rifamycin-sv and Chemical-and-Drug-Induced-Liver-Injury

Drug-induced liver injury (DILI) accounts for 20-40% of all instances of clinical hepatic failure and is a common reason for withdrawal of an approved drug or discontinuation of a potentially new drug from clinical/nonclinical development. Numerous individual risk factors contribute to the susceptibility to human DILI and its severity that are either compound- and/or patient-specific. Compound-specific primary mechanisms linked to DILI include: cytotoxicity, reactive metabolite formation, inhibition of bile salt export pump (BSEP), and mitochondrial dysfunction. Since BSEP is an energy-dependent protein responsible for the efflux of bile acids from hepatocytes, it was hypothesized that humans exposed to drugs that impair both mitochondrial energetics and BSEP functional activity are more sensitive to more severe manifestations of DILI than drugs that only have a single liability factor. As annotated in the United States National Center for Toxicological Research Liver Toxicity Knowledge Base (NCTR-LTKB), the inhibitory properties of 24 Most-DILI-, 28 Less-DILI-, and 20 No-DILI-concern drugs were investigated. Drug potency for inhibiting BSEP or mitochondrial activity was generally correlated across human DILI concern categories. However, drugs with dual potency as mitochondrial and BSEP inhibitors were highly associated with more severe human DILI, more restrictive product safety labeling related to liver injury, and appear more sensitive to the drug exposure (Cmax) where more restrictive labeling occurs.. These data affirm that severe manifestations of human DILI are multifactorial, highly associated with combinations of drug potency specifically related to known mechanisms of DILI (like mitochondrial and BSEP inhibition), and, along with patient-specific factors, lead to differences in the severity and exposure thresholds associated with clinical DILI.

Topics: Animals; ATP Binding Cassette Transporter, Subfamily B, Member 11; ATP-Binding Cassette Transporters; Chemical and Drug Induced Liver Injury; Humans; Male; Mitochondria, Liver; Rats; Rats, Sprague-Dawley; Severity of Illness Index

Inhibition of the activity of the human bile salt export pump (BSEP: ABCB11) has been proposed to play a role in drug-induced liver injury (DILI). To enhance understanding of the relationship between BSEP inhibition and DILI, inhibition of human BSEP (hBSEP) and its rat ortholog (rBsep) by 85 pharmaceuticals was investigated in vitro. This was explored using assays that quantified inhibition of ATP-dependent [(3)H]taurocholate uptake into inverted plasma membrane vesicles from Sf21 insect cells, which expressed the proteins. Of the pharmaceuticals, 40 exhibited evidence of in vitro transporter inhibition and overall a close correlation was observed between potency values for inhibition of hBSEP and rBsep activity (r(2) = 0.94), although 12 drugs exhibited >2-fold more potent inhibition of hBSEP than rBsep. The median potency of hBSEP inhibition was higher among drugs that caused cholestatic/mixed DILI than among drugs that caused hepatocellular or no DILI, as was the incidence of hBSEP inhibition with IC(50) <300 μM. All drugs with hBSEP IC(50) <300 μM had molecular weight >250, ClogP >1.5, and nonpolar surface area >180Å. A clear distinction was not evident between hBSEP IC(50) or unbound plasma concentration (C(max, u)) of the drugs in humans and whether the drugs caused DILI. However, all 17 of the drugs with hBSEP IC(50) <100 μM and C(max, u) >0.002 μM caused DILI. Overall, these data indicate that inhibition of hBSEP/rBsep correlates with the propensity of numerous pharmaceuticals to cause cholestatic DILI in humans and is associated with several of their physicochemical properties.

Topics: Animals; ATP Binding Cassette Transporter, Subfamily B, Member 11; ATP-Binding Cassette Transporters; Bile Acids and Salts; Cell Line; Chemical and Drug Induced Liver Injury; Cholestasis; Drug-Related Side Effects and Adverse Reactions; Humans; Insecta; Rats; Risk Factors

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