The number of emerging chemical substances grow faster and show their variety with the rapid development of industries and technologies. There are only 7.5% of the registered chemical substances have known toxicity. How to fill the data gap of the chemical substance with unknown toxicity become a critical issue in the management of chemical substances. Therefore, the application of computational toxicology, which is the integrative approach to toxicological research and chemical safety assessments via predictive modeling, provides a time-saving and cost-effective method for the toxicity testing of emerging chemical substances. Moreover, the approach may enhance the risk assessment and management of emerging chemical substances in the future. This report includes three major parts, which shows as follows, (1) Strengthening the hazard cross-reference (Read-Across) information of chemical substances, (2) Establishing a high-throughput toxicity screening model for chemical substances, (3) Applied chemistry Informatics (Cheminformatics) module for high-throughput risk assessment. We have built the toxicity prediction by Read Across and provided the validation methods in this report. For Projects 1, at least eight chemical substances were used to conduct the total toxicity prediction and different type toxicity prediction. The validation results suggested the areas under ROC curve (AUC) all over 0.8. However, the validation results of different type of toxicity were weak. Furthermore, we have established a platform for the integration of the databases, which could provide the average, maximum and minimum values of animal experiments and cell experiments from different databases. The dataset will use to build the modules of Read-across and ToxPi. For project 2, we have completed the establishment of high throughput cell models of H295R cells and HK-2 human renal proximal tubular epithelial cells, and applied the cell models established last year and this year to real water samples for high-throughput toxicological mechanism studies. The endocrine disruptors and liver toxicity were found in the real water samples obtained from groundwater of petroleum industries. In the future, the application of high-throughput toxicological study of environmental samples should add the high-throughput analysis results of chemical substances. For the QSAR model, the R2 (coefficient of determination) is 0.94 and 0.93, rtest2 (the correlation coefficient between the experimental value and the predicted value of the test set) is 0.7 and 0.71, and Q2 (10-fold cross-validation correlation coefficient) is 0.63 and 0.55, within the acceptable range for environmental toxicities. In addition, ToxPi module establishment and sensitivity test were completed. 6-Trichlorophenol, formaldehyde, 2,4,5-trichlorophenol and 3,3'-dichlorobenzidine all were ranked in the top 5 in ToxPi Score, and these substances should be paid priority attention. For real water samples, the results of the priority ranking of points suggested that 4 sampling sites should be prioritized into management considerations from 12 sampling areas.

Read-across (RAx), high throughput toxicity testing, cheminformatics, chemical substances hazard, computational toxicology