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Predictive modeling for chemical developmental toxicity assessments
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Sharma, Swati. Predictive modeling for chemical developmental toxicity assessments. Retrieved from https://doi.org/doi:10.7282/t3-795b-s140

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TitlePredictive modeling for chemical developmental toxicity assessments
NameSharma, Swati (author); Zhu, Hao (chair); Shende, Sunil (co-chair); Salas, David (co-chair); Rutgers University; Camden Graduate School
Date Created2020
Other Date2020-05 (degree)
SubjectDevelopmental toxicity, Read-across, Chemical toxic alerts, Molecularinitiating event, Developmental toxicology, Computational and Integrative Biology
Extent1 online resource (vii, 24 pages) : illustrations
DescriptionDevelopmental toxicity is a critical health hazard that needs to be addressed in chemical risk assessments. Traditional animal testing for developmental toxicity evaluations is expensive, time consuming and requires a large number of animals. High throughput screening is one such low cost method that has been used to test a large number of chemicals for toxicity and computational modeling is a promising approach to quickly evaluate chemical toxicity. In this paper, we collected a large database consisting of 1,365 unique chemicals with their in vivo developmental toxicity potentials from various sources. The database was searched against PubChem, a public repository, to establish in vitro bioprofiles of the chemicals. The PubChem in vitro assays were clustered based on relationships between structural fragments and cellular responses in the assays, to identify relevant assays that may reveal the underlying toxicity mechanisms for developmental toxicity. The PubChem in vitro assay clusters were used for read-across studies for predicting developmental toxicity and several clusters had high positive prediction rates in the cross-validation process. With further analysis, several interesting chemical toxic alerts were identified from the read-across studies for different clusters. These alerts can be used to establish molecular initiating events (MIE) in vitro and link to in vivo through an adverse outcome pathway (AOP), to improve the read-across prediction rates. The results indicate that if a chemical contains the chemical toxic alert and shows active response in the relevant bioassay, it has high potential to be a developmental toxicant. Novel mechanisms for developmental toxicity were also revealed in this study. This modeling approach can be used to predict a new chemical for developmental toxicity and can be extended for use in other toxicity end points.
NoteM.S.
NoteIncludes bibliographical references
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/t3-795b-s140
LanguageEnglish
CollectionCamden Graduate School Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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