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.

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The noradrenaline and dopamine reuptake inhibitor bupropion is metabolized by CYP2B6 and recommended by the FDA as the only sensitive substrate for clinical CYP2B6 drug–drug interaction (DDI) studies. The aim of this study was to build a whole-body physiologically based pharmacokinetic (PBPK) model of bupropion including its DDI-relevant metabolites, and to qualify the model using clinical drug–gene interaction (DGI) and DDI data. The model was built in PK-Sim® applying clinical data of 67 studies. It incorporates CYP2B6-mediated hydroxylation of bupropion, metabolism via CYP2C19 and 11β-HSD, as well as binding to pharmacological targets. The impact of CYP2B6 polymorphisms is described for normal, poor, intermediate, and rapid metabolizers, with various allele combinations of the genetic variants CYP2B6*1, *4, *5 and *6. DDI model performance was evaluated by prediction of clinical studies with rifampicin (CYP2B6 and CYP2C19 inducer), fluvoxamine (CYP2C19 inhibitor) and voriconazole (CYP2B6 and CYP2C19 inhibitor). Model performance quantification showed 20/20 DGI ratios of hydroxybupropion to bupropion AUC ratios (DGI AUCHBup/Bup ratios), 12/13 DDI AUCHBup/Bup ratios, and 7/7 DDGI AUCHBup/Bup ratios within 2-fold of observed values. The developed model is freely available in the Open Systems Pharmacology model repository.

Background Application of idarucizumab and hemodialysis are options to reverse the action of the oral anticoagulant dabigatran in emergency situations. Objectives The objectives of this study were to build and evaluate a mechanistic, whole-body physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) model of idarucizumab, including its effects on dabigatran plasma concentrations and blood coagulation, in healthy and renally impaired individuals, and to include the effect of hemodialysis on dabigatran exposure. Methods The idarucizumab model was built with the software packages PK-Sim® and MoBi® and evaluated using the full range of available clinical data. The default kidney structure in MoBi® was extended to mechanistically describe the renal reabsorption of idarucizumab and to correctly reproduce the reported fractions excreted into urine. To model the PD effects of idarucizumab on dabigatran plasma concentrations, and consequently also on blood coagulation, idarucizumab-dabigatran binding was implemented and a previously established PBPK model of dabigatran was expanded to a PBPK/PD model. The effect of hemodialysis on dabigatran was implemented by the addition of an extracorporeal dialyzer compartment with a clearance process governed by dialysate and blood flow rates. Results The established idarucizumab-dabigatran-hemodialysis PBPK/PD model shows a good descriptive and predictive performance. To capture the clinical data of patients with renal impairment, both glomerular filtration and tubular reabsorption were modeled as functions of the individual creatinine clearance. Conclusions A comprehensive and mechanistic PBPK/PD model to study dabigatran reversal has been established, which includes whole-body PBPK modeling of idarucizumab, the idarucizumab-dabigatran interaction, dabigatran hemodialysis, the pharmacodynamic effect of dabigatran on blood coagulation, and the impact of renal function in these different scenarios. The model was applied to explore different reversal scenarios for dabigatran therapy.

The antihypertensive felodipine is a calcium channel blocker of the dihydropyridine type, and its pharmacodynamic effect directly correlates with its plasma concentration. As a sensitive substrate of cytochrome P450 (CYP) 3A4 with high first-pass metabolism, felodipine shows low oral bioavailability and is susceptible to drug–drug interactions (DDIs) with CYP3A4 perpetrators. This study aimed to develop a physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) parent–metabolite model of felodipine and its metabolite dehydrofelodipine for DDI predictions. The model was developed in PK-Sim® and MoBi® using 49 clinical studies (94 plasma concentration–time profiles in total) that investigated different doses (1–40 mg) of the intravenous and oral administration of felodipine. The final model describes the metabolism of felodipine to dehydrofelodipine by CYP3A4, sufficiently capturing the first-pass metabolism and the subsequent metabolism of dehydrofelodipine by CYP3A4. Diastolic blood pressure and heart rate PD models were included, using an Emax function to describe the felodipine concentration–effect relationship. The model was tested in DDI predictions with itraconazole, erythromycin, carbamazepine, and phenytoin as CYP3A4 perpetrators, with all predicted DDI AUClast and Cmax ratios within two-fold of the observed values. The model will be freely available in the Open Systems Pharmacology model repository and can be applied in DDI predictions as a CYP3A4 victim drug.