vinflunine and Chemical-and-Drug-Induced-Liver-Injury

The role of pazopanib in the second-line setting of refractory metastatic transitional cell carcinoma of the urothelium has not been defined clearly. The aim of this phase I/II trial was to assess the safety, tolerability, and efficacy of combining pazopanib and vinflunine in patients with metastatic transitional cell carcinoma of the urothelium after failure of first-line platinum-containing therapy. From May 2011 to December 2011, five patients were enrolled in this trial. Pazopanib was the investigated compound; four levels were planned (200, 400, 600, and 800 mg/day). Vinflunine was dosed at 280 mg/m for the first dose and 320 mg/m every 3 weeks thereafter. After the definition of a tolerated dose for the combined therapy, a subsequent phase II study was planned. At the starting level, pazopanib 200 mg/day, dose-limiting toxicities were observed in two of five patients. One patient experienced grade 4 febrile neutropenia, which led to treatment discontinuation. A second patient showed grade 3 hepatobiliary disorder with an increase in γ-glutamyltransferase. The study was interrupted at dose level 1 for safety reasons. The initially planned phase II study was therefore not carried out. This phase I study showed that combined therapy of daily pazopanib (200 mg) and vinflunine (280/320 mg/m) every 3 weeks is poorly tolerated in patients with refractory advanced urothelial cancer.

Topics: Aged; Aged, 80 and over; Angiogenesis Inhibitors; Antineoplastic Combined Chemotherapy Protocols; Carcinoma, Transitional Cell; Cardiovascular Diseases; Chemical and Drug Induced Liver Injury; Dose-Response Relationship, Drug; Drug Resistance, Neoplasm; Early Termination of Clinical Trials; Humans; Indazoles; Male; Neutropenia; Organoplatinum Compounds; Palliative Care; Prospective Studies; Pyrimidines; Salvage Therapy; Sulfonamides; Thrombocytopenia; Urologic Neoplasms; Vinblastine

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