Dynamically simulating the interaction of midazolam and the CYP3A4 inhibitor itraconazole using individual coupled whole-body physiologically-based pharmacokinetic (WB-PBPK) models

Effects of CYP3A4 inhibition/induction and OATP inhibition on the pharmacokinetics of atogepant in healthy adults

Aim: Atogepant, a calcitonin gene-related peptide (CGRP) receptor antagonist, is a substrate of OATP and metabolized by CYP3A4. Effect of multiple-dose itraconazole (strong CYP3A4 inhibitor), single-dose rifampin (strong OATP inhibitor) and multiple-dose rifampin (strong CYP3A4 inducer) on single-dose pharmacokinetics (PK) and safety of atogepant were assessed. Methods: Two phase I, open-label, single-center, crossover trials enrolled healthy adults. Results: Cmax and AUC of atogepant increased when co-administered with itraconazole. Atogepant systemic exposure increased following co-administration with single-dose rifampin. Atogepant systemic exposure decreased with co-administration of multiple-dose rifampin. Treatment emergent adverse events (TEAEs) were predominantly mild or moderate, and included constipation, dizziness, headache and nauseas. Conclusion: Systemic exposure of atogepant increased significantly when co-administered with a strong CYP3A4 or OATP inhibitor and decreased significantly when co-administered with a strong CYP3A4 inducer.

Analysis of cellular kinetic models suggest that physiologically based model parameters may be inherently, practically unidentifiable

Physiologically-based pharmacokinetic and cellular kinetic models are used extensively to predict concentration profiles of drugs or adoptively transferred cells in patients and laboratory animals. Models are fit to data by the numerical optimisation of appropriate parameter values. When quantities such as the area under the curve are all that is desired, only a close qualitative fit to data is required. When the biological interpretation of the model that produced the fit is important, an assessment of uncertainties is often also warranted. Often, a goal of fitting PBPK models to data is to estimate parameter values, which can then be used to assess characteristics of the fit system or applied to inform new modelling efforts and extrapolation, to inform a prediction under new conditions. However, the parameters that yield a particular model output may not necessarily be unique, in which case the parameters are said to be unidentifiable. We show that the parameters in three published physiologically-based pharmacokinetic models are practically (deterministically) unidentifiable and that it is challenging to assess the associated parameter uncertainty with simple curve fitting techniques. This result could affect many physiologically-based pharmacokinetic models, and we advocate more widespread use of thorough techniques and analyses to address these issues, such as established Markov Chain Monte Carlo and Bayesian methodologies. Greater handling and reporting of uncertainty and identifiability of fit parameters would directly and positively impact interpretation and translation for physiologically-based model applications, enhancing their capacity to inform new model development efforts and extrapolation in support of future clinical decision-making.

A latent variable approach to account for correlated inputs in global sensitivity analysis

In drug development decision-making is often supported through model-based methods, such as physiologically-based pharmacokinetics (PBPK). Global sensitivity analysis (GSA) is gaining use for quality assessment of model-informed inference. However, the inclusion and interpretation of correlated factors in GSA has proven an issue. Here we developed and evaluated a latent variable approach for dealing with correlated factors in GSA. An approach was developed that describes the correlation between two model inputs through the causal relationship of three independent factors: the latent variable and the unique variances of the two correlated parameters. The latent variable approach was applied to a set of algebraic models and a case from PBPK. Then, this method was compared to Sobol’s GSA assuming no correlations, Sobol’s GSA with groups and the Kucherenko approach. For the latent variable approach, GSA was performed with Sobol’s method. By using the latent variable approach, it is possible to devise a unique and easy interpretation of the sensitivity indices while maintaining the correlation between the factors. Compared methods either consider the parameters independent, group the dependent variables into one unique factor or present difficulties in the interpretation of the sensitivity indices. In situations where GSA is called upon to support model-informed decision-making, the latent variable approach offers a practical method, in terms of ease of implementation and interpretability, for applying GSA to models with correlated inputs that does not violate the independence assumption. Prerequisites and limitations of the approach are discussed.

The effects of fasting on drug metabolism

Introduction: There is considerable variability in the rates and extent of drug metabolism between patients due to physiological, genetic, pharmacologic, environmental and nutritional factors such as fasting. This variability in drug metabolism may result in treatment failure or, conversely, in increased side effects or toxicity. Preclinical studies have shown that fasting alters drug metabolism by modulating the activity of drug metabolizing enzymes involved. However, until recently little was known about the effects of fasting on drug metabolism in humans.Areas covered: This review describes the effects of fasting on drug metabolism based on both preclinical studies and studies performed in humans.Expert opinion: A better understanding of the effects of fasting may improve the efficacy and safety of pharmacotherapy for individual patients. Fasting contributes to variability in human drug metabolism by differentially affecting drug metabolizing enzymes. Although the effects of fasting on drug metabolism appear to be small (between 10-20%), fasting may be relevant for drugs with a small therapeutic range and/or in combination with other factors that contribute to variability in drug metabolism such as physiological, genetic or pharmacological factors. Therefore, additional research on this topic is warranted.

