Physiologically Based Pharmacokinetic Simulation Research Articles

Pharmacokinetic assessment of alprazolam-induced neonatal abstinence syndrome using physiologically based pharmacokinetic model

Sustained benzodiazepine use during pregnancy can induce neonatal abstinence syndrome (NAS). In this study, the association between NAS and plasma alprazolam concentration was examined using the measured neonatal concentrations in the time series as well as simulated plasma concentrations of pregnant woman and neonate by physiologically based pharmacokinetic (PBPK) modeling. A neonate born to a mother taking alprazolam daily throughout pregnancy exhibited symptoms such as apnea and vomiting from 9 h to 4 days after birth. Finnegan score was 7 at birth and decreased to 0 by day 4. Apnea improved by 24 h post-delivery and gastrointestinal symptoms disappeared by day 4. The plasma alprazolam concentration in the neonate was 15.2 ng/mL immediately after birth and gradually decreased over 3 days. Measured neonate and estimated maternal plasma alprazolam concentrations were within the 90% prediction intervals of each concentration by PBPK simulation using “pregnancy” and “pediatrics” population parameters including in Simcyp population-based ADME simulator. In conclusion, NAS symptoms such as apnea and digestive events disappeared in parallel with the decrease of the neonate's plasma alprazolam concentrations. Moreover, PBPK modeling and simulation is a useful methodology for toxicological assessment in special characteristics populations lacking specific experimental data.

Physiologically Based Pharmacokinetic Modelling of Hyperforin to Predict Drug Interactions with St John's Wort.

Herb-drug interactions with St John's wort (SJW) have been widely studied in numerous clinical studies. The objective of this study was to develop and evaluate a physiologically based pharmacokinetic (PBPK) model for hyperforin (the constituent of SJW responsible for interactions), which has the potential to provide unique insights into SJW interactions and allow prediction of the likely extent of interactions with SJW compared to published interaction reports. A PBPK model of hyperforin accounting for the induction of cytochrome P450 (CYP) 3A, CYP2C9 and CYP2C19 was developed in the Simcyp® Simulator (version 17) and verified using published, clinically observed pharmacokinetic data. The predictive performance of this model based on the prediction fold-difference (expressed as the ratio of predicted and clinically observed change in systemic exposure of drug) was evaluated across a range of CYP substrates. The verified PBPK model predicted the change in victim drug exposure due to the induction by SJW (expressed as area under the plasma concentration-time curve (AUC) ratio) within 1.25-fold (0.80-1.25) of that reported in clinical studies. The PBPK simulation indicated that the unbound concentration of hyperforin in the liver was far lower than in the gut (enterocytes). Simulations revealed that induction of intestinal CYP enzymes by hyperforin was found to be more pronounced than the corresponding increase in liver CYP activity (15.5- vs. 1.1-fold, respectively, at a hyperforin dose of 45mg/day). In the current study, a PBPK model for hyperforin was successfully developed, with a predictive capability for the interactions of SJW with different CYP3A, CYP2C9 and CYP2C19 substrates. This PBPK model is valuable to predict the extent of herb-drug interactions with SJW and help design the clinical interaction studies, particularly for new drugs and previously unstudied clinical scenarios.

A physiologically-based pharmacokinetic model adequately predicted the human pharmacokinetic profiles of YH4808, a novel K+-competitive acid blocker

A physiologically-based pharmacokinetic (PBPK) model was developed for YH4808, a novel potassium-competitive acid blocker, using the SimCYP® Simulator based on the physicochemical, in vitro preclinical and clinical data of YH4808. The PBPK model was optimized using YH4808 concentrations obtained from the single-dose phase I clinical trial. Overall, the PBPK model adequately predicted the observed pharmacokinetic profiles of YH4808 in humans. The pharmacokinetic profiles of YH4808 after multiple oral administrations were predicted using a refined PBPK model. The ratios of model-predicted to observed Cmax, AUCinf and AUCτ values on Day 1 and Day 7 at 100 mg were 0.7–1.0. However, the model failed to predict a decreased exposure after multiple oral administration particularly at higher doses of 200 and 400 mg. The reduced solubility of YH4808 at higher pH was hypothesized as the main cause of the reduction in exposure such that absorption was decreased as pH was increased. This hypothesis was confirmed by PBPK modeling and simulation, where intragastric pH was increased by YH4808.

