Full Physiologically Based Pharmacokinetic Model Research Articles

Physiologically based pharmacokinetic modeling to predict the clinical effect of azole antifungal agents as CYP3A inhibitors on azelnidipine pharmacokinetics

In this study, a physiologically based pharmacokinetic (PBPK) model of the cytochrome P450 3A (CYP3A) substrate azelnidipine was developed using in vitro and clinical data to predict the effects of azole antifungals on azelnidipine pharmacokinetics. Modeling and simulations were conducted using the Simcyp™ PBPK simulator. The azelnidipine model consisted of a full PBPK model and a first-order absorption model. CYP3A was assumed as the only azelnidipine elimination route, and CYP3A clearance was optimized using the pharmacokinetic profile of single-dose 5-mg azelnidipine in healthy participants. The model reproduced the results of a clinical drug–drug interaction study and met validation criteria. PBPK model simulations using azole antifungals (itraconazole, voriconazole, posaconazole, fluconazole, fosfluconazole) and azelnidipine or midazolam (CYP3A index substrate) were performed. Increases in the simulated area under the plasma concentration–time curve from time zero extrapolated to infinity with inhibitors were comparable between azelnidipine (range, 2.11–6.47) and midazolam (range, 2.26–9.22), demonstrating that azelnidipine is a sensitive CYP3A substrate. Increased azelnidipine plasma concentrations are expected when co-administered with azole antifungals, potentially affecting azelnidipine safety. These findings support the avoidance of azole antifungals in patients taking azelnidipine and demonstrate the utility of PBPK modeling to inform appropriate drug use.

Physiologically Based Pharmacokinetic Modeling for Maribavir to Inform Dosing in Drug-Drug Interaction Scenarios with CYP3A4 Inducers and Inhibitors.

Maribavir, an orally available antiviral agent, has been approved in multiple countries for the treatment of patients with refractory post-transplant cytomegalovirus (CMV) infection and/or disease. Maribavir is primarily metabolized by CYP3A4; coadministration with CYP3A4 inducers and inhibitors may significantly alter maribavir exposure, thereby affecting its efficacy and safety. The effect of CYP3A4 inducers and inhibitors on maribavir exposure was evaluated based on a drug-drug interaction (DDI) study and physiologically-based pharmacokinetic (PBPK) modeling. The effect of rifampin (a strong inducer of CYP3A4 and moderate inducer of CYP1A2), administered at a 600mg dose once daily, on maribavir pharmacokinetics was assessed in a clinical phase1 DDI study in healthy participants. A full PBPK model for maribavir was developed and verified using invitro and clinical pharmacokinetic data from phase1 studies. The verified PBPK model was then used to simulate maribavir DDI interactions with various CYP3A4 inducers and inhibitors. The DDI study results showed that coadministration with rifampin decreased the maribavir maximum plasma concentration (Cmax), area under the plasma concentration-time curve (AUC), and trough concentration (Ctrough) by 39%, 60%, and 82%, respectively. Based on the results from the clinical DDI study, the coadministration of maribavir with rifampin is not recommended. The PBPK model did not predict a clinically significant effect of CYP3A4 inhibitors on maribavir exposure; however, it predicted that strong or moderate CYP3A4 inducers, including carbamazepine, efavirenz, phenobarbital, and phenytoin, may reduce maribavir exposure to a clinically significant extent, and may prompt the consideration of a maribavir dosing increase, in accordance with local approved labels and/or regulations.

