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Neurodegenerative diseases are being treated by modulating adenosine receptors with more effective, safe and selective antagonists. The objective of the study was to develop a methodology to obtain classification models based on decision tree algorithms and descriptors from 0D to 2D of non-congenital families of organic compounds to qualitatively predict lig-and-RAA2A affinity. For this purpose, a non-congeneric database of 315 antagonists was constructed and cured with its inhibition constant in nano molar, labeled as potent and weak. The Dragon and ISIDA / QSPR programs were used to calculate molecular descriptors and five groups of descriptors were obtained. In each group 50 descriptors were selected using the mRMR criterion. The database was divided into Training, Test and External series through a random selection and a generalized k-means cluster analysis. Classifiers were developed and validated using the WEKA program. The results were analyzed using the statistical tests of Friedman and Wilcoxon. The significant influence of parameter m of algorithm J48 on the predictivity was verified for the models that used the descriptors of the aug.a-b and hyb.aug.a groups of ISIDA / QSPR. The best performance model was obtained from the selected descriptors of the ISIDA-all group with a value of m = 6 and reached 90.6% prediction on the External series. The methodology developed to obtain classification models based on decision tree algorithms and descriptors from 0D to 2D of non-congenital families of organic compounds is effective in qualitatively predicting ligand- RAA2A affinity with accuracy, specificity and selectivity greater than 90 %. [ABSTRACT FROM AUTHOR]

Background: The systemic information enclosed in microarray data encodes relevant clues to overcome the poorly understood combination of genetic and environmental factors in Parkinson's disease (PD), which represents the major obstacle to understand its pathogenesis and to develop disease-modifying therapeutics. While several gene prioritization approaches have been proposed, none dominate over the rest. Instead, hybrid approaches seem to outperform individual approaches. Methods: A consensus strategy is proposed for PD related gene prioritization from mRNA microarray data based on the combination of three independent prioritization approaches: Limma, machine learning, and weighted gene co-expression networks. Results: The consensus strategy outperformed the individual approaches in terms of statistical significance, overall enrichment and early recognition ability. In addition to a significant biological relevance, the set of 50 genes prioritized exhibited an excellent early recognition ability (6 of the top 10 genes are directly associated with PD). 40 % of the prioritized genes were previously associated with PD including well-known PD related genes such as SLC18A2, TH or DRD2. Eight genes (CCNH, DLK1, PCDH8, SLIT1, DLD, PBX1, INSM1, and BMI1) were found to be significantly associated to biological process affected in PD, representing potentially novel PD biomarkers or therapeutic targets. Additionally, several metrics of standard use in chemoinformatics are proposed to evaluate the early recognition ability of gene prioritization tools. Conclusions: The proposed consensus strategy represents an efficient and biologically relevant approach for gene prioritization tasks providing a valuable decision-making tool for the study of PD pathogenesis and the development of disease-modifying PD therapeutics. [ABSTRACT FROM AUTHOR]

Background: The mechanism(s) responsible for breast capsular contracture (CC) remain unknown, but inflammatory pathways play a role. Various molecules have been attached to implant shells in the hope of modifying or preventing CC. The intrinsic antibacterial and antifungal activities of chitosan and related oligochitosan molecules lend themselves well to the study of the infectious hypothesis; chitosan's ability to bind to growth factors, its hemostatic action, and its ability to activate macrophages, cause cytokine stimulation, and increase the production of transforming growth factor (TGF)-β1 allow study of the hypertrophic scar hypothesis. Objective: The authors perform a comprehensive evaluation, in a rabbit model, of the relationship between CC and histological, microbiological, and immunological characteristics in the presence of a chitooligosaccharide (COS) mixture and a low molecular weight chitosan (LMWC). Methods: Eleven adult New Zealand rabbits were each implanted with three silicone implants: a control implant, one impregnated with COS, and one impregnated with LMWC. At four-week sacrifice, microdialysates were obtained in the capsule-implant interfaces for tumor necrosis factor alpha (TNF-α) and interleukin-8 (IL-8) level assessment. Histological and microbiological analyses were performed. Results: Baker grade III/IV contractures were observed in the LMWC group, with thick capsules, dense connective tissue, and decreased IL-8 levels (p < .05) compared to control and COS groups. Capsule tissue bacterial types and microdialysate TNF-α levels were similar among all groups. Conclusions: Chitosan-associated silicone implantation in a rabbit model resulted in Baker grade III/IV CC. This preclinical study may provide a model to test various mechanistic hypotheses of breast capsule formation and subsequent CC. [ABSTRACT FROM AUTHOR]

