{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T00:54:55Z","timestamp":1785891295102,"version":"3.56.0"},"reference-count":59,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2020,10,11]],"date-time":"2020-10-11T00:00:00Z","timestamp":1602374400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["analyticalsciencejournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J of Applied Toxicology"],"published-print":{"date-parts":[[2021,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Benchmark dose method is one of the most famous quantitative approaches available for toxicological risks prediction. However, it is not fully clear how occupational health professionals can use it for specific workplace scenarios requiring carcinogen risk assessment. The paper explores the hypothesis that benchmark dose method allows to effectively approximate dose\u2013response data on carcinogenic response, providing reasonable estimations of risks in the situations when a choice between more complex models is not warranted for practical purposes. Three case studies were analyzed for the agents with different levels of scientific confidence in human carcinogenicity: carbon nanotubes, amosite asbestos, and glyphosate. For each agent, a critical study was determined, and a dose\u2013response slope factor was quantified, based on the weighted average lower bound benchmark dose. The linear slope factors of 0.111 lifetime excess cases of lung carcinoma per mg\/m<jats:sup>3<\/jats:sup> of MWCNT\u20107 (in rats exposure equivalent), 0.009 cases of mesothelioma per f\/cc\u2010years of cumulative exposure to amosite asbestos, and 0.000094 cases of malignant lymphoma per mg\/kg\/day of glyphosate (in mice equivalent) were determined. The correlations between the proposed linear predictive models and observed data points were <jats:italic>R<\/jats:italic> = 0.96 (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> = 0.92) for carbon nanotubes, <jats:italic>R<\/jats:italic> = 0.97 (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> = 0.95) for amosite asbestos, and <jats:italic>R<\/jats:italic> = 0.89 (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> = 0.79) for glyphosate. In all three cases, the linear extrapolation yielded comparable level of risk estimations with the \u201cbest fit\u201d nonlinear model; for nanoparticles and amosite asbestos, linear estimations were more conservative. By performing a simulation study, it was demonstrated that a weighted average benchmark dose expressed the highest correlation with multistage and quantal\u2010linear models.<\/jats:p>","DOI":"10.1002\/jat.4063","type":"journal-article","created":{"date-parts":[[2020,10,11]],"date-time":"2020-10-11T04:55:40Z","timestamp":1602392140000},"page":"148-160","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Using benchmark dose modeling for the quantitative risk assessment: Carbon nanotubes, asbestos, glyphosate"],"prefix":"10.1002","volume":"41","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0456-3823","authenticated-orcid":false,"given":"Andrey","family":"Korchevskiy","sequence":"first","affiliation":[{"name":"Chemistry &amp; Industrial Hygiene, Inc.  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