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From in vitro to in vivo: Integration of the virtual cell based assay with physiologically based kinetic modelling.

ABSTRACT: Physiologically based kinetic (PBK) models and the virtual cell based assay can be linked to form so called physiologically based dynamic (PBD) models. This study illustrates the development and application of a PBK model for prediction of estragole-induced DNA adduct formation and hepatotoxicity in humans. To address the hepatotoxicity, HepaRG cells were used as a surrogate for liver cells, with cell viability being used as the in vitro toxicological endpoint. Information on DNA adduct formation was taken from the literature. Since estragole induced cell damage is not directly caused by the parent compound, but by a reactive metabolite, information on the metabolic pathway was incorporated into the model. In addition, a user-friendly tool was developed by implementing the PBK/D model into a KNIME workflow. This workflow can be used to perform in vitro to in vivo extrapolation and forward as backward dosimetry in support of chemical risk assessment.

SUBMITTER: Paini A

PROVIDER: S-EPMC5742636 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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From in vitro to in vivo: Integration of the virtual cell based assay with physiologically based kinetic modelling.

Paini Alicia A Sala Benito Jose Vicente JV Bessems Jos J Worth Andrew P AP

Toxicology in vitro : an international journal published in association with BIBRA 20170627 Pt 2

Physiologically based kinetic (PBK) models and the virtual cell based assay can be linked to form so called physiologically based dynamic (PBD) models. This study illustrates the development and application of a PBK model for prediction of estragole-induced DNA adduct formation and hepatotoxicity in humans. To address the hepatotoxicity, HepaRG cells were used as a surrogate for liver cells, with cell viability being used as the in vitro toxicological endpoint. Information on DNA adduct formatio ...[more]

PMID: 28663056

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Project description:Human biomonitoring (HBM) data can provide insight into co-exposure patterns resulting from exposure to multiple chemicals from various sources and over time. Therefore, such data are particularly valuable for assessing potential risks from combined exposure to multiple chemicals. One way to interpret HBM data is establishing safe levels in blood or urine, called Biomonitoring Equivalents (BE) or HBM health based guidance values (HBM-HBGV). These can be derived by converting established external reference values, such as tolerable daily intake (TDI) values. HBM-HBGV or BE values are so far agreed only for a very limited number of chemicals. These values can be established using physiologically based kinetic (PBK) modelling, usually requiring substance specific models and the collection of many input parameters which are often not available or difficult to find in the literature. The aim of this study was to investigate the suitability and limitations of generic PBK models in deriving BE values for several compounds with a view to facilitating the use of HBM data in the assessment of chemical mixtures at a screening level. The focus was on testing the methodology with two generic models, the IndusChemFate tool and High-Throughput Toxicokinetics package, for two different classes of compounds, phenols and phthalates. HBM data on Danish children and on Norwegian mothers and children were used to evaluate the quality of the predictions and to illustrate, by means of a case study, the overall approach of applying PBK models to chemical classes with HBM data in the context of chemical mixture risk assessment. Application of PBK models provides a better understanding and interpretation of HBM data. However, the study shows that establishing safety threshold levels in urine is a difficult and complex task. The approach might be more straightforward for more persistent chemicals that are analysed as parent compounds in blood but high uncertainties have to be considered around simulated metabolite concentrations in urine. Refining the models may reduce these uncertainties and improve predictions. Based on the experience gained with this study, the performance of the models for other chemicals could be investigated, to improve the accuracy of the simulations.

Dataset Information

From in vitro to in vivo: Integration of the virtual cell based assay with physiologically based kinetic modelling.

Publications

From in vitro to in vivo: Integration of the virtual cell based assay with physiologically based kinetic modelling.

Similar Datasets

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