Quantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships.

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dc.creatorAllen, Timothy EH
dc.creatorGoodman, Jonathan M
dc.creatorGutsell, Steve
dc.creatorRussell, Paul J
dc.date2019-12-19T00:31:07Z
dc.date2019-12-19T00:31:07Z
dc.date2020-02-17
dc.date.accessioned2026-08-03T02:24:21Z
dc.descriptionThe aim of human toxicity risk assessment is to determine a safe dose or exposure to a chemical for humans. This requires an understanding of the exposure of a person to a chemical and how much of the chemical is required to cause an adverse effect. To do this computationally, we need to understand how much of a chemical is required to perturb normal biological function in an adverse outcome pathway (AOP). The molecular initiating event (MIE) is the first step in an adverse outcome pathway and can be considered as a chemical interaction between a chemical toxicant and a biological molecule. Key chemical characteristics can be identified and used to model the chemistry of these MIEs. In this study, we do just this by using chemical substructures to categorize chemicals and 3D quantitative structure-activity relationships (QSARs) based on comparative molecular field analysis (CoMFA) to calculate molecular activity. Models have been constructed across a variety of human biological targets, the glucocorticoid receptor, mu opioid receptor, cyclooxygenase-2 enzyme, human ether-à-go-go related gene channel, and dopamine transporter. These models tend to provide molecular activity estimation well within one log unit and electronic and steric fields that can be visualized to better understand the MIE and biological target of interest. The outputs of these fields can be used to identify key aspects of a chemical's chemistry which can be changed to reduce its ability to activate a given MIE. With this methodology, the quantitative chemical activity can be predicted for a wide variety of MIEs, which can feed into AOP-based chemical risk assessments, and understanding of the chemistry behind the MIE can be gained.
dc.descriptionUnilever
dc.formatPrint-Electronic
dc.formatapplication/vnd.openxmlformats-officedocument.wordprocessingml.document
dc.identifier0893-228X
dc.identifierhttps://www.repository.cam.ac.uk/handle/1810/300099
dc.identifier10.17863/CAM.47173
dc.identifier1520-5010
dc.identifier.urihttps://repo.dare.co.zw/handle/123456789/166480
dc.languageeng
dc.languageeng
dc.publisherAmerican Chemical Society (ACS)
dc.publisherhttps://doi.org/10.1021/acs.chemrestox.9b00136
dc.rightsAll rights reserved
dc.subjectDatabases, Chemical
dc.subjectDrug-Related Side Effects and Adverse Reactions
dc.subjectHumans
dc.subjectMolecular Conformation
dc.subjectOrganic Chemicals
dc.subjectQuantitative Structure-Activity Relationship
dc.subjectRisk Assessment
dc.titleQuantitative Predictions for Molecular Initiating Events Using Three-Dimensional Quantitative Structure-Activity Relationships.
dc.typeArticle

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