Rim Kyung-Taek
Chemicals Research Bureau, Occupational Safety and Health Research Institute, Korea Occupational Safety and Health Agency, Daejeon, South Korea.
Mol Cell Toxicol. 2021;17(3):325-345. doi: 10.1007/s13273-021-00139-4. Epub 2021 May 5.
To solve current issues using big data, solve current issues related to the semiconductor and electronics industry, I tried to establish the data for each toxicity mechanism for adverse outcome pathway (AOP) for the exposure.
I planned to increase the efficiency of human hazard assessment by searching, analyzing, and linking test data on the relationship between key events occurred at each level, which are the biological targets of chemicals in semiconductor manufacturing.
It was found that 48 kinds of chemicals had 11 AOPs, while 103 chemicals had multiple AOPs, and 26 had case evidence. As a result of AOP analysis, it was found that a total of 320 chemicals had 42 AOPs, and 190 major chemicals corresponded to 11 AOPs. It was found necessary to develop a complex AOP and secure an (inhalation or dermal) exposure scenario for combined exposure at work. As a comparative search (41 out of 190 chemicals) of biomarkers specific to occupational diseases, 12 biomarkers were found to be related to breast cancer. The AOPs for 50 specific chemicals were presented, together with occupational disease-specific AOPs and key events relationship from 50 chemicals, and taxonomic classification for each AOP analysis could be found. With a comparative search, 41 out of 190 chemicals were associated with specific biomarkers for occupational diseases, and 12 mRNA or protein biomarkers were found to be related to breast cancer by cross-validation with the attached Table 24 of the Enforcement Regulations of the OSHAct and the CTD.
The mechanism of occupational diseases caused by chemicals was presented, together with pathological preventions. I believe that a strategy is needed to expand the target organization for each chemical by linking with activities, such as work environment measurement, and cooperating with screening items and methods suitable for toxic chemicals, like AOP tools.
The online version contains supplementary material available at 10.1007/s13273-021-00139-4.
为利用大数据解决当前问题，解决与半导体和电子行业相关的当前问题，我尝试为暴露的不良结局途径（AOP）的每种毒性机制建立数据。
我计划通过搜索、分析和关联关于在每个层面发生的关键事件之间关系的测试数据来提高人类危害评估的效率，这些关键事件是半导体制造中化学物质的生物学靶点。
发现48种化学物质有11条AOP，而103种化学物质有多种AOP，26种有病例证据。AOP分析结果显示，共有320种化学物质有42条AOP，190种主要化学物质对应11条AOP。发现有必要开发复杂的AOP并确保工作场所联合暴露的（吸入或经皮）暴露场景。作为对职业病特异性生物标志物的比较搜索（190种化学物质中的41种），发现12种生物标志物与乳腺癌有关。给出了50种特定化学物质的AOP，以及50种化学物质的职业病特异性AOP和关键事件关系，并且可以找到每种AOP分析的分类学分类。通过比较搜索，190种化学物质中的41种与职业病特异性生物标志物相关，通过与《职业安全与健康法》实施条例和CTD的附表24交叉验证，发现12种mRNA或蛋白质生物标志物与乳腺癌有关。
介绍了化学物质引起职业病的机制以及病理预防措施。我认为需要一种策略，通过与工作环境测量等活动相联系，并与适合有毒化学物质的筛查项目和方法（如AOP工具）合作，来扩大每种化学物质的目标组织。
在线版本包含可在10.1007/s13273-021-00139-4获取的补充材料。
