Administration mode matters for 5-fluorouracil therapy: Physiologically based pharmacokinetic evidence for avoidance of myelotoxicity by continuous infusion but not intravenous bolus.

AIMS: Pre-emptive prediction to avoid myelosuppression and harmful sequelae is difficult given the complex interplay among patients, drugs and treatment protocols. This study aimed to model plasma and bone marrow concentrations and the likelihood of myelotoxicity following administration of 5-fluorouracil (5-FU) by diverse intravenous (IV) bolus or continuous infusion (cIF) regimens. METHODS: Using physicochemical, in vitro and clinical data obtained from the literature consisting of various regimens and patient cohorts, a 5-FU physiologically based pharmacokinetic (PBPK) model was developed. The predicted and observed PK values were compared to assess model performance prior to examining myelotoxicity potential of IV bolus vs. cIF and DPYD wild type vs. genetic variant. RESULTS: The established model was verified by utilizing 5-FU concentration-time profiles of adequate heterogeneity contributed by 36 regimens from 15 studies. The study provided corroborative evidence to explain why cIF (vs. IV bolus) had lower myelotoxicity risk despite much higher total doses. The PBPK model was used to estimate the optimal dosage in patients heterozygous for the DPYD c.1905 + 1G > A allele and suggested that a dose reduction of at least 25% was needed (compared to the dose in wild-type subjects). CONCLUSION: A verified PBPK model was used to explain the lower myelotoxicity risk of cIF vs. IV bolus administration of 5-FU and to estimate the dose reduction needed in carriers of a DPYD variant. With appropriate data, expertise and resources, PBPK models have many potential uses in precision medicine application of oncology drugs.

An Application of a Physiologically Based Pharmacokinetic Approach to Predict Ceftazidime Pharmacokinetics in a Pregnant Population.

Physiological changes during pregnancy can alter maternal and fetal drug exposure. The objective of this work was to predict maternal and umbilical ceftazidime pharmacokinetics during pregnancy. Ceftazidime transplacental permeability was predicted from its physicochemical properties and incorporated into the model. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted ceftazidime concentrations in non-pregnant and pregnant subjects of different gestational weeks were within 2-fold of the observations, and the observed concentrations fell within the 5th-95th prediction interval from the PBPK simulations. The calculated transplacental clearance (0.00137 L/h/mL of placenta volume) predicted an average umbilical cord-to-maternal plasma ratio of 0.7 after the first dose, increasing to about 1.0 at a steady state, which also agrees well with clinical observations. The developed maternal PBPK model adequately predicted the observed exposure and kinetics of ceftazidime in the pregnant population. Using a verified population-based PBPK model provides valuable insights into the disposition of drug concentrations in special individuals that are otherwise difficult to study and, in addition, offers the possibility of supplementing sparse samples obtained in vulnerable populations with additional knowledge, informing the dosing adjustment and study design, and improving the efficacy and safety of drugs in target populations.

Exploring the impact of ethnicity on drug pharmacokinetics using PBPK models: A case study with lansoprazole in Japanese subjects.

AIMS: The aim of this study is to demonstrate the use of PBPK modelling to explore the impact of ethnic differences on drug PK. METHODS: A PBPK model developed for lansoprazole was used to predict the clinical PK of lansoprazole in Japanese subjects by incorporating the physiological parameters of a Japanese population into the model. Further verification of the developed Japanese population with clinical studies involving eight other CYP substrates-omeprazole, ticlopidine, alprazolam, midazolam, nifedipine, cinacalcet, paroxetine and dextromethorphan-was also carried out. RESULTS: The PK of lansoprazole in both Caucasian and Japanese subjects was well predicted by the model as the observed data were within the 5th and 95th percentiles across all the clinical studies. In age- and sex-matched simulations in both the Caucasian and Japanese populations, the predicted PK (mean ± SD) of a single oral dose of 30-mg lansoprazole was higher in the Japanese population in all cases, with more than twofold higher AUC of 5.98 ± 6.43 mg/L.h (95% CI: 4.72, 7.24) vs. 2.46 ± 2.45 mg/L.h (95% CI: 1.98, 2.94) in one scenario. In addition, in two out of the nine clinical DDIs of lansoprazole and the additional CYP substrates simulated using the Japanese population, the predicted DDI in Japanese was more than 1.25-fold that in Caucasians, indicating an increased DDI liability. CONCLUSIONS: By accounting for various physiological parameters that characterize a population in a PBPK model, the impact of the different identified interethnic differences on the drug's PK can be explored, which can inform the adoption of drugs from one region to another.

