The Project for Developing High-Throughput Screening Methods for
「The Project for Developing High-Throughput Screening Methods for」於資料集「EPQSummary_ChemicalSubstanceManagement」由單位「行政院環境保護署」的陳先生所提供，聯繫電話是02-2311-7722#2386，最近更新時間為：2023-07-30 01:01:48。 欄位Project Title的內容是The Project for Developing High-Throughput Screening Methods for Multi-Chemical Risk Management ， 欄位Project Subject的內容是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. ， 欄位Project Year的內容是2022 ， 欄位Organizer的內容是Toxic and Chemical Substances Bureau ， 欄位Executive Unit的內容是成功大學 ， 欄位Reporting download URL的內容是https://epq.epa.gov.tw/ProjectDoc/FileDownload?fid=291941&proj_id=1110845931&doc_kind=1 ， 欄位Publish Date的內容是20230110 。
Project Title
The Project for Developing High-Throughput Screening Methods for Multi-Chemical Risk Management
Project Subject
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.
Project Year
2022
Organizer
Toxic and Chemical Substances Bureau
Executive Unit
成功大學
Reporting download URL
https://epq.epa.gov.tw/ProjectDoc/FileDownload?fid=291941&proj_id=1110845931&doc_kind=1
Publish Date
20230110
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