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.
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QSAR Models and Virtual Screening for Discovery of New Analgesic Leads
Published:
20 January 2017
by MDPI
in MOL2NET'16, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 2nd ed.
congress CHEMBIOMOL-02: Chem. Biol. & Med. Chem. Workshop, Rostock, Germany-Bilbao, Spain-Galveston, Texas, USA, 2016
Abstract:
Keywords: TOMOCOMD-CARDD Software, Non-Stochastic and Stochastic Linear Indices, Classification Model, Learning Machine-based QSAR, Analgesic Activity
Comments on this paper
Naivi Flores Balmaseda
23 January 2017
Dear Authors:
Good job, have you considered performing QSAR Artificial Intelligence models?