A Physiologically Based Pharmacokinetic Model of Voriconazole Integrating Time-Dependent Inhibition of CYP3A4, Genetic Polymorphisms of CYP2C19 and Predictions of Drug-Drug Interactions.

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®. The model was evaluated quantitatively with the predicted/observed ratio of the area under the plasma concentration-time curve (AUC), maximum concentration (Cmax), and trough concentrations for multiple dosings (Ctrough), 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 aggregate Cmax 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 aggregate Cmax 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 < 5-6 mg/L for Ctrough, while 400 mg twice daily might be more suitable for rapid metabolizers (RMs; *1/*17, *17/*17) and normal metabolizers (NMs; *1/*1). When the model was integrated with independently developed CYP3A4 substrate models (midazolam and alfentanil), the observed AUC change of substrates by voriconazole was inside the 90% confidence interval of the predicted AUC change, indicating that CYP3A4 inhibition was appropriately incorporated into the voriconazole model. CONCLUSIONS: Both the in vitro assay and model-based simulations support TDI of CYP3A4 by voriconazole as a pivotal characteristic of this drug's PK. The PBPK model developed here could support individual dose adjustment of voriconazole according to genetic polymorphisms of CYP2C19, and DDI risk management. The applicability of modeling results for patients remains to be confirmed in future studies.

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Model of Dabigatran Etexilate, Dabigatran and Dabigatran Glucuronide in Healthy Adults and Renally Impaired Patients.

BACKGROUND AND OBJECTIVES: The thrombin inhibitor dabigatran is administered as the prodrug dabigatran etexilate, which is a substrate of esterases and P-glycoprotein (P-gp). Dabigatran is eliminated via renal excretion but is also a substrate of uridine 5'-diphospho (UDP)-glucuronosyltransferases (UGTs). The objective of this study was to build a physiologically based pharmacokinetic (PBPK) model comprising dabigatran etexilate, dabigatran, and dabigatran 1-O-acylglucuronide to describe the pharmacokinetics in healthy adults and renally impaired patients mechanistically. METHODS: Model development and evaluation were carried out using (i) physicochemical and absorption, distribution, metabolism, and excretion (ADME) parameter values of all three analytes; (ii) concentration-time profiles from 13 studies of healthy and renally impaired individuals after varying doses (0.1-300 mg), intravenous (dabigatran) and oral (dabigatran etexilate) administration, and different formulations of dabigatran etexilate (capsule, solution); and (iii) drug-drug interaction studies of dabigatran with the P-gp perpetrators rifampin (inducer) and clarithromycin (inhibitor). RESULTS: A PBPK model of dabigatran was successfully developed. The predicted area under the plasma concentration-time curve, trough concentration, and half-life values of the assessed clinical studies satisfied the two-fold acceptance criterion. Metabolic clearances of dabigatran etexilate and dabigatran were implemented using data on carboxylesterase 1/2 enzymes and UGT subtype 2B15. In severe renal impairment, the UGT2B15 metabolism and the P-gp transport in the model were reduced to 67% and 65% of the rates in healthy adults. CONCLUSION: This is the first implementation of a PBPK model for dabigatran to distinguish between the prodrug, active moiety, and main active metabolite. Following adjustment of the UGT2B15 metabolism and P-gp transport rates, the PBPK model accurately predicts the pharmacokinetics in renally impaired patients.

PBPK Models for CYP3A4 and P-gp DDI Prediction: A Modeling Network of Rifampicin, Itraconazole, Clarithromycin, Midazolam, Alfentanil, and Digoxin.

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.

The feasibility of physiologically based pharmacokinetic modeling in forensic medicine illustrated by the example of morphine.

In forensic medicine, expert opinion is often required concerning dose and time of intake of a substance, especially in the context of fatal intoxications. In the present case, a 98-year-old man died 4 days after admission to a hospital due to a femur neck fracture following a domestic fall in his retirement home. As he had obtained high morphine doses in the context of palliative therapy and a confusion of his supplemental magnesium tablets with a diuretic by the care retirement home was suspected by the relatives, a comprehensive postmortem examination was performed. Forensic toxicological GC- and LC-MS analyses revealed, besides propofol, ketamine, and a metamizole metabolite in blood and urine, toxic blood morphine concentrations of approximately 3 mg/l in femoral and 5 mg/l in heart blood as well as 2, 7, and 10 mg/kg morphine in brain, liver, and lung, respectively. A physiologically based pharmacokinetic (PBPK) model was developed and applied to examine whether the morphine concentrations were (i) in agreement with the morphine doses documented in the clinical records or (ii) due to an excessive morphine administration. PBPK model simulations argue against an overdosing of morphine. The immediate cause of death was respiratory and cardiovascular failure due to pneumonia following a fall, femur neck fracture, and immobilization accompanied by a high and probably toxic concentration of morphine, attributable to the administration under palliative care conditions. The presented case indicates that PBPK modeling can be a useful tool in forensic medicine, especially in question of a possible drug overdosing.

A physiologically based pharmacokinetic (PBPK) parent-metabolite model of the chemotherapeutic zoptarelin doxorubicin-integration of in vitro results, Phase I and Phase II data and model application for drug-drug interaction potential analysis.

PURPOSE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

A physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model of the histone deacetylase (HDAC) inhibitor vorinostat for pediatric and adult patients and its application for dose specification.

PURPOSE: 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. METHODS: 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. RESULTS: 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%. CONCLUSIONS: 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.

Clarithromycin, Midazolam, and Digoxin: Application of PBPK Modeling to Gain New Insights into Drug-Drug Interactions and Co-medication Regimens.

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.