Robust design of some selective matrix metalloproteinase-2 inhibitors over matrix metalloproteinase-9 through in silico/fragment-based lead identification and de novo lead modification: Syntheses and biological assays
Broad range of selectivity possesses serious limitation for the development of matrix metalloproteinase-2 (MMP-2) inhibitors for clinical purposes. To develop potent and selective MMP-2 inhibitors, initially multiple molecular modeling techniques were adopted for robust design. Predictive and validated regression models (2D and 3D QSAR and ligand-based pharmacophore mapping studies) were utilized for estimating the potency whereas classification models (Bayesian and recursive partitioning analyses) were used for determining the selectivity of MMP-2 inhibitors over MMP-9. Bayesian model fingerprints were used to design selective lead molecule which was modified using structure-based de novo technique. A series of designed molecules were prepared and screened initially for inhibitions of MMP-2 and MMP-9, respectively, as these are designed followed by other MMPs to observe the broader selectivity. The best active MMP-2 inhibitor had IC50 value of 24nM whereas the best selective inhibitor (IC50=51nM) showed at least 4 times selectivity to MMP-2 against all tested MMPs. Active derivatives were non-cytotoxic against human lung carcinoma cell line-A549. At non-cytotoxic concentrations, these inhibitors reduced intracellular MMP-2 expression up to 78% and also exhibited satisfactory anti-migration and anti-invasive properties against A549 cells. Some of these active compounds may be used as adjuvant therapeutic agents in lung cancer after detailed study..
Medienart: |
Artikel |
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Erscheinungsjahr: |
2016 |
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Erschienen: |
2016 |
Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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Enthalten in: |
Bioorganic & medicinal chemistry - 24(2016), 18, Seite 4291-4309 |
Sprache: |
Englisch |
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Beteiligte Personen: |
Adhikari, Nilanjan [VerfasserIn] |
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Links: |
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RVK: |
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doi: |
10.1016/j.bmc.2016.07.023 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
OLC1986063836 |
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