Current pharmaceutical design · 2026
Design of Quinazoline Dual Inhibitors Targeting Focal Adhesion Kinase and Polo-like Kinase 1 for Metastatic Cancers: QSAR, Pharmacophore Modelling, and Molecular Docking Approaches.
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Garhewal Mayank, Pal Dipti, Gupta Ajay Kumar, Thakur Gajendra Singh, Mishra Achal, Verma Shekhar, Annadurai Sivakumar, Jain Sanmati Kumar, Vaishnav Yogesh
Аннотация
INTRODUCTION: FAK and PLK1 are regulators of oncogenic signaling and metastasis, and their coinhibition offers a synergistic anticancer strategy. This study employed computational modeling to design quinazoline-based scaffolds as potential dual FAK-PLK1 inhibitors.
METHODS: 54 quinazoline derivatives were analyzed using 2D/3D-QSAR, pharmacophore mapping, pharmacokinetic (ADMET) profile prediction, and molecular docking (PyRx-AutoDock Vina) to identify structural features and evaluate their dual FAK-PLK1 inhibitory potential with favourable pharmacokinetics properties.
RESULTS: 2D-QSAR modeling of 54 quinazoline derivatives yielded statistically significant models, with Model 2 exhibiting the best performance (r² = 0.6504, q² = 0.5704, Pred_r² = 0.5360, F = 31.63). The 3DQSAR kNN-MFA analysis produced a reasonably reliable predictive model (q² = 0.6970, Pred_r² = 0.6528), confirming the contribution of steric and electrostatic fields to dual FAK-PLK1 inhibition. ADMET predictions supported favourable pharmacokinetic and safety profiles for compounds 28 and 33. Molecular docking revealed that compounds 28 and 33 displayed the highest binding affinities toward FAK and PLK1, outperforming reference inhibitors PF-573228 and BI 2536. RMSD values (1.18-2.31 Å) indicated stable ligand conformations.
DISCUSSION: The 2D- and 3D-QSAR models showed strong predictive ability, confirming the role of steric and electrostatic factors in dual FAK-PLK1 inhibition. Docking and ADMET analyses identified compounds 28 and 33 as potent, stable, and pharmacokinetically favourable dual inhibitors.
CONCLUSION: This integrated computational approach combining QSAR modeling, pharmacophore analysis, ADMET prediction, and molecular docking identified promising quinazoline scaffolds as potential dual FAK-PLK1 inhibitors for the treatment of metastatic cancers, such as breast, colon, and non-small cell lung cancer.
METHODS: 54 quinazoline derivatives were analyzed using 2D/3D-QSAR, pharmacophore mapping, pharmacokinetic (ADMET) profile prediction, and molecular docking (PyRx-AutoDock Vina) to identify structural features and evaluate their dual FAK-PLK1 inhibitory potential with favourable pharmacokinetics properties.
RESULTS: 2D-QSAR modeling of 54 quinazoline derivatives yielded statistically significant models, with Model 2 exhibiting the best performance (r² = 0.6504, q² = 0.5704, Pred_r² = 0.5360, F = 31.63). The 3DQSAR kNN-MFA analysis produced a reasonably reliable predictive model (q² = 0.6970, Pred_r² = 0.6528), confirming the contribution of steric and electrostatic fields to dual FAK-PLK1 inhibition. ADMET predictions supported favourable pharmacokinetic and safety profiles for compounds 28 and 33. Molecular docking revealed that compounds 28 and 33 displayed the highest binding affinities toward FAK and PLK1, outperforming reference inhibitors PF-573228 and BI 2536. RMSD values (1.18-2.31 Å) indicated stable ligand conformations.
DISCUSSION: The 2D- and 3D-QSAR models showed strong predictive ability, confirming the role of steric and electrostatic factors in dual FAK-PLK1 inhibition. Docking and ADMET analyses identified compounds 28 and 33 as potent, stable, and pharmacokinetically favourable dual inhibitors.
CONCLUSION: This integrated computational approach combining QSAR modeling, pharmacophore analysis, ADMET prediction, and molecular docking identified promising quinazoline scaffolds as potential dual FAK-PLK1 inhibitors for the treatment of metastatic cancers, such as breast, colon, and non-small cell lung cancer.