A rule-based expert system for chemical prioritization using effects-based chemical categories.

A rule-based expert system (ES) was developed to predict chemical binding to the estrogen receptor (ER) patterned on the research approaches championed by Gilman Veith to whom this article and journal issue are dedicated. The ERES was built to be mechanistically transparent and meet the needs of a specific application, i.e. predict for all chemicals within two well-defined inventories (industrial chemicals used as pesticide inerts and antimicrobial pesticides). These chemicals all lack structural features associated with high affinity binders and thus any binding should be low affinity. Similar to the high-quality fathead minnow database upon which Veith QSARs were built, the ERES was derived from what has been termed gold standard data, systematically collected in assays optimized to detect even low affinity binding and maximizing confidence in the negatives determinations. The resultant logic-based decision tree ERES, determined to be a robust model, contains seven major nodes with multiple effects-based chemicals categories within each. Predicted results are presented in the context of empirical data within local chemical structural groups facilitating informed decision-making. Even using optimized detection assays, the ERES applied to two inventories of >600 chemicals resulted in only ~5% of the chemicals predicted to bind ER.

Effects-based chemical category approach for prioritization of low affinity estrogenic chemicals.

Regulatory agencies are charged with addressing the endocrine disrupting potential of large numbers of chemicals for which there is often little or no data on which to make decisions. Prioritizing the chemicals of greatest concern for further screening for potential hazard to humans and wildlife is an initial step in the process. This paper presents the collection of in vitro data using assays optimized to detect low affinity estrogen receptor (ER) binding chemicals and the use of that data to build effects-based chemical categories following QSAR approaches and principles pioneered by Gilman Veith and colleagues for application to environmental regulatory challenges. Effects-based chemical categories were built using these QSAR principles focused on the types of chemicals in the specific regulatory domain of concern, i.e. non-steroidal industrial chemicals, and based upon a mechanistic hypothesis of how these non-steroidal chemicals of seemingly dissimilar structure to 17ß-estradiol (E2) could interact with the ER via two distinct binding types. Chemicals were also tested to solubility thereby minimizing false negatives and providing confidence in determination of chemicals as inactive. The high-quality data collected in this manner were used to build an ER expert system for chemical prioritization described in a companion article in this journal.

Alternatives to in vivo tests to detect endocrine disrupting chemicals (EDCs) in fish and amphibians--screening for estrogen, androgen and thyroid hormone disruption.

Endocrine disruption is considered a highly relevant hazard for environmental risk assessment of chemicals, plant protection products, biocides and pharmaceuticals. Therefore, screening tests with a focus on interference with estrogen, androgen, and thyroid hormone pathways in fish and amphibians have been developed. However, they use a large number of animals and short-term alternatives to animal tests would be advantageous. Therefore, the status of alternative assays for endocrine disruption in fish and frogs was assessed by a detailed literature analysis. The aim was to (i) determine the strengths and limitations of alternative assays and (ii) present conclusions regarding chemical specificity, sensitivity, and correlation with in vivo data. Data from 1995 to present were collected related to the detection/testing of estrogen-, androgen-, and thyroid-active chemicals in the following test systems: cell lines, primary cells, fish/frog embryos, yeast and cell-free systems. The review shows that the majority of alternative assays measure effects directly mediated by receptor binding or resulting from interference with hormone synthesis. Other mechanisms were rarely analysed. A database was established and used for a quantitative and comparative analysis. For example, a high correlation was observed between cell-free ligand binding and cell-based reporter cell assays, between fish and frog estrogenic data and between fish embryo tests and in vivo reproductive effects. It was concluded that there is a need for a more systematic study of the predictive capacity of alternative tests and ways to reduce inter- and intra-assay variability.

Discriminating redox cycling and arylation pathways of reactive chemical toxicity in trout hepatocytes.

The toxicity of four quinones, 2,3-dimethoxy-1,4-naphthoquinone (DMONQ), 2-methyl-1,4-naphthoquinone (MNQ), 1,4-naphthoquinone (NQ), and 1,4-benzoquinone (BQ), which redox cycle or arlyate in mammalian cells, was determined in isolated trout (Oncorhynchus mykiss) hepatocytes. More than 70% of cells died in 3 h when exposed to BQ or NQ; 50% died in 7 h when exposed to MNQ, with no mortality compared to controls after 7 h DMONQ exposure. A suite of biochemical parameters was assessed for ability to discriminate these reactivity pathways in fish. Rapid depletion of glutathione (GSH) with appearance of glutathione disulfide (GSSG) and increased dichlorofluoroscein fluorescence were used as indicators of redox cycling, noted with DMONQ, MNQ, and NQ. Depletion of GSH with no GSSG accumulation, and loss of free protein thiol (PrSH) groups (nonreducible) indicated direct arylation by BQ. All toxicants rapidly oxidized NADH, with changes in NADPH noted later (BQ, NQ, MNQ) or not at all (DMONQ). Biochemical measures including cellular energy status, cytotoxicity, and measures of reactive oxygen species, along with the key parameters of GSH and PrSH redox status, allowed differentiation of responses associated with lethality. Chemicals that arylate were more potent than redox cyclers. Toxic pathway discrimination is needed to group chemicals for potency predictions and identification of structural parameters associated with distinct types of reactive toxicity, a necessary step for development of mechanistically based quantitative structure-activity relationships (QSARs) to predict chemical toxic potential. The commonality of reactivity mechanisms between rodents and fish was also demonstrated, a step essential for species extrapolations.