Adapting physiologically-based pharmacokinetic models for machine learning applications

Both machine learning and physiologically-based pharmacokinetic models are becoming essential components of the drug development process. Integrating the predictive capabilities of physiologically-based pharmacokinetic (PBPK) models within machine learning (ML) pipelines could offer significant benefits in improving the accuracy and scope of drug screening and evaluation procedures. Here, we describe the development and testing of a self-contained machine learning module capable of faithfully recapitulating summary pharmacokinetic (PK) parameters produced by a full PBPK model, given a set of input drug-specific and regimen-specific information. Because of its widespread use in characterizing the disposition of orally administered drugs, the PBPK model chosen to demonstrate the methodology was an open-source implementation of a state-of-the-art compartmental and transit model called OpenCAT. The model was tested for drug formulations spanning a large range of solubility and absorption characteristics, and was evaluated for concordance against predictions of OpenCAT and relevant experimental data. In general, the values predicted by the ML models were within 20% of those of the PBPK model across the range of drug and formulation properties. However, summary PK parameter predictions from both the ML model and full PBPK model were occasionally poor with respect to those derived from experiments, suggesting deficiencies in the underlying PBPK model.

Incorporation and Performance Verification of Hepatic Portal Blood Flow Shunting in Minimal and Full PBPK Models of Liver Cirrhosis.

Patho-physiological changes in liver cirrhosis create portacaval shunts that allow blood flow to bypass the hepatic portal vein into the systemic circulation affecting drug pharmacokinetics (PKs). The objectives of this work were to implement a physiologically-based pharmacokinetic (PBPK) framework describing shunted blood flows in virtual patients with differing degrees of liver cirrhosis; and to assess the minimal and full PBPK model's performance using drugs with intermediate to high hepatic extraction. Single dose concentration-time profiles and PK parameters for oral ibrutinib, midazolam, propranolol, and buspirone were simulated in healthy volunteers (HVs) and subjects with cirrhosis (Child-Pugh severity score (CP-A, CP-B, or CP-C)). Model performance was verified by comparing predicted to observed fold-changes in PK parameters between HVs and cirrhotic subjects. The verified model was used to simulate the PK changes for simvastatin in patients with cirrhosis. The predicted area under the curve ratios (AUCCirr :AUCHV ) for ibrutinib were 3.38, 6.87, and 11.46 using the minimal PBPK model with shunt and 1.61, 2.58, and 4.33 without the shunt, these compared with observed values of 4.33, 8.14, and 9.04, respectively. For ibrutinib, propranolol, and buspirone, including a shunt in the PBPK model improved the prediction of the AUCCirr :AUCHV and maximum plasma concentration ratios (CmaxCirr :CmaxHV ). For midazolam, an intermediate extraction drug, the differences were less clear. Simulated simvastatin dose adjustments in cirrhosis suggested that 20 mg in CP-A and 10 mg in CP-B could be used clinically. A mechanistic model-informed understanding of the anatomic and pathophysiology of cirrhosis will facilitate improved dose prediction and adjustment in this vulnerable population.

A physiologically based pharmacokinetic model for open acid and lactone forms of atorvastatin and metabolites to assess the drug-gene interaction with SLCO1B1 polymorphisms

Atorvastatin is the most prescribed 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitor used to lower cardiovascular risk and constitutes one of the best-selling drugs world-wide. Several physiologically based pharmacokinetic (PBPK) models have been developed to assess its non-straightforward pharmacokinetics (PK) as well as that of its metabolites and have been only applied to assess drug-drug interactions (DDI). Here we present a full PBPK model for atorvastatin and its metabolites able to predict within a 2-fold error their PK after the administration of a solid oral dosage form containing the calcium salt of atorvastatin in single and multiple dosing schedules at 20, 40, and 80 mg and 10 mg dose levels, respectively. Internal validation with data from Phase 1 clinical trials as well as external validation in predicting clinically relevant DDIs consolidated model structure and parameterization. The model has been used to quantitatively assess the drug-gene interaction (DGI) between SLCO1B1 polymorphisms and atorvastatin exposure and revealed that patients with a reduced activity in hepatic uptake of atorvastatin are at increased risk of suffering muscle discomfort because of a 30% lower clearance (p < 0.01), leading to a 40% and 33% higher (p < 0.05) atorvastatin AUC and Cmax, respectively. These findings could explain the reported hazard ratio of 1.4 (95% CI: 1.1–1.7, p = 0.02) for suffering statin-induced myopathies and the treatment discontinuation among these patients (odds ratio 1.67, p = 0.0001) observed in the context of routine clinical care.