Nutritional Status Differentially Alters Cytochrome P450 3A4 (CYP3A4) and Uridine 5'-Diphospho-Glucuronosyltransferase (UGT) Mediated Drug Metabolism: Effect of Short-Term Fasting and High Fat Diet on Midazolam Metabolism

BACKGROUND AND OBJECTIVES

Previous studies have shown that nutritional status can alter drug metabolism which may result in treatment failure or untoward side effects. This study assesses the effect of two nutritional conditions, short-term fasting, and a short-term high fat diet (HFD) on cytochrome P450 3A4 (CYP3A4) and uridine 5'-diphospho-glucuronosyltransferase (UGT) mediated drug metabolism by studying the pharmacokinetics of midazolam and its main metabolites.

METHODS

In a randomized-controlled cross-over trial, nine healthy subjects received a single intravenous administration of 0.015 mg/kg midazolam after: (1) an overnight fast (control); (2) 36 h of fasting; and (3) an overnight fast after 3 days of a HFD consisting of 500 ml of cream supplemented to their regular diet. Pharmacokinetic parameters were analyzed simultaneously using non-linear mixed-effects modeling.

RESULTS

Short-term fasting increased CYP3A4-mediated midazolam clearance by 12% (p < 0.01) and decreased UGT-mediated metabolism apparent 1-OH-midazolam clearance by 13% (p < 0.01) by decreasing the ratio of clearance and the fraction metabolite formed (ΔCL_1-OH-MDZ/f_1-OH-MDZ). Furthermore, short-term fasting decreased apparent clearance of 1-OH-midazolam-O-glucuronide (CL_{1-OH-MDZ-glucuronide}/(f_{1-OH-MDZ-glucuronide} × f_1-OH-MDZ)) by 20% (p < 0.01). The HFD did not affect systemic clearance of midazolam or metabolites.

CONCLUSIONS

Short-term fasting differentially alters midazolam metabolism by increasing CYP3A4-mediated metabolism but by decreasing UGT-mediated metabolism. In contrast, a short-term HFD did not affect systemic clearance of midazolam.

Assessment of Pharmacokinetic Drug-Drug Interactions in Humans: In Vivo Probe Substrates for Drug Metabolism and Drug Transport Revisited

Pharmacokinetic parameters of selective probe substrates are used to quantify the activity of an individual pharmacokinetic process (PKP) and the effect of perpetrator drugs thereon in clinical drug-drug interaction (DDI) studies. For instance, oral caffeine is used to quantify hepatic CYP1A2 activity, and oral dagibatran etexilate for intestinal P-glycoprotein (P-gp) activity. However, no probe substrate depends exclusively on the PKP it is meant to quantify. Lack of selectivity for a given enzyme/transporter and expression of the respective enzyme/transporter at several sites in the human body are the main challenges. Thus, a detailed understanding of the role of individual PKPs for the pharmacokinetics of any probe substrate is essential to allocate the effect of a perpetrator drug to a specific PKP; this is a prerequisite for reliably informed pharmacokinetic models that will allow for the quantitative prediction of perpetrator effects on therapeutic drugs, also in respective patient populations not included in DDI studies.

Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model

The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites—hydroxybupropion, threohydrobupropion and erythrohydrobupropion—based on a mixed “bottom-up” and “top-down” approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (C_max) area under the concentration-time curve (AUC) values and C_max and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and C_max ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

Use of Human Plasma Samples to Identify Circulating Drug Metabolites that Inhibit Cytochrome P450 Enzymes

Drug interactions elicited through inhibition of cytochrome P450 (P450) enzymes are important in pharmacotherapy. Recently, greater attention has been focused on not only parent drugs inhibiting P450 enzymes but also on possible inhibition of these enzymes by circulating metabolites. In this report, an ex vivo method whereby the potential for circulating metabolites to be inhibitors of P450 enzymes is described. To test this method, seven drugs and their known plasma metabolites were added to control human plasma at concentrations previously reported to occur in humans after administration of the parent drug. A volume of plasma for each drug based on the known inhibitory potency and time-averaged concentration of the parent drug was extracted and fractionated by high-pressure liquid chromatography-mass spectrometry, and the fractions were tested for inhibition of six human P450 enzyme activities (CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, and CYP3A4). Observation of inhibition in fractions that correspond to the retention times of metabolites indicates that the metabolite has the potential to contribute to P450 inhibition in vivo. Using this approach, norfluoxetine, hydroxyitraconazole, desmethyldiltiazem, desacetyldiltiazem, desethylamiodarone, hydroxybupropion, erythro-dihydrobupropion, and threo-dihydrobupropion were identified as circulating metabolites that inhibit P450 activities at a similar or greater extent as the parent drug. A decision tree is presented outlining how this method can be used to determine when a deeper investigation of the P450 inhibition properties of a drug metabolite is warranted.

