Synthesis and pharmacological evaluation of novel isoquinoline N-sulphonylhydrazones designed as ROCK inhibitors.

In this study, we synthesized a new congener series of N-sulphonylhydrazones designed as candidate ROCK inhibitors using the molecular hybridization of the clinically approved drug fasudil (1) and the IKK-ß inhibitor LASSBio-1524 (2). Among the synthesized compounds, the N-methylated derivative 11 (LASSBio-2065) showed the best inhibitory profile for both ROCK isoforms, with IC50 values of 3.1 and 3.8 µM for ROCK1 and ROCK2, respectively. Moreover, these compounds were also active in the scratch assay performed in human breast cancer MDA-MB 231 cells and did not display toxicity in MTT and LDH assays. Molecular modelling studies provided insights into the possible binding modes of these N-sulphonylhydrazones, which present a new molecular architecture capable of being optimized and developed as therapeutically useful ROCK inhibitors.

Novel orally active analgesic and anti-inflammatory cyclohexyl-N-acylhydrazone derivatives.

The N-acylhydrazone (NAH) moiety is considered a privileged structure, being present in many compounds with diverse pharmacological activities. Among the activities attributed to NAH derivatives anti-inflammatory and analgesic ones are recurrent. As part of a research program aiming at the design of new analgesic and anti-inflammatory lead-candidates, a series of cyclohexyl-N-acylhydrazones 10-26 were structurally designed from molecular modification on the prototype LASSBio-294, representing a new class of cycloalkyl analogues. Compounds 10-26 and their conformationally restricted analogue 9 were synthetized and evaluated as analgesic and anti-inflammatory agents in classical pharmacologic protocols. The cyclohexyl-N-acylhydrazones 10-26 and the cyclohexenyl analogue 9 showed great anti-inflammatory and/or analgesic activities, but compound 13 stood out as a new prototype to treat acute and chronic painful states due to its important analgesic activity in a neuropathic pain model.

Graph cuts and neural networks for segmentation and porosity quantification in Synchrotron Radiation X-ray µCT of an igneous rock sample.

X-ray Synchrotron Radiation Micro-Computed Tomography (SR-µCT) allows a better visualization in three dimensions with a higher spatial resolution, contributing for the discovery of aspects that could not be observable through conventional radiography. The automatic segmentation of SR-µCT scans is highly valuable due to its innumerous applications in geological sciences, especially for morphology, typology, and characterization of rocks. For a great number of µCT scan slices, a manual process of segmentation would be impractical, either for the time expended and for the accuracy of results. Aiming the automatic segmentation of SR-µCT geological sample images, we applied and compared Energy Minimization via Graph Cuts (GC) algorithms and Artificial Neural Networks (ANNs), as well as the well-known K-means and Fuzzy C-Means algorithms. The Dice Similarity Coefficient (DSC), Sensitivity and Precision were the metrics used for comparison. Kruskal-Wallis and Dunn's tests were applied and the best methods were the GC algorithms and ANNs (with Levenberg-Marquardt and Bayesian Regularization). For those algorithms, an approximate Dice Similarity Coefficient of 95% was achieved. Our results confirm the possibility of usage of those algorithms for segmentation and posterior quantification of porosity of an igneous rock sample SR-µCT scan.