Abundance of Phase 1 and 2 Drug-Metabolizing Enzymes in Alcoholic and Hepatitis C Cirrhotic Livers: A Quantitative Targeted Proteomics Study.

To predict the impact of liver cirrhosis on hepatic drug clearance using physiologically based pharmacokinetic (PBPK) modeling, we compared the protein abundance of various phase 1 and phase 2 drug-metabolizing enzymes (DMEs) in S9 fractions of alcoholic (n = 27) or hepatitis C (HCV, n = 30) cirrhotic versus noncirrhotic (control) livers (n = 25). The S9 total protein content was significantly lower in alcoholic or HCV cirrhotic versus control livers (i.e., 38.3 ± 8.3, 32.3 ± 12.8, vs. 51.1 ± 20.7 mg/g liver, respectively). In general, alcoholic cirrhosis was associated with a larger decrease in the DME abundance than HCV cirrhosis; however, only the abundance of UGT1A4, alcohol dehydrogenase (ADH)1A, and ADH1B was significantly lower in alcoholic versus HCV cirrhotic livers. When normalized to per gram of tissue, the abundance of nine DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, CYP1A2, ADH1A, ADH1B, aldehyde oxidase (AOX)1, and carboxylesterase (CES)1) in alcoholic cirrhosis and five DMEs (UGT1A6, UGT1A4, CYP3A4, UGT2B7, and CYP1A2) in HCV cirrhosis was <25% of that in control livers. The abundance of most DMEs in cirrhotic livers was 25% to 50% of control livers. CES2 abundance was not affected by cirrhosis. Integration of UGT2B7 abundance in cirrhotic livers into the liver cirrhosis (Child Pugh C) model of Simcyp improved the prediction of zidovudine and morphine PK in subjects with Child Pugh C liver cirrhosis. These data demonstrate that protein abundance data, combined with PBPK modeling and simulation, can be a powerful tool to predict drug disposition in special populations.

Implications of intercorrelation between hepatic CYP3A4-CYP2C8 enzymes for the evaluation of drug-drug interactions: a case study with repaglinide.

Statistically significant positive correlations are reported for the abundance of hepatic drug-metabolizing enzymes. We investigate, as an example, the impact of CYP3A4-CYP2C8 intercorrelation on the predicted interindividual variabilities of clearance and drug-drug interactions (DDIs) for repaglinide using physiologically based pharmacokinetic (PBPK) modelling. PBPK modelling and simulation were employed using Simcyp Simulator (v15.1). Virtual populations were generated assuming intercorrelations between hepatic CYP3A4-CYP2C8 abundances derived from observed values in 24 human livers. A repaglinide PBPK model was used to predict PK parameters in the presence and absence of gemfibrozil in virtual populations, and the results were compared with a clinical DDI study. Coefficient of variation (CV) of oral clearance was 52.5% in the absence of intercorrelation between CYP3A4-CYP2C8 abundances, which increased to 54.2% when incorporating intercorrelation. In contrast, CV for predicted DDI (as measured by AUC ratio before and after inhibition) was reduced from 46.0% in the absence of intercorrelation between enzymes to 43.8% when incorporating intercorrelation: these CVs were associated with 5th/95th percentiles (2.48-11.29 vs. 2.49-9.69). The range of predicted DDI was larger in the absence of intercorrelation (1.55-77.06) than when incorporating intercorrelation (1.79-25.15), which was closer to clinical observations (2.6-12). The present study demonstrates via a systematic investigation that population-based PBPK modelling incorporating intercorrelation led to more consistent estimation of extreme values than those observed in interindividual variabilities of clearance and DDI. As the intercorrelations more realistically reflect enzyme abundances, virtual population studies involving PBPK and DDI should avoid using Monte Carlo assignment of enzyme abundance.

