首页> 外文期刊>Regulatory Toxicology and Pharmacology: RTP >Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals. II. Using oral slope factor as a measure of carcinogenic potency.
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Development of quantitative structure-activity relationship (QSAR) models to predict the carcinogenic potency of chemicals. II. Using oral slope factor as a measure of carcinogenic potency.

机译:建立定量构效关系(QSAR)模型以预测化学物质的致癌性。二。使用口腔坡度因子作为致癌力的量度。

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摘要

The overall risk associated with exposure to a chemical is determined by combining quantitative estimates of exposure to the chemical with their known health effects. For chemicals that cause carcinogenicity, oral slope factors (OSFs) and inhalation unit risks are used to quantitatively estimate the carcinogenic potency or the risk associated with exposure to the chemical by oral or inhalation route, respectively. Frequently, there is a lack of animal or human studies in the literature to determine OSFs. This study aims to circumvent this problem by developing quantitative structure-activity relationship (QSAR) models to predict the OSFs of chemicals. The OSFs of 70 chemicals based on male/female human, rat, and mouse bioassay data were obtained from the United States Environmental Protection Agency's Integrated Risk Information System (IRIS) database. A global QSAR model that considered all 70 chemicals as well as species and/or sex-specific QSARs were developed in this study. Study results indicate that the species and sex-specific QSARs (r(2)>0.8, q(2)>0.7) had a better predictive abilities than the global QSAR developed using data from all species and sexes (r(2)=0.77, q(2)=0.73). The QSARs developed in this study were externally validated, and demonstrated reasonable predictive abilities.
机译:与化学物质接触相关的总体风险是通过将化学物质接触的定量估计值与其已知的健康影响相结合来确定的。对于引起致癌性的化学物质,分别采用口服斜率因子(OSF)和吸入单位风险来定量估计致癌性或与通过口服或吸入途径接触该化学物质有关的风险。通常,文献中缺乏确定OSF的动物或人体研究。这项研究旨在通过开发定量构效关系(QSAR)模型来预测化学品的OSF来规避这一问题。基于雄性/雌性人类,大鼠和小鼠生物测定数据的70种化学物质的OSF从美国环境保护局的综合风险信息系统(IRIS)数据库获得。这项研究建立了一个全球QSAR模型,该模型考虑了所有70种化学物质以及特定物种和/或性别的QSAR。研究结果表明,物种和性别特异性QSAR(r(2)> 0.8,q(2)> 0.7)具有比使用所有物种和性别的数据开发的全球QSAR更好的预测能力(r(2)= 0.77 ,q(2)= 0.73)。在这项研究中开发的QSAR在外部进行了验证,并显示出合理的预测能力。

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