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 transportermediated DDIs during model-informed drug development. All presented models are provided open-source and transparently documented

Physiologically Based Pharmacokinetic Models for Prediction of Complex CYP2C8 and OATP1B1 (SLCO1B1) Drug-Drug-Gene Interactions : A Modeling Network of Gemfibrozil, Repaglinide, Pioglitazone, Rifampicin, Clarithromycin and Itraconazole

Background Drug–drug interactions (DDIs) and drug–gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the efect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogenous regarding their genetic background. Objectives The frst objective of this study was to provide whole-body physiologically based pharmacokinetic (PBPK) models of important cytochrome P450 (CYP) 2C8 perpetrator and victim drugs, built and evaluated for DDI and DGI studies. The second objective was to apply these models to describe complex interactions with more than two interacting partners. Methods PBPK models of the CYP2C8 and organic-anion-transporting polypeptide (OATP) 1B1 perpetrator drug gemfbrozil (parent–metabolite model) and the CYP2C8 victim drugs repaglinide (also an OATP1B1 substrate) and pioglitazone were developed using a total of 103 clinical studies. For evaluation, these models were applied to predict 34 diferent DDI studies, establishing a CYP2C8 and OATP1B1 PBPK DDI modeling network. Results The newly developed models show a good performance, accurately describing plasma concentration–time profles, area under the plasma concentration–time curve (AUC) and maximum plasma concentration (Cmax) values, DDI studies as well as DGI studies. All 34 of the modeled DDI AUC ratios (AUC during DDI/AUC control) and DDI Cmax ratios (Cmax during DDI/Cmax control) are within twofold of the observed values. Conclusions Whole-body PBPK models of gemfbrozil, repaglinide, and pioglitazone have been built and qualifed for DDI and DGI prediction. PBPK modeling is applicable to investigate complex interactions between multiple drugs and genetic polymorphisms

Physiologically-Based Pharmacokinetic Models for CYP1A2 Drug-Drug Interaction Prediction: A Modeling Network of Fluvoxamine, Theophylline, Caffeine, Rifampicin, and Midazolam

This study provides whole-body physiologically-based pharmacokinetic models of the strong index cytochrome P450 (CYP)1A2 inhibitor and moderate CYP3A4 inhibitor fluvoxamine and of the sensitive CYP1A2 substrate theophylline. Both models were built and thoroughly evaluated for their application in drug-drug interaction (DDI) prediction in a network of perpetrator and victim drugs, combining them with previously developed models of caffeine (sensitive index CYP1A2 substrate), rifampicin (moderate CYP1A2 inducer), and midazolam (sensitive index CYP3A4 substrate). Simulation of all reported clinical DDI studies for combinations of these five drugs shows that the presented models reliably predict the observed drug concentrations, resulting in seven of eight of the predicted DDI area under the plasma curve (AUC) ratios (AUC during DDI/AUC control) and seven of seven of the predicted DDI peak plasma concentration (Cmax ) ratios (Cmax during DDI/Cmax control) within twofold of the observed values. Therefore, the models are considered qualified for DDI prediction. All models are comprehensively documented and publicly available, as tools to support the drug development and clinical research community

Physiologically based pharmacokinetic modeling of tacrolimus for food-drug and CYP3A drug-drug-gene interaction predictions

