Acute systemic toxicity testing, which is broadly utilized in pharmaceutical industrials and environmental protection agencies, is conducted to determine the relative health hazard of various chemicals. The traditional approaches for acute toxicity testing are costly, time-consuming, and require the use of many testing animals. Cell based assays, such as cytotoxicity testing, have been used as possible alternatives to animal acute testing. However, earlier studies have found poor correlation between these bioassay data and animal acute toxicity. To develop a predictive acute toxicity model, in this project we posit to construct diverse biological profiles of chemicals of environmental interest using large public toxicity bioassay data pools. To this end, we will implement an automated data mining method to explore the PubChem bioassay database. We have succeeded in preliminary studies to create a novel Quantitative Structure In vitro-In vivo Relationship (QSIIR) modeling workflow. In this project, we expect to apply this strategy to develop predictive animal acute toxicity models by employing the bioassay profiles for the modeling set compounds as extra biological descriptors. Moreover, we want to reveal the associated toxicity mechanisms by recognizing specific chemical-bioassay features in the modeling process. We expect to use the resulting model to prioritize toxic compounds of environmental interest for future animal toxicity studies.
Many drug candidates are eliminated as pharmaceutical leads due to unsuitable toxicity established in the course of the drug development process or in clinical studies. The cheminformatics animal toxicity predictor developed in this project is expected to directly evaluate chemical toxicity potential. In addition, the chemical-biological profiles for chemicals of environmental and pharmaceutical interest generated in this study could be used for in future studies of other complex toxicity endpoints.
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|Russo, Daniel P; Kim, Marlene T; Wang, Wenyi et al. (2017) CIIPro: a new read-across portal to fill data gaps using public large-scale chemical and biological data. Bioinformatics 33:464-466|
|Zhao, Linlin; Wang, Wenyi; Sedykh, Alexander et al. (2017) Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do. ACS Omega 2:2805-2812|
|Wang, Wenyi; Sedykh, Alexander; Sun, Hainan et al. (2017) Predicting Nano-Bio Interactions by Integrating Nanoparticle Libraries and Quantitative Nanostructure Activity Relationship Modeling. ACS Nano 11:12641-12649|
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|Liu, Yin; Su, Gaoxing; Wang, Fei et al. (2017) Elucidation of the Molecular Determinants for Optimal Perfluorooctanesulfonate Adsorption Using a Combinatorial Nanoparticle Library Approach. Environ Sci Technol 51:7120-7127|
|Ribay, Kathryn; Kim, Marlene T; Wang, Wenyi et al. (2016) Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data. Front Environ Sci 4:|
|Xiang, Jinbao; Zhang, Zhuoqi; Mu, Yan et al. (2016) Discovery of Novel Tricyclic Thiazepine Derivatives as Anti-Drug-Resistant Cancer Agents by Combining Diversity-Oriented Synthesis and Converging Screening Approach. ACS Comb Sci 18:230-5|
|Russo, Daniel P; Zhu, Hao (2016) Accessing the High-Throughput Screening Data Landscape. Methods Mol Biol 1473:153-9|
|Luechtefeld, Thomas; Maertens, Alexandra; Russo, Daniel P et al. (2016) Analysis of publically available skin sensitization data from REACH registrations 2008-2014. ALTEX 33:135-48|
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