Dataset Information

Extrapolation of Hepatic Concentrations of Industrial Chemicals Using Pharmacokinetic Models to Predict Hepatotoxicity.

ABSTRACT: In this review, we describe the absorption rates (Caco-2 cell permeability) and hepatic/plasma pharmacokinetics of 53 diverse chemicals estimated by modeling virtual oral administration in rats. To ensure that a broad range of chemical structures is present among the selected substances, the properties described by 196 chemical descriptors in a chemoinformatics tool were calculated for 50,000 randomly selected molecules in the original chemical space. To allow visualization, the resulting chemical space was projected onto a two-dimensional plane using generative topographic mapping. The calculated absorbance rates of the chemicals based on cell permeability studies were found to be inversely correlated to the no-observed-effect levels for hepatoxicity after oral administration, as obtained from the Hazard Evaluation Support System Integrated Platform in Japan (r = -0.88, p < 0.01, n = 27). The maximum plasma concentrations and the areas under the concentration-time curves (AUC) of a varied selection of chemicals were estimated using two different methods: simple one-compartment models (i.e., high-throughput toxicokinetic models) and simplified physiologically based pharmacokinetic (PBPK) modeling consisting of chemical receptor (gut), metabolizing (liver), and central (main) compartments. The results obtained from the two methods were consistent. Although the maximum concentrations and AUC values of the 53 chemicals roughly correlated in the liver and plasma, inconsistencies were apparent between empirically measured concentrations and the PBPK-modeled levels. The lowest-observed-effect levels and the virtual hepatic AUC values obtained using PBPK models were inversely correlated (r = -0.78, p < 0.05, n = 7). The present simplified PBPK models could estimate the relationships between hepatic/plasma concentrations and oral doses of general chemicals using both forward and reverse dosimetry. These methods are therefore valuable for estimating hepatotoxicity.

SUBMITTER: Yamazaki H

PROVIDER: S-EPMC6791659 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Extrapolation of Hepatic Concentrations of Industrial Chemicals Using Pharmacokinetic Models to Predict Hepatotoxicity.

Yamazaki Hiroshi H Kamiya Yusuke Y

Toxicological research 20191015 4

In this review, we describe the absorption rates (Caco-2 cell permeability) and hepatic/plasma pharmacokinetics of 53 diverse chemicals estimated by modeling virtual oral administration in rats. To ensure that a broad range of chemical structures is present among the selected substances, the properties described by 196 chemical descriptors in a chemoinformatics tool were calculated for 50,000 randomly selected molecules in the original chemical space. To allow visualization, the resulting chemic ...[more]

PMID: 31636840

Similar Datasets

Project description:Hepatic in vitro biotransformation assays, in combination with in vitro-in vivo extrapolation (IVIVE) and bioaccumulation modeling, can be used to support regulatory bioaccumulation assessments. In most applications, however, these methods ignore the possibility of extrahepatic metabolism. Here we evaluated intestinal biotransformation in rainbow trout using S9 fractions prepared from the upper intestinal (GIT) epithelium. Measured levels of activity determined using standard substrates for phase I and phase II biotransformation enzymes were within 2-fold of activities measured in hepatic S9 fractions. In vitro intrinsic clearance rates for 2-ethylhexyl-4-methoxycinnamate (EHMC; an organic sunscreen agent) and two polycyclic aromatic hydrocarbons (pyrene [PYR] and benzo(a)pyrene [BAP]) were significantly higher in liver S9 fractions than in GIT S9 fractions. For octocrylene (OCT; a second sunscreen agent), however, in vitro intrinsic clearance rates were higher in GIT S9 fractions compared to liver S9 fractions. An existing 'liver only' IVIVE model was expanded to consider biotransformation in both the liver and GIT. Relevant IVIVE scaling factors were developed by morphological, histological, and biochemical evaluation of trout intestines. For chemicals biotransformed at higher rates by hepatic S9 fractions (i.e., BAP, PYR, EHMC), the 'liver & GIT' model yielded whole-body biotransformation rate constants (kMET) that were within 1.2 to 1.4-fold of those estimated using the 'liver only' model. In contrast to these findings, the mean kMET for OCT obtained using the 'liver & GIT' model was 3.3 times higher than the mean kMET derived using the 'liver only' model and was in good agreement with empirical kMET estimates determined previously for trout (<20 % difference). The results of this study suggest that current 'liver only' IVIVE approaches may underestimate in vivo biotransformation rates for chemicals that undergo substantial biotransformation in the GIT.

