Inhibition of DNA-topoisomerase I by acylated triterpene saponins from pittosporum angustifolium Lodd

Previous phytochemical investigation of the leaves and seeds of Pittosporum angustifolium Lodd. led to the isolation and structural elucidation of polyphenols and triterpene saponins. Evaluation for cytotoxicity of isolated saponins revealed that the predominant structural feature for a cytotoxic activity are acyl substituents at the oleanane aglycon backbone. The present work reports the results of a screening of 10 selected acylated saponins for their potential to inhibit the human DNA-topoisomerase I, giving rise to IC50 values in a range of 2.8-46.5 microM. To clarify the mode of observed cytotoxic action and, moreover, to distinguish from a pure surfactant effect which is commonly accompanied with saponins, these results indicate an involvement of the topoisomerase I and its role as a possible target structure for a cytotoxic activity. In addition, computational predictions of the fitting of saponins to the topoisomerase I-DNA complex, indicate a similar binding mode to that of clinically used topoisomerase I inhibitors. Ten acylated triterpene saponins from Pittosporum angustifolium were investigated for their potential to inhibit the human DNA-topoisomerase I and computational predictions of the fitting of saponins to the topoisomerase I-DNA complex were carried out

WITHDRAWN-a resource for withdrawn and discontinued drugs

Post-marketing drug withdrawals can be associated with various events, ranging from safety issues such as reported deaths or severe side-effects, to a multitude of non-safety problems including lack of efficacy, manufacturing, regulatory or business issues. During the last century, the majority of drugs voluntarily withdrawn from the market or prohibited by regulatory agencies was reported to be related to adverse drug reactions. Understanding the underlying mechanisms of toxicity is of utmost importance for current and future drug discovery. Here, we present WITHDRAWN, a resource for withdrawn and discontinued drugs publicly accessible at http://cheminfo.charite.de/withdrawn. Today, the database comprises 578 withdrawn or discontinued drugs, their structures, important physico-chemical properties, protein targets and relevant signaling pathways. A special focus of the database lies on the drugs withdrawn due to adverse reactions and toxic effects. For approximately one half of the drugs in the database, safety issues were identified as the main reason for withdrawal. Withdrawal reasons were extracted from the literature and manually classified into toxicity types representing adverse effects on different organs. A special feature of the database is the presence of multiple search options which will allow systematic analyses of withdrawn drugs and their mechanisms of toxicity

Computational prediction of immune cell cytotoxicity

Immunotoxicity, defined as adverse effects of xenobiotics on the immune system, is gaining increasing attention in the approval process of industrial chemicals and drugs. In-vivo and ex-vivo experiments have been the gold standard in immunotoxicity assessment so far, so the development of in-vitro and in-silico alternatives is an important issue. In this paper we describe a widely applicable, easy-to use computational approach which can serve as an initial immunotoxicity screen of new chemical entities. Molecular fingerprints describing chemical structure were used as parameters in a machine-learning approach based on the Naïve-Bayes learning algorithm. The model was trained using blood-cell growth inhibition data from the NCI database and validated externally with several in-house and literature-derived data sets tested in cytotoxicity assays on different types on immune cells. Both cross-validations and external validations resulted in areas under the receiver operator curves (ROC/AUC) of 75% or higher. The classification of the validation data sets occurred with excellent specificities and fair to excellent selectivities, depending on the data set. This means that the probability of actual immunotoxicity is very high for compounds classified as immunotoxic, while the fraction of false negative predictions might vary. Thus, in a multistep immunotoxicity screening scheme, the classification as immunotoxic can be accepted without additional confirmation, while compounds classified as not immunotoxic will have to be subjected to further investigation

Molecular similarity searching based on deep belief networks with different molecular descriptors

Molecular 2D similarity searching is one of the most widely used techniques for ligand-based virtual screening (LBVS). This study has used the concepts of deep learning by adapted deep belief networks (DBN) and data fusion concept with DBN to enhance the molecular similarity searching of chemical compounds in LBVS. The MDDR Datasets represented by different descriptors to convert the molecule shape to numerical values and each descriptor has different important features rather than the others. The DBN with data fusion is adapted to obtain a lower detection error probability and a higher reliability by using data from multiple distributed descriptors and analyzing the performance of combination and individual descriptors target by target and showed that the combination descriptor did better than both original descriptors. The overall results of this research showed that the use of DBN with data fusion in similarity-based is found to significantly outperform the conventional, industry-standard Tanimoto-based similarity search systems and some others benchmarks witch have been adapted by others researchers, with 1 % and 5% performance improvement in the average recall rates