Physiologically Based Pharmacokinetic Modeling in Pregnant Women Suggests Minor Decrease in Maternal Exposure to Olanzapine.

Pregnancy is accompanied by significant physiological changes that might affect the in vivo drug disposition. Olanzapine is prescribed to pregnant women with schizophrenia, while its pharmacokinetics during pregnancy remains unclear. This study aimed to develop a physiologically based pharmacokinetic (PBPK) model of olanzapine in the pregnant population. With the contributions of each clearance pathway determined beforehand, a full PBPK model was developed and validated in the non-pregnant population. This model was then extrapolated to predict steady-state pharmacokinetics in the three trimesters of pregnancy by introducing gestation-related alterations. The model adequately simulated the reported time-concentration curves. The geometric mean fold error of Cmax and AUC was 1.14 and 1.09, respectively. The model predicted that under 10 mg daily dose, the systematic exposure of olanzapine had minor changes (less than 28%) throughout pregnancy. We proposed that the reduction in cytochrome P4501A2 activity is counteracted by the induction of other enzymes, especially glucuronyltransferase1A4. In conclusion, the PBPK model simulations suggest that, at least at the tested stages of pregnancy, dose adjustment of olanzapine can hardly be recommended for pregnant women if effective treatment was achieved before the onset of pregnancy and if fetal toxicity can be ruled out.

Experimental Data and PBPK Modeling Quantify Antibody Interference in PEGylated Drug Carrier Delivery.

Physiologically-based pharmacokinetic (PBPK) modeling is a popular drug development tool that integrates physiology, drug physicochemical properties, preclinical data, and clinical information to predict drug systemic disposition. Since PBPK models seek to capture complex physiology, parameter uncertainty and variability is a prevailing challenge: there are often more compartments (e.g., organs, each with drug flux and retention mechanisms, and associated model parameters) than can be simultaneously measured. To improve the fidelity of PBPK modeling, one approach is to search and optimize within the high-dimensional model parameter space, based on experimental time-series measurements of drug distributions. Here, we employ Latin Hypercube Sampling (LHS) on a PBPK model of PEG-liposomes (PL) that tracks biodistribution in an 8-compartment mouse circulatory system, in the presence (APA+) or absence (naïve) of anti-PEG antibodies (APA). Near-continuous experimental measurements of PL concentration during the first hour post-injection from the liver, spleen, kidney, muscle, lung, and blood plasma, based on PET/CT imaging in live mice, are used as truth sets with LHS to infer optimal parameter ranges for the full PBPK model. The data and model quantify that PL retention in the liver is the primary differentiator of biodistribution patterns in naïve versus APA+ mice, and spleen the secondary differentiator. Retention of PEGylated nanomedicines is substantially amplified in APA+ mice, likely due to PL-bound APA engaging specific receptors in the liver and spleen that bind antibody Fc domains. Our work illustrates how applying LHS to PBPK models can further mechanistic understanding of the biodistribution and antibody-mediated clearance of specific drugs.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11538-021-00950-z.

Bridging the gap between in silico and in vivo by modeling opioid disposition in a kidney proximal tubule microphysiological system

Opioid overdose, dependence, and addiction are a major public health crisis. Patients with chronic kidney disease (CKD) are at high risk of opioid overdose, therefore novel methods that provide accurate prediction of renal clearance (CLr) and systemic disposition of opioids in CKD patients can facilitate the optimization of therapeutic regimens. The present study aimed to predict renal clearance and systemic disposition of morphine and its active metabolite morphine-6-glucuronide (M6G) in CKD patients using a vascularized human proximal tubule microphysiological system (VPT-MPS) coupled with a parent-metabolite full body physiologically-based pharmacokinetic (PBPK) model. The VPT-MPS, populated with a human umbilical vein endothelial cell (HUVEC) channel and an adjacent human primary proximal tubular epithelial cells (PTEC) channel, successfully demonstrated secretory transport of morphine and M6G from the HUVEC channel into the PTEC channel. The in vitro data generated by VPT-MPS were incorporated into a mechanistic kidney model and parent-metabolite full body PBPK model to predict CLr and systemic disposition of morphine and M6G, resulting in successful prediction of CLr and the plasma concentration–time profiles in both healthy subjects and CKD patients. A microphysiological system together with mathematical modeling successfully predicted renal clearance and systemic disposition of opioids in CKD patients and healthy subjects.