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 systems approach reveals species differences in hepatic stress response capacity.

To minimize the occurrence of unexpected toxicities in early phase preclinical studies of new drugs, it is vital to understand fundamental similarities and differences between preclinical species and humans. Species differences in sensitivity to acetaminophen (APAP) liver injury have been related to differences in the fraction of the drug that is bioactivated to the reactive metabolite N-acetyl-p-benzoquinoneimine (NAPQI). We have used physiologically based pharmacokinetic modeling to identify oral doses of APAP (300 and 1000 mg/kg in mice and rats, respectively) yielding similar hepatic burdens of NAPQI to enable the comparison of temporal liver tissue responses under conditions of equivalent chemical insult. Despite pharmacokinetic and biochemical verification of the equivalent NAPQI insult, serum biomarker and tissue histopathology analyses revealed that mice still exhibited a greater degree of liver injury than rats. Transcriptomic and proteomic analyses highlighted the stronger activation of stress response pathways (including the Nrf2 oxidative stress response and autophagy) in the livers of rats, indicative of a more robust transcriptional adaptation to the equivalent insult. Components of these pathways were also found to be expressed at a higher basal level in the livers of rats compared with both mice and humans. Our findings exemplify a systems approach to understanding differential species sensitivity to hepatotoxicity. Multiomics analysis indicated that rats possess a greater basal and adaptive capacity for hepatic stress responses than mice and humans, with important implications for species selection and human translation in the safety testing of new drug candidates associated with reactive metabolite formation.

Non-specific binding of compounds in in vitro metabolism assays: a comparison of microsomal and hepatocyte binding in different species and an assessment of the accuracy of prediction models.

Non-specific binding in in vitro metabolism systems leads to an underestimation of the true intrinsic metabolic clearance of compounds being studied. Therefore in vitro binding needs to be accounted for when extrapolating in vitro data to predict the in vivo metabolic clearance of a compound. While techniques exist for experimentally determining the fraction of a compound unbound in in vitro metabolism systems, early in drug discovery programmes computational approaches are often used to estimate the binding in the in vitro system.Experimental fraction unbound data (n = 60) were generated in liver microsomes (fumic) from five commonly used pre-clinical species (rat, mouse, dog, minipig, monkey) and humans. Unbound fraction in incubations with mouse, rat or human hepatocytes was determined for the same 60 compounds. These data were analysed to determine the relationship between experimentally determined binding in the different matrices and across different species. In hepatocytes there was a good correlation between fraction unbound in human and rat (r2=0.86) or mouse (r2=0.82) hepatocytes. Similar correlations were observed between binding in human liver microsomes and microsomes from rat, mouse, dog, Göttingen minipig or monkey liver microsomes (r2 of >0.89, n = 51 - 52 measurements in different species). Physicochemical parameters (logP, pKa and logD) were predicted for all evaluated compounds. In addition, logP and/or logD were measured for a subset of compounds.Binding to human hepatocytes predicted using 5 different methods was compared to the measured data for a set of 59 compounds. The best methods evaluated used measured microsomal binding in human liver microsomes to predict hepatocyte binding. The collated physicochemical data were used to predict the human fumic using four different in silico models for a set of 53-60 compounds. The correlation (r2) and root mean square error between predicted and observed microsomal binding was 0.69 & 0.20, 0.47 & 0.23, 0.56 & 0.21 and 0.54 & 0.26 for the Turner-Simcyp, Austin, Hallifax-Houston and Poulin models, respectively. These analyses were extended to include measured literature values for binding in human liver microsomes for a larger set of compounds (n=697). For the larger dataset of compounds, microsomal binding was well predicted for neutral compounds (r2=0.67 - 0.70) using the Poulin, Austin, or Turner-Simcyp methods but not for acidic or basic compounds (r2<0.5) using any of the models. While the lipophilicity-based models can be used, the in vitro binding should be measured for compounds where more certainty is needed, using appropriately calibrated assays and possibly established weak, moderate, and strong binders as reference compounds to allow comparison across databases.

Application of physiologically based pharmacokinetic models for therapeutic proteins and other novel modalities.

