Development of a deep neural network model based on high throughput screening data for predicting synergistic estrogenic activity of binary mixtures for consumer products

Abstract

A paradigm of chemical risk assessment is continuously extending from focusing on ‘single substances’ to more comprehensive approaches that examines the combined toxicity among different components in ‘mixtures.’ This change aims to account for the cocktail effect arising from the toxicological interactions in mixtures, which can lead to increased risks. More than 1,000 potential endocrine-disrupting chemicals (EDCs) have been reported, and they can be included in different industrial and consumer chemical products and released to the environment as pollutants of emerging environmental concern. Although extensive studies involving both experiments and predictions have investigated individual EDCs, predictions of their synergistic effects are still relatively lacking, an area that requires further investigation. In this study, we extensively investigated substances in consumer products, mainly marketed in South Korea, that might exhibit estrogenic activity or reproductive toxicity. A high throughput screening (HTS) assay based on OECD Test Guideline 455 for hERαHeLa-9903 cells was constructed, and 435 substances were screened using the HTS. Thirty-five (potential) estrogenic agonists were selected, and their 1,412 binary mixtures that could be prepared in four different ratios were systematically tested, considering the available effective concentrations of substances and the solubility of their resulting mixtures. The best empirical dose-response curves of 35 substances and 917 mixtures were derived in this study. Based on the HTS data, a deep neural network model was developed (area under the curve (AUC): 0.837-0.881) and compared with a random forest model (AUC: 0.656-0.829) to screen for the synergistic estrogenic activity of binary mixtures.