article · ACS Omega
Abnormal expression of c-Met tyrosine kinase is linked to the proliferation of non-small-cell lung cancer cells. To identify potential inhibitors, computational modeling was conducted on forty small molecules derived from cyclohexane-1,3-dione. Researchers analysed the links between chemical properties and biological inhibitory activity using quantitative structure-activity relationship techniques, artificial neural networks, density-functional theory, and pharmacokinetic profiling. From this initial evaluation, a compound designated 6d emerged as the most promising scaffold for subsequent drug design. Using computer-aided optimization, a new set of thirty-six cyclohexane-1,3-dione derivatives was designed and evaluated. The in silico screening process identified nine lead compounds with potential to target the c-Met protein. These candidate molecules were further examined and validated through molecular docking and molecular dynamics simulations lasting one hundred nanoseconds, comparing their behaviour against the known drug Foretinib.
Non-small-cell lung cancer is often driven by the abnormal activity of proteins such as c-Met kinase. Using computational modeling to identify and evaluate promising molecular structures helps researchers pinpoint potential drug candidates much faster than traditional laboratory screening alone, offering a focused foundation for developing future targeted lung cancer therapies.
This work represents very early-stage discovery research conducted entirely through computational modeling and simulation. The identified lead compounds could serve as starting points for pharmaceutical developers and medicinal chemists seeking targeted c-Met inhibitors for non-small-cell lung cancer. Substantial laboratory synthesis, in vitro validation, and preclinical animal testing will be required before any therapeutic commercialisation pathway can be established.
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The abnormal expression of the c-Met tyrosine kinase has been linked to the proliferation of several human cancer cell lines, including non-small-cell lung cancer (NSCLC). In this context, the identification of new c-Met inhibitors based on heterocyclic small molecules could pave the way for the development of a new cancer therapeutic pathway. Using multiple linear regression (MLR)-quantitative structure–activity relationship (QSAR) and artificial neural network (ANN)-QSAR modeling techniques, we look at the quantitative relationship between the biological inhibitory activity of 40 small molecules derived from cyclohexane-1,3-dione and their topological, physicochemical, and electronic properties against NSCLC cells. In this regard, screening methods based on QSAR modeling with density-functional theory (DFT) computations, in silico pharmacokinetic/pharmacodynamic (ADME-Tox) modeling, and molecular docking with molecular electrostatic potential (MEP) and molecular mechanics-generalized Born surface area (MM-GBSA) computations were used. Using physicochemical (stretch–bend, hydrogen bond acceptor, Connolly molecular area, polar surface area, total connectivity) and electronic (total energy, highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energy levels) molecular descriptors, compound 6d is identified as the optimal scaffold for drug design based on in silico screening tests. The computer-aided modeling developed in this study allowed us to design, optimize, and screen a new class of 36 small molecules based on cyclohexane-1,3-dione as potential c-Met inhibitors against NSCLC cell growth. The in silico rational drug design approach used in this study led to the identification of nine lead compounds for NSCLC therapy via c-Met protein targeting. Finally, the findings are validated using a 100 ns series of molecular dynamics simulations in an aqueous environment on c-Met free and complexed with samples of the proposed lead compounds and Foretinib drug.
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DOI: 10.1021/acsomega.2c07585
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