Interaction Between Domperidone and Ketoconazole: Toward Prediction of Consequent QTc Prolongation Using Purely In Vitro Information

Beyond the Michaelis-Menten: Accurate Prediction of In Vivo Hepatic Clearance for Drugs With Low KM

Clearance (CL) is the major pharmacokinetic parameter for evaluating systemic exposure of drugs in the body and, thus, for developing new drugs. To predict in vivo CL, the ratio between the maximal rate of metabolism and Michaelis‐Menten constant (V_max/K_M estimated from in vitro metabolism study has been widely used. This canonical approach is based on the Michaelis‐Menten equation, which is valid only when the K_M value of a drug is much higher than the hepatic concentration of the enzymes, especially cytochrome P450, involved in its metabolism. Here, we find that such a condition does not hold for many drugs with low K_M, and, thus, the canonical approach leads to considerable error. Importantly, we propose an alternative approach, which incorporates the saturation of drug metabolism when concentration of the enzymes is not sufficiently lower than K_M. This new approach dramatically improves the accuracy of prediction for in vivo CL of high‐affinity drugs with low K_M. This indicates that the proposed approach in this study, rather than the canonical approach, should be used to predict in vivo hepatic CL for high‐affinity drugs, such as midazolam and propafenone.

Integrating in vitro data and physiologically based kinetic modeling-facilitated reverse dosimetry to predict human cardiotoxicity of methadone

Development of novel testing strategies to detect adverse human health effects is of interest to replace in vivo-based drug and chemical safety testing. The aim of the present study was to investigate whether physiologically based kinetic (PBK) modeling-facilitated conversion of in vitro toxicity data is an adequate approach to predict in vivo cardiotoxicity in humans. To enable evaluation of predictions made, methadone was selected as the model compound, being a compound for which data on both kinetics and cardiotoxicity in humans are available. A PBK model for methadone in humans was developed and evaluated against available kinetic data presenting an adequate match. Use of the developed PBK model to convert concentration–response curves for the effect of methadone on human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) in the so-called multi electrode array (MEA) assay resulted in predictions for in vivo dose–response curves for methadone-induced cardiotoxicity that matched the available in vivo data. The results also revealed differences in protein plasma binding of methadone to be a potential factor underlying variation between individuals with respect to sensitivity towards the cardiotoxic effects of methadone. The present study provides a proof-of-principle of using PBK modeling-based reverse dosimetry of in vitro data for the prediction of cardiotoxicity in humans, providing a novel testing strategy in cardiac safety studies.

Development of Prodrugs for PDT-Based Combination Therapy Using a Singlet-Oxygen-Sensitive Linker and Quantitative Systems Pharmacology

Photodynamic therapy (PDT) has become an effective treatment for certain types of solid tumors. The combination of PDT with other therapies has been extensively investigated in recent years to improve its effectiveness and expand its applications. This focused review summarizes the development of a prodrug system in which anticancer drugs are activated locally at tumor sites during PDT treatment. The development of a singlet-oxygen-sensitive linker that can be conveniently conjugated to various drugs and efficiently cleaved to release intact drugs is recapitulated. The initial design of prodrugs, preliminary efficacy evaluation, pharmacokinetics study, and optimization using quantitative systems pharmacology is discussed. Current treatment optimization in animal models using physiologically based a pharmacokinetic (PBPK) modeling approach is also explored.

Cardiovascular disease models: A game changing paradigm in drug discovery and screening

Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.

Humans Vary, So Cardiac Models Should Account for That Too!

The utilization of mathematical modeling and simulation in drug development encompasses multiple mathematical techniques and the location of a drug candidate in the development pipeline. Historically speaking they have been used to analyze experimental data (i.e., Hill equation) and clarify the involved physical and chemical processes (i.e., Fick laws and drug molecule diffusion). In recent years the advanced utilization of mathematical modeling has been an important part of the regulatory review process. Physiologically based pharmacokinetic (PBPK) models identify the need to conduct specific clinical studies, suggest specific study designs and propose appropriate labeling language. Their application allows the evaluation of the influence of intrinsic (e.g., age, gender, genetics, disease) and extrinsic [e.g., dosing schedule, drug-drug interactions (DDIs)] factors, alone or in combinations, on drug exposure and therefore provides accurate population assessment. A similar pathway has been taken for the assessment of drug safety with cardiac safety being one the most advanced examples. Mechanistic mathematical model-informed safety evaluation, with a focus on drug potential for causing arrhythmias, is now discussed as an element of the Comprehensive in vitro Proarrhythmia Assay. One of the pillars of this paradigm is the use of an in silico model of the adult human ventricular cardiomyocyte to integrate in vitro measured data. Existing examples (in vitro—in vivo extrapolation with the use of PBPK models) suggest that deterministic, epidemiological and clinical data based variability models can be merged with the mechanistic models describing human physiology. There are other methods available, based on the stochastic approach and on population of models generated by randomly assigning specific parameter values (ionic current conductance and kinetic) and further pruning. Both approaches are briefly characterized in this manuscript, in parallel with the drug-specific variability.