The immunosuppressant and narrow therapeutic index drug tacrolimus is metabolized mainly via cytochrome P450 (CYP) 3A4 and CYP3A5. For its pharmacokinetics (PK), high inter- and intra-individual variability can be observed. Underlying causes include the effect of food intake on tacrolimus absorption as well as genetic polymorphism in the CYP3A5 gene. Furthermore, tacrolimus is highly susceptible to drug–drug interactions, acting as a victim drug when coadministered with CYP3A perpetrators. This work describes the development of a whole-body physiologically based pharmacokinetic model for tacrolimus as well as its application for investigation and prediction of (i) the impact of food intake on tacrolimus PK (food–drug interactions [FDIs]) and (ii) drug–drug(−gene) interactions (DD[G]Is) involving the CYP3A perpetrator drugs voriconazole, itraconazole, and rifampicin. The model was built in PK-Sim® Version 10 using a total of 37 whole blood concentration–time profiles of tacrolimus (training and test) compiled from 911 healthy individuals covering the administration of tacrolimus as intravenous infusions as well as immediate-release and extended-release capsules. Metabolism was incorporated via CYP3A4 and CYP3A5, with varying activities implemented for different CYP3A5 genotypes and study populations. The good predictive model performance is demonstrated for the examined food effect studies with 6/6 predicted FDI area under the curve determined between first and last concentration measurements (AUClast) and 6/6 predicted FDI maximum whole blood concentration (Cmax) ratios within twofold of the respective observed ratios. In addition, 7/7 predicted DD(G)I AUClast and 6/7 predicted DD(G)I Cmax ratios were within twofold of their observed values. Potential applications of the final model include model-informed drug discovery and development or the support of model-informed precision dosing

Population pharmacokinetic and pharmacodynamic modeling of epinephrine administered using a mobile inhaler

Inhaled epinephrine is a potential alternative to self-administered intramuscular epinephrine in imminent anaphylactic reactions. The objective was to develop a pharmacokinetic-pharmacodynamic model describing exposure and effects on heart rate of inhaled epinephrine. Data from a 4-phase cross-over clinical trial in 9 healthy volunteers including 0.3 mg intramuscular epinephrine, two doses of inhaled epinephrine (4 mg/mL solution administered during [mean] 18 and 25 min, respectively) using a mobile pocket inhaler, and an inhaled placebo were analyzed using mixed-effects modeling. Inhaled epinephrine was available almost immediately and more rapidly than via the intramuscular route (absorption half-live 29 min). Epinephrine plasma concentrations declined rapidly after terminating inhalation (elimination half-life 4.1 min) offering the option to stop exposure in case of adverse events. While the expected maximum concentration was higher for inhaled epinephrine, this was not associated with safety concerns due to only moderate additional hemodynamic effects compared to intramuscular administration. Bioavailability after inhalation (4.7%) was subject to high interindividual and inter-occasional variability highlighting that training of inhalation would be essential for patients. The proposed model suggests that the use of a highly concentrated epinephrine solution via inhalation may offer an effective treatment option in anaphylaxis, while efficacy in patients remains to be shown. Copyright (C) 2015, The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved

Optimization of linezolid therapy in the critically ill: the effect of adjusted infusion regimens

Objectives: Insufficient linezolid levels, which are associated with a poorer outcome, are often observed in ICU patients who receive standard dosing. Although strategies to overcome these insufficient levels have been discussed, appropriate alternative dosing regimens remain to be identified. Methods: Various infusion regimens (1200-3600 mg/day; q6h, q8h, q12h and continuous) were simulated in 67000 ICU patients. The probability of attaining pharmacodynamic targets (T > MIC >= 85%, AUC/MIC >= 100, cumulative fraction of response for Staphylococcus aureus and Enterococcus spp., PTA for an MIC of 0.5-4 mg/L) as well as the avoidance of toxic concentrations and concentrations constantly below the MIC (lack of antibiotic effect) or inside amutant selection window (resistance development) were evaluated. Results: Best target attainment according to T > MIC was observed for continuous infusions, followed by q6h, q8h and q12h. A substantially reduced target attainment was observed in patients with acute respiratory distress syndrome (ARDS). In patients without ARDS, 1200 mg/day was insufficient irrespective of the regimen, while a dose of 1400 mg/day administered q6h or by continuous infusions provided an acceptable target attainment (e.g. cumulative fraction of response with regards to T > MIC >= 93%). Higher rates of potentially toxic trough concentrations (28% versus 12%) and concentrations constantly inside the mutant selection window (15% versus, 0.1%) were observed with continuous infusions compared with q6h infusions (1400 mg/day, patients without ARDS). Conclusions: Irrespective of the regimen, 1200 mg/day linezolid might be insufficient for the treatment of ICU patients. Patients without ARDS might particularly benefit from q6h infusions with increased daily doses (e.g. 1400mg/day)

