Physiologically-based Pharmacokinetic (PBPK) Modeling of Simvastatin Drug-Drug Interactions with Rifampin, Clarithromycin and Drug-Gene Interaction with ABCG2
Jan-Georg Wojtyniak (1,2), Nina Hanke (1), Roman Tremmel (2), Matthias Schwab (2,3) and Thorsten Lehr (1)
(1) Clinical Pharmacy, Saarland University, Saarbruecken, Germany (2) Dr. Margarete Fischer-Bosch-Institut für Klinische Pharmakologie, Stuttgart, Germany, (3) Department of Clinical Pharmacology, University Hospital Tübingen, Germany
Objectives: To build and evaluate a PBPK model for simvastatin lactone (SL) for the prediction of drug-drug interactions (DDIs) with CYP3A4 inducers or inhibitors like rifampin or clarithromycin. Additionally, drug-gene interaction (DGI) due to the ABCG2 c.421C>A polymorphism was included.
Methods: PBPK models were built in PK-Sim® modeling software (Version 6.3.2) [1].
For model development physicochemical parameters as well as mean plasma and intestinal concentration-time profiles of SL after oral single dose (SD) and multiple dose (MD) (range 10 to 80 mg) were obtained from literature. For ABCG2 c.421C>A polymorphisms mean hetero- and homozygote profiles were available. Data were separated into internal and external datasets for model development and evaluation, respectively. After model establishment the simvastatin model was coupled to previously developed rifampin and clarithromycin models to predict DDIs [3, 4].
Results: The final model accurately describes the plasma concentration-time profiles of all SL internal and external dataset profiles. Based on homozygote ABCG2 c.421C>A data the heterozygote profile could be successfully predicted with an AUC ratio predicted vs. observed of 0.99. Based on literature ABCG2 c.421C>A increases AUC by 60% and ABCG2 c.421A>A by 111% compared to ABCG2 c.421C>C wild type. Model predicted AUC increase were 37.5% and 105.4% for ABCG2 c.421C>A and ABCG2 c.421A>A, respectively. Furthermore, the DDI with rifampin and clarithromycin were adequately predicted. Co-treatment with rifampin showed significant decreases in AUC of 89.1% for SL. Model predicted rifampin effect on AUC was 91.0%. On the other hand, clarithromycin increases the AUC of SL by 885%. Here the model predicted AUC increases of 801% for SL.
Conclusions: A SL PBPK model including CYP3A4 metabolism as well as ABCG2 transport was successfully developed. The model predicts the effects of genetic polymorphism as well as DDIs due to concomitant use of CYP3A4 inducers or inhibitors. Overall, this model can help to reduce the risk of adverse drug events by improving dosing schemes in personalized medicine.
References:
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