Quantitative systems toxicology

The overarching goal of modern drug development is to optimize therapeutic benefits while minimizing adverse effects. However, inadequate efficacy and safety concerns remain to be the major causes of drug attrition in clinical development. For the past 80 years, toxicity testing has consisted of evaluating the adverse effects of drugs in animals to predict human health risks. The U.S. Environmental Protection Agency recognized the need to develop innovative toxicity testing strategies and asked the National Research Council to develop a long-range vision and strategy for toxicity testing in the 21st century. The vision aims to reduce the use of animals and drug development costs through the integration of computational modeling and in vitro experimental methods that evaluates the perturbation of toxicity-related pathways. Towards this vision, collaborative quantitative systems pharmacology and toxicology modeling endeavors (QSP/QST) have been initiated amongst numerous organizations worldwide. In this article, we discuss how quantitative structure-activity relationship (QSAR), network-based, and pharmacokinetic/pharmacodynamic modeling approaches can be integrated into the framework of QST models. Additionally, we review the application of QST models to predict cardiotoxicity and hepatotoxicity of drugs throughout their development. Cell and organ specific QST models are likely to become an essential component of modern toxicity testing, and provides a solid foundation towards determining individualized therapeutic windows to improve patient safety.

A physiologically based pharmacokinetic modeling approach to predict drug-drug interactions between domperidone and inhibitors of CYP3A4

Domperidone is a dopamine receptor antagonist and a substrate of CYP3A4, hence there is a potential for CYP3A inhibition-based drug-drug interactions (DDI). A physiologically based pharmacokinetic model was developed to describe DDIs between domperidone and three different inhibitors of CYP3A4. Simcyp V13.1 was used to simulate human domperidone pharmacokinetics and DDIs. Inputs included domperidone chemical and physical properties (LogP, pKa, etc.), in vitro human liver microsomal data and pharmacokinetic parameters from single-dose intravenous clinical studies in healthy participants. The simulated mean maximum domperidone plasma concentration and AUC after single- and multiple-oral doses under diverse conditions were within 1.1-1.4 fold of the observed values. The simulated intestinal availability, hepatic availability and the fraction absorbed were 0.45 ± 0.14, 0.31 ± 0.10 and 0.89 ± 0.11, respectively, and comparable to observed in vivo values. The simulated ratios of AUC and C(max) in the presence of ketoconazole, erythromycin or itraconazole to baseline were consistent with the observed ratios. Simulated ketoconazole, erythromycin, itraconazole and C(max,ss) and AUC(ss) were within 1.5-fold of the observed values.

The promises of quantitative systems pharmacology modelling for drug development

Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.

Pharmacokinetic-pharmacodynamic modelling of drug-induced QTc interval prolongation in man: prediction from in vitro human ether-à-go-go-related gene binding and functional inhibition assays and conscious dog studies

BACKGROUND AND PURPOSE

Functional measures of human ether-à-go-go-related gene (hERG; Kv 11.1) channel inhibition have been prioritized as an in vitro screening tool for candidate molecules. However, it is unclear how these results can be translated to humans. Here, we explore how data on drug binding and functional inhibition in vitro relate to QT prolongation in vivo. Using cisapride, sotalol and moxifloxacin as paradigm compounds, we assessed the relationship between drug concentrations, binding, functional measures and in vivo effects in preclinical species and humans.

EXPERIMENTAL APPROACH

Pharmacokinetic-pharmacodynamic modelling was used to characterize the drug effects in hERG functional patch clamp, hERG radio-labelled dofetilide displacement experiments and QT interval in conscious dogs. Data were analysed in parallel to identify potential correlations between pharmacological activity in vitro and in vivo.

KEY RESULTS

An Emax model could not be used due to large variability in the functional patch clamp assay. Dofetilide displacement revealed that binding curves are unrelated to the in vivo potency estimates for QTc prolongation in dogs and humans. Mean in vitro potency estimates ranged from 99.9 nM for cisapride to 1030 μM for moxifloxacin.