A generic framework for the physiologically‐based pharmacokinetic platform qualification of PK‐Sim and its application to predicting cytochrome P450 3A4–mediated drug–drug interactions

Abstract The success of applications of physiologically‐based pharmacokinetic (PBPK) modeling in drug development and drug labeling has triggered regulatory agencies to demand rigorous demonstration of the predictive capability of the specific PBPK platform for a particular intended application purpose. The effort needed to comply with such qualification requirements exceeds the costs for any individual PBPK application. Because changes or updates of a PBPK platform would require (re‐)qualification, a reliable and efficient generic qualification framework is needed. We describe the development and implementation of an agile and sustainable technical framework for automatic PBPK platform (re‐)qualification of PK‐Sim® embedded in the open source and open science GitHub landscape of Open Systems Pharmacology. The qualification approach enables the efficient assessment of all aspects relevant to the qualification of a particular purpose and provides transparency and traceability for all stakeholders. As a showcase example for the power and versatility of the qualification framework, we present the qualification of PK‐Sim® for the intended purpose of predicting cytochrome P450 3A4 (CYP3A4)–mediated drug–drug interactions (DDIs). Several perpetrator PBPK models featuring various degrees of CYP3A4 modulation and different types of mechanisms (competitive inhibition, mechanism‐based inactivation, and induction) were coupled with a set of PBPK models of sensitive CYP3A4 victim drugs. Simulations were compared to a comprehensive data set of 135 observations from published clinical DDI studies. The platform's overall predictive performance showed reasonable accuracy and precision (geometric mean fold error of 1.4 for both area under the plasma concentration‐time curve ratios and peak plasma concentration ratios with/without perpetrator) and suggests that PK‐Sim® can be applied to quantitatively assess CYP3A4‐mediated DDI in clinically untested scenarios

Modeling Tumor Dynamics and Overall Survival in Advanced Non-Small-Cell Lung Cancer Treated with Erlotinib

Introduction: Pharmacostatistical models can quantify different relationships and improve decision making in personalized medicine and drug development. Our objectives were to develop models describing non-small-cell lung cancer (NSCLC) dynamics during first-line treatment with erlotinib, and survival of the cohort. Methods: Data from patients with advanced NSCLC (n = 39) treated first-line with erlotinib (150 mg/day) were analyzed using nonlinear mixed effects modeling. Exposure-driven disease-drug models were built to describe tumor metabolic and proliferative dynamics evaluated by positron emission tomography (PET) using 2'-deoxy-2'-[F-18] fluoro-D-glucose (FDG) and 3'-[F-18] fluoro-3'-deoxy-L-thymidine (FLT), respectively, at baseline, weeks 1 and 6 after starting erlotinib treatment. A parametric time-to-event model was built to describe overall survival (OS). Demographics, histology, mutational, smoking, and baseline performance statuses were tested for their effects on models developed, in addition to tumor dynamics on survival. Results: An exponential relationship described progression, and a concentration-driven drug effect model described erlotinib effect. An activating epidermal growth factor receptor (EGFR) mutation increased the drug effect as assessed using FDG-PET by 2.19-fold (95% confidence interval [CI]: 1.35-4.44). An exponential distribution described the times-to-death distribution. Baseline FDG uptake (p=0.0005; hazard ratio [HR] = 1.26 for every unit increase, 95% CI: 1.13-1.42) and relative change in FDG uptake after 1 week of treatment (p=0.0073; HR=0.84 for every 10% drop, 95% CI: 0.71-0.91) were significant OS predictors irrespective of the EGFR mutational status. FLT-PET was statistically less significant than FDG-PET for OS prediction. Conclusion: Models describing tumor dynamics and survival of advanced NSCLC patients first-treated with erlotinib were developed. The impacts of different covariates were quantified