Developing a Physiologically-Based Pharmacokinetic Model Knowledgebase in Support of Provisional Model Construction

Developing physiologically-based pharmacokinetic (PBPK) models for chemicals can be resource-intensive, as neither chemical-specific parameters nor in vivo pharmacokinetic data are easily available for model construction. Previously developed, well-parameterized, and thoroughly-vetted models can be a great resource for the construction of models pertaining to new chemicals. A PBPK knowledgebase was compiled and developed from existing PBPK-related articles and used to develop new models. From 2,039 PBPK-related articles published between 1977 and 2013, 307 unique chemicals were identified for use as the basis of our knowledgebase. Keywords related to species, gender, developmental stages, and organs were analyzed from the articles within the PBPK knowledgebase. A correlation matrix of the 307 chemicals in the PBPK knowledgebase was calculated based on pharmacokinetic-relevant molecular descriptors. Chemicals in the PBPK knowledgebase were ranked based on their correlation toward ethylbenzene and gefitinib. Next, multiple chemicals were selected to represent exact matches, close analogues, or non-analogues of the target case study chemicals. Parameters, equations, or experimental data relevant to existing models for these chemicals and their analogues were used to construct new models, and model predictions were compared to observed values. This compiled knowledgebase provides a chemical structure-based approach for identifying PBPK models relevant to other chemical entities. Using suitable correlation metrics, we demonstrated that models of chemical analogues in the PBPK knowledgebase can guide the construction of PBPK models for other chemicals.

Physiologically based pharmacokinetic and pharmacodynamic modeling in cancer drug development: status, potential and gaps

INTRODUCTION

Modeling and simulation have become important means of answering questions relevant to the development of a drug, making it possible to assess risks early and to reduce costs. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models contribute to a comprehensive understanding of the drug, covering specific questions from early discovery through lifecycle management stages. As for other disease areas, in oncology, PBPK and PD models are important topics that remain to be addressed.

AREAS COVERED

This review describes current PBPK and PD approaches, their applicability in drug development in general and specifically in the area of oncology. It discusses the current status and then focuses on key challenges and the potential for future use. It provides cases in which modeling currently cannot answer the questions and assesses the requirements to close gaps for PBPK/PD in oncology.

EXPERT OPINION

PBPK/PD models have led to improvements in identifying risks and reducing costs during the drug development process. Nevertheless, there is a lot of potential, where more rigorous integration of biological knowledge and specific experimental design would result in a more comprehensive biological picture. Ideally, such approaches would reveal the extent to which preclinical work can be extrapolated to clinical settings, thus enabling reliable prediction and, ultimately, reducing failed trials in clinical oncology.

Applications of physiologically based pharmacokinetic modeling for the optimization of anti-infective therapies

INTRODUCTION

The pharmacokinetic properties of anti-infective drugs are a determinant part of treatment success. Pathogen replication is inhibited if adequate drug levels are achieved in target sites, whereas excessive drug concentrations linked to toxicity are to be avoided. Anti-infective distribution can be predicted by integrating in vitro drug properties and mathematical descriptions of human anatomy in physiologically based pharmacokinetic models. This method reduces the need for animal and human studies and is used increasingly in drug development and simulation of clinical scenario such as, for instance, drug-drug interactions, dose optimization, novel formulations and pharmacokinetics in special populations.

AREAS COVERED

We have assessed the relevance of physiologically based pharmacokinetic modeling in the anti-infective research field, giving an overview of mechanisms involved in model design and have suggested strategies for future applications of physiologically based pharmacokinetic models.

EXPERT OPINION

Physiologically based pharmacokinetic modeling provides a powerful tool in anti-infective optimization, and there is now no doubt that both industry and regulatory bodies have recognized the importance of this technology. It should be acknowledged, however, that major challenges remain to be addressed and that information detailing disease group physiology and anti-infective pharmacodynamics is required if a personalized medicine approach is to be achieved.

A semiphysiological population pharmacokinetic model for dynamic inhibition of liver and gut wall cytochrome P450 3A by voriconazole. Clin Pharmacokinet. 2013;52:763-781. [PMID: 23653047 DOI: 10.1007/s40262-013-0070-9]

BACKGROUND

Accurate predictions of cytochrome P450 (CYP) 3A-mediated drug-drug interactions (DDIs) account for dynamic changes of CYP3A activity at both major expression sites (liver and gut wall) by considering the full pharmacokinetic profile of the perpetrator and the substrate. Physiological-based in vitro-in vivo extrapolation models have become of increasing interest. However, due to discrepancies between the predicted and observed magnitude of DDIs, the role of models fully based on in vivo data is still essential.

OBJECTIVE

The primary objective of this study was to develop a coupled dynamic model for the interaction of the CYP3A inhibitor voriconazole and the prototypical CYP3A substrate midazolam.