The past two decades have seen diversification of drug development pipelines and approvals from traditional small molecule therapies to alternative modalities including monoclonal antibodies, engineered proteins, antibody drug conjugates (ADCs), oligonucleotides and gene therapies. At the same time, physiologically based pharmacokinetic (PBPK) models for small molecules have seen increased industry and regulatory acceptance.This review focusses on the current status of the application of PBPK models to these newer modalities and give a perspective on the successes, challenges and future directions of this field.There is greatest experience in the development of PBPK models for therapeutic proteins, and PBPK models for ADCs benefit from prior experience for both therapeutic proteins and small molecules. For other modalities, the application of PBPK models is in its infancy.Challenges are discussed and a common theme is lack of availability of physiological and experimental data to characterise systems and drug parameters to enable a priori prediction of pharmacokinetics. Furthermore, sufficient clinical data are required to build confidence in developed models.The PBPK modelling approach provides a quantitative framework for integrating knowledge and data from multiple sources and can be built on as more data becomes available.

Performance Verification of CYP2C19 Enzyme Abundance Polymorphism Settings within the Simcyp Simulator v21.

Physiologically based pharmacokinetic (PBPK) modeling has a number of applications, including assessing drug−drug interactions (DDIs) in polymorphic populations, and should be iteratively refined as science progresses. The Simcyp Simulator is annually updated and version 21 included updates to hepatic and intestinal CYP2C19 enzyme abundance, including addition of intermediate and rapid metabolizer phenotypes and changes to the ultra-rapid metabolizer enzyme abundance, with implications for population clearance and DDI predictions. This work details verification of the updates with sensitive CYP2C19 substrates, omeprazole and lansoprazole, using available clinical data from literature. Multiple assessments were performed, including recovery of areas under the concentration-time curve (AUC) and Cmax from compiled datasets for each drug, recovery of victim DDI ratios with CYP2C19 and/or CYP3A4 inhibition and recovery of relative exposure between phenotypes. Simulated data were within respective acceptance criteria for >80% of omeprazole AUC values, >70% of lansoprazole AUC and Cmax, >60% of AUC and Cmax DDI ratios and >80% of exposure ratios between different phenotypes. Recovery of omeprazole Cmax was lower (>50−70% within 2-fold) and possibly attributed to the variety of formulations used in the clinical dataset. Overall, the results demonstrated that the updated data used to parameterize CYP2C19 phenotypes reasonably described the pharmacokinetics of omeprazole and lansoprazole in genotyped or phenotyped individuals.

SimRFlow: An R-based workflow for automated high-throughput PBPK simulation with the Simcyp® simulator.

SimRFlow is a high-throughput physiologically based pharmacokinetic (PBPK) modelling tool which uses Certara's Simcyp® simulator. The workflow is comprised of three main modules: 1) a Data Collection module for automated curation of physicochemical (from ChEMBL and the Norman Suspect List databases) and experimental data (i.e.: clearance, plasma-protein binding, and blood-to-plasma ratio, from httk-R package databases), 2) a Simulation module which activates the Simcyp® simulator and runs Monte Carlo simulations on virtual subjects using the curated data, and 3) a Data Visualisation module for understanding the simulated compound-specific profiles and predictions. SimRFlow has three administration routes (oral, intravenous, dermal) and allows users to change some simulation parameters including the number of subjects, simulation duration, and dosing. Users are only expected to provide a file of the compounds they wish to simulate, and in return the workflow provides summary statistics, concentration-time profiles of various tissue types, and a database file (containing in-depth results) for each simulated compound. This is presented within a guided and easy-to-use R Shiny interface which provides many plotting options for the visualisation of concentration-time profiles, parameter distributions, trends between the different parameters, as well as comparison of predicted parameters across all batch-simulated compounds. The in-built R functions can be assembled in user-customised scripts which allows for the modification of the workflow for different purposes. SimRFlow proves to be a time-efficient tool for simulating a large number of compounds without any manual curation of physicochemical or experimental data necessary to run Simcyp® simulations.

In vitro/in silico prediction of drug induced steatosis in relation to oral doses and blood concentrations by the Nile Red assay.