In Vitro-In Vivo Correlation Using In Silico Modeling of Physiological Properties, Metabolites, and Intestinal Metabolism

Recently, pharmaceutical research has focused on in vitro-in vivo correlation as a novel challenge, and in silico modeling has been an important component. As in silico models are highly representative of practical use, regulatory agencies such as the US Food and Drug Administration and European Medicines Agency have recognized and utilized in silico modeling as a useful tool; this allows pharmaceutical organizations to use Physiologically Based Pharmacokinetic (PBPK) models for decision-making, which may aid the financial efficiency of a clinical trial. However, some studies have shown differences of up to approximately 40% in pharmacokinetic parameters such as area under the curve or maximum serum concentration between observed and simulated data. Gastroplus™ was used to demonstrate current PBPK simulation. 46 research papers were compared with each other's applications of PBPK simulation. To improve the accuracy of simulation, additional factors may need to be considered, such as precise volume of gastrointestinal sections, specific metabolism of the target drug, and physicochemical data of drug metabolites. Furthermore, the results of these simulations would be extremely valuable to the relevant applications. Simulation programs using Advanced Compartmental Absorption and Transit (ACAT)/PBPK modeling could be a powerful tool for companies performing pre-clinical experiments, and could provide a solution for the ethical issues and economic constraints of clinical trials. If in silico modeling produced more precise results that could closely match clinical data, it could be more readily used to screen drug pharmacodynamics in bodily systems, and the efficiency of clinical trials would be improved. However, simulation programs are currently limited in their accuracy of pharmacodynamic predictions. In developing new drugs, pharmaceutical companies should address this issue in order to improve in silico/PBPK modeling in the future.

Coupled in silico platform: Computational fluid dynamics (CFD) and physiologically-based pharmacokinetic (PBPK) modelling

One of the critical components of the respiratory drug delivery is the manner in which the inhaled aerosol is deposited in respiratory tract compartments. Depending on formulation properties, device characteristics and breathing pattern, only a certain fraction of the dose will reach the target site in the lungs, while the rest of the drug will deposit in the inhalation device or in the mouth–throat region. The aim of this study was to link the Computational fluid dynamics (CFD) with physiologically-based pharmacokinetic (PBPK) modelling in order to predict aerolisolization of different dry powder formulations, and estimate concomitant in vivo deposition and absorption of amiloride hydrochloride. Drug physicochemical properties were experimentally determined and used as inputs for the CFD simulations of particle flow in the generated 3D geometric model of Aerolizer® dry powder inhaler (DPI). CFD simulations were used to simulate air flow through Aerolizer® inhaler and Discrete Phase Method (DPM) was used to simulate aerosol particles deposition within the fluid domain. The simulated values for the percent emitted dose were comparable to the values obtained using Andersen cascade impactor (ACI). However, CFD predictions indicated that aerosolized DPI have smaller particle size and narrower size distribution than assumed based on ACI measurements. Comparison with the literature in vivo data revealed that the constructed drug-specific PBPK model was able to capture amiloride absorption pattern following oral and inhalation administration. The PBPK simulation results, based on the CFD generated particle distribution data as input, illustrated the influence of formulation properties on the expected drug plasma concentration profiles. The model also predicted the influence of potential changes in physiological parameters on the extent of inhaled amiloride absorption. Overall, this study demonstrated the potential of the combined CFD-PBPK approach to model inhaled drug bioperformance, and suggested that CFD generated results might serve as input for the prediction of drug deposition pattern in vivo.

Impact of acute fat mobilisation on the pharmacokinetics of the highly fat distributed compound TAK-357, investigated by physiologically based pharmacokinetic (PBPK) modeling and simulation.

