High-throughput screening tools facilitate calculation of a combined exposure-bioactivity index for chemicals with endocrine activity

Abstract

High-throughput and computational tools provide a new opportunity to calculate combined bioactivity of exposure to diverse chemicals acting through a common mechanism. We used high throughput in vitro bioactivity data and exposure predictions from the U.S. EPA’s Toxicity and Exposure Forecaster (ToxCast and ExpoCast) to estimate combined estrogen receptor (ER) agonist activity of non-pharmaceutical chemical exposures for the general U.S. population. High-throughput toxicokinetic (HTTK) data provide conversion factors that relate bioactive concentrations measured in vitro (μM), to predicted population geometric mean exposure rates (mg/kg/day). These data were available for 22 chemicals with ER agonist activity and were estimated for other ER bioactive chemicals based on the geometric mean of HTTK values across chemicals. For each chemical, ER bioactivity across ToxCast assays was compared to predicted population geometric mean exposure at different levels of in vitro potency and model certainty. Dose additivity was assumed in calculating a Combined Exposure-Bioactivity Index (CEBI), the sum of exposure/bioactivity ratios. Combined estrogen bioactivity was also calculated in terms of the percent maximum bioactivity of chemical mixtures in human plasma using a concentration-addition model. Estimated CEBIs vary greatly depending on assumptions used for exposure and bioactivity. In general, CEBI values were <1 when using median of the estimated general population chemical intake rates, while CEBI were ≥1 when using the upper 95th confidence bound for those same intake rates for all chemicals. Concentration-addition model predictions of mixture bioactivity yield comparable results. Based on current in vitro bioactivity data, HTTK methods, and exposure models, combined exposure scenarios sufficient to influence estrogen bioactivity in the general population cannot be ruled out. Future improvements in screening methods and computational models could reduce uncertainty and better inform the potential combined effects of estrogenic chemicals.

Evaluating consumer exposure to disinfecting chemicals against coronavirus disease 2019 (COVID-19) and associated health risks

“…In addition, it is important to develop new knowledge and best practice of how to use this high-throughput toxicity data in risk assessment, given the inconsistent results from traditional risk assessment methods using animal-based toxicity data. Despite pioneering works utilizing high-throughput in vitro bioactivity data in risk prioritization ( Shin et al, 2015 , Wegner et al, 2020 , Wetmore et al, 2015 ), the value from the rapidly increasing amount of high-throughput toxicity data is unlikely to be fully realized without accompanying methodologies of its application in human health risk assessment that is recognized by a wide range of stakeholders.…”

Molecular Characterization of Estrogen Receptor Agonists during Sewage Treatment Processes Using Effect-Directed Analysis Combined with High-Resolution Full-Scan Screening

“…Diphenoxylate, clonidine, cinnamic acid, and amygdalin are pharmaceutical compounds, while hydramethylon is used in insecticides. Hydramethylon and cinnamic acid were previously identified as ER agonists. , Diphenoxylate is a metabolite of difenoxin and is considered to be formed during the sewage treatment process.…”

“…Hydramethylon and cinnamic acid were previously identified as ER agonists. 58,59 Diphenoxylate is a metabolite of difenoxin 60 and is considered to be formed during the sewage treatment process.…”

Utilizing High-Throughput Screening Data, Integrative Toxicological Prioritization Index Score, and Exposure–Activity Ratios for Chemical Prioritization: A Case Study of Endocrine-Active Pesticides in Food Crops

“…The 3R principle in animal research (i.e., replacement, reduction, and refinement) has driven the development of new approach methodologies (NAMs), such as in vitro cell-based models, − HTS assays, , and in silico modeling, which can eventually be incorporated into the health risk assessment and chemical prioritization. Indeed, the publicly available HTS data from ToxCast and Tox21 programs have been used to prioritize chemicals in the environment and ecosystem. ,− A risk-based prioritization allows for the regulatory agencies to distribute resources on the concerning chemicals for more in-depth risk assessments and cost-effective management. For the first time, this study used state-of-the-art computational tools in exposure science and toxicology and applied the EAR profiling to prioritize the endocrine-active pesticides in food crops for the Taiwanese and U.S. populations.…”