METHODS

Raw concentration data were obtained from a DDI study. Ten subjects were given either no pretreatment (control) or voriconazole twice daily orally. Midazolam was given either intravenously or orally after the last voriconazole dose and during control phases. Data analysis was performed by the population pharmacokinetic approach using non-linear mixed effects modelling (NONMEM 7.2.0). Model evaluation was performed using visual predictive checks and bootstrap analysis.

RESULTS

A semiphysiological model was able to describe the pharmacokinetics of midazolam, its major metabolite and voriconazole simultaneously. By considering the temporal disposition of all three substances in the liver and gut wall, a time-varying CYP3A inhibition process was implemented. Only the incorporation of hypothetical enzyme site compartments resulted in an adequate fit, suggesting a sustained inhibitory effect through accumulation. Novel key features of this analysis are the identification of (1) an apparent sustained inhibitory effect by voriconazole due to a proposed quasi accumulation at the enzyme site, (2) a significantly reduced inhibitory potency of intravenous voriconazole for oral substrates, (3) voriconazole as a likely uridine diphosphate glucuronosyltransferase (UGT) 2B inhibitor and (4) considerable sources of interindividual variability.

CONCLUSION

The proposed semiphysiological modelling approach generated a mechanistic description of the complex DDI occurring at major CYP3A expression sites and thus may serve as a powerful tool to maximise information acquired from clinical DDI studies. The model has been shown to draw precise and accurate predictions. Therefore, simulations based on this kind of models may be used for various clinical scenarios to improve pharmacotherapy.

Predicting drug-drug interactions: application of physiologically based pharmacokinetic models under a systems biology approach

The development of in vitro-in vivo extrapolation (IVIVE), a 'bottom-up' approach, to predict pharmacokinetic parameters and drug-drug interactions (DDIs) has accelerated mainly due to an increase in the understanding of the multiple mechanisms involved in these interactions and the availability of appropriate in vitro systems that act as surrogates for delineating various elements of the interactions relevant to absorption, distribution, metabolism and elimination. Recent advances in the knowledge of the population variables required for IVIVE (demographic, anatomical, genetic and physiological parameters) have also contributed to the appreciation of the sources of variability and wider use of this approach for different scenarios within the pharmaceutical industry. Initially, the authors present an overview of the integration of IVIVE into 'static' and 'dynamic' models for the quantitative prediction of DDIs. The main purpose of this review is to discuss the application of IVIVE in conjunction with physiologically based pharmacokinetic modeling under a systems biology approach to characterize the potential DDIs in individual patients, including those who cannot be investigated in formal clinical trials for ethical reasons. In addition, we address the issues related to the prediction of complex DDIs involving the inhibition of cytochrome P- and transporter-mediated activities through multiple drugs.

Impact of physiologically based pharmacokinetic modeling and simulation in drug development

Physiologically based pharmacokinetic modeling and simulation can be used to predict the pharmacokinetics of drugs in human populations and to explore the effects of varying physiologic parameters that result from aging, ethnicity, or disease. In addition, the effects of concomitant medications on drug exposure can be investigated; prediction of the magnitude of drug interactions can impact regulatory communications or internal decision-making regarding the requirement for a clinical drug interaction study. Modeling and simulation can also help to inform the design and timings of clinical drug interaction studies, resulting in more efficient use of limited resources and improved planning in addition to promoting mechanistic understanding of observed drug interactions. These approaches have been used in GlaxoSmithKline from drug discovery to registration and have been applied to 41 drugs from a number of therapeutic areas. This report highlights the variety of questions that can be addressed by prospective or retrospective application of modeling and simulation and the impact this can have on clinical drug development (from candidate selection through clinical development to regulatory submissions).

A clinical study to assess CYP1A2 and CYP3A4 induction by AZD7325, a selective GABA(A) receptor modulator - an in vitro and in vivo comparison

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

• AZD7325 is an orally administered, potent, selective gamma-amino-butyric acid (GABA(A) ) α2,3 receptor modulator intended for the treatment of anxiety. • The induction effects of AZD7325 on CYP1A2 and CYP3A4 have not been systematically studied.

WHAT THIS STUDY ADDS

• The in vitro studies showed that AZD7325 was a moderate CYP1A2 inducer and potent CYP3A4 inducer. • The follow-up clinical studies in healthy volunteers demonstrated that the expected efficacious daily dose of AZD7325 only weakly induced the pharmacokinetics of the CYP3A4 sensitive substrate, midazolam, and had no effect on the pharmacokinetics of the CYP1A2 substrate, caffeine. There was no apparent change in AZD7325 exposure following co-administration of midazolam or caffeine compared with AZD7325 alone. • The study demonstrated that clinical exposure of the inducer plays a critical role in the determination of cytochrome P450 induction risk of a drug candidate.

AIM(S)

To investigate the potential of AZD7325 to induce CYP1A2 and CYP3A4 enzyme activities.

