Influence of components structure and composition of binary mixtures of organic compounds on toxicity towards Daphnia Magna

Simplex representation of molecular structure is employed for consensus QSAR analysis of toxicity towards Daphnia magna of sodium salts of α-alkoxycarbonylsulfuric acids, aliphatic n-alcohols, phenols and their binary mixtures. The structure of a mixture is represented both using descriptors of individual compounds included in the mixture and using novel specific mixture parameters termed unconnected simplexes. The studied dataset included 15 single compounds and 20 mixtures. The logarithm of EC50 (mmole/l) is used as a target function. Aims of the research are the determination of molecular fragments with positive or negative influence on the toxicity and developing QSAR models capable to predict properly the toxicity of new compounds and mixtures from their structure and composition. Successful consensus model based on forty best QSAR models (R2=0,86…0,97; Q2=0,74…0,96; R2test=0,86…0,99) is obtained using different training and test sets

Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results