sodium-benzoate and Chemical-and-Drug-Induced-Liver-Injury

Liver damage is a global health concern associated with a high mortality rate. Sodium benzoate (SB) is a widely used preservative in the food industry with a wide range of applications. However, there's a lack of scientific reports on its effect on lipopolysaccharide-induced hepatic dysfunction.. The present study investigated the influence of SB on lipopolysaccharide (LPS)-induced liver injury.. Twenty-eight rats were randomly allocated into four groups: control (received distilled water), SB (received 600 mg/kg), LPS (received 0.25 mg/kg), and LPS + SB (received LPS, 0.25 mg/kg, and SB, 600 mg/kg). SB was administered orally for 14 days while LPS was administered intraperitoneally for 7 days.. Administration of SB to rats with hepatocyte injury exacerbated liver damage with a significant increase in the activities of serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphatase (ALP). We also observed that SB aggravated LPS-mediated hepatic oxidative stress occasioned by a marked decrease in antioxidant status with a concomitant increase in lipid peroxidation. Furthermore, LPS - mediated increase in inflammatory biomarkers as well as histological deterioration in the liver was exacerbated following the administration of SB to rats.. Taken together, the study provides experimental evidence that SB exacerbates hepatic oxidative stress and inflammation in LPS-mediated liver injury.

Topics: Animals; Chemical and Drug Induced Liver Injury; Chemical and Drug Induced Liver Injury, Chronic; Inflammation; Lipopolysaccharides; Liver; Oxidative Stress; Rats; Sodium Benzoate

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