alvocidib and Chemical-and-Drug-Induced-Liver-Injury

The cyclin-dependent kinase (CDK) inhibitor flavopiridol is currently being tested in clinical trials as anticancer drug. Beyond its cell death-inducing action, we hypothesized that flavopiridol affects inflammatory processes. Therefore, we elucidated the action of flavopiridol on leukocyte-endothelial cell interaction and endothelial activation in vivo and in vitro and studied the underlying molecular mechanisms.. Flavopiridol suppressed concanavalin A-induced hepatitis and neutrophil infiltration into liver tissue. Flavopiridol also inhibited tumor necrosis factor-α-induced leukocyte-endothelial cell interaction in the mouse cremaster muscle. Endothelial cells were found to be the major target of flavopiridol, which blocked the expression of endothelial cell adhesion molecules (intercellular adhesion molecule-1, vascular cell adhesion molecule-1, and E-selectin), as well as NF-κB-dependent transcription. Flavopiridol did not affect inhibitor of κB (IκB) kinase, the degradation and phosphorylation of IκBα, nuclear translocation of p65, or nuclear factor-κB (NF-κB) DNA-binding activity. By performing a cellular kinome array and a kinase activity panel, we found LIM domain kinase-1 (LIMK1), casein kinase 2, c-Jun N-terminal kinase (JNK), protein kinase C (PKC), CDK4, CDK6, CDK8, and CDK9 to be influenced by flavopiridol. Using specific inhibitors, as well as RNA interference (RNAi), we revealed that only CDK9 is responsible for the action of flavopiridol.. Our study highlights flavopiridol as a promising antiinflammatory compound and inhibition of CDK9 as a novel approach for the treatment of inflammation-associated diseases.

Topics: Animals; Cell Adhesion; Cell Communication; Cell Movement; Cells, Cultured; Chemical and Drug Induced Liver Injury; Concanavalin A; Cyclin-Dependent Kinase 9; Disease Models, Animal; E-Selectin; Endothelium, Vascular; Flavonoids; Humans; Inflammation; Intercellular Adhesion Molecule-1; Leukocytes; Male; Mice; Mice, Inbred C57BL; NF-kappa B; Piperidines; Protein Kinase Inhibitors; Vascular Cell Adhesion Molecule-1

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