Existing computational methods for drug repositioning either rely only on the gene expression response of cell lines after treatment, or on drug-to-disease relationships, merging several information levels. However, the noisy nature of the gene expression and the scarcity of genomic data for many diseases are important limitations to such approaches. Here we focused on a drug-centered approach by predicting the therapeutic class of FDA-approved compounds, not considering data concerning the diseases. We propose a novel computational approach to predict drug repositioning based on state-of-the-art machine-learning algorithms. We have integrated multiple layers of information: i) on the distances of the drugs based on how similar are their chemical structures, ii) on how close are their targets within the protein-protein interaction network, and iii) on how correlated are the gene expression patterns after treatment. Our classifier reaches high accuracy levels (78%), allowing us to re-interpret the top misclassifications as re-classifications, after rigorous statistical evaluation. Efficient drug repurposing has the potential to significantly impact the whole field of drug development. The results presented here can significantly accelerate the translation into the clinics of known compounds for novel therapeutic uses.

Covering: up to December 2011 This review highlights the potential of natural and semisynthetic ursane-type triterpenoids as candidates for the design of multi-target bioactive compounds, with focus on their anticancer effects. A brief illustration of the biosynthesis, sources, and general biological effects of the main classes of naturally occurring pentacyclic triterpenoids (PTs) are provided.

Potent drugs are desperately needed to counteract bacterial biofilm infections, especially those caused by gram-positive organisms, such as Staphylococcus aureus. Moreover, anti-biofilm compounds/agents that can be used as chemical tools are also needed for basic in vitro or in vivo studies aimed at exploring biofilms behavior and functionability. In this contribution, a collection of naturally-occurring abietane-type diterpenes and their derivatives was tested against S. aureus biofilms using a platform consisting of two phenotypic assays that have been previously published by our group. Three active compounds were identified: nordehydroabietylamine (1), (+)-dehydroabietic acid (2) and (+)-dehydroabietylamine (3) that prevented biofilm formation in the low micromolar range, and unlike typical antibiotics, only 2 to 4-fold higher concentrations were needed to significantly reduce viability and biomass of existing biofilms. Compound 2, (+)-dehydroabietic acid, was the most selective towards biofilm bacteria, achieving high killing efficacy (based on log Reduction values) and it was best tolerated by three different mammalian cell lines. Since (+)-dehydroabietic acid is an easily available compound, it holds great potential to be used as a molecular probe in biofilms-related studies as well as to serve as inspirational chemical model for the development of potent drug candidates.