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Prediction of Organ Toxicity Endpoints by QSAR Modeling Based on Precise Chemical‐Histopathology Annotations
Author(s) -
Myshkin Eugene,
Brennan Richard,
Khasanova Tatiana,
Sitnik Tatiana,
Serebriyskaya Tatiana,
Litvinova Elena,
Guryanov Alexey,
Nikolsky Yuri,
Nikolskaya Tatiana,
Bureeva Svetlana
Publication year - 2012
Publication title -
chemical biology and drug design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 77
eISSN - 1747-0285
pISSN - 1747-0277
DOI - 10.1111/j.1747-0285.2012.01411.x
Subject(s) - toxicity , nephrotoxicity , quantitative structure–activity relationship , drug , kidney , computational biology , pharmacology , computer science , medicine , bioinformatics , biology
The ability to accurately predict the toxicity of drug candidates from their chemical structure is critical for guiding experimental drug discovery toward safer medicines. Under the guidance of the MetaTox consortium (Thomson Reuters, CA, USA), which comprised toxicologists from the pharmaceutical industry and government agencies, we created a comprehensive ontology of toxic pathologies for 19 organs, classifying pathology terms by pathology type and functional organ substructure. By manual annotation of full‐text research articles, the ontology was populated with chemical compounds causing specific histopathologies. Annotated compound‐toxicity associations defined histologically from rat and mouse experiments were used to build quantitative structure–activity relationship models predicting subcategories of liver and kidney toxicity: liver necrosis, liver relative weight gain, liver lipid accumulation, nephron injury, kidney relative weight gain, and kidney necrosis. All models were validated using two independent test sets and demonstrated overall good performance: initial validation showed 0.80–0.96 sensitivity (correctly predicted toxic compounds) and 0.85–1.00 specificity (correctly predicted non‐toxic compounds). Later validation against a test set of compounds newly added to the database in the 2 years following initial model generation showed 75–87% sensitivity and 60–78% specificity. General hepatotoxicity and nephrotoxicity models were less accurate, as expected for more complex endpoints.