In a dog toxicokinetic study, an unusual plasma concentration increase of the highly lipophilic compound TAK-357 was observed 2weeks after termination of a 2-week repeated dosing in one dog with acute body weight loss. The present study investigates the cause of this increase. A physiologically based pharmacokinetic (PBPK) model was constructed using the rat and dog pharmacokinetic data. Using the constructed model, the TAK-357 concentration profile in the case of body weight change was simulated. The PBPK model-derived simulation suggested that redistribution from adipose tissues to plasma due to a loss of body fat caused the observed concentration increase of TAK-357 in dog plasma. The analysis demonstrates that the disposition of a highly lipophilic and fat-distributed compound can be affected by acute changes in adipose tissue mass. PBPK modeling and simulation proved to be efficient tools for the quantitative hypothesis testing of apparently atypical PK phenomena resulting from acute physiological changes.

Physiologically Based Pharmacokinetic Modelling and Prediction of Metformin Pharmacokinetics in Renal/Hepatic-Impaired Young Adults and Elderly Populations.

Physiologically based pharmacokinetic (PBPK) modelling and simulation enable researchers to overcome practical limitations for clinical trials on special populations. This study was conducted to investigate how the PBPK model describes the pharmacokinetics of metformin in young adult and elderly populations and to predict the pharmacokinetics of metformin in patients with renal or hepatic impairment in both populations. A first-order absorption/PBPK model for metformin was built in the Simcyp simulator version 14 release 1. A full PBPK model was constructed for metformin based on physicochemical properties and clinical observations. The model was refined and validated using clinical plasma concentration data obtained in healthy young adults and elderly after the oral administration of metformin. Metformin pharmacokinetics in patients with renal or hepatic impairment were then investigated and compared by simulation. The PBPK model reasonably predicted the pharmacokinetic profiles of metformin for both young adults and the elderly. The predicted pharmacokinetic parameters, including maximum concentration, area under the time-concentration curve, and apparent oral clearance values, were within 1.5-fold of the observed data of metformin. In the simulation results, the systemic exposure of metformin was expected to be markedly increased not only with a decrease in renal function but also with severe hepatic impairments. The PBPK model adequately characterised the pharmacokinetics of metformin in both young adult and elderly populations. PBPK modelling and simulation can be used as a useful tool to investigate and compare the pharmacokinetics in geriatric populations incorporating various disease conditions.

Prediction of pharmacokinetics and drug-drug interaction potential using physiologically based pharmacokinetic (PBPK) modeling approach: A case study of caffeine and ciprofloxacin.

Over the last decade, physiologically based pharmacokinetics (PBPK) application has been extended significantly not only to predicting preclinical/human PK but also to evaluating the drug-drug interaction (DDI) liability at the drug discovery or development stage. Herein, we describe a case study to illustrate the use of PBPK approach in predicting human PK as well as DDI using in silico, in vivo and in vitro derived parameters. This case was composed of five steps such as: simulation, verification, understanding of parameter sensitivity, optimization of the parameter and final evaluation. Caffeine and ciprofloxacin were used as tool compounds to demonstrate the “fit for purpose” application of PBPK modeling and simulation for this study. Compared to caffeine, the PBPK modeling for ciprofloxacin was challenging due to several factors including solubility, permeability, clearance and tissue distribution etc. Therefore, intensive parameter sensitivity analysis (PSA) was conducted to optimize the PBPK model for ciprofloxacin. Overall, the increase in Cmax of caffeine by ciprofloxacin was not significant. However, the increase in AUC was observed and was proportional to the administered dose of ciprofloxacin. The predicted DDI and PK results were comparable to observed clinical data published in the literatures. This approach would be helpful in identifying potential key factors that could lead to significant impact on PBPK modeling and simulation for challenging compounds.

Evaluation of Cytochrome P450 3A4-Mediated Drug-Drug Interaction Potential for Cobimetinib Using Physiologically Based Pharmacokinetic Modeling and Simulation.