CONCLUSIONS AND IMPLICATIONS

The lack of standardized protocols for in vitro assays leads to significant differences in experimental conditions, making the assessment of in vitro-in vivo correlations unreliable. Identification of an accurate safety window during the screening of candidate molecules requires a quantitative framework that disentangles system- from drug-specific properties under physiological conditions, enabling translation of the results to humans. Similar considerations will be relevant for the comprehensive in vitro pro-arrhythmia assay initiative.

Top-down, Bottom-up and Middle-out Strategies for Drug Cardiac Safety Assessment via Modeling and Simulations

Cardiac safety is an issue causing early terminations at various stages of drug development. Efforts are put into the elimination of false negatives as well as false positives resulting from the current testing paradigm. In silico approaches offer mathematical system and data description from the ion current, through cardiomyocytes level, up to incorporation of inter-individual variability at the population level. The article aims to review three main modelling and simulation approaches, i.e. “top-down” which refers to models built on the observed data, “bottom-up”, which stands for a mechanistic description of human physiology, and “middle-out” which combines both strategies. Modelling and simulation is a well-established tool in the assessment of drug proarrhythmic potency with an impact on research and development as well as on regulatory decisions, and it is certainly here to stay. What is more, the shift to systems biology and physiology-based models makes the cardiac effect more predictable.

How computer simulations of the human heart can improve anti-arrhythmia therapy

Over the last decade, the state-of-the-art in cardiac computational modelling has progressed rapidly. The electrophysiological function of the heart can now be simulated with a high degree of detail and accuracy, opening the doors for simulation-guided approaches to anti-arrhythmic drug development and patient-specific therapeutic interventions. In this review, we outline the basic methodology for cardiac modelling, which has been developed and validated over decades of research. In addition, we present several recent examples of how computational models of the human heart have been used to address current clinical problems in cardiac electrophysiology. We will explore the use of simulations to improve anti-arrhythmic pacing and defibrillation interventions; to predict optimal sites for clinical ablation procedures; and to aid in the understanding and selection of arrhythmia risk markers. Together, these studies illustrate how the tremendous advances in cardiac modelling are poised to revolutionize medical treatment and prevention of arrhythmia.

Drug-drug interactions in pharmacologic management of gastroparesis

BACKGROUND

Gastroparesis is a disorder characterized by delayed gastric emptying due to chronic abnormal gastric motility. The treatment of the disease often entails the co-administration of several classes of pharmacological agents. These agents may be metabolized via the same pathway. Inhibition or induction of a shared metabolic pathway leads to change in the systemic levels of prescribed drugs, possibly leading to undesired clinical outcomes.

PURPOSE

This review discusses different pharmacological treatment for gastroparesis patients and describes the potential for drug-drug interactions (DDIs) in some of the combinations that are currently used. Prokinetic agents such as metoclopramide and domperidone are the cornerstone in treatment of gastroparesis. Antiemetic agents such as promethazine and ondansetron are frequently administered to gastroparesis patients to reduce nausea and vomiting. Gastroparesis is prevalent in diabetic patients and therefore antidiabetic agents are also prescribed. Many of these co-administered drugs are metabolized via common drug metabolizing enzymes and this can trigger potential DDIs. The scientific literature was reviewed from the years 1975-2014 for original research articles and reviews that evaluated DDIs in gastroparesis. Many commonly prescribed combinations were predicted to cause potential DDIs in gastroparesis patients. This review will help inform about potential hazardous combinations. This information will hopefully lead to less adverse effects and more successful gastroparesis management.

Modeling and Simulation Approaches for Cardiovascular Function and Their Role in Safety Assessment

Systems pharmacology modeling and pharmacokinetic-pharmacodynamic (PK/PD) analysis of drug-induced effects on cardiovascular (CV) function plays a crucial role in understanding the safety risk of new drugs. The aim of this review is to outline the current modeling and simulation (M&S) approaches to describe and translate drug-induced CV effects, with an emphasis on how this impacts drug safety assessment. Current limitations are highlighted and recommendations are made for future effort in this vital area of drug research.

Predicting QTc Prolongation in Man From Only In Vitro Data

Mishra et al.(1) in their article "Interaction between domperidone and ketoconazole: toward prediction of consequent QTc prolongation using purely in vitro information" describe the use of physiologically based pharmacokinetic (PBPK) modeling and pharmacodynamic models of cardiac repolarization to predict clinical data from preclinical data. Eliminating the risk of cardiac arrhythmias through delayed repolarization often relies on preclinical data during compound selection. Although there are some limitations, there appears to be significant promise in using this modeling approach.