Abstract
In this study we propose a virtual screening strategy based on the generation of a pharmacophore hypothesis, followed by an in silico evaluation of some ADME-TOX properties with the aim to apply it to the hit finding process and, specifically, to characterize new chemical entities with potential to control inflammatory processes mediated by T lymphocytes such as multiple sclerosis, systemic lupus erithematosus or rheumatoid arthritis. As a result, three compounds with completely novel scaffolds were selected as final hits for future hit-to-lead optimization due to their anti-inflammatory profile. The biological results showed that the selected compounds increased the intracellular cAMP levels and inhibited cell proliferation in T lymphocytes. Moreover, two of these compounds were able to increase the production of IL-4, an immunoregulatory cytokine involved in the selective deviation of T helper (Th) immune response Th type 2 (Th2), which has been proved to have anti-inflammatory properties in several animal models for autoimmune pathologies as multiple sclerosis or rheumatoid arthritis. Thus our pharmacological strategy has shown to be useful to find molecules with biological activity to control immune responses involved in many inflammatory disorders. Such promising data suggested that this in silico strategy might be useful as hit finding process for future drug development.
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Acknowledgments
The authors acknowledge computational resources provided by Center for Scientific and Academic Services of Catalonia (CESCA), the technical assistance of Beatriz Bravo and Raquel Gómez, and the support of the FIS project PS09/00604 and Ministerio de Economia y Competitividad (project: CTQ2010-19690). Miguel Jerez gratefully acknowledges financial support from Ministerio de Economía y Competitividad through the Grant ECO2011-23972.
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Jerez, M.J., Jerez, M., González-García, C. et al. Combined use of pharmacophoric models together with drug metabolism and genotoxicity “in silico” studies in the hit finding process. J Comput Aided Mol Des 27, 79–90 (2013). https://doi.org/10.1007/s10822-012-9627-1
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DOI: https://doi.org/10.1007/s10822-012-9627-1