Telomerase is a reverse transcriptase enzyme that activates in more than 85% of cancer cells and it is associated with the acquisition of a malignant phenotype. Some experimental strategies have been suggested in order to avoid the enzyme effect on unstopped telomere elongation. One of them, the stabilization of the G-quartet structure, has been widely studied. Nevertheless, no QSAR studies to predict this activity have been developed. In the present study a classification model was carried out to identify, through molecular descriptors with structural fragments and groups information, those acridinic derivatives with better inhibitory concentration on telomerase enzyme. A linear discriminant model was developed to classify a data set of 90 acridinic derivatives (48 more potent derivatives with IC(50) < 1 microM and 42 less potent with IC(50) > or = 1 microM). The final model fit the data with sensitivity of 87.50% and specificity of 82.85%, for a final accuracy of 85.33%. The predictive ability of the model was assessed by a prediction set (15 compounds of 90% and 82.29% of prediction accuracy); a tenfold full cross-validation procedure (removing 15 compounds in each cycle, 84.80% of good prediction) and the prediction of inhibitory concentration on telomerase enzyme for external data of 10 novel acridines (90% of good prediction). The results of this study suggest that the established model has a strong predictive ability and can be prospectively used in the molecular design and action mechanism analysis of this kind of compounds with anticancer activity.

This paper reports a QSAR study for predicting the complexation of a large and heterogeneous variety of substances (233 organic compounds) with beta-cyclodextrins (beta-CDs). Several different theoretical molecular descriptors, calculated solely from the molecular structure of the compounds under investigation, and an efficient variable selection procedure, like the Genetic Algorithm, led to models with satisfactory global accuracy and predictivity. But the best-final QSAR model is based on Topological descriptors meanwhile offering a reasonable interpretation. This QSAR model was able to explain ca. 84% of the variance in the experimental activity, and displayed very good internal cross-validation statistics and predictivity on external data. It shows that the driving forces for CD complexation are mainly hydrophobic and steric (van der Waals) interactions. Thus, the results of our study provide a valuable tool for future screening and priority testing of beta-CDs guest molecules.

Chemical carcinogenicity is of primary interest because it drives much of the current regulatory actions regarding new and existing chemicals and conventional experimental tests take around 3 years to design, conduct, and interpret in addition to costing hundreds of millions of dollars, millions of skilled personnel hours, and millions of animal lives. Thus, theoretical approaches such as the one proposed here, quantitative structure-activity relationship (QSAR), are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach, aimed at predicting the rodent carcinogenicity of a set of nitroso compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises 26 nitroso compounds, divided into N-nitrosoureas, N-nitrosamines, and C-nitroso compounds, which have been bioassayed in female rats using gavage as a route of administration. Here, we are especially concerned in discerning the role of structural parameters on the carcinogenic activity of this family of compounds. First, the regression model derived, upon removal of two identified nitrosamine outliers, is able to account for more than 86% of the variance in the experimental activity. Second, TOPS-MODE afforded the bond contributions (expressed as fragment contributions to the carcinogenic activity) that can be interpreted and provided tools for better understanding of the mechanisms of carcinogenesis. Finally and, most importantly, we demonstrate the potential use of this approach toward the recognition of structural alerts for carcinogenicity predictions.

The TOPological Sub-Structural MOlecular DEsign (TOPS-MODE) approach has been applied to the study of the permeability coefficient of various compounds through low-density polyethylene at 0 degrees C. A model able to describe more than 92% of the variance in the experimental permeability of 38 organic compounds was developed with the use of the mentioned approach. In contrast, none of eight different approaches, including the use of constitutional, topological, BCUT, 2D autocorrelations, geometrical, RDF, 3D Morse, and GETAWAY descriptors was able to explain more than 75% of the variance in the mentioned property with the same number of descriptors. In addition, the TOPS-MODE approach permitted to find the contribution of different fragments to the permeability coefficients, giving to the model a straightforward structural interpretability.