Cobimetinib is eliminated mainly through cytochrome P450 (CYP) 3A4-mediated hepatic metabolism in humans. A clinical drug-drug interaction (DDI) study with the potent CYP3A4 inhibitor itraconazole resulted in an approximately sevenfold increase in cobimetinib exposure. The DDI risk for cobimetinib with other CYP3A4 inhibitors and inducers needs to be assessed in order to provide dosing instructions. A physiologically based pharmacokinetic (PBPK) model was developed for cobimetinib using in vitro data. It was then optimized and verified using clinical pharmacokinetic data and itraconazole-cobimetinib DDI data. The contribution of CYP3A4 to the clearance of cobimetinib in humans was confirmed using sensitivity analysis in a retrospective simulation of itraconazole-cobimetinib DDI data. The verified PBPK model was then used to predict the effect of other CYP3A4 inhibitors and inducers on cobimetinib pharmacokinetics. The PBPK model described cobimetinib pharmacokinetic profiles after both intravenous and oral administration of cobimetinib well and accurately simulated the itraconazole-cobimetinib DDI. Sensitivity analysis suggested that CYP3A4 contributes ~78% of the total clearance of cobimetinib. The PBPK model predicted no change in cobimetinib exposure (area under the plasma concentration-time curve, AUC) with the weak CYP3A inhibitor fluvoxamine and a three to fourfold increase with the moderate CYP3A inhibitors, erythromycin and diltiazem. Similarly, cobimetinib exposure in the presence of strong (rifampicin) and moderate (efavirenz) CYP3A inducers was predicted to decrease by 83 and 72%, respectively. This study demonstrates the value of using PBPK simulation to assess the clinical DDI risk inorder to provide dosing instructions with other CYP3A4 perpetrators.

Quantitative prediction and clinical evaluation of an unexplored herb-drug interaction mechanism in healthy volunteers.

Quantitative prediction of herb-drug interaction risk remains challenging. A quantitative framework to assess a potential interaction was used to evaluate a mechanism not previously tested in humans. The semipurified milk thistle product, silibinin, was selected as an exemplar herbal product inhibitor of raloxifene intestinal glucuronidation. Physiologically based pharmacokinetic (PBPK) model simulations of the silibinin-raloxifene interaction predicted up to 30% increases in raloxifene area under the curve (AUC0-inf) and maximal concentration (Cmax). Model-informed clinical evaluation of the silibinin-raloxifene interaction indicated minimal clinical interaction liability, with observed geometric mean raloxifene AUC0-inf and Cmax ratios lying within the predefined no effect range (0.75-1.33). Further refinement of PBPK modeling and simulation approaches will enhance confidence in predictions and facilitate generalizability to additional herb-drug combinations. This quantitative framework can be used to develop guidances to evaluate potential herb-drug interactions prospectively, providing evidenced-based information about the risk or safety of these interactions.

Pharmacists in Australian general practice: an opportunity for expertise in precision medicine.

Pharmacists in Australian general practice: an opportunity for expertise in precision medicine.

Investigation of Low Bioavailability Using Physiologically Based Pharmacokinetic Modeling: A Case Example (Lassbio-596)

Aim: Investigate the possible mechanism (s) of the poor bioavailability of a lipophilic compound in rats using the physiologically based pharmacokinetic (PBPK) modeling approach. Methodology: A rat PBPK model was constructed using data from intravenous administration, and verified by comparing predicted tissue concentrations (kidneys, liver and lungs) with experimental Method Article Vieira et al.; BJAST, 5(6): 613-620, 2015; Article no.BJAST.2015.059 614 data from tissue distribution studies. Using parameter sensitivity analysis, the model was used to investigate the absorption characteristics of the compound and the probable causes of the low absorbed fraction. Results: Sensitivity analysis of absorption parameters was performed to understand the absorption characteristic of the compound in regard to permeability, solubility and intra-gut degradation. Taking in consideration the latter factor, the oral pharmacokinetics of the tested compound was satisfactorily predicted in rats. Conclusion: The PBPK simulation results suggest that chemical or/and bacterial degradation of the compound in the gastrointestinal tract may be a probable cause of the low bioavailability observed.

