Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides-Triazole Hybrids with Anticancer Activity

In the presented work, a series of 22 hybrids of 8-quinolinesulfonamide and 1,4-disubstituted triazole with antiproliferative activity were designed and synthesised. The title compounds were designed using molecular modelling techniques. For this purpose, machine-learning, molecular docking, and molecular dynamics methods were used. Calculations of the pharmacokinetic parameters (connected with absorption, distribution, metabolism, excretion, and toxicity) of the hybrids were also performed. The new compounds were synthesised via a copper-catalysed azide-alkyne cycloaddition reaction (CuAAC). 8-N-Methyl-N-{[1-(7-chloroquinolin-4-yl)-1H-1,2,3-triazol-4-yl]methyl}quinolinesulfonamide was identified in in silico studies as a potential strong inhibitor of Rho-associated protein kinase and as a compound that has an appropriate pharmacokinetic profile. The results obtained from in vitro experiments confirm the cytotoxicity of derivative 9b in four selected Cancer cell lines and the lack of cytotoxicity of this derivative towards normal cells. The results obtained from silico and in vitro experiments indicate that the introduction of another quinolinyl fragment into the inhibitor molecule may have a significant impact on increasing the level of cytotoxicity toward Cancer cells and indicate a further direction for future research in order to find new substances suitable for clinical applications in Cancer treatment.

ADMET; anticancer activity; machine learning; molecular docking; quinolinesulfonamides; triazoles.