Development of a Physiologically Based Pharmacokinetic Model for Prediction of Ethanol Concentration-Time Profile in Different Organs.

A physiologically based pharmacokinetic (PBPK) modeling approach was used to simulate the concentration-time profile of ethanol (EtOH) in stomach, duodenum, plasma and other tissues upon consumption of beer and whiskey under fasted and fed conditions. A full PBPK model was developed for EtOH using the advanced dissolution, absorption and metabolism (ADAM) model fully integrated into the Simcyp Simulator® 15 (Simcyp Ltd., Sheffield, UK). The prediction performance of the developed model was verified and the EtOH concentration-time profile in different organs was predicted. Simcyp simulation showed ≤ 2-fold difference in values of EtOH area under the concentration-time curve (AUC) in stomach and duodenum as compared to the observed values. Moreover, the simulated EtOH maximum concentration (Cmax), time to reach Cmax (Tmax) and AUC in plasma were comparable to the observed values. We showed that liver is exposed to the highest EtOH concentration, faster than other organs (Cmax=839.50mg/L and Tmax=0.53h), while brain exposure of EtOH (AUC=1139.43mg·h/L) is the highest among all other organs. Sensitivity analyses (SAs) showed direct proportion of EtOH rate and extent of absorption with administered EtOH dose and inverse relationship with gastric emptying time (GE) and steady-state volume of distribution (Vss). The current PBPK model approach might help with designing in vitro experiments in the area of alcohol organ damage or alcohol-drug interaction studies.

Physiologically Based Pharmacokinetic Modeling Approach to Identify the Drug–Drug Interaction Mechanism of Nifedipine and a Proton Pump Inhibitor, Omeprazole

Proton pump inhibitors (PPIs) can affect the intragastric release of other drugs from their dosage forms by elevating the gastric pH. They may also influence drug absorption and metabolism by interacting with P-glycoprotein or with the cytochrome P450 (CYP) enzyme system. Nifedipine is a Biopharmaceutics Classification System (BCS) class II drug with low solubility across physiologic pH and high permeability. Previous studies have demonstrated that drug-drug interaction (DDI) existed between omeprazole and nifedipine with significantly increased systemic exposure of nifedipine in subjects after pre-treatment for 7days with omeprazole compared to the subjects without omeprazole treatment. It was shown that omeprazole not only induced an increase in intragastric pH, but also inhibited the CYP3A4 activity, while CYP3A4-mediated oxidation is the main metabolic pathway of nifedipine. The purpose of this study is to apply a physiologically based pharmacokinetic (PBPK) modeling approach to investigate the DDI mechanism for an immediate release formulation of nifedipine with omeprazole. A previously published model for omeprazole was modified to integrate metabolites and to update CYP inhibition based on the most updated published in vitro data. We simulated the nifedipine pharmacokinetics in healthy subjects with or without the multiple-dose pretreatment of omeprazole (20mg) following oral administrations of immediate-release (IR) (10mg) nifedipine. Nifedipine solubility at different pHs was used to simulate the nifedipine pharmacokinetics for both clinical arms. Multiple sensitivity analyses were performed to understand the impact of gastric pH and the CYP3A4-mediated gut and liver first pass metabolism on the overall nifedipine pharmacokinetics. The developed PBPK model properly described the pharmacokinetics of nifedipine and predicted the inhibitory effect of multiple-dose omeprazole on CYP3A4 activity. With the incorporation of the physiologic effect of omeprazole on both gastric pH and CYP3A4 to the PBPK model, the verified PBPK model allows evaluating the impact of the increase in gastric pH and/or CYP3A4 inhibition. The simulated results show that the nifedipine metabolic inhibition by omeprazole may play an important role in the DDI between nifedipine and omeprazole for IR nifedipine formulation. The developed full PBPK model with the capability to simulate DDI by considering gastric pH change and metabolic inhibition provides a mechanistic understanding of the observed DDI of nifedipine with a PPI, omeprazole.

