, Sebastian Frechen, Maaike van der Lee, Dirk Jan A R Moes, Jesse J Swen, Thorsten Lehr CPT Pharmacometrics Syst Pharmacol. 2023 Feb 19. doi: 10.1002/psp4.12946
Abstract
The immunosuppressant and narrow therapeutic index drug tacrolimus is metabolized mainly via cytochrome P450 (CYP) 3A4 and CYP3A5. For its pharmacokinetics (PK), a high inter- and intra-individual variability can be observed. Underlying causes include the effect of food intake on tacrolimus absorption as well as genetic polymorphism in the CYP3A5 gene. Furthermore, tacrolimus is highly susceptible to drug-drug interactions (DDIs), acting as a victim drug when co-administered with CYP3A perpetrators. This work describes the development of a whole-body physiologically based pharmacokinetic (PBPK) model for tacrolimus as well as its application for investigation and prediction of (i) the impact of food intake on tacrolimus PK (food-drug interactions, FDIs) and (ii) drug-drug(-gene) interactions (DD(G)Is) involving the CYP3A perpetrator drugs voriconazole, itraconazole, and rifampicin. The model was built in PK-Sim® Version 10 using a total of 37 whole blood concentration-time profiles of tacrolimus (training and test) compiled from 911 healthy individuals covering the administration of tacrolimus as intravenous infusions as well as immediate-release and extended-release capsules. Metabolism was incorporated via CYP3A4 and CYP3A5, with varying activities implemented for different CYP3A5 genotypes and study populations. The good predictive model performance is demonstrated for the examined food effect studies with 6/6 predicted FDI AUClast and 6/6 predicted FDI Cmax ratios within the limits proposed by Guest et al. In addition, 7/7 predicted DD(G)I AUClast and 6/7 predicted DD(G)I Cmax ratios were within the Guest-limits. Potential applications of the final model include model-informed drug discovery and development or the support of model-informed precision dosing.