Project description:BackgroundDrug-induced liver injury (DILI) is a critical issue in drug development because DILI causes failures in clinical trials and the withdrawal of approved drugs from the market. There have been many attempts to predict the risk of DILI based on in vivo and in silico identification of hepatotoxic compounds. In the current study, we propose the in silico prediction model predicting DILI using weighted molecular fingerprints.ResultsIn this study, we used 881 bits of molecular fingerprint and used as features describing presence or absence of each substructure of compounds. Then, the Bayesian probability of each substructure was calculated and labeled (positive or negative for DILI), and a weighted fingerprint was determined from the ratio of DILI-positive to DILI-negative probability values. Using weighted fingerprint features, the prediction models were trained and evaluated with the Random Forest (RF) and Support Vector Machine (SVM) algorithms. The constructed models yielded accuracies of 73.8% and 72.6%, AUCs of 0.791 and 0.768 in cross-validation. In independent tests, models achieved accuracies of 60.1% and 61.1% for RF and SVM, respectively. The results validated that weighted features helped increase overall performance of prediction models. The constructed models were further applied to the prediction of natural compounds in herbs to identify DILI potential, and 13,996 unique herbal compounds were predicted as DILI-positive with the SVM model.ConclusionsThe prediction models with weighted features increased the performance compared to non-weighted models. Moreover, we predicted the DILI potential of herbs with the best performed model, and the prediction results suggest that many herbal compounds could have potential to be DILI. We can thus infer that taking natural products without detailed references about the relevant pathways may be dangerous. Considering the frequency of use of compounds in natural herbs and their increased application in drug development, DILI labeling would be very important.

Project description:BackgroundDifferent parametric survival models can lead to widely discordant extrapolations and decision uncertainty in cost-effectiveness analyses. The use of excess hazard (EH) methods, which incorporate general population mortality data, has the potential to reduce model uncertainty. This review highlights key practical considerations of EH methods for estimating long-term survival.MethodsDemonstration of methods used a case study of 686 patients from the German Breast Cancer Study Group, followed for a maximum of 7.3 y and divided into low (1/2) and high (3) grade cancers. Seven standard parametric survival models were fit to each group separately. The same 7 distributions were then used in an EH framework, which incorporated general population mortality rates, and fitted both with and without a cure parameter. Survival extrapolations, restricted mean survival time (RMST), and difference in RMST between high and low grades were compared up to 30 years along with Akaike information criterion goodness-of-fit and cure fraction estimates. The sensitivity of the EH models to lifetable misspecification was investigated.ResultsIn our case study, variability in survival extrapolations was extensive across the standard models, with 30-y RMST ranging from 7.5 to 14.3 y. Incorporation of general population mortality rates using EH cure methods substantially reduced model uncertainty, whereas EH models without cure had less of an effect. Long-term treatment effects approached the null for most models but at varying rates. Lifetable misspecification had minimal effect on RMST differences.ConclusionsEH methods may be useful for survival extrapolation, and in cancer, EHs may decrease over time and be easier to extrapolate than all-cause hazards. EH cure models may be helpful when cure is plausible and likely to result in less extrapolation variability.HighlightsIn health economic modeling, to help anchor long-term survival extrapolation, it has been recommended that survival models incorporate background mortality rates using excess hazard (EH) methods.We present a thorough description of EH methods with and without the assumption of cure and demonstrate user-friendly software to aid researchers wishing to use these methods.EH models are applied to a case study, and we demonstrate that EHs are easier to extrapolate and that the use of the EH cure model, when cure is plausible, can reduce extrapolation variability.EH methods are relatively robust to lifetable misspecification.

Dataset Information

Extrapolation of Hepatic Concentrations of Industrial Chemicals Using Pharmacokinetic Models to Predict Hepatotoxicity.

Publications

Extrapolation of Hepatic Concentrations of Industrial Chemicals Using Pharmacokinetic Models to Predict Hepatotoxicity.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets