Mechanistic Computational Model for Extrapolating In Vitro Thyroid Peroxidase (TPO) Inhibition Data to Predict Serum Thyroid Hormone Levels in Rats.

High-throughput in vitro assays are developed to screen chemicals for their potential to inhibit thyroid hormones (THs) synthesis. Some of these experiments, such as the thyroid peroxidase (TPO) inhibition assay, are based on thyroid microsomal extracts. However, the regulation of thyroid disruption chemicals is based on THs in vivo serum levels. This necessitates the estimation of thyroid disruption chemicals in vivo tissue levels in the thyroid where THs synthesis inhibition by TPO takes place. The in vivo tissue levels of chemicals are controlled by pharmacokinetic determinants such as absorption, distribution, metabolism, and excretion, and can be described quantitatively in physiologically based pharmacokinetic (PBPK) models. An integrative computational model including chemical-specific PBPK and TH kinetics models provides a mechanistic quantitative approach to translate thyroidal high-throughput in vitro assays to in vivo measures of circulating THs serum levels. This computational framework is developed to quantitatively establish the linkage between applied dose, chemical thyroid tissue levels, thyroid TPO inhibition potential, and in vivo TH serum levels. Once this link is established quantitatively, the overall model is used to calibrate the TH kinetics parameters using experimental data for THs levels in thyroid tissue and serum for the 2 drugs, propylthiouracil and methimazole. The calibrated quantitative framework is then evaluated against literature data for the environmental chemical ethylenethiourea. The linkage of PBPK and TH kinetics models illustrates a computational framework that can be extrapolated to humans to screen chemicals based on their exposure levels and potential to disrupt serum THs levels in vivo.

Extrapolating In Vitro Screening Assay Data for Thyroperoxidase Inhibition to Predict Serum Thyroid Hormones in the Rat.

Thyroperoxidase (TPO) is an enzyme essential for thyroid hormone (TH) synthesis and a target site for a number of xenobiotics that disrupt TH homeostasis. An in vitro high-throughput screening assay for TPO inhibition, the Amplex UltraRed-TPO (AUR-TPO), has been used to screen the ToxCast chemical libraries for this action. Output from this assay would be most useful if it could be readily translated into an in vivo response, namely a reduction of TH in serum. To this end, the relationship between TPO inhibition in vitro and serum TH decreases was examined in rats exposed to 2 classic TPO inhibitors, propylthiouracil (PTU) and methimazole (MMI). Serum and gland PTU, MMI, and TH levels were quantified using tandem liquid chromatography mass spectrometry. Thyroperoxidase activity was determined in thyroid gland microsomes treated with PTU or MMI in vitro and ex vivo from thyroid gland microsomes prepared from exposed animals. A quantitative model was constructed by contrasting in vitro and ex vivo AUR-TPO results and the in vivo time-course and dose-response analysis. In vitro:ex vivo correlations of AUR-TPO outputs indicated that less than 30% inhibition of TPO in vitro was sufficient to reduce serum T4 by 20%, a degree of regulatory significance. Although further testing of model estimates using other TPO inhibitors is essential for verification of these initial findings, the results of this study provide a means to translate in vitro screening assay results into predictions of in vivo serum T4 changes to inform risk assessment.

Identification of differentially expressed genes and networks related to hepatic lipid dysfunction.

A range of chemical exposures that resulted in the specific pathology of hepatic lipid dysfunction in rats were selected from DrugMatrix, a publicly available toxicogenomic database. Raw microarray data collected from these exposures were further analyzed using bioinformatic tools to generate a differentially expressed genes (DEGs) dataset associated with hepatic lipid dysfunction. Further analysis of the DEGs dataset resulted in 324 upregulated genes, and 275 genes that were down regulated. Meanwhile, 36 genes were either up regulated or down regulated in different chemical treatments. All identified genes were uploaded in the web application for Database for Annotation, Visualization and Integrated Discovery (DAVID) for gene ontology enrichments and to identify Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. Some of the identified pathways included glycolysis/gluconeogenesis, steroid hormone biosynthesis, retinol metabolism, and metabolism of xenobiotics by cytochrome P450. The same DEGs dataset was also analyzed using Ingenuity Pathway Analysis (IPA) software. IPA identified several pathways including PXR/RXR activation, Aryl hydrocarbon receptor signaling, and xenobiotic metabolism signaling. Furthermore, the generated DEGs lists were uploaded into NCATS BioPlanet platform. Some of the identified pathways were related to fatty acid omega oxidation, lipid and lipoprotein metabolism, and adipogenesis. The enrichment and clarification of the pathways and biological networks obtained from the DEGs dataset provide prior knowledge on the underlying biological key events and molecular mechanisms for the computational development of putative adverse outcome pathways (AOPs) for hepatic lipid dysfunction as a precursor to hepatic steatosis.