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Updated: Mar 14, 2026
Screening and Identification of Small Peptides Targeting Fibroblast Growth Factor Receptor2 using a Phage Display Peptide Library
Published on: September 30, 2019
Computer-aided discovery of new FGFR-1 inhibitors followed by in vitro validation
Shada J Alabed1, Mohammad Khanfar1, Mutasem O Taha2
1Department of Pharmaceutical Sciences, Faculty of Pharmacy, University of Jordan, Amman, Jordan.
Aim:
FGFR-1 is an oncogenic kinase involved in several cancers. FGFR1-specific inhibitors have shown promising results against several human cancers prompting us to model this interesting target. Toward the end, we implemented elaborate ligand-based and structure-based computational workflows to explore the pharmacophoric requirements for potent FGFR-1 inhibitors. Results & methodology: Structure-based and ligand-based modeling applied on 59 diverse FGFR-1 inhibitors yielded novel pharmacophore and quantitative structure-activity relationship models that were used to scan the National Cancer Institute's structural database for novel leads. Four potent hits were captured, with the most active having IC50 of 426 nM. Identities and purities of active hits were established using nuclear magnetic resonance and mass spectroscopy.
Conclusion:
Elaborate ligand-based (pharmacophore/quantitaive structure-activity relationship) and structure-based (docking-based comparative intermolecular contacts analysis) modeling provided deep understanding of ligand binding within FGFR-1 as evidenced by the virtually captured new potent leads.
Insights
Computational modeling identified new potent inhibitors for fibroblast growth factor receptor 1 (FGFR-1), a key target in cancer therapy. This research advances the development of novel FGFR-1 inhibitors for cancer treatment.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Oncology
Background:
- Fibroblast growth factor receptor 1 (FGFR-1) is an oncogenic kinase implicated in various human cancers.
- FGFR1-specific inhibitors have demonstrated therapeutic potential in preclinical and clinical settings.
Purpose of the Study:
- To computationally model FGFR-1 and explore pharmacophoric requirements for potent inhibitors.
- To identify novel FGFR-1 inhibitor leads using advanced computational techniques.
Main Methods:
- Employed ligand-based and structure-based computational modeling on 59 diverse FGFR-1 inhibitors.
- Developed novel pharmacophore and quantitative structure-activity relationship (QSAR) models.
- Screened the National Cancer Institute's structural database for potential drug candidates.
Main Results:
- Generated robust pharmacophore and QSAR models for FGFR-1 inhibition.
- Identified four potent novel inhibitor hits from the database screen.
- The most active compound exhibited an IC50 of 426 nM.
Conclusions:
- Integrated computational approaches provided significant insights into FGFR-1 ligand binding.
- Successfully identified novel, potent FGFR-1 inhibitor leads with therapeutic potential.
- Validated findings through structural and spectroscopic analyses (NMR, mass spectrometry).
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