Integrating in vitro testing and physiologically-based pharmacokinetic (PBPK) modelling for chemical liver toxicity assessment—A case study of troglitazone

In vitro to in vivo extrapolation (IVIVE) for next-generation risk assessment (NGRA) of chemicals requires computational modeling and faces unique challenges. Using mitochondria-related toxicity data of troglitazone (TGZ), a prototype drug known for liver toxicity, from HepaRG, HepG2, HC-04, and primary human hepatocytes, we explored inherent uncertainties in IVIVE, including cell models, cellular response endpoints, and dose metrics. A human population physiologically-based pharmacokinetic (PBPK) model for TGZ was developed to predict in vivo doses from in vitro point-of-departure (POD) concentrations. Compared to the 200–800 mg/d dose range of TGZ where liver injury was observed clinically, the predicted POD doses for the mean and top one percentile of the PBPK population were 28–372 and 15–178 mg/d respectively based on C_max dosimetry, and 185–2552 and 83–1010 mg/d respectively based on AUC. In conclusion, although with many uncertainties, integrating in vitro assays and PBPK modeling is promising in informing liver toxicity-inducing TGZ doses.     

Derivation of internal dose-based thresholds of toxicological concern for occupational inhalation exposure to systemically acting organic chemicals

This study aimed at deriving occupational thresholds of toxicological concern for inhalation exposure to systemically-acting organic chemicals using predicted internal doses. The latter were also used to evaluate the quantitative relationship between occupational exposure limit and internal dose. Three internal dose measures were identified for investigation: (i) the daily area under the venous blood concentration vs. time curve, (ii) the daily rate of the amount of parent chemical metabolized, and (iii) the maximum venous blood concentration at the end of an 8-hr work shift. A dataset of 276 organic chemicals with 8-hr threshold limit values-time-weighted average was compiled along with their molecular structure and Cramer classes (Class I: low toxicity, Class II: intermediate toxicity, Class III: suggestive of significant toxicity). Using a human physiologically-based pharmacokinetic model, the three identified dose metrics were predicted for an 8-hr occupational inhalation exposure to the threshold limit value for each chemical. Distributional analyses of the predicted dose metrics were performed to identify the percentile values corresponding to the occupational thresholds of toxicological concern. Also, simple linear regression analyses were performed to evaluate the relationship between the 8-hr threshold limit value and each of the predicted dose metrics, respectively. No threshold of toxicological concern could be derived for class II due to few chemicals. Based on the daily rate of the amount of parent chemical metabolized, the proposed internal dose-based occupational thresholds of toxicological concern were 5.61?×?10^?2 and 9?×?10^?4 mmol/d at the 10th percentile level for classes I and III, respectively, while they were 4.55?×?10^?1 and 8.5?×?10^?3 mmol/d at the 25th percentile level. Even though high and significant correlations were observed between the 8-hr threshold limit values and the predicted dose metrics, the one with the rate of the amount of chemical metabolized was remarkable regardless of the Cramer class (r² = 0.81; n = 276). The proposed internal dose-based occupational thresholds of toxicological concern are potentially useful for screening-level assessments as well as prioritization within an integrated occupational risk assessment framework.     

Tissue dosimetry expansion and cross-validation of rat and mouse physiologically based pharmacokinetic models for trichloroethylene.

Trichloroethylene (TCE), a volatile liquid used as a degreasing agent, is a common environmental pollutant. In 2001, the EPA published a draft risk assessment for TCE that incorporates dosimetry predictions of physiologically based pharmacokinetic (PBPK) models. The current modeling effort represents an expansion and extensive tissue dosimetry validation of rodent PBPK models for TCE. The pharmacokinetics of TCE in male Sprague-Dawley (S-D) rats were characterized (1) during and after inhalation exposure to 50 or 500 ppm TCE, (2) following administration of 8 mg/kg TCE PO, and (3) following intra-arterial injection of 8 mg/kg TCE. Blood and tissues (including liver, kidney, fat, skeletal muscle, heart, spleen, gastrointestinal tract, and brain) were collected at selected time-points from 5 min up to 24 h post initial exposure. The fat compartment was modified to be diffusion-limited to predict the observed slow release of TCE from the fat. The addition of a deep liver compartment was necessary to accurately predict the slower hepatic clearance of TCE for all three exposure routes. Simulations of liver concentrations following gavage of male B6C3F1 mice with 300-2000 mg/kg TCE were also improved with the addition of a deep liver compartment. Liver predictions were calibrated and validated using a cross-validation technique novel to PBPK modeling. Splitting of compartments did not significantly affect predictions of TCE concentrations in the liver, fat, or venous blood. This model expansion and validation increases both the utility and our confidence in the current use of rodent TCE PBPK models in human health risk assessment.     

