Gelsolin Contributes to the Motility of A375 Melanoma Cells and This Activity Is Mediated by the Fibrous Extracellular Matrix Protein Profile.

Skin melanocytes reside on the basement membrane (BM), which is mainly composed of laminin, collagen type IV, and proteoglycans. For melanoma cells, in order to invade into the skin, melanocytes must cross the BM. It has been reported that changes in the composition of the BM accompany melanocytes tumorigenesis. Previously, we reported high gelsolin (GSN)-an actin-binding protein-levels in melanoma cell lines and GSN's importance for migration of A375 cells. Here we investigate whether melanoma cells migrate differently depending on the type of fibrous extracellular matrix protein. We obtained A375 melanoma cells deprived of GSN synthesis and tested their migratory properties on laminin, collagens type I and IV, fibronectin, and Matrigel, which resembles the skin's BM. We applied confocal and structured illuminated microscopy (SIM), gelatin degradation, and diverse motility assays to assess GSN's influence on parameters associated with cells' ability to protrude. We show that GSN is important for melanoma cell migration, predominantly on laminin, which is one of the main components of the skin's BM.

Modelling the toxicity of a large set of metal and metal oxide nanoparticles using the OCHEM platform.

Inorganic nanomaterials have become one of the new areas of modern knowledge and technology and have already found an increasing number of applications. However, some nanoparticles show toxicity to living organisms, and can potentially have a negative influence on environmental ecosystems. While toxicity can be determined experimentally, such studies are time consuming and costly. Computational toxicology can provide an alternative approach and there is a need to develop methods to reliably assess Quantitative Structure-Property Relationships for nanomaterials (nano-QSPRs). Importantly, development of such models requires careful collection and curation of data. This article overviews freely available nano-QSPR models, which were developed using the Online Chemical Modeling Environment (OCHEM). Multiple data on toxicity of nanoparticles to different living organisms were collected from the literature and uploaded in the OCHEM database. The main characteristics of nanoparticles such as chemical composition of nanoparticles, average particle size, shape, surface charge and information about the biological test species were used as descriptors for developing QSPR models. QSPR methodologies used Random Forests (WEKA-RF), k-Nearest Neighbors and Associative Neural Networks. The predictive ability of the models was tested through cross-validation, giving cross-validated coefficients q2 = 0.58-0.80 for regression models and balanced accuracies of 65-88% for classification models. These results matched the predictions for the test sets used to develop the models. The proposed nano-QSPR models and uploaded data are freely available online at http://ochem.eu/article/103451 and can be used for estimation of toxicity of new and emerging nanoparticles at the early stages of nanomaterial development.

New promising antifouling agent based on polymeric biocide polyhexamethylene guanidine molybdate.

A new polymeric biocide polyhexamethylene guanidine (PHMG) molybdate has been synthesized. The obtained cationic polymer has limited water solubility of 0.015 g/100 mL and is insoluble in paint solvents. The results of acute toxicity studies indicate moderate toxicity of PHMG molybdate, which has a median lethal dose at 48 h of 0.7 mg/L for Daphnia magna and at 96 h of 17 mg/L for Danio rerio (zebrafish) freshwater model organisms. Commercial ship paint was then modified by the addition of a low concentration of polymeric biocide 5% (w/w). The painted steel panels were kept in Dnipro River water for the evaluation of the dynamics of fouling biomass. After 129-d exposure, Bryozoa dominated in biofouling of tested substrates, forming 86% (649 g/m2 ) of the total biomass on control panel surfaces. However, considerably lower Bryozoa fouling biomass (15 g/m2 ) was detected for coatings containing PHMG molybdate. Dreissenidae mollusks were found to form 88% (2182 g/m2 ) of the fouling biomass on the control substrates after 228 d of exposure, whereas coatings containing PHMG molybdate showed a much lower biomass value of 23.6 g/m2 . The leaching rate of PHMG molybdate in water was found to be similar to rates for conventional booster biocides ranging from 5.7 µg/cm2 /d at the initial stage to 2.2 µg/cm2 /d at steady state. Environ Toxicol Chem 2017;36:2543-2551. © 2017 SETAC.

QSAR Studies, Synthesis and Antibacterial Assessment of New Inhibitors Against Multidrug-Resistant Mycobacterium tuberculosis.

BACKGROUND: The increasing rate of appearance of multidrug-resistant strains of Mycobacterium tuberculosis (MDR-TB) is a serious problem at the present time. MDR-TB forms do not respond to the standard treatment with the commonly used drugs and can take some years or more to treat with drugs that are less potent, more toxic and much more expensive. OBJECTIVE: The goal of this work is to identify the novel effective drug candidates active against MDR-TB strains through the use of methods of cheminformatics and computeraided drug design. METHODS: This paper describes Quantitative Structure-Activity Relationships (QSAR) studies using Artificial Neural Networks, synthesis and in vitro antitubercular activity of several potent compounds against H37Rv and resistant Mycobacterium tuberculosis (Mtb) strains. RESULTS: Eight QSAR models were built using various types of descriptors with four publicly available structurally diverse datasets, including recent data from PubChem and ChEMBL. The predictive power of the obtained QSAR models was evaluated with a cross-validation procedure, giving a q2=0.74-0.78 for regression models and overall accuracy 78.9-94.4% for classification models. The external test sets were predicted with accuracies in the range of 84.1-95.0% (for the active/inactive classifications) and q2=0.80- 0.83 for regressions. The 15 synthesized compounds showed inhibitory activity against H37Rv strain whereas the compounds 1-7 were also active against resistant Mtb strain (resistant to isoniazid and rifampicin). CONCLUSION: The results indicated that compounds 1-7 could serve as promising leads for further optimization as novel antibacterial inhibitors, in particular, for the treatment of drug resistance of Mtb forms.

Design and synthesis of new potent inhibitors of farnesyl pyrophosphate synthase.

Predictive QSAR models for the inhibition activities of nitrogen-containing bisphosphonates (N-BPs) against farnesyl pyrophosphate synthase (FPPS) from Leishmania major (LeFPPS) were developed using a data set of 97 compounds. The QSAR models were developed through the use of Artificial Neural Networks and Random Forest learning procedures. The predictive ability of the models was tested by means of leave-one-out cross-validation; Q(2)values ranging from 0.45-0.79 were obtained for the regression models. The consensus prediction for the external evaluation set afforded high predictive power (Q(2)=0.76 for 35 compounds). The robustness of the QSAR models was also evaluated using a Y-randomization procedure. A small set of 6 new N-BPs were designed and synthesized applying the Michael reaction of tetrakis (trimethylsilyl) ethenylidene bisphosphonate with amines. The inhibition activities of these compounds against LeFPPS were predicted by the developed QSAR models and were found to correlate with their fungistatic activities against Candida albicans. The antifungal activities of N-BPs bearing n-butyl and cyclopropyl side chains exceeded the activities of Fluconazole, a triazole-containing antifungal drug. In conclusion, the N-BPs developed here present promising candidate drugs for the treatment of fungal diseases.

Prediction of thrombin and factor xa inhibitory activity with associative neural networks.

Quantitative structure-activity relationship studies on a series of selective inhibitors of thrombin and factor Xa were performed by using Associative Neural Network. To overcome the problem of overfitting due to descriptor selection, 5-fold cross-validation with variable selection in each step of the analysis was performed. The predictive ability of the models was tested through leave-one-out cross-validation, giving a Q2=0.74-0.87 for regression models. Predictions for the external evaluation sets obtained accuracies in the range of 0.71-0.82 for regressions. The proposed models can be potential tools for finding new drug candidates.