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Quick Access to Potential Trichomonacidals through Bond Linear Indices-Trained Ligand-Based virtual Screening Models
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1  Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy and Department of Drug Design, Chemical Bioactive Center. Central University of Las Villas, Santa Clara, 54830, Villa Clara, C
2  Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P.O. Box 22085, E-46071, València, Spain
3  Departamento de Parasitología, Facultad de Farmacia, UCM, Pza. Ramón y Cajal s/n, 28040 Madrid, Spain
4  Bioinformatics Group, Center of Studies on Informatics (CEI), Faculty of Mathematics, Physics and Computer Science. Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba
5  Department of Parasitology, Faculty of Veterinarian Medicinal and Zootecnic, UNAM, Mexico, D.F. 04510, Mexico fMediscovery, Inc. Suite 1050, 601 Carlson Parkway, Minnetonka, MN 55305, USA
6  Laboratorio de Electrónica Molecular, Departamento de Química, Modulo II, grano de Oro, Facultad Experimental de Ciencias, La Universidad del Zulia (LUZ), Venezuela
7  Universität Rostock, Institut für Chemie, Abteilung für Organische Chemie, Albert-Einstein-Straße 3a, 18059 Rostock, Germany

Abstract: Trichomonas vaginalis (Tv) is the causative agent of the most common, nonviral, sexually transmitted disease in women and men world-wide. Since 1959 metronidazole (MTZ) has been the drug of choice in the systemic treatment of trichomoniasis. However resistance to MTZ in some patients and the great cost associated to the development of new trichomonacidals make necessary the development of computational methods that shorten the drug discovery pipeline. Toward this end, bond-based linear indices, new TOMOCOMD-CARDD molecular descriptors, and linear discriminant analysis (LDA) were used to discover novel trichomonacidal chemicals. The obtained models, using non-stochastic and stochastic indices, were able to classify correctly 89.01% (87.50%) and 82.42% (84.38%) of the chemicals in training (test) sets, respectively. These results validate the models for use in the ligand-based virtual screening. Also they showed large Matthews’ correlation coefficients (C) of 0.78 (0.71) and 0.65 (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 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 applied to a library of seven chemicals in order to find novel antitrichomonal agents. These compounds were synthesized and tested for in vitro activity against Tv. As a result, experimental observations approached to theoretical predictions since it was obtained a correct classification of 85.71% (6 out of 7) of the chemicals. Besides, out of the seven compounds that were screened, synthesized and biologically assayed, six compounds (VA7-34, VA7-35, VA7-37, VA7-38, VA7-68, VA7-70) showed pronounced cytocidal activity at the concentration of 100µg/ml at 24h (48h) within the range of 98.66%-100% (99.40%-100%) while only two molecules (chemicals VA7-37 and VA7-38) showed high cytocidal activity at the concentration of 10µg/ml at 24h (48h): 98.38% (94.23%) and 97.59% (98.10%) correspondingly. The LDA-assisted QSAR models presented here could significantly reduce the number of synthesized and tested compounds and increase the chance of finding new chemical entities with trichomonacidal activity.
Keywords: <i>TOMOCOMD-CARDD</i> Software, Bond-based Linear Indices, LDAassisted QSAR Models, virtual Screening, Trichomonacidal, in vitro Cytostatic and Cytocidal Activity