title: Profiling animal toxicants by automatically mining public bioassay data: a big data approach for computational toxicology.
abstract: In vitro bioassays have been developed and are currently being evaluated as potential alternatives to traditional animal toxicity models. Already, the progress of high throughput screening techniques has resulted in an enormous amount of publicly available bioassay data having been generated for a large collection of compounds. When a compound is tested using a collection of various bioassays, all the testing results can be considered as providing a unique bio-profile for this compound, which records the responses induced when the compound interacts with different cellular systems or biological targets. Profiling compounds of environmental or pharmaceutical interest using useful toxicity bioassay data is a promising method to study complex animal toxicity. In this study, we developed an automatic virtual profiling tool to evaluate potential animal toxicants. First, we automatically acquired all PubChem bioassay data for a set of 4,841 compounds with publicly available rat acute toxicity results. Next, we developed a scoring system to evaluate the relevance between these extracted bioassays and animal acute toxicity. Finally, the top ranked bioassays were selected to profile the compounds of interest. The resulting response profiles proved to be useful to prioritize untested compounds for their animal toxicity potentials and form a potential in vitro toxicity testing panel. The protocol developed in this study could be combined with structure-activity approaches and used to explore additional publicly available bioassay datasets for modeling a broader range of animal toxicities.
authors.affiliation: Department of Chemistry, Rutgers University, Camden, New Jersey, United States of America; The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey, United States of America.
authors.forename: Jun
authors.initials: J
authors.lastname: Zhang
authors.affiliation: Biomolecular Screening Branch, Division of National Toxicology Program, National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina, United States of America.
authors.forename: Jui-Hua
authors.initials: JH
authors.lastname: Hsieh
authors.affiliation: Department of Chemistry, Rutgers University, Camden, New Jersey, United States of America; The Rutgers Center for Computational and Integrative Biology, Camden, New Jersey, United States of America.
authors.forename: Hao
authors.initials: H
authors.lastname: Zhu
mesh_terms: D000818:Animals; D000900:Anti-Bacterial Agents / Q000633:toxicity; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D004353:Drug Evaluation, Preclinical; D015386:Hazardous Substances / Q000633:toxicity*; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D051381:Rats; D013329:Structure-Activity Relationship; D018675:Toxicity Tests; D014116:Toxicology / Q000379:methods*
chemical_list: D000900:Anti-Bacterial Agents; D015386:Hazardous Substances