IDENTIFICATION OF POTENTIAL NOVEL EGFR INHIBITORS USING A COMBINATION OF PHARMACOPHORE AND DOCKING METHODS
Keywords:EGFR inhibitors, 3D-QSAR, Pharmacophore, Docking, Virtual screening
Objective: Identifying new inhibitors of Epidermal Growth Factor Receptor (EGFR) by virtual screening using a pharmacophore model followed by docking.
Methods: A pharmacophore model was developed using a dataset of 77 chemically diverse EGFR inhibitors using PHASE. Statistically valid Three Dimensional Quantitative Structure Activity Relationship (3D-QSAR) equations were generated for the pharmacophore model. This was followed by database screening to obtain probable hits. Docking of the probable hits into the crystal structure of EGFR was used as a second filter. Docking studies were carried out using GLIDE. Calculation of ADME properties of the probable hits arising out of docking further reduced the number of hits.
Results: A five-point pharmacophore was generated for EGFR inhibitors reported in literature. The pharmacophore indicated that the presence of two aromatic ring features (R), one acceptor feature (A), one donor feature (D) and one hydrophobic feature (H) is necessary for potent inhibitory activity. The generated pharmacophore yielded statistically significant 3D-QSAR model, with a correlation coefficient r2 of 0.9905 and q2 of 0.8764. Virtual screening using the best pharmacophore model resulted in 372 hits. Docking studies as a second filter reduced the hits to 8. Application of drug-likeness as a third filter gave 6 leads.
Conclusion: 6 leads with satisfactory pharmacokinetics properties were identified as potential EGFR inhibitors. This study may facilitate development of some new potential EGFR inhibitors.
Sawyers C. Targeted cancer therapy. Nature 2004;432:294-7.
Arteaga C. Targeting HER1/EGFR: a molecular approach to cancer therapy. Paper presented at the Seminars in oncology; 2003.
Yarden Y. The EGFR family and its ligands in human cancer: signalling mechanisms and therapeutic opportunities. Eur J Cancer 2001;37:3-8.
Ritter CA, Arteaga CL. The epidermal growth factor receptor-tyrosine kinase: A promising therapeutic target in solid tumors. Paper presented at the Seminars in oncology; 2003.
Mass RD. The HER receptor family: a rich target for therapeutic development. Int J Radiation Oncol Biol Physics 2004;58:932-40.
Lurje G, Lenz HJ. EGFR signaling and drug discovery. Oncol 2010;77:400-10.
Eberhard DA, Johnson BE, Amler LC, Goddard AD, Heldens SL, Herbst RS, et al. Mutations in the epidermal growth factor receptor and in KRAS are predictive and prognostic indicators in patients with non-small-cell lung cancer treated with chemotherapy alone and in combination with erlotinib. J Clin Oncol 2005;23:5900-9.
Burris HA, Hurwitz HI, Dees EC, Dowlati A, Blackwell KL, O'Neil B, et al. Phase I safety, pharmacokinetics, and clinical activity study of lapatinib (GW572016), a reversible dual inhibitor of epidermal growth factor receptor tyrosine kinases, in heavily pretreated patients with metastatic carcinomas. J Clin Oncol 2005;23:5305-13.
Aparna V, Rambabu G, Panigrahi SK, Sarma JARP, Desiraju GR. Virtual screening of 4-anilinoquinazoline analogues as EGFR kinase inhibitors: importance of hydrogen bonds in the evaluation of poses and scoring functions. J Chem Inf Model 2005;45:725-38.
Liu LT, Yuan T, Liu H, Chena S, Wu Y. Synthesis and biological evaluation of substituted 6-alkynyl-4-anilinoquinazoline derivatives as potent EGFR inhibitors. Bioorg Med Chem Lett 2007;17:6373â€“7.
Kubinyi H. ed. 3D QSAR in drug design: volume 1: Theory methods and applications. Vol. 1. Springer Science & Business Media; 1993.
Dixon SL, Smondyrev AM, Rao SN. PHASE: a novel approach to pharmacophore modeling and 3d database searching. Chem Biol Drug Des 2006;67(5):370-2.
Kurogi Y, Guner OF. Pharmacophore modeling and three-dimensional database searching for drug design using catalyst. Curr Med Chem 2001;8(9):1035-55.
La Motta C, Sartini S, Tuccinardi T, Nerini E, Da Settimo F, Martinelli A. Computational studies of epidermal growth factor receptor: docking reliability, three-dimensional quantitative structure-activity relationship analysis, and virtual screening studies. J Med Chem 2009;52:964-75.
Traxler P, Green J, Mett H, Urs SeÂ´quin, Furet P. Use of a pharmacophore model for the design of EGFR tyrosine kinase inhibitors: isoflavones and 3-phenyl-4(1H)-quinolones. J Med Chem 1999;42:1018-26.
Rakesh JG, Shaheera B, Aarumugam P, Vaiyshnavi R, Thulasibabu R. Emerging drug discovery paradigm in non small cell lung cancer: pharmacophore modeling, atom-based 3D-QSAR and virtual screening of novel EGFR inhibitors. J Drug Discovery Ther 2014;2(23):9-17.
Sudha A, Srinivasan P, Rameshthangam P. Exploration of potential EGFR inhibitors: a combination of pharmacophore-based virtual screening, atom-based 3D-QSAR and molecular docking analysis. J Recept Signal Transduction Res 2014;35(2):137-48.
Gupta AK, Bhunia SS, Balaramnavar VM, Saxena AK. Pharmacophore modelling, molecular docking and virtual screening for EGFR (HER 1) tyrosine kinase inhibitors. SAR QSAR Environ Res 2011;22(3-4):239-63.
Hou T, Zhu L, Chen L, Xu X. Mapping the binding site of a large set of quinazoline type EGF-R inhibitors using molecular field analyses and molecular docking studies. J Chem Inf Comp Sci 2003;43(1):273-87.