Distribution of Articles published per year
CONFERENCE-ARTICLE 6 Reads 0 Citations QSAR & Network-based multi-species activity models for antifungals Published: 30 November 2007
There are many pathogen microbial species with very different antimicrobial drugs susceptibility. In this work, we selected pairs of antifungal drugs with similar/dissimilar species predicted-activity profile and represented it as a large network, which may be used to identify drugs with similar mechanism of action. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) susbtantialy increases the potentialities of this kind of networks avoiding time and resources consming experiments. Unfortunately, almost QSAR models are unspecific or predict activity against only one species. To solve this problem we developed here a multi-species QSAR classification model, which outputs were the inputs of the above-mentioned network. Overall model classification accuracy was 87.0% (161/185 compounds) in training, 83.4% (50/61) in validation, and 83.7% for 288 additional antifungal compounds used to extent model validation for network construction. The network predicted has 59 nodes (compounds), 648 edges (pairs of compounds with similar activity), low coverage density d = 37.8%, and distribution more close to normal than to exponential. These results are more characteristic of a not-overestimated random network, clustering different drug mechanisms of actions, than of a less useful power-law network with few mechanisms (network hubs).
CONFERENCE-ARTICLE 2 Reads 0 Citations Bond-Based Quadratic <i>TOMOCOMD-CARDD</i> Molecular Indices & Statistical Techniques for New Antitrichomonal Drug-like ... Published: 30 November 2006
New antitrichomonal agents are needed to combat emerging metronidazoleresistant trichomoniasis and reduce the side-effects associated with currently available drugs. Toward this end, bond-based quadratic indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel, potent, and non-toxic lead trichomonacidal chemicals. Two discriminant functions were obtained with the use of non-stochastic and stochastic total and bond-type quadratic indices for heteroatoms. The obtained LDA-based QSAR models, using non-stochastic and stochastic indices, were able to classify correctly 87.91% (87.50%) and 89.01% (84.38%) of the chemicals in training (test) sets, respectively. They showed large Matthews’ correlation coefficients (C) of 0.75 (0.71) and 0.78 (0.65) for the training (test) sets, correspondingly. The result of predictions on the 10% full-out cross-validation test also evidenced the robustness of the obtained models. Later, both models were applied to the virtual screening of 12 compounds already proved against Trichomonas Vaginalis (Tv). As a result, they correctly classified 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is a more important criterion for validating the models. In addition, these classification functions were also applied to a library of twenty-one chemicals in order to find new lead antitrichomonal agents. These compounds were synthesized and tested for in vitro activity against Tv. As expected, theoretical results almost coincided with experimental ones since there was obtained a correct classification for both models of 95.24% (20 out of 21) of the chemicals. Out of the twenty-one compounds that were screened, and synthesized, two molecules (chemicals G-1, UC-245), showed high to moderate cytocidal activity at the concentration of 10µg/ml, other two compounds (G-0 and CRIS-148) showed high cytocidal activity only at the concentration of 100µg/ml, and the remaining chemicals (from CRIS-105 to CRIS-153 except CRIS-148) were inactive at these assayed concentrations. Finally, the best candidate, G-1 (cytocidal activity of 100% at 10µg/ml) was in vivo assayed in ovariectomized Wistar rats achieving promissory results as a trichomonacidal drug-like compound. The LDA-based QSAR models presented here can be considered as a computer-assisted system that could potentially significantly reduce the number of synthesized and tested compounds and increase the chance of finding new chemical entities with antitrichomonal activity.