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Comput Biol Chem ; 74: 294-303, 2018 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-29698921

RESUMO

Based on modern literature data about biological activity of E7010 derivatives, a series of new sulfonamides as potential anticancer drugs were rationally designed by QSAR modeling methods Сlassification learning QSAR models to predict the tubulin polymerization inhibition activity of novel sulfonamides as potential anticancer agents were created using the Online Chemical Modeling Environment (OCHEM) and are freely available online on OCHEM server at https://ochem.eu/article/107790. A series of sulfonamides with predicted activity were synthesized and tested against 60 human cancer cell lines with growth inhibition percent values. The highest antiproliferative activity against leukemia (cell lines K-562 and MOLT-4), non-small cell lung cancer (cell line NCI-H522), colon cancer (cell lines NT29 and SW-620), melanoma (cell lines MALME-3M and UACC-257), ovarian cancer (cell lines IGROV1 and OVCAR-3), renal cancer (cell lines ACHN and UO-31), breast cancer (cell line T-47D) was found for compounds 4-9. According to the docking results the compounds 4-9 induce cytotoxicity by the disruption of the microtubule dynamics by inhibiting tubulin polymerization via effective binding into colchicine domain, similar the E7010.


Assuntos
Antineoplásicos/farmacologia , Desenho de Fármacos , Sulfonamidas/farmacologia , Antineoplásicos/síntese química , Antineoplásicos/química , Linhagem Celular Tumoral , Proliferação de Células/efeitos dos fármacos , Ensaios de Seleção de Medicamentos Antitumorais , Humanos , Aprendizado de Máquina , Modelos Moleculares , Estrutura Molecular , Relação Quantitativa Estrutura-Atividade , Sulfonamidas/síntese química , Sulfonamidas/química
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