The accumulation of lipid droplets in hepatocytes is a key feature of drug-induced liver injury (DILI) and can be induced by a subset of hepatotoxic compounds. In the present study, we optimized and evaluated an in vitro technique based on the fluorescent dye Nile Red, further named Nile Red assay to quantify lipid droplets induced by the exposure to chemicals. The Nile Red assay and a cytotoxicity test (CTB assay) were then performed on cells exposed concentration-dependently to 60 different compounds. Of these, 31 were known to induce hepatotoxicity in humans, and 13 were reported to also cause steatosis. In order to compare in vivo relevant blood concentrations, pharmacokinetic models were established for all compounds to simulate the maximal blood concentrations (Cmax) at therapeutic doses. The results showed that several hepatotoxic compounds induced an increase in lipid droplets at sub-cytotoxic concentrations. To compare how well (1) the cytotoxicity test alone, (2) the Nile Red assay alone, and (3) the combination of the cytotoxicity test and the Nile Red assay (based on the lower EC10 of both assays) allow the differentiation between hepatotoxic and non-hepatotoxic compounds, a previously established performance metric, the Toxicity Separation Index (TSI) was calculated. In addition, the Toxicity Estimation Index (TEI) was calculated to determine how well blood concentrations that cause an increased DILI risk can be estimated for hepatotoxic compounds. Our findings indicate that the combination of both assays improved the TSI and TEI compared to each assay alone. In conclusion, the study demonstrates that inclusion of the Nile Red assay into in vitro test batteries may improve the prediction of DILI compounds.

Physiologically-Based Pharmacokinetic Models of CYP2D6 Substrate and Inhibitors Nebivolol, Cinacalcet and Mirabegron to Simulate Drug-Drug Interactions.

BACKGROUND AND OBJECTIVES: Index substrates and inhibitors to investigate the role of the polymorphic enzyme, cytochrome P450 (CYP) 2D6, in the metabolism of new compounds have been proposed by regulatory agencies. This work describes the development and verification of physiologically-based pharmacokinetic (PBPK) models for the CYP2D6-sensitive substrate, nebivolol and the index CYP2D6 inhibitors, mirabegron and cinacalcet. METHODS: PBPK models for nebivolol, mirabegron and cinacalcet were developed using in vitro and clinical data. The performance of the PBPK models was verified by comparing the simulated results against reported human systemic exposure and clinical drug-drug interactions (DDIs) studies. RESULTS: The exposure of nebivolol, cinacalcet and mirabegron predicted by the PBPK models was verified against pharmacokinetic data from 13, 3 and 9 clinical studies, respectively. For nebivolol, the predicted mean maximum plasma concentration (Cmax) and area under the plasma concentration-time (AUC) values in CYP2D6 extensive metaboliser subjects were within 0.9- to 1.49-fold of the observed values. In poor metaboliser CYP2D6 subjects, the predicted Cmax and AUC values were within 0.41- to 0.81-fold of observed values. For cinacalcet, the predicted Cmax and AUC values were within 0.97- to 1.32-fold of the observed data. For mirabegron, the predicted AUC values across all the studies investigated were within 0.71- to 1.88-fold of observed values. The PBPK model-predicted DDIs were in good agreement (within 2-fold) with observed DDIs in all verification studies (n = 8) assessed. The overall precision was 1.26 and 1.21 for Cmax and the AUC ratio, respectively. CONCLUSIONS: The developed PBPK models can be used to assess the DDI potential liability of new chemical entities that are substrates or inhibitors of CYP2D6.

Application of a Physiologically Based Pharmacokinetic Approach to Predict Theophylline Pharmacokinetics Using Virtual Non-Pregnant, Pregnant, Fetal, Breast-Feeding, and Neonatal Populations.

Perinatal pharmacology is influenced by a myriad of physiological variables that are changing dynamically. The influence of these covariates has not been assessed systemically. The objective of this work was to use theophylline as a model drug and to predict its pharmacokinetics before, during (including prediction of the umbilical cord level), and after pregnancy as well as in milk (after single and multiple doses) and in neonates using a physiological-based pharmacokinetic (PBPK) model. Neonatal theophylline exposure from milk consumption was projected in both normal term and preterm subjects. Predicted infant daily doses were calculated using theophylline average and maximum concentration in the milk as well as an estimate of milk consumption. Predicted concentrations and parameters from the PBPK model were compared to the observed data. PBPK predicted theophylline concentrations in non-pregnant and pregnant populations at different gestational weeks were within 2-fold of the observations and the observed concentrations fell within the 5th-95th prediction interval from the PBPK simulations. The PBPK model predicted an average cord-to-maternal plasma ratio of 1.0, which also agrees well with experimental observations. Predicted postpartum theophylline concentration profiles in milk were also in good agreement with observations with a predicted milk-to-plasma ratio of 0.68. For an infant of 2 kg consuming 150 ml of milk per day, the lactation model predicted a relative infant dose (RID) of 12 and 17% using predicted average (Cavg,ss) and maximum (Cmax,ss) concentration in milk at steady state. The maximum RID of 17% corresponds to an absolute infant daily dose of 1.4 ± 0.5 mg/kg/day. This dose, when administered as 0.233 mg/kg every 4 h, to resemble breastfeeding frequency, resulted in plasma concentrations as high as 3.9 (1.9-6.8) mg/L and 2.8 (1.3-5.3) (5th-95th percentiles) on day 7 in preterm (32 GW) and full-term neonatal populations.

