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[] QSAR Models and Virtual Screening for Discovery of New Analgesic Leads

1 Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Department of Pharmacy, Faculty of Chemical-Pharmacy. Central University of Las Villas, Santa Clara, 54830, Villa Clara, Cuba
2 Bioinformatic Research in Systems & Computer Engineering, Carleton University, Ottawa, Canada
3 Grupo de Investigación en Estudios Químicos y Biológicos, Facultad de Ciencias Básicas, Universidad Tecnológica de Bolívar, Cartagena de Indias, Bolívar, Colombia
4 Instituto de Química Medica (IQM), Consejo Superior de Investigaciones Científicas (CSIC), c/Juan de la Cierva 3, 28006, Madrid, Spain
* Author to whom correspondence should be addressed.
20 January 2017
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The search for new selective pharmacological agents with no significant side-effects is an increasing requirement for the development of new drugs to be used in the treatment of acute and chronic pain. In the present study, a new series of compounds (VAM 1, 6, 10, 11, 12, 2-4) has been screening in QSAR-LDA mathematic models and pharmacologically evaluated. The antinociceptive properties of the new analgesic candidates obtained of virtual screening have been investigated in animal models of pain at the doses of 100, 150 and 200 mg/kg, and in vitro tests. Compounds VAM 10 and VAM 2-4 are the most potent antinociceptive agents from this series using different models of nociception in mice. A mild affinity for μ opioid receptor has been observed for the compound VAM 1 and 10. The pre-treatment with the compounds VAM 1, 2-4, 6, 10, 11, 12, showed a potent inhibition of IL-6 on RAW cells. The blocking efficacy of nineteen compounds on several isoforms of voltage-dependent sodium channels, expressed in Xenopus laevis oocytes, was tested (Nav1.3, Nav1.5, Nav1.6, Nav1.7, and Nav1.8). An exception was Nav1.6, where VAM 2-4 compound to result in substantial block indicating that acts specifically at this peculiar isoform. These results indicate the potential of the compound VAM 2-4 to treat pain conditions.


TOMOCOMD-CARDD Software, Non-Stochastic and Stochastic Linear Indices, Classification Model, Learning Machine-based QSAR, Analgesic Activity

Cite this article as

Castillo-Garit, J.; López, A.; Marrero-Ponce, Y.; Casañola-Martín, G.; Arán, V. QSAR Models and Virtual Screening for Discovery of New Analgesic Leads. In Proceedings of the MOL2NET, International Conference on Multidisciplinary Sciences, 15 January–30 December 2016; Sciforum Electronic Conference Series, Vol. 2, 2017 ; doi:10.3390/mol2net-02-03887


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