The Effect of Liver and Kidney Disease on the Pharmacokinetics of Clozapine and Sildenafil: A Physiologically Based Pharmacokinetic Modeling.

Background and ObjectivesPhysiologically based pharmacokinetic (PBPK) modeling permits clinical scientists to reduce practical constraints for clinical trials on patients with special diseases. In this study, simulations were carried out to validate the pharmacokinetic parameters of clozapine and sildenafil using Simcyp® simulator in young male adults and compare the effect of renal or hepatic impairment on the pharmacokinetic parameters of clozapine and sildenafil. Also, the effect of age on pharmacokinetic parameters of both drugs was investigated in healthy population and in patients with renal and hepatic impairment.MethodsA full PBPK model was built in the simulator for clozapine and sildenafil based on physicochemical properties and observed clinical results. The model used was Advanced, Dissolution, Absorption and Metabolism (ADAM) for both drugs.ResultsThe PBPK model adequately predicted the pharmacokinetic parameters of clozapine and sildenafil for the healthy adult population. In the simulation results, the bioavailability of both drugs was remarkably raised in both renal and hepatic impairment in young and elderly populations.ConclusionPBPK modeling could be helpful in the investigation and comparison of the pharmacokinetics in populations with specific disease conditions.

Physiologically Based Pharmacokinetic Modeling of Oxycodone in Children to Support Pediatric Dosing Optimization.

Physiologically-based pharmacokinetic (PBPK) modeling offers a unique modality to predict age-specific pharmacokinetics. The objective of this study was to assess the ability of PBPK model to predict plasma exposure of oxycodone, a widely used opioid for pain management, in adults and children. A full PBPK model of oxycodone following intravenous and oral administration was developed using a 'bottom-up' and 'top-down' combined strategy. The model was then extrapolated to pediatrics through a reasonable scaling method. The adult and pediatric model was evaluated using data from 17 clinical PK studies by testing predicted/observed goodness of fit. The mean fold error for PK parameters was calculated. Finally, we used the validated PBPK model to visualize adult-children dose conversion for oxycodone. The developed PBPK model successfully predicted the oxycodone disposition in adults, wherein the predicted versus observed AUC, Cmax, and tmax were within 0.90 to 1.20-fold difference. After scaling anatomy/physiology, protein binding, and clearance, the model showed satisfactory prediction performance for pediatric populations as predicted AUC were within the 1.50-fold range of the observed values. According to the application of PBPK model, we found that different intravenous doses should be given in children of different ages compared to a standard 0.1mg/kg in adults, while a progressive increasing dose with age growth following oral administration is recommended for children. The current example provides the opportunity for using the PBPK model to guide dose adjustment of oxycodone in the design of future pediatric clinical studies.

Physiologically based pharmacokinetic modelling to predict exposure differences in healthy volunteers and subjects with renal impairment: Ceftazidime case study.