Understanding Interindividual Variability in the Drug Interaction of a Highly Extracted CYP1A2 Substrate Tizanidine: Application of a Permeability-Limited Multicompartment Liver Model in a Population Based Physiologically Based Pharmacokinetic Framework.

Tizanidine, a centrally acting skeletal muscle relaxant, is predominantly metabolized by CYP1A2 and undergoes extensive hepatic first-pass metabolism after oral administration. As a highly extracted drug, the systemic exposure to tizanidine exhibits considerable interindividual variability and is altered substantially when coadministered with CYP1A2 inhibitors or inducers. The aim of the current study was to compare the performance of a permeability-limited multicompartment liver (PerMCL) model, which operates as an approximation of the dispersion model, and the well stirred model (WSM) for predicting tizanidine drug-drug interactions (DDIs). Physiologically based pharmacokinetic models were developed for tizanidine, incorporating the PerMCL model and the WSM, respectively, to simulate the interaction of tizanidine with a range of CYP1A2 inhibitors and inducers. Whereas the WSM showed a tendency to underpredict the fold change of tizanidine area under the plasma concentration-time curve (AUC ratio) in the presence of perpetrators, the use of PerMCL model increased precision (absolute average-fold error: 1.32-1.42 versus 1.58) and decreased bias (average-fold error: 0.97-1.25 versus 0.63) for the predictions of mean AUC ratios as compared with the WSM. The PerMCL model captured the observed range of individual AUC ratios of tizanidine as well as the correlation between individual AUC ratios and CYP1A2 activities without interactions, whereas the WSM was not able to capture these. The results demonstrate the advantage of using the PerMCL model over the WSM in predicting the magnitude and interindividual variability of DDIs for a highly extracted sensitive substrate tizanidine. SIGNIFICANCE STATEMENT: This study demonstrates the advantages of the PerMCL model, which operates as an approximation of the dispersion model, in mitigating the tendency of the WSM to underpredict the magnitude and variability of DDIs of a highly extracted CYP1A2 substrate tizanidine when it is administered with CYP1A2 inhibitors or inducers. The physiologically based pharmacokinetic modeling approach described herein is valuable to the understanding of drug interactions of highly extracted substrates and the source of its interindividual variability.

Prediction of CYP-mediated DDIs involving inhibition: Approaches to address the requirements for system qualification of the Simcyp Simulator.

Physiologically-based pharmacokinetic (PBPK) modeling is being increasingly used in drug development to avoid unnecessary clinical drug-drug interaction (DDI) studies and inform drug labels. Thus, regulatory agencies are recommending, or indeed requesting, more rigorous demonstration of the prediction accuracy of PBPK platforms in the area of their intended use. We describe a framework for qualification of the Simcyp Simulator with respect to competitive and mechanism-based inhibition (MBI) of CYP1A2, CYP2D6, CYP2C8, CYP2C9, CYP2C19, and CYP3A4/5. Initially, a DDI matrix, consisting of a range of weak, moderate, and strong inhibitors and substrates with varying fraction metabolized by specific CYP enzymes that were susceptible to different degrees of inhibition, were identified. Simulations were run with 123 clinical DDI studies involving competitive inhibition and 78 clinical DDI studies involving MBI. For competitive inhibition, the overall prediction accuracy was good with an average fold error (AFE) of 0.91 and 0.92 for changes in the maximum plasma concentration (Cmax ) and area under the plasma concentration (AUC) time profile, respectively, as a consequence of the DDI. For MBI, an AFE of 1.03 was determined for both Cmax and AUC. The prediction accuracy was generally comparable across all CYP enzymes, irrespective of the isozyme and mechanism of inhibition. These findings provide confidence in application of the Simcyp Simulator (V19 R1) for assessment of the DDI potential of drugs in development either as inhibitors or victim drugs of CYP-mediated interactions. The approach described herein and the identified DDI matrix can be used to qualify subsequent versions of the platform.