METHODS

Induction of CYP1A2 and CYP3A4 by AZD7325 was first evaluated using cultured human hepatocytes. The effect of multiple doses of 10 mg AZD7325 on the pharmacokinetics of midazolam and caffeine was then examined in healthy subjects.

RESULTS

The highest CYP1A2 and CYP3A4 induction responses were observed in human hepatocytes treated with 1 or 10 µm of AZD7325, in the range of 17.9%-54.9% and 76.9%-85.7% of the positive control responses, respectively. The results triggered the further clinical evaluation of AZD7325 induction potential. AZD7325 reached a plasma C(max) of 0.2 µm after 10 mg daily dosing to steady-state. AZD7325 decreased midazolam geometric mean AUC by 19% (0.81-fold, 90% CI 0.77, 0.87), but had no effect on midazolam C(max) (90% CI 0.82, 0.97). The mean CL/F of midazolam increased from 62 l h(-1) (midazolam alone) to 76 l h(-1) when co-administered with AZD7325. The AUC and C(max) of caffeine were not changed after co-administration of AZD7325, with geometric mean ratios (90% CI) of 1.17 (1.12, 1.23) and 0.99 (0.95, 1.03), respectively.

CONCLUSIONS

While AZD7325 appeared to be a potent CYP3A4 inducer and a moderate CYP1A2 inducer from in vitro studies, the expected efficacious dose of AZD7325 had no effect on CYP1A2 activity and only a weak inducing effect on CYP3A4 activity. This comparison of in vitro and in vivo results demonstrates the critical role that clinical exposure plays in evaluating the CYP induction risk of a drug candidate.

Use of physiologically based pharmacokinetic modeling for assessment of drug-drug interactions

Interactions between co-administered medicines can reduce efficacy or lead to adverse effects. Understanding and managing such interactions is essential in bringing safe and effective medicines to the market. Ideally, interaction potential should be recognized early and minimized in compounds that reach late stages of drug development. Physiologically based pharmacokinetic models combine knowledge of physiological factors with compound-specific properties to simulate how a drug behaves in the human body. These software tools are increasingly used during drug discovery and development and, when integrating relevant in vitro data, can simulate drug interaction potential. This article provides some background and presents illustrative examples. Physiologically based models are an integral tool in the discovery and development of drugs, and can significantly aid our understanding and prediction of drug interactions.

Pharmacogenomics of codeine, morphine, and morphine-6-glucuronide: model-based analysis of the influence of CYP2D6 activity, UGT2B7 activity, renal impairment, and CYP3A4 inhibition

BACKGROUND AND OBJECTIVE

The analgesic effect of codeine depends on the formation of the opioid metabolites morphine and morphine-6-glucuronide. Different factors have been shown or suspected to affect the safety and efficacy of codeine treatment. The objective of the current study is to assess and quantify the impact of important pharmacokinetic factors, using a mechanistic modeling approach.

METHODS

By means of a generic modeling approach integrating prior physiologic knowledge, we systematically investigated the complex dependence of opioid exposure on cytochrome P450 2D6 and 3A4 (CYP2D6 and CYP3A4), and uridine diphosphate glucuronosyltransferase 2B7 (UGT2B7) activity, as well as renal function, by means of a virtual clinical trial.

RESULTS

First, the known dominant role of CYP2D6 activity for morphine exposure was reproduced. Second, the model demonstrated that mild and moderate renal impairment and co-administration of CYP3A4 inhibitors have only minor influences on opioid exposure. Third, the model showed - in contrast to current opinion - that increased UGT2B7 activity is associated with a decrease in active opioid exposure.

CONCLUSION

Overall, the model-based analysis predicts a wide range of morphine levels after codeine administration and supports recent doubts about safe and efficacious use of codeine for analgesia in non-genotyped individuals.

Prediction of a potentially effective dose in humans for BAY 60-5521, a potent inhibitor of cholesteryl ester transfer protein (CETP) by allometric species scaling and combined pharmacodynamic and physiologically-based pharmacokinetic modelling

AIMS

The purpose of this work was to support the prediction of a potentially effective dose for the CETP-inhibitor, BAY 60-5521, in humans.

METHODS

A combination of allometric scaling of the pharmacokinetics of the CETP-inhibitor BAY 60-5521 with pharmacodynamic studies in CETP-transgenic mice and in human plasma with physiologically-based pharmacokinetic (PBPK) modelling was used to support the selection of the first-in-man dose.

RESULTS

The PBPK approach predicts a greater extent of distribution for BAY 60-5521 in humans compared with the allometric scaling method as reflected by a larger predicted volume of distribution and longer elimination half-life. The combined approach led to an estimate of a potentially effective dose for BAY 60-5521 of 51 mg in humans.

CONCLUSION

The approach described in this paper supported the prediction of a potentially effective dose for the CETP-inhibitor BAY 60-5521 in humans. Confirmation of the dose estimate was obtained in a first-in-man study.

