nifurtimox and Chemical-and-Drug-Induced-Liver-Injury

Treatment of Chagas disease, caused by Trypanosoma cruzi, relies on nifurtimox and benznidazole (BZL), which present side effects in adult patients, and natural resistance in some parasite strains. Hydroxymethylnitrofurazone (NFOH) is a new drug candidate with demonstrated trypanocidal activity; however, its safety is not known.. HepG2 cells dose response to NFOH and BZL (5-100 µM) was assessed by measurement of ROS, DNA damage and survival. Swiss mice were treated with NFOH or BZL for short-term (ST, 21 d) or long-term (LT, 60 d) periods. Sera levels of cellular injury markers, liver inflammatory and oxidative stress, and fibrotic remodeling were monitored.. HepG2 cells exhibited mild stress, evidenced by increased ROS and DNA damage, in response to NFOH, while BZL at 100 µM concentration induced >33% cell death in 24 h. In mice, NFOH ST treatment resulted in mild-to-no increase in the liver injury biomarkers (GOT, GPT), and liver levels of inflammatory (myeloperoxidase, TNF-α), oxidative (lipid peroxides) and nitrosative (3-nitrotyrosine) stress. These stress responses in NFOH LT treated mice were normalized to control levels. BZL-treated mice exhibited a >5-fold increase in GOT, GPT and TNF-α (LT) and a 20-40% increase in liver levels of MPO activity (ST and LT) in comparison with NFOH-treated mice. The liver inflammatory infiltrate was noted in the order of BZL>vehicle≥NFOH and BZL>NFOH≥vehicle, respectively, after ST and LT treatments. Liver fibrotic remodeling, identified after ST treatment, was in the order of BZL>vehicle>NFOH; lipid deposits, indicative of mitochondrial dysfunction and in the order of NFOH>vehicle>BZL were evidenced after LT treatment.. NFOH induces mild ST hepatotoxicity that is normalized during LT treatment in mice. Our results suggest that additional studies to determine the efficacy and toxicity of NFOH are warranted.

Topics: Animals; Cell Death; Cell Line, Tumor; Chagas Disease; Chemical and Drug Induced Liver Injury; DNA Damage; Female; Hep G2 Cells; Humans; Liver; Male; Mice; Mitochondria; Nifurtimox; Nitrofurazone; Nitroimidazoles; Parasites; Reactive Oxygen Species; Trypanocidal Agents; Trypanosoma cruzi; Tumor Necrosis Factor-alpha; Tyrosine

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