Evaluation of Potential Toxicity from Co-Exposure to Three CNS Depressants (Toluene, Ethylbenzene, and Xylene) Under Resting and Working Conditions Using PBPK Modeling
Bibliographic record
Abstract
Under OSHA and American Conference of Governmental Industrial Hygienists (ACGIH®) guidelines, the mixture formula (unity calculation) provides a method for evaluating exposures to mixtures of chemicals that cause similar toxicities. According to the formula, if exposures are reduced in proportion to the number of chemicals and their respective exposure limits, the overall exposure is acceptable. This approach assumes that responses are additive, which is not the case when pharmacokinetic interactions occur. To determine the validity of the additivity assumption, we performed unity calculations for a variety of exposures to toluene, ethylbenzene, and/or xylene using the concentration of each chemical in blood in the calculation instead of the inhaled concentration. The blood concentrations were predicted using a validated physiologically based pharmacokinetic (PBPK) model to allow exploration of a variety of exposure scenarios. In addition, the Occupational Safety and Health Administration and ACGIH® occupational exposure limits were largely based on studies of humans or animals that were resting during exposure. The PBPK model was also used to determine the increased concentration of chemicals in the blood when employees were exercising or performing manual work. At rest, a modest overexposure occurs due to pharmacokinetic interactions when exposure is equal to levels where a unity calculation is 1.0 based on threshold limit values (TLVs®). Under work load, however, internal exposure was 87% higher than provided by the TLVs. When exposures were controlled by a unity calculation based on permissible exposure limits (PELs), internal exposure was 2.9 and 4.6 times the exposures at the TLVs at rest and workload, respectively. If exposure was equal to PELs outright, internal exposure was 12.5 and 16 times the exposure at the TLVs at rest and workload, respectively. These analyses indicate the importance of (1) Bruckner, J. V. and Warren, D. A. 2001. “Toxic effects of solvents and vapors”. In Casarett and Doull's Toxicology: The Basic Science of Poisons, Edited by: Klaassen, C. D. 869–916. New York: McGraw-Hill. [Google Scholar] selecting appropriate exposure limits, (2) Caprino, L. and Togna, G. I. 1998. Potential health effects of gasoline and its constituents: A review of current literature (1990–1997) on toxicological data. Environ. Health Perspect., 106: 115–125. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=9452413http://www4.infotrieve.com/newmedline/detail.asp?NameID=9452413&Session=&searchQuery=Environ%2E+Health+Perspect%2E%5BJournal+Name%5D+AND+106%5BVolume%5D+AND+115%5BPage+Number%5D+AND+1998%5BPublication+Date%5D+AND+Caprino%5BAuthor+Name%5D&count=1http://www.csa.com/htbin/linkabst.cgi?issn=0091-6765&vol=106&iss=&firstpage=115[Crossref], [PubMed], [Web of Science ®] , [Google Scholar] performing unity calculations, and (3) Dobrev, I. D., Andersen, M. E. and Yang, R. S. 2002. In silico toxicology: Simulating interaction thresholds for human exposure to mixtures of trichloroethylene, tetrachloroethylene, and 1,1,1-trichloroethane. Environ. Health Perspect., 110: 1031–1039. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=pubmed&dopt=Abstract&list_uids=12361929http://www4.infotrieve.com/newmedline/detail.asp?NameID=12361929&Session=&searchQuery=Environ%2E+Health+Perspect%2E%5BJournal+Name%5D+AND+110%5BVolume%5D+AND+1031%5BPage+Number%5D+AND+2002%5BPublication+Date%5D+AND+Dobrev%5BAuthor+Name%5D&count=1http://www.csa.com/htbin/linkabst.cgi?issn=0091-6765&vol=110&iss=&firstpage=1031[Crossref], [PubMed], [Web of Science ®] , [Google Scholar] considering the effect of work load on internal doses, and they illustrate the utility of PBPK modeling in occupational health risk assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".