EventsMOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed.
Published
This submission belongs to the session 02. CHEMBIOMOL-02: Chem. Biol. & Med. Chem. Workshop, Rostock, Germany-Bilbao, Spain-Galveston, Texas, USA, 2016 of the event MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed.
Published date
20 Jan, 2017
Citation
Juan Alberto Castillo-Garit, Arelys López, Yovani Marrero-Ponce, Gerardo M. Casañola-Martín, Vicente J. Arán, QSAR Models and Virtual Screening for Discovery of New Analgesic Leads, in Proceedings of MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed., 15 October–20 October 2022, MDPI: Basel, Switzerland, doi: 10.3390/mol2net-02-03887
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QSAR Models and Virtual Screening for Discovery of New Analgesic Leads

Yovani Marrero-Ponce 3
Vicente J. Arán 4
1. Unidad de Toxicología Experimental, Universidad de Ciencias Médicas de Villa Clara, Santa Clara, Villa Clara, Cuba. CP: 50200, 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
Abstract

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.

Keywords
TOMOCOMD-CARDD Software
Non-Stochastic and Stochastic Linear Indices
Classification Model
Learning Machine-based QSAR
Analgesic Activity
Poster
Analgesics.pdf
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