Artículo

Martins Alho, M.A.; Marrero-Ponce, Y.; Barigye, S.J.; Meneses-Marcel, A.; Machado Tugores, Y.; Montero-Torres, A.; Gómez-Barrio, A.; Nogal, J.J.; García-Sánchez, R.N.; Vega, M.C.; Rolón, M.; Martínez-Fernández, A.R.; Escario, J.A.; Pérez-Giménez, F.; Garcia-Domenech, R.; Rivera, N.; Mondragón, R.; Mondragón, M. (...) Arán, V.J. "Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones" (2014) Bioorganic and Medicinal Chemistry
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Abstract:

Protozoan parasites have been one of the most significant public health problems for centuries and several human infections caused by them have massive global impact. Most of the current drugs used to treat these illnesses have been used for decades and have many limitations such as the emergence of drug resistance, severe side-effects, low-to-medium drug efficacy, administration routes, cost, etc. These drugs have been largely neglected as models for drug development because they are majorly used in countries with limited resources and as a consequence with scarce marketing possibilities. Nowadays, there is a pressing need to identify and develop new drug-based antiprotozoan therapies. In an effort to overcome this problem, the main purpose of this study is to develop a QSARs-based ensemble classifier for antiprotozoan drug-like entities from a heterogeneous compounds collection. Here, we use some of the TOMOCOMD-CARDD molecular descriptors and linear discriminant analysis (LDA) to derive individual linear classification functions in order to discriminate between antiprotozoan and non-antiprotozoan compounds as a way to enable the computational screening of virtual combinatorial datasets and/or drugs already approved. Firstly, we construct a wide-spectrum benchmark database comprising of 680 organic chemicals with great structural variability (254 of them antiprotozoan agents and 426 to drugs having other clinical uses). This series of compounds was processed by a k-means cluster analysis in order to design training and predicting sets. In total, seven discriminant functions were obtained, by using the whole set of atom-based linear indices. All the LDA-based QSAR models show accuracies above 85% in the training set and values of Matthews correlation coefficients ( C ) vary from 0.70 to 0.86. The external validation set shows rather-good global classifications of around 80% (92.05% for best equation). Later, we developed a multi-agent QSAR classification system, in which the individual QSAR outputs are the inputs of the aforementioned fusion approach. Finally, the fusion model was used for the identification of a novel generation of lead-like antiprotozoan compounds by using ligand-based virtual screening of 'available' small molecules (with synthetic feasibility) in our 'in-house' library. A new molecular subsystem (quinoxalinones) was then theoretically selected as a promising lead series, and its derivatives subsequently synthesized, structurally characterized, and experimentally assayed by using in vitro screening that took into consideration a battery of five parasite-based assays. The chemicals 11(12) and 16 are the most active (hits) against apicomplexa (sporozoa) and mastigophora (flagellata) subphylum parasites, respectively. Both compounds depicted good activity in every protozoan in vitro panel and they did not show unspecific cytotoxicity on the host cells. The described technical framework seems to be a promising QSAR-classifier tool for the molecular discovery and development of novel classes of broad-antiprotozoan-spectrum drugs, which may meet the dual challenges posed by drug-resistant parasites and the rapid progression of protozoan illnesses. © 2014 Elsevier Ltd. All rights reserved.

Registro:

Documento: Artículo
Título:Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones
Autor:Martins Alho, M.A.; Marrero-Ponce, Y.; Barigye, S.J.; Meneses-Marcel, A.; Machado Tugores, Y.; Montero-Torres, A.; Gómez-Barrio, A.; Nogal, J.J.; García-Sánchez, R.N.; Vega, M.C.; Rolón, M.; Martínez-Fernández, A.R.; Escario, J.A.; Pérez-Giménez, F.; Garcia-Domenech, R.; Rivera, N.; Mondragón, R.; Mondragón, M.; Ibarra-Velarde, F.; Lopez-Arencibia, A.; Martín-Navarro, C.; Lorenzo-Morales, J.; Cabrera-Serra, M.G.; Piñero, J.; Tytgat, J.; Chicharro, R.; Arán, V.J.
Filiación:CIHIDECAR (CONICET), Departamento de Química Orgánica, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, C1428EGA Buenos Aires, Argentina
Unit of Computer-Aided Molecular 'Biosilico' Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Universidad Central 'Marta Abreu' de Las Villas, Santa Clara 54830, Villa Clara, Cuba
Environmental and Computational Chemistry Group, Facultad de Química Farmacéutica, Universidad de Cartagena, Cartagena de Indias, Bolivar, Colombia
Unidad de Investigación de Diseño de Fármacos y Conectividad Molecular, Departamento de Química Física, Facultad de Farmacia, Universitat de València, Spain
Laboratory of Toxicology, University of Leuven (KULeuven), Campus Gasthuisberg, O and N2, PO Box 922, Herestraat 49, 3000 Leuven, Belgium
Departamento de Parasitología, Facultad de Farmacia, Universidad Complutense, 28040 Madrid, Spain
Centro para el Desarrollo de la Investigación Científica (CEDIC), Fundación Moisés Bertoni/Díaz Gill Medicina Laboratorial, Pai Perez 1165, Asunción, Paraguay
Laboratorio de Investigación de Productos Naturales Antiparasitarios de la Amazonía, Universidad Nacional de la Amazonía Peruana, Pasaje Los Paujiles s/n, A A.H H Nuevo San Lorenzo, San Juan Bautista, Iquitos, Peru
Departamento de Bioquímica, Centro de Investigaciones y Estudios Avanzados del IPN, Av. Instituto Politécnico Nacional No 2508, Col. San Pedro Zacatenco, México DF 07360, Mexico
Departamento de Microbiología y Parasitología, Facultad de Medicina, UNAM, México DF 04510, Mexico
Department of Parasitology, Faculty of Veterinarian Medicinal and Zootecnic, UNAM, Mexico DF 04510, Mexico
Instituto Universitario de Enfermedades Tropicales y Salud Pública de Canarias, University of La Laguna, Avda. Astrofísico Fco. Sánchez, S/N, 38203 La Laguna, Tenerife, Canary Islands, Spain
Instituto de Química Orgánica General, CSIC, c/Juan de la Cierva 3, 28006 Madrid, Spain
Instituto de Química Médica, CSIC, c/Juan de la Cierva 3, 28006 Madrid, Spain
Idioma: Inglés
Palabras clave:Antimalarial; Antiprotozoan database; Antitoxoplasma; Antitrichomonas; Antitrypanosomal; Classification model; Cyt; In silico study; In vitro assay; Leishmanicide; Machine learning-based QSAR; Non-stochastic and stochastic linear indices; TOMOCOMD-CARDD software
Año:2014
DOI: http://dx.doi.org/10.1016/j.bmc.2014.01.036
Título revista:Bioorganic and Medicinal Chemistry
Título revista abreviado:Bioorg. Med. Chem.
ISSN:09680896
CODEN:BMECE
Registro:http://digital.bl.fcen.uba.ar/collection/paper/document/paper_09680896_v_n_p_MartinsAlho

Citas:

---------- APA ----------
Martins Alho, M.A., Marrero-Ponce, Y., Barigye, S.J., Meneses-Marcel, A., Machado Tugores, Y., Montero-Torres, A., Gómez-Barrio, A.,..., Arán, V.J. (2014) . Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones. Bioorganic and Medicinal Chemistry.
http://dx.doi.org/10.1016/j.bmc.2014.01.036
---------- CHICAGO ----------
Martins Alho, M.A., Marrero-Ponce, Y., Barigye, S.J., Meneses-Marcel, A., Machado Tugores, Y., Montero-Torres, A., et al. "Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones" . Bioorganic and Medicinal Chemistry (2014).
http://dx.doi.org/10.1016/j.bmc.2014.01.036
---------- MLA ----------
Martins Alho, M.A., Marrero-Ponce, Y., Barigye, S.J., Meneses-Marcel, A., Machado Tugores, Y., Montero-Torres, A., et al. "Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones" . Bioorganic and Medicinal Chemistry, 2014.
http://dx.doi.org/10.1016/j.bmc.2014.01.036
---------- VANCOUVER ----------
Martins Alho, M.A., Marrero-Ponce, Y., Barigye, S.J., Meneses-Marcel, A., Machado Tugores, Y., Montero-Torres, A., et al. Antiprotozoan lead discovery by aligning dry and wet screening: Prediction, synthesis, and biological assay of novel quinoxalinones. Bioorg. Med. Chem. 2014.
http://dx.doi.org/10.1016/j.bmc.2014.01.036