Predicting the oral pharmacokinetic profiles of multiple-unit (pellet) dosage forms using a modeling and simulation approach coupled with biorelevant dissolution testing: Case example diclofenac sodium

The objective of this research was to characterize the dissolution profile of a poorly soluble drug, diclofenac, from a commercially available multiple-unit enteric coated dosage form, Diclo-Puren® capsules, and to develop a predictive model for its oral pharmacokinetic profile.The paddle method was used to obtain the dissolution profiles of this dosage form in biorelevant media, with the exposure to simulated gastric conditions being varied in order to simulate the gastric emptying behavior of pellets. A modified Noyes–Whitney theory was subsequently fitted to the dissolution data. A physiologically-based pharmacokinetic (PBPK) model for multiple-unit dosage forms was designed using STELLA® software and coupled with the biorelevant dissolution profiles in order to simulate the plasma concentration profiles of diclofenac from Diclo-Puren® capsule in both the fasted and fed state in humans. Gastric emptying kinetics relevant to multiple-units pellets were incorporated into the PBPK model by setting up a virtual patient population to account for physiological variations in emptying kinetics.Using in vitro biorelevant dissolution coupled with in silico PBPK modeling and simulation it was possible to predict the plasma profile of this multiple-unit formulation of diclofenac after oral administration in both the fasted and fed state. This approach might be useful to predict variability in the plasma profiles for other drugs housed in multiple-unit dosage forms.

Precipitation in the small intestine may play a more important role in the in vivo performance of poorly soluble weak bases in the fasted state: Case example nelfinavir

The aim of this study was to evaluate the utility of biorelevant dissolution tests coupled with in silico simulation technology to forecast in vivo bioperformance of poorly water-soluble bases, using nelfinavir mesylate as a model compound. An in silico physiologically based pharmacokinetic (PBPK) model for poorly water-soluble, weakly basic drugs was used to generate plasma profiles of nelfinavir by coupling dissolution results and estimates of precipitation with standard gastrointestinal (GI) parameters and the disposition pharmacokinetics of nelfinavir. In vitro dissolution of nelfinavir mesylate film-coated tablets was measured in biorelevant and compendial media. Drug precipitation in the small intestine was estimated from crystal growth theory. GI parameters (gastric emptying rate and fluid volume) appropriate to the dosing conditions (fasting and fed states) were used in the PBPK model. The disposition parameters of nelfinavir were estimated by fitting compartmental models to the in vivo oral PK data. The in vivo performance in each prandial state was simulated with the PBPK model, and predicted values for AUC and C max were compared to observed values. Dissolution results in FaSSIF-V2 and FeSSIF-V2, simulating the fasting and fed small intestinal conditions, respectively, correctly predicted that there would be a positive food effect for nelfinavir mesylate, but overestimated the food effect observed in healthy human volunteers. In order to better predict the food effect, an in silico PBPK simulation model using STELLA® software was evolved. Results with the model indicated that invoking drug precipitation in the small intestine is necessary to describe the in vivo performance of nelfinavir mesylate in the fasted state, whereas a good prediction under fed state conditions is obtained without assuming any precipitation. In vitro– in silico– in vivo relationships (IVISIV-R) may thus be a helpful tool in understanding the critical parameters that affect the oral absorption of poorly soluble weak bases.

Applications of Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation During Regulatory Review

Physiologically based pharmacokinetic (PBPK) modeling and simulation is a tool that can help predict the pharmacokinetics of drugs in humans and evaluate the effects of intrinsic (e.g., organ dysfunction, age, genetics) and extrinsic (e.g., drug-drug interactions) factors, alone or in combinations, on drug exposure. The use of this tool is increasing at all stages of the drug development process. This report reviews recent instances of the use of PBPK in decision-making during regulatory review. The examples are based on Center for Drug Evaluation and Research reviews of several submissions for investigational new drugs (INDs) and new drug applications (NDAs) received between July 2008 and June 2010. The use of PBPK modeling and simulation facilitated the following types of decisions: the need to conduct specific clinical pharmacology studies, specific study designs, and appropriate labeling language. The report also discusses the challenges encountered when PBPK modeling and simulation were used in these cases and recommends approaches to facilitating full utilization of this tool.