Ceftazidime is a widely used β-lactam antibiotic and almost entirely excreted via glomerular filtration in kidney. The objective of this analysis was to assess the ability of physiologically based pharmacokinetic (PBPK) model to predict ceftazidime exposure in healthy volunteers and subjects with renal impairment. A full PBPK model of ceftazidime was developed using physiochemical properties and clinical data. The total clearance of 115mL/min and renal clearance of 100mL/min were obtained from ceftazidime package insert. Healthy and chronic kidney disease (CKD) populations were applied for sampling of virtual subjects. The established PBPK model predicted mean plasma AUCinf were 138.5±19.6, 230.7±22.2, 369.3±53.1 and 561.8±92.4hµg/mL in healthy, mild, moderate and severe renal impairment subjects, respectively, after administration of 1g ceftazidime intravenous bolus dose. The predicted values were in close agreement with the weighted mean of the five reported clinical studies. The exposure was slightly under predicted in subjects with severely impaired renal function, but still within 1.5-fold range. The concentration-time profiles of ceftazidime were also well captured in healthy volunteers and subjects with renal impairment. The developed PBPK model along with systems pharmacokinetics (PK) (renal impaired populations) well predicted the ceftazidime exposure. PBPK models verified with clinical study in healthy volunteers could be potentially applied to predict PK and recommend dose adjustment for CKD patients.

Physiologically Based Pharmacokinetic Model of the CYP2D6 Probe Atomoxetine: Extrapolation to Special Populations and Drug-Drug Interactions.

Physiologically based pharmacokinetic (PBPK) modeling of drug disposition and drug-drug interactions (DDIs) has become a key component of drug development. PBPK modeling has also been considered as an approach to predict drug disposition in special populations. However, whether models developed and validated in healthy populations can be extrapolated to special populations is not well established. The goal of this study was to determine whether a drug-specific PBPK model validated using healthy populations could be used to predict drug disposition in specific populations and in organ impairment patients. A full PBPK model of atomoxetine was developed using a training set of pharmacokinetic (PK) data from CYP2D6 genotyped individuals. The model was validated using drug-specific acceptance criteria and a test set of 14 healthy subject PK studies. Population PBPK models were then challenged by simulating the effects of ethnicity, DDIs, pediatrics, and renal and hepatic impairment on atomoxetine PK. Atomoxetine disposition was successfully predicted in 100% of healthy subject studies, 88% of studies in Asians, 79% of DDI studies, and 100% of pediatric studies. However, the atomoxetine area under the plasma concentration versus time curve (AUC) was overpredicted by 3- to 4-fold in end stage renal disease and hepatic impairment. The results show that validated PBPK models can be extrapolated to different ethnicities, DDIs, and pediatrics but not to renal and hepatic impairment patients, likely due to incomplete understanding of the physiologic changes in these conditions. These results show that systematic modeling efforts can be used to further refine population models to improve the predictive value in this area.

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.

More Power to OATP1B1: An Evaluation of Sample Size in Pharmacogenetic Studies Using a Rosuvastatin PBPK Model for Intestinal, Hepatic, and Renal Transporter-Mediated Clearances.

Rosuvastatin is a substrate of choice in clinical studies of organic anion‐transporting polypeptide (OATP)1B1‐ and OATP1B3‐associated drug interactions; thus, understanding the effect of OATP1B1 polymorphisms on the pharmacokinetics of rosuvastatin is crucial. Here, physiologically based pharmacokinetic (PBPK) modeling was coupled with a power calculation algorithm to evaluate the influence of sample size on the ability to detect an effect (80% power) of OATP1B1 phenotype on pharmacokinetics of rosuvastatin. Intestinal, hepatic, and renal transporters were mechanistically incorporated into a rosuvastatin PBPK model using permeability‐limited models for intestine, liver, and kidney, respectively, nested within a full PBPK model. Simulated plasma rosuvastatin concentrations in healthy volunteers were in agreement with previously reported clinical data. Power calculations were used to determine the influence of sample size on study power while accounting for OATP1B1 haplotype frequency and abundance in addition to its correlation with OATP1B3 abundance. It was determined that 10 poor‐transporter and 45 intermediate‐transporter individuals are required to achieve 80% power to discriminate the AUC0‐48h of rosuvastatin from that of the extensive‐transporter phenotype. This number was reduced to 7 poor‐transporter and 40 intermediate‐transporter individuals when the reported correlation between OATP1B1 and 1B3 abundance was taken into account. The current study represents the first example in which PBPK modeling in conjunction with power analysis has been used to investigate sample size in clinical studies of OATP1B1 polymorphisms. This approach highlights the influence of interindividual variability and correlation of transporter abundance on study power and should allow more informed decision making in pharmacogenomic study design.