A Mechanistic Absorption and Disposition Model of Ritonavir to Predict Exposure and Drug-Drug Interaction Potential of CYP3A4/5 and CYP2D6 Substrates.

BACKGROUND AND OBJECTIVES: Due to health authority warnings and the recommended limited use of ketoconazole as a model inhibitor of cytochrome P450 (CYP) 3A4 in clinical drug-drug interaction (DDI) studies, there is a need to search for alternatives. Ritonavir is a strong inhibitor for CYP3A4/5-mediated DDIs and has been proposed as a suitable alternative to ketoconazole. It can also be used as a weak inhibitor for CYP2D6-mediated DDIs. Most of the currently available physiologically based pharmacokinetic (PBPK) inhibitor models developed for predicting DDIs use first-order absorption models, which do not mechanistically capture the effect of formulations on the systemic exposure of the inhibitor. Thus, the main purpose of the current study was to verify the predictive performance of a mechanistic absorption and disposition model of ritonavir when it was applied to the inhibition of CYP2D6 and CYP3A4/5 by ritonavir. METHODS: A PBPK model that incorporates formulation characteristics and enzyme kinetic parameters for post-absorptive pharmacokinetic processes of ritonavir was constructed. Key absorption-related parameters in the model were determined using mechanistic modelling of in vitro biopharmaceutics experiments. The model was verified for systemic exposure and DDI risk assessment using clinical observations from 13 and 18 studies, respectively. RESULTS: Maximal inhibition of hepatic (3.53% of the activity remaining) and gut (5.16% of the activity remaining) CYP3A4 activity was observed when ritonavir was orally administered in doses of 100 mg or higher. The PBPK model accurately described the concentrations of ritonavir in the different simulated studies. The prediction accuracy for maximum concentration (Cmax) and area under the plasma concentration versus time curve (AUC) were assessed. The bias (average fold error, AFE) for the prediction of Cmax and AUC was 0.92 and 1.06, respectively, and the precision (absolute average fold error, AAFE) was 1.29 and 1.23, respectively. The PBPK model predictions for all Cmax and AUC ratios when ritonavir was used as an inhibitor of CYP metabolism fell within twofold of the clinical observations. The prediction accuracy for Cmax and AUC ratios had a bias (AFE) of 0.85 and 0.99, respectively, and a precision (AAFE) of 1.21 and 1.33, respectively. CONCLUSIONS: The current model, which incorporates formulation characteristics and mechanistic disposition parameters, can be used to assess the DDI potential of CYP3A4/5 and CYP2D6 substrates administered with a twice-daily dose of 100 mg of ritonavir for 14 days.

Guide to development of compound files for PBPK modeling in the Simcyp population-based simulator.

The Simcyp Simulator is a software platform for population physiologically-based pharmacokinetic (PBPK) modeling and simulation. It links in vitro data to in vivo absorption, distribution, metabolism, excretion and pharmacokinetic/pharmacodynamic outcomes to explore clinical scenarios and support drug development decisions, including regulatory submissions and drug labels. This tutorial describes the different input parameters required, as well as the considerations needed when developing a PBPK model within the Simulator, for a small molecule intended for oral administration. A case study showing the development and application of a PBPK model for ondansetron is herein used to aid the understanding of different PBPK model development concepts.

Influence of bile acids on the cytotoxicity of chemicals in cultivated human hepatocytes.

Bile acids (BA) are known to influence the susceptibility of hepatocytes to chemicals. We investigated the cytotoxicity of 18 compounds with known hepatotoxicity status and pharmacokinetics in cultivated primary human hepatocytes with and without the addition of a BA mix to the cell culture medium. This BA mix consisted of physiological ratios of the most abundant human BA at a cholestatic sum concentration of 0.5 mM, which corresponds to 50% of the EC10 (cytotoxicity) of the mix. The BA mix decreased the EC10 of 7 compounds by a factor greater than 1.5, but also increased the EC10 of 5 compounds. The compounds with increased susceptibility include the known hepatotoxicants and BSEP/MRP2 inhibitors rifampicin, ketoconazole, atorvastatin, and cyclosporin A. However, the cytotoxicity of some non-hepatotoxic compounds was also enhanced, among them glucose, which is not known to be an inhibitor of canalicular bile acid export. A recently established technique to quantify how well hepatotoxic and non-hepatotoxic compounds are separated by an in vitro test indicated that the addition of the BA mix did not improve separation. In conclusion, the addition of BA to cultivated hepatocytes leads to a complex situation with increased and decreased susceptibilities depending on the specific compound.