Modelling and PBPK Simulation in Drug Discovery

Physiologically based pharmacokinetic (PBPK) models are composed of a series of differential equations and have been implemented in a number of commercial software packages. These models require species-specific and compound-specific input parameters and allow for the prediction of plasma and tissue concentration time profiles after intravenous and oral administration of compounds to animals and humans. PBPK models allow the early integration of a wide variety of preclinical data into a mechanistic quantitative framework. Use of PBPK models allows the experimenter to gain insights into the properties of a compound, helps to guide experimental efforts at the early stages of drug discovery, and enables the prediction of human plasma concentration time profiles with minimal (and in some cases no) animal data. In this review, the application and limitations of PBPK techniques in drug discovery are discussed. Specific reference is made to its utility (1) at the lead development stage for the prioritization of compounds for animal PK studies and (2) at the clinical candidate selection and "first in human" stages for the prediction of human PK.

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

BackgroundDrug-drug interactions resulting from the inhibition of an enzymatic process can have serious implications for clinical drug therapy. Quantification of the drugs internal exposure increase upon administration with an inhibitor requires understanding to avoid the drug reaching toxic thresholds. In this study, we aim to predict the effect of the CYP3A4 inhibitors, itraconazole (ITZ) and its primary metabolite, hydroxyitraconazole (OH-ITZ) on the pharmacokinetics of the anesthetic, midazolam (MDZ) and its metabolites, 1' hydroxymidazolam (1OH-MDZ) and 1' hydroxymidazolam glucuronide (1OH-MDZ-Glu) using mechanistic whole body physiologically-based pharmacokinetic simulation models. The model is build on MDZ, 1OH-MDZ and 1OH-MDZ-Glu plasma concentration time data experimentally determined in 19 CYP3A5 genotyped adult male individuals, who received MDZ intravenously in a basal state. The model is then used to predict MDZ, 1OH-MDZ and 1OH-MDZ-Glu concentrations in an CYP3A-inhibited state following ITZ administration.ResultsFor the basal state model, three linked WB-PBPK models (MDZ, 1OH-MDZ, 1OH-MDZ-Glu) for each individual were elimination optimized that resulted in MDZ and metabolite plasma concentration time curves that matched individual observed clinical data. In vivo Km and Vmax optimized values for MDZ hydroxylation were similar to literature based in vitro measures. With the addition of the ITZ/OH-ITZ model to each individual coupled MDZ + metabolite model, the plasma concentration time curves were predicted to greatly increase the exposure of MDZ as well as to both increase exposure and significantly alter the plasma concentration time curves of the MDZ metabolites in comparison to the basal state curves. As compared to the observed clinical data, the inhibited state curves were generally well described although the simulated concentrations tended to exceed the experimental data between approximately 6 to 12 hours following MDZ administration. This deviations appeared to be greater in the CYP3A5 *1/*1 and CYP3A5 *1/*3 group than in the CYP3A5 *3/*3 group and was potentially the result of assuming that ITZ/OH-ITZ inhibits both CYP3A4 and CYP3A5, whereas in vitro inhibition is due to CYP3A4.ConclusionThis study represents the first attempt to dynamically simulate metabolic enzymatic drug-drug interactions via coupled WB-PBPK models. The workflow described herein, basal state optimization followed by inhibition prediction, is novel and will provide a basis for the development of other inhibitor models that can be used to guide, interpret, and potentially replace clinical drug-drug interaction trials.

Physiologically-based pharmacokinetic simulation modelling

Drug selection is now widely viewed as an important and relatively new, yet largely unsolved, bottleneck in the drug discovery and development process. In order to achieve an efficient selection process, high quality, rapid, predictive and correlative ADME models are required in order for them to be confidently used to support critical financial decisions. Systems that can be relied upon to accurately predict performance in humans have not existed, and decisions have been made using tools whose capabilities could not be verified until candidates went to clinical trial, leading to the high failure rates historically observed. However, with the sequencing of the human genome, advances in proteomics, the anticipation of the identification of a vastly greater number of potential targets for drug discovery, and the potential of pharmacogenomics to require individualized evaluation of drug kinetics as well as drug effects, there is an urgent need for rapid and accurately computed pharmacokinetic properties.