Using expression data for quantification of active processes in physiologically based pharmacokinetic modeling

Active processes involved in drug metabolization and distribution mediated by enzymes, transporters, or binding partners mostly occur simultaneously in various organs. However, a quantitative description of active processes is difficult because of limited experimental accessibility of tissue-specific protein activity in vivo. In this work, we present a novel approach to estimate in vivo activity of such enzymes or transporters that have an influence on drug pharmacokinetics. Tissue-specific mRNA expression is used as a surrogate for protein abundance and activity and is integrated into physiologically based pharmacokinetic (PBPK) models that already represent detailed anatomical and physiological information. The new approach was evaluated using three publicly available databases: whole-genome expression microarrays from ArrayExpress, reverse transcription-polymerase chain reaction-derived gene expression estimates collected from the literature, and expressed sequence tags from UniGene. Expression data were preprocessed and stored in a customized database that was then used to build PBPK models for pravastatin in humans. These models represented drug uptake by organic anion-transporting polypeptide 1B1 and organic anion transporter 3, active efflux by multidrug resistance protein 2, and metabolization by sulfotransferases in liver, kidney, and/or intestine. Benchmarking of PBPK models based on gene expression data against alternative models with either a less complex model structure or randomly assigned gene expression values clearly demonstrated the superior model performance of the former. Besides accurate prediction of drug pharmacokinetics, integration of relative gene expression data in PBPK models offers the unique possibility to simultaneously investigate drug-drug interactions in all relevant organs because of the physiological representation of protein-mediated processes.

Physiologically based pharmacokinetic modeling: methodology, applications, and limitations with a focus on its role in pediatric drug development. J Biomed Biotechnol. 2011;2011:907461. [PMID: 21716673 PMCID: PMC3118302 DOI: 10.1155/2011/907461]

The concept of physiologically based pharmacokinetic (PBPK) modeling was introduced years ago, but it has not been practiced significantly. However, interest in and implementation of this modeling technique have grown, as evidenced by the increased number of publications in this field. This paper demonstrates briefly the methodology, applications, and limitations of PBPK modeling with special attention given to discuss the use of PBPK models in pediatric drug development and some examples described in detail. Although PBPK models do have some limitations, the potential benefit from PBPK modeling technique is huge. PBPK models can be applied to investigate drug pharmacokinetics under different physiological and pathological conditions or in different age groups, to support decision-making during drug discovery, to provide, perhaps most important, data that can save time and resources, especially in early drug development phases and in pediatric clinical trials, and potentially to help clinical trials become more “confirmatory” rather than “exploratory”.

Simulation of differential drug pharmacokinetics under heat and exercise stress using a physiologically based pharmacokinetic modeling approach

Under extreme conditions of heat exposure and exercise stress, the human body undergoes major physiological changes. Perturbations in organ blood flows, gastrointestinal properties, and vascular physiology may impact the body's ability to absorb, distribute, and eliminate drugs. Clinical studies on the effect of these stressors on drug pharmacokinetics demonstrate that the likelihood of pharmacokinetic alteration is dependent on drug properties and the intensity of the stressor. The objectives of this study were to use literature data to quantify the correlation between exercise and heat exposure intensity to changing physiological parameters and further, to use this information for the parameterization of a whole-body, physiologically based pharmacokinetic model for the purposes of determining those drug properties most likely to demonstrate altered drug pharmacokinetics under stress. Cardiac output and most organ blood flows were correlated with heart rate using regression analysis. Other altered parameters included hematocrit and intravascular albumin concentration. Pharmacokinetic simulations of intravenous and oral administration of hypothetical drugs with either a low or high value of lipophilicity, unbound fraction in plasma, and unbound intrinsic hepatic clearance demonstrated that the area under the curve of those drugs with a high unbound intrinsic clearance was most affected (up to a 130% increase) following intravenous administration, whereas following oral administration, pharmacokinetic changes were smaller (<40% increase in area under the curve) for all hypothetical compounds. A midazolam physiologically based pharmacokinetic model was also used to demonstrate that simulated changes in pharmacokinetic parameters under exercise and heat stress were generally consistent with those reported in the literature.

Evaluating pharmacokinetic and pharmacodynamic interactions with computational models in supporting cumulative risk assessment

Simultaneous or sequential exposure to multiple chemicals may cause interactions in the pharmacokinetics (PK) and/or pharmacodynamics (PD) of the individual chemicals. Such interactions can cause modification of the internal or target dose/response of one chemical in the mixture by other chemical(s), resulting in a change in the toxicity from that predicted from the summation of the effects of the single chemicals using dose additivity. In such cases, conducting quantitative cumulative risk assessment for chemicals present as a mixture is difficult. The uncertainties that arise from PK interactions can be addressed by developing physiologically based pharmacokinetic (PBPK) models to describe the disposition of chemical mixtures. Further, PK models can be developed to describe mechanisms of action and tissue responses. In this article, PBPK/PD modeling efforts conducted to investigate chemical interactions at the PK and PD levels are reviewed to demonstrate the use of this predictive modeling framework in assessing health risks associated with exposures to complex chemical mixtures.