Combining the ‘bottom up’ and ‘top down’ approaches in pharmacokinetic modelling: fitting PBPK models to observed clinical data

Pharmacokinetic models range from being entirely exploratory and empirical, to semi-mechanistic and ultimately complex physiologically based pharmacokinetic (PBPK) models. This choice is conditional on the modelling purpose as well as the amount and quality of the available data. The main advantage of PBPK models is that they can be used to extrapolate outside the studied population and experimental conditions. The trade-off for this advantage is a complex system of differential equations with a considerable number of model parameters. When these parameters cannot be informed from in vitro or in silico experiments they are usually optimized with respect to observed clinical data. Parameter estimation in complex models is a challenging task associated with many methodological issues which are discussed here with specific recommendations. Concepts such as structural and practical identifiability are described with regards to PBPK modelling and the value of experimental design and sensitivity analyses is sketched out. Parameter estimation approaches are discussed, while we also highlight the importance of not neglecting the covariance structure between model parameters and the uncertainty and population variability that is associated with them. Finally the possibility of using model order reduction techniques and minimal semi-mechanistic models that retain the physiological-mechanistic nature only in the parts of the model which are relevant to the desired modelling purpose is emphasized. Careful attention to all the above issues allows us to integrate successfully information from in vitro or in silico experiments together with information deriving from observed clinical data and develop mechanistically sound models with clinical relevance.     

Physiologically based pharmacokinetics model predicts the lack of inhibition by repaglinide on the metabolism of pioglitazone

Purpose: Repaglinide and pioglitazone are both CYP2C8 and CYP3A4 substrates. This study was to determine whether repaglinide has an inhibitory effect on the metabolism of pioglitazone in vitro, in silico and in vivo. Method: In vitro, the effect of repaglinide on the metabolism of pioglitazone was assessed in pooled human liver microsomes. In silico, an IVIVE‐PBPK linked model was built with Simcyp®. Then, a randomized, 2‐phase cross‐over clinical study was conducted in 12 healthy volunteers. Results: Repaglinide showed a strong inhibitory effect on the metabolism of pioglitazone in vitro (K_i = 0.0757 µm ), [I]/K_i > 0.1. The Simcyp® prediction ratios of AUC and C_max between the two treatment groups were both about 1.01. The pharmacokinetics of pioglitazone in clinical trials showed no significant difference between these two treatment groups (p > 0.05). Conclusion: Repaglinide has no significant inhibitory effect on the metabolism of pioglitazone in vivo, which is inconsistent with the in vitro results. The lack of an inhibitory effect was partly due to extensive plasma protein binding and to the high in vivo clearance of repaglinide, for the concentration of repaglinide in vivo was far smaller than in vitro. Copyright © 2015 John Wiley & Sons, Ltd.     

PBPK modeling of irbesartan: incorporation of hepatic uptake

Physiological based pharmacokinetic (PBPK) modeling is now commonly used in drug development to integrate human or animal physiological data in order to predict pharmacokinetic profiles. The aim of this work was to construct and refine a PBPK model of irbesartan taking into account its active uptake via OATP1B1/B3 in order to predict more accurately its pharmacokinetic profile using Simcyp^®. The activity and expression of the human hepatocyte transporters OATP1B1 and OATP1B3 were studied. The relative activity factors (RAFs) for OATP1B1 and OATP1B3 transporters were calculated from intrinsic clearances obtained by concentration dependent uptake experiments in human hepatocytes and HEK overexpressing cells: RAF_1B1 using estrone‐3‐sulfate and pitavastatine clearances, and RAF_1B3 using cholecystokinine octapeptide (CCK‐8) clearances. The relative expression factor (REF) was calculated by comparing immunoblotting of hepatocytes (REF_HH) or tissues (REF_tissue) with those of overexpressing HEK cells for each transporter. These scaling factors were applied in a PBPK model of irbesartan using the Simcyp® simulator. Pharmacokinetic simulation using REF_HH (1.82 for OATP1B1, 8.03 for OATP1B3) as an extrapolation factor was the closest to the human clinical pharmacokinetic profile of irbesartan. These investigations show the importance of integrating the contribution of the active uptake of a drug in the liver to improve PBPK modeling. Copyright © 2015 John Wiley & Sons, Ltd.     

Evaluation of three physiologically based pharmacokinetic (PBPK) modeling tools for emergency risk assessment after acute dichloromethane exposure

Introduction

Physiologically based pharmacokinetic (PBPK) models may be useful in emergency risk assessment, after acute exposure to chemicals, such as dichloromethane (DCM). We evaluated the applicability of three PBPK models for human risk assessment following a single exposure to DCM: one model is specifically developed for DCM (Bos) and the two others are semi-generic ones (Mumtaz and Jongeneelen).

Materials and methods

We assessed the accuracy of the models’ predictions by simulating exposure data from a previous healthy volunteer study, in which six subjects had been exposed to DCM for 1 h. The time-course of both the blood DCM concentration and percentage of carboxyhemoglobin (HbCO) were simulated.

Results

With all models, the shape of the simulated time course resembled the shape of the experimental data. For the end of the exposure, the predicted DCM blood concentration ranged between 1.52–4.19 mg/L with the Bos model, 1.42–4.04 mg/L with the Mumtaz model, and 1.81–4.31 mg/L with the Jongeneelen model compared to 0.27–5.44 mg/L in the experimental data. % HbCO could be predicted only with the Bos model. The maximum predicted % HbCO ranged between 3.1 and 4.2% compared to 0.4–2.3% in the experimental data. The % HbCO predictions were more in line with the experimental data after adjustment of the Bos model for the endogenous HbCO levels.

Conclusions

The Bos Mumtaz and Jongeneelen PBPK models were able to simulate experimental DCM blood concentrations reasonably well. The Bos model appears to be useful for calculating HbCO concentrations in emergency risk assessment.