scholarly journals Pharmacokinetics of the CYP3A4 and CYP2B6 Inducer Carbamazepine and Its Drug–Drug Interaction Potential: A Physiologically Based Pharmacokinetic Modeling Approach

Pharmaceutics ◽  
2021 ◽  
Vol 13 (2) ◽  
pp. 270
Author(s):  
Laura Maria Fuhr ◽  
Fatima Zahra Marok ◽  
Nina Hanke ◽  
Dominik Selzer ◽  
Thorsten Lehr
Keyword(s):  
Drug Interaction ◽  
Glomerular Filtration ◽  
Open Systems ◽  
Term Therapy ◽  
Time Profiles ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Drug Drug Interaction ◽  
Long Term Therapy

The anticonvulsant carbamazepine is frequently used in the long-term therapy of epilepsy and is a known substrate and inducer of cytochrome P450 (CYP) 3A4 and CYP2B6. Carbamazepine induces the metabolism of various drugs (including its own); on the other hand, its metabolism can be affected by various CYP inhibitors and inducers. The aim of this work was to develop a physiologically based pharmacokinetic (PBPK) parent−metabolite model of carbamazepine and its metabolite carbamazepine-10,11-epoxide, including carbamazepine autoinduction, to be applied for drug–drug interaction (DDI) prediction. The model was developed in PK-Sim, using a total of 92 plasma concentration−time profiles (dosing range 50–800 mg), as well as fractions excreted unchanged in urine measurements. The carbamazepine model applies metabolism by CYP3A4 and CYP2C8 to produce carbamazepine-10,11-epoxide, metabolism by CYP2B6 and UDP-glucuronosyltransferase (UGT) 2B7 and glomerular filtration. The carbamazepine-10,11-epoxide model applies metabolism by epoxide hydroxylase 1 (EPHX1) and glomerular filtration. Good DDI performance was demonstrated by the prediction of carbamazepine DDIs with alprazolam, bupropion, erythromycin, efavirenz and simvastatin, where 14/15 DDI AUClast ratios and 11/15 DDI Cmax ratios were within the prediction success limits proposed by Guest et al. The thoroughly evaluated model will be freely available in the Open Systems Pharmacology model repository.

scholarly journals A Mechanistic, Enantioselective, Physiologically Based Pharmacokinetic Model of Verapamil and Norverapamil, Built and Evaluated for Drug–Drug Interaction Studies

Pharmaceutics ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 556 ◽  
Author(s):  
Nina Hanke ◽  
Denise Türk ◽  
Dominik Selzer ◽  
Sabrina Wiebe ◽  
Éric Fernandez ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Pharmacokinetic Model ◽  
Competitive Inhibition ◽  
Open Systems ◽  
Whole Body ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Drug Drug Interaction ◽  
Enantioselective Metabolism

The calcium channel blocker and antiarrhythmic agent verapamil is recommended by the FDA for drug–drug interaction (DDI) studies as a moderate clinical CYP3A4 index inhibitor and as a clinical Pgp inhibitor. The purpose of the presented work was to develop a mechanistic whole-body physiologically based pharmacokinetic (PBPK) model to investigate and predict DDIs with verapamil. The model was established in PK-Sim®, using 45 clinical studies (dosing range 0.1–250 mg), including literature as well as unpublished Boehringer Ingelheim data. The verapamil R- and S-enantiomers and their main metabolites R- and S-norverapamil are represented in the model. The processes implemented to describe the pharmacokinetics of verapamil and norverapamil include enantioselective plasma protein binding, enantioselective metabolism by CYP3A4, non-stereospecific Pgp transport, and passive glomerular filtration. To describe the auto-inhibitory and DDI potential, mechanism-based inactivation of CYP3A4 and non-competitive inhibition of Pgp by the verapamil and norverapamil enantiomers were incorporated based on in vitro literature. The resulting DDI performance was demonstrated by prediction of DDIs with midazolam, digoxin, rifampicin, and cimetidine, with 21/22 predicted DDI AUC ratios or Ctrough ratios within 1.5-fold of the observed values. The thoroughly built and qualified model will be freely available in the Open Systems Pharmacology model repository to support model-informed drug discovery and development.

scholarly journals Prediction of Drug–Drug Interaction Potential of Tegoprazan Using Physiologically Based Pharmacokinetic Modeling and Simulation

Pharmaceutics ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1489
Author(s):  
Deok Yong Yoon ◽  
SeungHwan Lee ◽  
In-Jin Jang ◽  
Myeongjoong Kim ◽  
Heechan Lee ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Pbpk Model ◽  
Phase 1 ◽  
Dose Range ◽  
Systemic Exposure ◽  
Multiple Dosing ◽  
Suppression Effect ◽  
Physiologically Based Pharmacokinetic ◽  
Physiologically Based ◽  
Drug Drug Interaction

This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of tegoprazan and to predict the drug–drug interaction (DDI) potential between tegoprazan and cytochrome P450 (CYP) 3A4 perpetrators. The PBPK model of tegoprazan was developed using SimCYP Simulator® and verified by comparing the model-predicted pharmacokinetics (PKs) of tegoprazan with the observed data from phase 1 clinical studies, including DDI studies. DDIs between tegoprazan and three CYP3A4 perpetrators were predicted by simulating the difference in tegoprazan exposure with and without perpetrators, after multiple dosing for a clinically used dose range. The final PBPK model adequately predicted the biphasic distribution profiles of tegoprazan and DDI between tegoprazan and clarithromycin. All ratios of the predicted-to-observed PK parameters were between 0.5 and 2.0. In DDI simulation, systemic exposure to tegoprazan was expected to increase about threefold when co-administered with the maximum recommended dose of clarithromycin or ketoconazole. Meanwhile, tegoprazan exposure was expected to decrease to ~30% when rifampicin was co-administered. Based on the simulation by the PBPK model, it is suggested that the DDI potential be considered when tegoprazan is used with CYP3A4 perpetrator, as the acid suppression effect of tegoprazan is known to be associated with systemic exposure.

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