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This study aimed at recommending pediatric dosages of the histone deacetylase (HDAC) inhibitor vorinostat and potentially more effective adult dosing regimens than the approved standard dosing regimen of 400 mg/day, using a comprehensive physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling approach. A PBPK/PD model for vorinostat was developed for predictions in adults and children. It includes the maturation of relevant metabolizing enzymes. The PBPK model was expanded by (1) effect compartments to describe vorinostat concentration–time profiles in peripheral blood mononuclear cells (PBMCs), (2) an indirect response model to predict the HDAC inhibition, and (3) a thrombocyte model to predict the dose-limiting thrombocytopenia. Parameterization of drug and system-specific processes was based on published and unpublished in silico, in vivo, and in vitro data. The PBPK modeling software used was PK-Sim and MoBi. The PBPK/PD model suggests dosages of 80 and 230 mg/m2 for children of 0–1 and 1–17 years of age, respectively. In comparison with the approved standard treatment, in silico trials reveal 11 dosing regimens (9 oral, and 2 intravenous infusion rates) increasing the HDAC inhibition by an average of 31%, prolonging the HDAC inhibition by 181%, while only decreasing the circulating thrombocytes to a tolerable 53%. The most promising dosing regimen prolongs the HDAC inhibition by 509%. Thoroughly developed PBPK models enable dosage recommendations in pediatric patients and integrated PBPK/PD models, considering PD biomarkers (e.g., HDAC activity and platelet count), are well suited to guide future efficacy trials by identifying dosing regimens potentially superior to standard dosing regimens.

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 Voriconazole, a first-line antifungal drug, exhibits nonlinear pharmacokinetics (PK), together with large interindividual variability but a narrow therapeutic range, and markedly inhibits cytochrome P450 (CYP) 3A4 in vivo. This causes difficulties in selecting appropriate dosing regimens of voriconazole and coadministered CYP3A4 substrates. Objective This study aimed to investigate the metabolism of voriconazole in detail to better understand dose- and time-dependent alterations in the PK of the drug, to provide the model basis for safe and effective use according to CYP2C19 genotype, and to assess the potential of voriconazole to cause drug-drug interactions (DDIs) with CYP3A4 substrates in more detail. Methods In vitro assays were carried out to explore time-dependent inhibition (TDI) of CYP3A4 by voriconazole. These results were combined with 93 published concentration-time datasets of voriconazole from clinical trials in healthy volunteers to develop a whole-body physiologically based PK (PBPK) model in PK-Sim(R). The model was evaluated quantitatively with the predicted/observed ratio of the area under the plasma concentration-time curve (AUC), maximum concentration (C-max), and trough concentrations for multiple dosings (C-trough), the geometric mean fold error, as well as visually with the comparison of predicted with observed concentration-time datasets over the full range of recommended intravenous and oral dosing regimens. Results The result of the half maximal inhibitory concentration (IC50) shift assay indicated that voriconazole causes TDI of CYP3A4. The PBPK model evaluation demonstrated a good performance of the model, with 71% of predicted/observed aggregate AUC ratios and all aggregateC(max)ratios from 28 evaluation datasets being within a 0.5- to 2-fold range. For those studies reporting CYP2C19 genotype, 89% of aggregate AUC ratios and all aggregateC(max)ratios were inside a 0.5- to 2-fold range of 44 test datasets. The results of model-based simulations showed that the standard oral maintenance dose of voriconazole 200 mg twice daily would be sufficient for CYP2C19 intermediate metabolizers (IMs; *1/*2, *1/*3, *2/*17, and *2/*2/*17) to reach the tentative therapeutic range of > 1-2 mg/L to

According to current US Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidance documents, physiologically based pharmacokinetic (PBPK) modeling is a powerful tool to explore and quantitatively predict drug-drug interactions (DDIs) and may offer an alternative to dedicated clinical trials. This study provides whole-body PBPK models of rifampicin, itraconazole, clarithromycin, midazolam, alfentanil, and digoxin within the Open Systems Pharmacology (OSP) Suite. All models were built independently, coupled using reported interaction parameters, and mutually evaluated to verify their predictive performance by simulating published clinical DDI studies. In total, 112 studies were used for model development and 57 studies for DDI prediction. 93% of the predicted area under the plasma concentration-time curve (AUC) ratios and 94% of the peak plasma concentration (Cmax ) ratios are within twofold of the observed values. This study lays a cornerstone for the qualification of the OSP platform with regard to reliable PBPK predictions of enzyme-mediated and transporter-mediated DDIs during model-informed drug development. All presented models are provided open-source and transparently documented.

Clarithromycin is a substrate and mechanism-based inhibitor of cytochrome P450 (CYP) 3A4 as well as a substrate and competitive inhibitor of P-glycoprotein (P-gp) and organic anion-transporting polypeptides (OATP) 1B1 and 1B3. Administered concomitantly, clarithromycin causes drug–drug interactions (DDI) with the victim drugs midazolam (CYP3A4 substrate) and digoxin (P-gp substrate). The objective of the presented study was to build a physiologically based pharmacokinetic (PBPK) DDI model for clarithromycin, midazolam, and digoxin and to exemplify dosing adjustments under clarithromycin co-treatment. The PBPK model development included an extensive literature search for representative PK studies and for compound characteristics of clarithromycin, midazolam, and digoxin. Published concentration-time profiles were used for model development (training dataset), and published and unpublished individual profiles were used for model evaluation (evaluation dataset). The developed single-compound PBPK models were linked for DDI predictions. The full clarithromycin DDI model successfully predicted the metabolic (midazolam) and transporter (digoxin) DDI, the acceptance criterion (0.5 ≤ AUCratio,predicted/AUCratio,observed ≤ 2) was met by all predictions. During co-treatment with 250 or 500 mg clarithromycin (bid), the midazolam and digoxin doses should be reduced by 74 to 88% and by 21 to 22%, respectively, to ensure constant midazolam and digoxin exposures (AUC). With these models, we provide highly mechanistic tools to help researchers understand and characterize the DDI potential of new molecular entities and inform the design of DDI studies with potential CYP3A4 and P-gp substrates.