Physiologically-based pharmacokinetics in drug development and regulatory science

The application of physiologically-based pharmacokinetic (PBPK) modeling is coming of age in drug development and regulation, reflecting significant advances over the past 10 years in the predictability of key pharmacokinetic (PK) parameters from human in vitro data and in the availability of dedicated software platforms and associated databases. Specific advances and contemporary challenges with respect to predicting the processes of drug clearance, distribution, and absorption are reviewed, together with the ability to anticipate the quantitative extent of PK-based drug-drug interactions and the impact of age, genetics, disease, and formulation. The value of this capability in selecting and designing appropriate clinical studies, its implications for resource-sparing techniques, and a more holistic view of the application of PK across the preclinical/clinical divide are considered. Finally, some attention is given to the positioning of PBPK within the drug development and approval paradigm and its future application in truly personalized medicine.

A computational systems biology software platform for multiscale modeling and simulation: integrating whole-body physiology, disease biology, and molecular reaction networks

Today, in silico studies and trial simulations already complement experimental approaches in pharmaceutical R&D and have become indispensable tools for decision making and communication with regulatory agencies. While biology is multiscale by nature, project work, and software tools usually focus on isolated aspects of drug action, such as pharmacokinetics at the organism scale or pharmacodynamic interaction on the molecular level. We present a modeling and simulation software platform consisting of PK-Sim^® and MoBi^® capable of building and simulating models that integrate across biological scales. A prototypical multiscale model for the progression of a pancreatic tumor and its response to pharmacotherapy is constructed and virtual patients are treated with a prodrug activated by hepatic metabolization. Tumor growth is driven by signal transduction leading to cell cycle transition and proliferation. Free tumor concentrations of the active metabolite inhibit Raf kinase in the signaling cascade and thereby cell cycle progression. In a virtual clinical study, the individual therapeutic outcome of the chemotherapeutic intervention is simulated for a large population with heterogeneous genomic background. Thereby, the platform allows efficient model building and integration of biological knowledge and prior data from all biological scales. Experimental in vitro model systems can be linked with observations in animal experiments and clinical trials. The interplay between patients, diseases, and drugs and topics with high clinical relevance such as the role of pharmacogenomics, drug–drug, or drug–metabolite interactions can be addressed using this mechanistic, insight driven multiscale modeling approach.

Physiologically based predictions of the impact of inhibition of intestinal and hepatic metabolism on human pharmacokinetics of CYP3A substrates

The first objective of the present study was to predict the pharmacokinetics of selected CYP3A substrates administered at a single oral dose to human. The second objective was to predict pharmacokinetics of the selected drugs in presence of inhibitors of the intestinal and/or hepatic CYP3A activity. We developed a whole-body physiologically based pharmacokinetics (WB-PBPK) model accounting for presystemic elimination of midazolam (MDZ), alprazolam (APZ), triazolam (TRZ), and simvastatin (SMV). The model also accounted for concomitant administration of the above-mentioned drugs with CYP3A inhibitors, namely ketoconazole (KTZ), itraconazole (ITZ), diltiazem (DTZ), saquinavir (SQV), and a furanocoumarin contained in grape-fruit juice (GFJ), namely 6',7'-dihydroxybergamottin (DHB). Model predictions were compared to published clinical data. An uncertainty analysis was performed to account for the variability and uncertainty of model parameters when predicting the model outcomes. We also briefly report on the results of our efforts to develop a global sensitivity analysis and its application to the current WB-PBPK model. Considering the current criterion for a successful prediction, judged satisfied once the clinical data are captured within the 5th and 95th percentiles of the predicted concentration-time profiles, a successful prediction has been obtained for a single oral administration of MDZ and SMV. For APZ and TRZ, however, a slight deviation toward the 95th percentile was observed especially for C(max) but, overall, the in vivo profiles were well captured by the PBPK model. Moreover, the impact of DHB-mediated inhibition on the extent of intestinal pre-systemic elimination of MDZ and SMV has been accurately predicted by the proposed PBPK model. For concomitant administrations of MDZ and ITZ, APZ and KTZ, as well as SMV and DTZ, the in vivo concentration-time profiles were accurately captured by the model. A slight deviation was observed for SMV when coadministered with ITZ, whereas more important deviations have been obtained between the model predictions and in vivo concentration-time profiles of MDZ coadministered with SQV. The same observation was made for TRZ when administered with KTZ. Most of the pharmacokinetic parameters predicted by the PBPK model were successfully predicted within a two-fold error range either in the absence or presence of metabolism-based inhibition. Overall, the present study demonstrated the ability of the PBPK model to predict DDI of CYP3A substrates with promising accuracy.

