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Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds

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Abstract

Trichomonas vaginalis (Tv) is the causative agent of the most common, non-viral, sexually transmitted disease in women and men worldwide. 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 with 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 were used to discover novel trichomonacidal chemicals. The obtained models, using non-stochastic and stochastic indices, are able to classify correctly 89.01% (87.50%) and 82.42% (84.38%) of the chemicals in the training (test) sets, respectively. These results validate the models for their use in the ligand-based virtual screening. In addition, they show 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 evidences the robustness of the obtained models. Later, both models are applied to the virtual screening of 12 compounds already proved against Tv. As a result, they correctly classify 10 out of 12 (83.33%) and 9 out of 12 (75.00%) of the chemicals, respectively; which is the most important criterion for validating the models. Besides, these classification functions are applied to a library of seven chemicals in order to find novel antitrichomonal agents. These compounds are 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. Moreover, out of the seven compounds that are screened, synthesized and biologically assayed, six compounds (VA7-34, VA7-35, VA7-37, VA7-38, VA7-68, VA7-70) show pronounced cytocidal activity at the concentration of 100 μg/ml at 24 h (48 h) within the range of 98.66%–100% (99.40%–100%), while only two molecules (chemicals VA7-37 and VA7-38) show high cytocidal activity at the concentration of 10 μg/ml at 24 h (48 h): 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 could increase the chance of finding new chemical entities with anti-trichomonal activity.

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Acknowledgements

The authors wish to express their gratitude to Prof. Dr. Jorge Gálvez for his attention to this work and valuable suggestions. Yovani Marrero-Ponce (M.-P. Y) acknowledges the Valencia University for kind hospitality during the second semester of 2007. M.-P. Y thanks are given to the international relationships of Valencia University, (Spain) for partial financial support as well as the program ‘Estades Temporals per an Investigadors Convidats’ for a fellowship to work at Valencia University. Some authors’ thanks support from Spanish MEC (Project Reference: SAF2006-04698). Finally, F.T. thanks support from Spanish MEC DGI (Project No. CTQ2004-07768-C02-01/BQU) and Generalitat Valenciana (DGEUI INF01-051 and INFRA03-047, and OCYT GRUPOS03-173). Last but not least, Yovani Marrero-Ponce would like to express thanks for the partial support received from the project entitled Strengthening postgraduate education and research in Pharmaceutical Sciences. This project is funded by the Flemish Interuniversity Council (VLIR) of Belgium.

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Authors and Affiliations

  1. Faculty of Chemistry-Pharmacy, Unit of Computer-Aided Molecular “Biosilico” Discovery and Bioinformatic Research (CAMD-BIR Unit), Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba

    Yovani Marrero-Ponce, Alfredo Meneses-Marcel, Oscar M. Rivera-Borroto & Alina Montero

  2. Institut Universitari de Ciència Molecular, Universitat de València, Edifici d’Instituts de Paterna, P.O. Box 22085, Valencia, 46071, Spain

    Yovani Marrero-Ponce & Francisco Torrens

  3. Departamento de Química Física, Facultad de Farmacia, Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Universitat de València, Valencia, Spain

    Yovani Marrero-Ponce, Ramón García-Domenech & Jesus Vicente De Julián-Ortiz

  4. Departamento de Parasitología, Facultad de Farmacia, UCM, Pza. Ramón y Cajal s/n, Madrid, 28040, Spain

    Alfredo Meneses-Marcel, José Antonio Escario, Alicia Gómez Barrio, David Montero Pereira & Juan José Nogal

  5. Faculty of Mathemathics, Physics & Computer Science, Center of Studies on Informatics, Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba

    Oscar M. Rivera-Borroto & Ricardo Grau

  6. Institut für Chemie, Universität Rostock, Abteilung für Organische Chemie, Albert-Einstein-Straße 3a, 18059, Rostock, Germany

    Christian Vogel

  7. Instituto de Química Médica, CSIC, c/ Juan de la Cierva 3, Madrid, 28006, Spain

    Vicente J. Arán

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  1. Yovani Marrero-Ponce
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  2. Alfredo Meneses-Marcel
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  4. Ramón García-Domenech
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Correspondence to Yovani Marrero-Ponce.

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Marrero-Ponce, Y., Meneses-Marcel, A., Rivera-Borroto, O.M. et al. Bond-based linear indices in QSAR: computational discovery of novel anti-trichomonal compounds. J Comput Aided Mol Des 22, 523–540 (2008). https://doi.org/10.1007/s10822-008-9171-1

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