The hepatocyte export carrier inhibition assay improves the separation of hepatotoxic from non-hepatotoxic compounds.

An in vitro/in silico method that determines the risk of human drug induced liver injury in relation to oral doses and blood concentrations of drugs was recently introduced. This method utilizes information on the maximal blood concentration (Cmax) for a specific dose of a test compound, which can be estimated using physiologically-based pharmacokinetic modelling, and a cytotoxicity test in cultured human hepatocytes. In the present study, we analyzed if the addition of an assay that measures the inhibition of bile acid export carriers, like BSEP and/or MRP2, to the existing method improves the differentiation of hepatotoxic and non-hepatotoxic compounds. Therefore, an export assay for 5-chloromethylfluorescein diacetate (CMFDA) was established. We tested 36 compounds in a concentration-dependent manner for which the risk of hepatotoxicity for specific oral doses and the capacity to inhibit hepatocyte export carriers are known. Compared to the CTB cytotoxicity test, substantially lower EC10 values were obtained using the CMFDA assay for several known BSEP and/or MRP2 inhibitors. To quantify if the addition of the CMFDA assay to our test system improves the overall separation of hepatotoxic from non-hepatotoxic compounds, the toxicity separation index (TSI) was calculated. We obtained a better TSI using the lower alert concentration from either the CMFDA or the CTB test (TSI: 0.886) compared to considering the CTB test alone (TSI: 0.775). In conclusion, the data show that integration of the CMFDA assay with an in vitro test battery improves the differentiation of hepatotoxic and non-hepatotoxic compounds in a set of compounds that includes bile acid export carrier inhibitors.

Integrate mechanistic evidence from new approach methodologies (NAMs) into a read-across assessment to characterise trends in shared mode of action.

Read-across approaches often remain inconclusive as they do not provide sufficient evidence on a common mode of action across the category members. This read-across case study on thirteen, structurally similar, branched aliphatic carboxylic acids investigates the concept of using human-based new approach methods, such as in vitro and in silico models, to demonstrate biological similarity. Five out of the thirteen analogues have preclinical in vivo studies. Three out of them induced lipid accumulation or hypertrophy in preclinical studies with repeated exposure, which leads to the read-across hypothesis that the analogues can potentially induce hepatic steatosis. To confirm the selection of analogues, the expression patterns of the induced differentially expressed genes (DEGs) were analysed in a human liver model. With increasing dose, the expression pattern within the tested analogues got more similar, which serves as a first indication of a common mode of action and suggests differences in the potency of the analogues. Hepatic steatosis is a well-known adverse outcome, for which over 55 adverse outcome pathways have been identified. The resulting adverse outcome pathway (AOP) network, comprised a total 43 MIEs/KEs and enabled the design of an in vitro testing battery. From the AOP network, ten MIEs, early and late KEs were tested to systematically investigate a common mode of action among the grouped compounds. The targeted testing of AOP specific MIE/KEs shows that biological activity in the category decreases with side chain length. A similar trend was evident in measuring liver alterations in zebra fish embryos. However, activation of single MIEs or early KEs at in vivo relevant doses did not necessarily progress to the late KE "lipid accumulation". KEs not related to the read-across hypothesis, testing for example general mitochondrial stress responses in liver cells, showed no trend or biological similarity. Testing scope is a key issue in the design of in vitro test batteries. The Dempster-Shafer decision theory predicted those analogues with in vivo reference data correctly using one human liver model or the CALUX reporter assays. The case study shows that the read-across hypothesis is the key element to designing the testing strategy. In the case of a good mechanistic understanding, an AOP facilitates the selection of reliable human in vitro models to demonstrate a common mode of action. Testing DEGs, MIEs and early KEs served to show biological similarity, whereas the late KEs become important for confirmation, as progression from MIEs to AO is not always guaranteed.