In Silico Molecular Docking, Molecular dynamic, ADME Study of Naproxen Analogues as Epidermal Growth Factor Receptor Inhibitors
Article info
2025-01-02
2025-03-12
2025-04-13
None - None
Keywords
- 1
- Molecular docking
- EGFR inhibitors
- ADME
- Cancer Therapy
- Osimertinib
- 3
- 4-oxadiazole
- Naproxen analogues
Abstract
Background: Cancer remains a leading cause of morbidity and mortality worldwide, necessitating continuous efforts to discover novel anticancer agents. Among various therapeutic targets, the Epidermal Growth Factor Receptor (EGFR) plays a critical role in tumor progression, while resistance to first- and second-generation tyrosine kinase inhibitors (TKIs) underscores the need for next-generation therapies. Objective: Naproxen derivatives with 1,3,4-oxadiazole substitutions are investigated as potential next-generation EGFR inhibitors to address drug resistance in cancer patients. Methods: We performed molecular docking (using GOLD software and Schrödinger Suite), ADME profiling (via the SwissADME webserver), and molecular dynamics (MD) simulations (50 ns) on the selected Naproxen analogues. Osimertinib was used as a reference to benchmark binding affinities and compare pharmacokinetic properties. Results: Docking revealed that several Naproxen–1,3,4-oxadiazole derivatives interacted favorably with key EGFR residues (e.g., Met793, Lys745, Val726) with higher ChemPLP docking scores than Osimertinib in certain cases. ADME analysis indicated acceptable drug-likeness profiles, adherence to Lipinski’s Rule of Five, and favorable absorption predictions. MD simulations demonstrated stable protein–ligand complexes, with RMSD values generally fluctuating within 1–3 Å for both protein and ligand. Conclusion: These computational findings support the potential of Naproxen–1,3,4-oxadiazole derivatives as novel EGFR inhibitors. While they appear promising, further in vitro and in vivo validation is essential to confirm their efficacy and potential to overcome EGFR-mediated drug resistance in cancer therapy.
In Silico Molecular Docking, Molecular dynamic, ADME Study of Naproxen Analogues as Epidermal Growth Factor Receptor Inhibitors
معلومات المقال
2025-01-02
2025-03-12
2025-04-13
None - None
الكلمات الإفتتاحية
- 1
- Molecular docking
- EGFR inhibitors
- ADME
- Cancer Therapy
- Osimertinib
- 3
- 4-oxadiazole
- Naproxen analogues
الملخص
Background: Cancer remains a leading cause of morbidity and mortality worldwide, necessitating continuous efforts to discover novel anticancer agents. Among various therapeutic targets, the Epidermal Growth Factor Receptor (EGFR) plays a critical role in tumor progression, while resistance to first- and second-generation tyrosine kinase inhibitors (TKIs) underscores the need for next-generation therapies. Objective: Naproxen derivatives with 1,3,4-oxadiazole substitutions are investigated as potential next-generation EGFR inhibitors to address drug resistance in cancer patients. Methods: We performed molecular docking (using GOLD software and Schrödinger Suite), ADME profiling (via the SwissADME webserver), and molecular dynamics (MD) simulations (50 ns) on the selected Naproxen analogues. Osimertinib was used as a reference to benchmark binding affinities and compare pharmacokinetic properties. Results: Docking revealed that several Naproxen–1,3,4-oxadiazole derivatives interacted favorably with key EGFR residues (e.g., Met793, Lys745, Val726) with higher ChemPLP docking scores than Osimertinib in certain cases. ADME analysis indicated acceptable drug-likeness profiles, adherence to Lipinski’s Rule of Five, and favorable absorption predictions. MD simulations demonstrated stable protein–ligand complexes, with RMSD values generally fluctuating within 1–3 Å for both protein and ligand. Conclusion: These computational findings support the potential of Naproxen–1,3,4-oxadiazole derivatives as novel EGFR inhibitors. While they appear promising, further in vitro and in vivo validation is essential to confirm their efficacy and potential to overcome EGFR-mediated drug resistance in cancer therapy.
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