Fourches, Denis published the artcileCheminformatics Analysis of Assertions Mined from Literature that Describe Drug-Induced Liver Injury in Different Species, Recommanded Product: Sulindac sulfone, the publication is Chemical Research in Toxicology (2010), 23(1), 171-183, database is CAplus and MEDLINE.
Drug-induced liver injury is one of the main causes of drug attrition. The ability to predict the liver effects of drug candidates from their chem. structures is critical to help guide exptl. drug discovery projects toward safer medicines. In this study, the authors have compiled a data set of 951 compounds reported to produce a wide range of effects in the liver in different species, comprising humans, rodents, and nonrodents. The liver effects for this data set were obtained as assertional metadata, generated from MEDLINE abstracts using a unique combination of lexical and linguistic methods and ontol. rules. The authors have analyzed this data set using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chem. determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. The authors found that the concordance of liver effects was relatively low (â?9-44%) between different species, raising the possibility that species specificity could depend on specific features of chem. structure. Compounds were clustered by their chem. similarity, and similar compounds were examined for the expected similarity of their species-dependent liver effect profiles. In most cases, similar profiles were observed for members of the same cluster, but some compounds appeared as outliers. The outliers were the subject of focused assertion regeneration from MEDLINE as well as other data sources. In some cases, addnl. biol. assertions were identified, which were in line with expectations based on compounds’ chem. similarities. The assertions were further converted to binary annotations of underlying chems. (i.e., liver effect vs. no liver effect), and binary quant. structure-activity relationship (QSAR) models were generated to predict whether a compound would be expected to produce liver effects in humans. Despite the apparent heterogeneity of data, models have shown good predictive power assessed by external 5-fold cross-validation procedures. The external predictive power of binary QSAR models was further confirmed by their application to compounds that were retrieved or studied after the model was developed. To the best of the authors’ knowledge, this is the first study for chem. toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automated text mining with limited manual curation, opening up new opportunities for generating and modeling chem. toxicol. data.
Chemical Research in Toxicology published new progress about 59973-80-7. 59973-80-7 belongs to naphthyridine, auxiliary class Immunology/Inflammation,COX, name is Sulindac sulfone, and the molecular formula is C20H17FO4S, Recommanded Product: Sulindac sulfone.
Referemce:
https://en.wikipedia.org/wiki/1,8-Naphthyridine,
1,8-Naphthyridine | C8H6N2 – PubChem