Predictions of metabolic drug-drug interactions using physiologically based modelling: Two cytochrome P450 3A4 substrates coadministered with ketoconazole or verapamil

Nowadays, evaluation of potential risk of metabolic drug-drug interactions (mDDIs) is of high importance within the pharmaceutical industry, in order to improve safety and reduce the attrition rate of new drugs. Accurate and early prediction of mDDIs has become essential for drug research and development, and in vitro experiments designed to evaluate potential mDDIs are systematically included in the drug development plan prior to clinical assessment. The aim of this study was to illustrate the value and limitations of the classical and new approaches available to predict risks of DDIs in the research and development processes. The interaction of cytochrome P450 (CYP) 3A4 inhibitors (ketoconazole and verapamil) with midazolam was predicted using the inhibitor concentration/inhibition constant ([I]/K(i)) approach, the static approach with added variability (Simcyp(R)), and whole-body physiologically based pharmacokinetic (WB-PBPK) modelling (acslXtreme(R)). Then an in-house reference drug was used to challenge the different approaches based on the midazolam experience. Predicted values (pharmacokinetic parameters, the area under the plasma concentration-time curve [AUC] ratio and plasma concentrations) were compared with observed values obtained after intravenous and oral administration in order to assess the accuracy of the prediction methods. With the [I]/K(i) approach, the interaction risk was always overpredicted for the midazolam substrate, regardless of its route of administration and the coadministered inhibitor. However, the predictions were always satisfactory (within 2-fold) for the reference drug. For the Simcyp(R) calculations, two of the three interaction results for midazolam were overpredicted, both when midazolam was given orally, whereas the prediction obtained when midazolam was administered intravenously was satisfactory. For the reference drug, all predictions could be considered satisfactory. For the WB-PBPK approach, all predictions were satisfactory, regardless of the substrate, route of administration, dose and coadministered inhibitor. DDI risk predictions are performed throughout the research and development processes and are now fully integrated into decision-making processes. The regulatory approach is useful to provide alerts, even at a very early stage of drug development. The 'steady state' approach in Simcyp(R) improves the prediction by using physiological knowledge and mechanistic assumptions. The DDI predictions are very useful, as they provide a range of AUC ratios that include individuals at the extremes of the population, in addition to the 'average tendency'. Finally, the WB-PBPK approach improves the predictions by simulating the concentration-time profiles and calculating the related pharmacokinetic parameters, taking into account the time of administration of each drug - but it requires a good understanding of the absorption, distribution, metabolism and excretion properties of the compound.

Predicting drug-drug interactions: an FDA perspective

Pharmacokinetic drug interactions can lead to serious adverse events, and the evaluation of a new molecular entity's drug-drug interaction potential is an integral part of drug development and regulatory review prior to its market approval. Alteration of enzyme and/or transporter activities involved in the absorption, distribution, metabolism, or excretion of a new molecular entity by other concomitant drugs may lead to a change in exposure leading to altered response (safety or efficacy). Over the years, various in vitro methodologies have been developed to predict drug interaction potential in vivo. In vitro study has become a critical first step in the assessment of drug interactions. Well-executed in vitro studies can be used as a screening tool for the need for further in vivo assessment and can provide the basis for the design of subsequent in vivo drug interaction studies. Besides in vitro experiments, in silico modeling and simulation may also assist in the prediction of drug interactions. The recent FDA draft drug interaction guidance highlighted the in vitro models and criteria that may be used to guide further in vivo drug interaction studies and to construct informative labeling. This report summarizes critical elements in the in vitro evaluation of drug interaction potential during drug development and uses a case study to highlight the impact of in vitro information on drug labeling.

Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulation

BACKGROUND

In accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of in silico prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring (unknown) possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion (OHA) framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL).

RESULTS

The concept of the Ontology-Driven Hypothetic Assertion (OHA) framework was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. Four drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction proteins were automatically detected from Drug Interaction Ontology (DIO). The effect of the two interactions involving CYP3A4 were quantitatively evaluated with numerical simulation. The co-administration of ketoconazole may increase AUC and Cmax of SN-38(active metabolite of irinotecan) to 108% and 105%, respectively. We also estimates the potential effects of genetic variations: the AUC and Cmax of SN-38 may increase to 208% and 165% respectively with the genetic variation UGT1A1*28/*28 which reduces the expression of UGT1A1 down to 30%.

CONCLUSION

These results demonstrate that the Ontology-Driven Hypothetic Assertion framework is a promising approach for in silico prediction of drug interactions. The following future researches for the in silico prediction of individual differences in the response to the drug and drug interactions after the administration of multiple drugs: expansion of the Drug Interaction Ontology for other drugs, and incorporation of virtual population model for genetic variation analysis, as well as refinement of the pathway generation rules, the drug interaction detection rules, and the numerical simulation models.