Physiologically based pharmacokinetic modeling for sequential metabolism: effect of CYP2C19 genetic polymorphism on clopidogrel and clopidogrel active metabolite pharmacokinetics.

Clopidogrel is a prodrug that needs to be converted to its active metabolite (clopi-H4) in two sequential cytochrome P450 (P450)-dependent steps. In the present study, a dynamic physiologically based pharmacokinetic (PBPK) model was developed in Simcyp for clopidogrel and clopi-H4 using a specific sequential metabolite module in four populations with phenotypically different CYP2C19 activity (poor, intermediate, extensive, and ultrarapid metabolizers) receiving a loading dose of 300 mg followed by a maintenance dose of 75 mg. This model was validated using several approaches. First, a comparison of predicted-to-observed area under the curve (AUC)0-24 obtained from a randomized crossover study conducted in four balanced CYP2C19-phenotype metabolizer groups was performed using a visual predictive check method. Second, the interindividual and intertrial variability (on the basis of AUC0-24 comparisons) between the predicted trials and the observed trial of individuals, for each phenotypic group, were compared. Finally, a further validation, on the basis of drug-drug-interaction prediction, was performed by comparing observed values of clopidogrel and clopi-H4 with or without dronedarone (moderate CYP3A4 inhibitor) coadministration using a previously developed and validated physiologically based PBPK dronedarone model. The PBPK model was well validated for both clopidogrel and its active metabolite clopi-H4, in each CYP2C19-phenotypic group, whatever the treatment period (300-mg loading dose and 75-mg last maintenance dose). This is the first study proposing a full dynamic PBPK model able to accurately predict simultaneously the pharmacokinetics of the parent drug and of its primary and secondary metabolites in populations with genetically different activity for a metabolizing enzyme.

Prediction of drug‐drug interactions between various antidepressants and ritonavir using a physiologically based pharmacokinetic model

Depression can impact on the treatment of HIV infection, and effective treatment of depressive conditions can have a beneficial effect improving adherence. However antidepressant treatment requires long‐term maintenance, and is prone to pharmacokinetic drug‐drug interactions (DDI) with antiretrovirals. The aim of this study was to predict the magnitude of DDI between ritonavir (RTV) and the most commonly prescribed antidepressants using a physiologically based pharmacokinetic (PBPK) model simulating virtual clinical trials. In vitro data describing the physiochemical properties, absorption, metabolism, induction and inhibitory potential of RTV and five antidepressants were obtained from published literature. Interactions between RTV and antidepressants were evaluated using the full PBPK model implemented in the Simcyp Population‐based Simulator (Version 11.1, Simcyp Limited, UK) and virtual clinical studies were simulated on 50 Caucasian subjects receiving 100mg bid of RTV for 21 days plus sertraline (100mg qd), citalopram (40mg qd), fluoxetine (20mg qd), venlafaxine (25mg qd) and then from day 14–21. Simulated pharmacokinetic parameters were compared with observed values available in the literature. The simulated PK parameters of RTV, sertraline, citalopram, fluoxetine, mirtazepine and venlafaxine given alone at standard dosage were similar to reference values obtain from published clinical studies. The effect of simulated RTV co‐administration on sertaline, fluoxetine and venlaflaxine was an AUC decrease of 40%, 26% and 6%, respectively and on mirtazepine and citalopram, an AUC increase of 60% and 20% respectively. The magnitude of the simulated DDI between RTV and the antidepressants was overall weak to moderate according to the classification of the FDA. The modest magnitude of these drug‐drug interactions could be explained by the fact that antidepressants are substrates of multiple isoforms thus metabolism can still occur through CYPs that are not or weakly impacted by RTV. Although from a pharmacokinetic point of view, venlafaxine or citalopram represent better candidates for patients on RTV, there are many considerations in seeking to optimize antidepressant therapy. The next stage in this work is to simulate DDI between boosted protease inhibitors and antidepressants. IVIVE is a useful tool for both prediction of drug‐drug interactions and design of prospective clinical trials, simulating optimal sample size, and selection of doses.

