Matrix metalloproteinases (MMPs) are identifiable members of proteolytic enzymes that can degrade a wide range of proteins in the extracellular matrix (ECM). MMPs can be categorized into six groups based on their substrate specificity and structural differences: collagenases, gelatinases, stromelysins, matrilysins, metalloelastase, and membrane-type MMPs. MMPs have been linked to a wide variety of biological processes, such as cell transformation and carcinogenesis. Over time, MMPs have been evaluated for their role in cancer progression, migration, and metastasis. Accordingly, various MMPs have become attractive therapeutic targets for anticancer drug development. The first generations of broad-spectrum MMP inhibitors displayed effective inhibitory activities but failed in clinical trials due to poor selectivity. Thanks to the evolution of X-ray crystallography, NMR analysis, and homology modeling studies, it has been possible to characterize the active sites of various MMPs and, consequently, to develop more selective, second-generation MMP inhibitors. In this review, we summarize the computational and synthesis approaches used in the development of MMP inhibitors and their evaluation as potential anticancer agents.

Lung cancer remains a major cause of cancer-related death, highlighting the need for new molecular targets and novel therapeutics. Matrix metalloproteinases are key regulators of invasion and microenvironment remodeling, and among them, matrix metalloproteinase-12 (MMP12) is a particularly attractive candidate whose network-level effects in cancer are still poorly defined. Herein, we applied an integrative strategy that combines bioinformatics methods with experimental validation in non-small cell lung cancer (NSCLC) cells. Protein–protein interaction (PPI) and pathway analyses of MMP12-regulated genes identified 113 downstream targets enriched in the extracellular matrix, PI3K–AKT, and immune pathways, from which an eight-gene panel (MMP12, CD44, ADAM9, NFKBIA, PSME3, SPARCL1, CCL15, and APOA1) was prioritized as a biomarker signature. Guided by these predictions, we screened a 31-compound MMP12 inhibitor library and selected five leads (C1, C7, C9, C10, and C15) for testing in H1299 cells, with C9 showing the strongest antiproliferative activity. These compounds showed antimigratory activity (C1 achieving a 90% inhibition of wound closure at its IC₅₀ concentration), reduced clonogenic growth, cell cycle perturbation, and induction of apoptosis. Gene- and protein-expression analyses confirmed MMP12 suppression and modulation of the eight-gene panel. Upstream regulator predictions implicated reduced AKT signaling alongside an ADAM9-centered adaptive axis. Collectively, these findings highlight C1, C7, C9, C10, and C15 as promising MMP12 inhibitors, supporting their further development in preclinical lung cancer and nominating the eight-gene panel as a pharmacodynamic signature for MMP12-targeted therapies.

Matrix metalloproteinase-9 (MMP9) is involved in extracellular matrix remodeling, inflammation, and metastasis, and its overexpression is associated with poor prognosis in lung cancer. However, the systems-level effects of MMP9 inhibition remain incompletely understood. We combined systems biology with experimental validation to characterize MMP9-centered signaling in non-small cell lung cancer (NSCLC) and identify downstream pharmacodynamic biomarkers. Network and pathway analyses revealed extracellular matrix- and inflammation-related interaction modules and prioritized an MMP9-associated gene panel. Synthesized compounds were evaluated using purified enzyme assays and A549 NSCLC cells. At 50 μM, compounds M34 and M33 showed the strongest inhibition of MMP9 activity (65.01% and 61.32%, respectively). Seven compounds (M1, M2, M8, M9, M10, M27, and M34) demonstrated antiproliferative activity at 72 h (IC₅₀ = 40–115 μM), suppressed migration and colony formation, induced apoptosis, and reduced MMP9 protein expression. Quantitative PCR confirmed coordinated downregulation of MMP9, VEGFA, APP, and ETV4, with upregulation of CDH1, COL5A1, COL6A2, CCL2, and CCL17. Enrichment analysis linked these changes to inflammatory signaling, immune activation, and extracellular matrix remodeling. These findings establish an experimentally supported eight-gene pharmacodynamic biomarker signature and support biomarker-guided development of MMP9-targeted strategies in NSCLC.