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Zoptarelin doxorubicin is a fusion molecule of the chemotherapeutic doxorubicin and a luteinizing hormone-releasing hormone receptor (LHRHR) agonist, designed for drug targeting to LHRHR positive tumors. The aim of this study was to establish a physiologically based pharmacokinetic (PBPK) parent-metabolite model of zoptarelin doxorubicin and to apply it for drug–drug interaction (DDI) potential analysis. The PBPK model was built in a two-step procedure. First, a model for doxorubicin was developed, using clinical data of a doxorubicin study arm. Second, a parent-metabolite model for zoptarelin doxorubicin was built, using clinical data of three different zoptarelin doxorubicin studies with a dosing range of 10–267 mg/m2, integrating the established doxorubicin model. DDI parameters determined in vitro were implemented to predict the impact of zoptarelin doxorubicin on possible victim drugs. In vitro, zoptarelin doxorubicin inhibits the drug transporters organic anion-transporting polypeptide 1B3 (OATP1B3) and organic cation transporter 2 (OCT2). The model was applied to evaluate the in vivo inhibition of these transporters in a generic manner, predicting worst-case scenario decreases of 0.5% for OATP1B3 and of 2.5% for OCT2 transport rates. Specific DDI simulations using PBPK models of simvastatin (OATP1B3 substrate) and metformin (OCT2 substrate) predict no significant changes of the plasma concentrations of these two victim drugs during co-administration. The first whole-body PBPK model of zoptarelin doxorubicin and its active metabolite doxorubicin has been successfully established. Zoptarelin doxorubicin shows no potential for DDIs via OATP1B3 and OCT2.

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. Electronic supplementary material The online version of this article (10.1007/s40262-019-00857-y) contains supplementary material, which is available to authorized users.

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.

The beta-blocker metoprolol (the sixth most commonly prescribed drug in the USA in 2017) is subject to considerable drug&ndash, gene interaction (DGI) effects caused by genetic variations of the CYP2D6 gene. CYP2D6 poor metabolizers (5.7% of US population) show approximately five-fold higher metoprolol exposure compared to CYP2D6 normal metabolizers. This study aimed to develop a whole-body physiologically based pharmacokinetic (PBPK) model to predict CYP2D6 DGIs with metoprolol. The metoprolol (R)- and (S)-enantiomers as well as the active metabolite &alpha, hydroxymetoprolol were implemented as model compounds, employing data of 48 different clinical studies (dosing range 5&ndash, 200 mg). To mechanistically describe the effect of CYP2D6 polymorphisms, two separate metabolic CYP2D6 pathways (&alpha, hydroxylation and O-demethylation) were incorporated for both metoprolol enantiomers. The good model performance is demonstrated in predicted plasma concentration&ndash, time profiles compared to observed data, goodness-of-fit plots, and low geometric mean fold errors of the predicted AUClast (1.27) and Cmax values (1.23) over all studies. For DGI predictions, 18 out of 18 DGI AUClast ratios and 18 out of 18 DGI Cmax ratios were within two-fold of the observed ratios. The newly developed and carefully validated model was applied to calculate dose recommendations for CYP2D6 polymorphic patients and will be freely available in the Open Systems Pharmacology repository.