Steady‐State Solutions to PBPK Models and Their Applications to Risk Assessment I: Route‐to‐Route Extrapolation of Volatile Chemicals

Although analysis of in vivo pharmacokinetic data necessitates use of time-dependent physiologically-based pharmacokinetic (PBPK) models, risk assessment applications are often driven primarily by steady-state and/or integrated (e.g., AUC) dosimetry. To that end, we present an analysis of steady-state solutions to a PBPK model for a generic volatile chemical metabolized in the liver. We derive an equivalent model that is much simpler and contains many fewer parameters than the full PBPK model. The state of the system can be specified by two state variables-the rate of metabolism and the rate of clearance by exhalation. For a given oral dose rate or inhalation exposure concentration, the system state only depends on the blood-air partition coefficient, metabolic constants, and the rates of blood flow to the liver and of alveolar ventilation. At exposures where metabolism is close to linear, only the effective first-order metabolic rate is needed. Furthermore, in this case, the relationship between cumulative exposure and average internal dose (e.g., AUCs) remains the same for time-varying exposures. We apply our analysis to oral-inhalation route extrapolation, showing that for any dose metric, route equivalence only depends on the parameters that determine the system state. Even if the appropriate dose metric is unknown, bounds can be placed on the route-to-route equivalence with very limited data. We illustrate this analysis by showing that it reproduces exactly the PBPK-model-based route-to-route extrapolation in EPA's 2000 risk assessment for vinyl chloride. Overall, we find that in many cases, steady-state solutions exactly reproduce or closely approximate the solutions using the full PBPK model, while being substantially more transparent. Subsequent work will examine the utility of steady-state solutions for analyzing cross-species extrapolation and intraspecies variability.

INVESTIGATION OF THE POTENTIAL IMPACT OF BENCHMARK DOSE AND PHARMACOKINETIC MODELING IN NONCANCER RISK ASSESSMENT

There has been relatively little attention given to incorporating knowledge of mode of action or of dosimetry of active toxic chemical to target tissue sites in the calculation of noncancer exposure guidelines. One exception is the focus in the revised reference con centration (RfC) process on delivered dose adjustments for inhaled materials. The studies reported here attempt to continue in the spirit of the new RfC guidelines by incorporating both mechanistic and delivered dose information using a physiologically based pharmaco kinetic (PBPK) model, along with quantitative dose-response information using the bench mark dose (BMD) method, into the noncancer risk assessment paradigm. Two examples of the use of PBPK and BMD techniques in noncancer risk assessment are described: methylene chloride, and trichloroethylene. Minimal risk levels (MRLs) based on PBPK analysis of these chemicals were generally similar to those based on the traditional process, but individual MRLs ranged from roughly 10-fold higher to more than 10-fold lower than existing MRLs that were not based on PBPK modeling. Only two MRLs were based on critical studies that presented adequate data for BMD modeling, and in these two cases the BMD models were unable to provide an acceptable fit to the overall dose-response of the data, even using pharmacokinetic dose metrics. A review of 10 additional chemicals indicated that data reporting in the toxicological literature is often inadequate to support BMD modeling. Three general observations regarding the use of PBPK and BMD modeling in noncancer risk assessment were noted. First, a full PBPK model may not be necessary to support a more accurate risk assessment; often only a simple pharmacokinetic description, or an understanding of basic pharmacokinetic principles, is needed. Second, pharmacokinetic and mode of action considerations are a crucial factor in conducting non cancer risk assessments that involve animal-to-human extrapolation. Third, to support the application of BMD modeling in noncancer risk assessment, reporting of toxicity results in the toxicological literature should include both means and standard deviations for each dose group in the case of quantitative endpoints, such as relative organ weights or testing scores, and should report the number of animals affected in the case of qualitative endpoints.