sitosterol--(3beta)-isomer and Chemical-and-Drug-Induced-Liver-Injury

Beta-sitosterol (Sit) widely exists in natural plants, is classed as phytosterol and known as the "key of life". Most pharmacological studies and clinical applications are limited because of the fact that Sit is difficult to be solved. Therefore, it is viable to enhance pharmacologic activities of Sit by using its derivatives which can be obtained through the modification of Sit. In this study, 4 kinds of new Sit derivatives were obtained by the esterification reaction. Further, the hepatoprotective effects of Sit and its derivatives were investigated against acute liver injury induced by lipopolysaccharide/d-galactosamine (LPS/GalN) in mice and its mechanism was illustrated by western blot analysis and real-time PCR. The results demonstrated that among its derivatives, 2-naphthoyl Sit ester (Sit-N) (50 mg/kg) showed the strongest activities against acute liver injury. Final experimental results showed that Sit-N significantly decreased the serum activity of aspartate transaminase (AST) and alanine aminotransferase (ALT); Sit-N also markedly reduced tumor necrosis factor (TNF-α), interleukin-1β (IL-1β) and interleukin-6 (IL-6) levels. Meanwhile, Sit-N drastically improved the activities of antioxidant enzymes such as superoxide dismutase (SOD), glutathione (GSH) and catalase (CAT), and suppressed the expression of malondialdehyde (MDA). Results also displayed that over-expression of Toll like receptor 4 (TLR4) and nuclear factor-kappa B (NF-κB) induced by LPS/Gal N were inhibited by Sit-N. Meanwhile, the expression of nuclear respiratory factor2 (Nrf2) and heme oxygenase-1 (HO-1) were enhanced. The results all above verified the effectiveness of Sit-N against acute liver injury induced by LPS/GalN mediated by TLR4 and Nrf2 pathways.

Topics: Animals; Anti-Inflammatory Agents; Cell Survival; Chemical and Drug Induced Liver Injury; Dose-Response Relationship, Drug; Galactosamine; Inflammation; Lipopolysaccharides; Mice; Molecular Structure; Oxidation-Reduction; Sitosterols; Structure-Activity Relationship

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 chemical structures is critical to help guide experimental drug discovery projects toward safer medicines. In this study, we 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 ontological rules. We have analyzed this data set using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chemical determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. We found that the concordance of liver effects was relatively low (ca. 39-44%) between different species, raising the possibility that species specificity could depend on specific features of chemical structure. Compounds were clustered by their chemical 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, additional biological assertions were identified, which were in line with expectations based on compounds' chemical similarities. The assertions were further converted to binary annotations of underlying chemicals (i.e., liver effect vs no liver effect), and binary quantitative 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 our knowledge, this is the first study for chemical toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automate

Topics: Animals; Chemical and Drug Induced Liver Injury; Cluster Analysis; Databases, Factual; Humans; MEDLINE; Mice; Models, Chemical; Molecular Conformation; Quantitative Structure-Activity Relationship