Investigation of a Novel PDEδ Inhibitor Targeting K-Ras in Colorectal Cancer: An in Silico and Computer-Aided Drug Design Approach
Year: 2025
Type: article
Abstract: Introduction: Colorectal cancer is frequently associated with mutations in the KRAS gene, leading to abnormal activation of the KRas protein. Direct targeting of KRas remains a major therapeutic challenge due to the absence of suitable binding sites for small molecules. An alternative strategy involves inhibiting phosphodiesterase δ (PDEδ), a key regulator of KRas oncogenic signaling. Objective: This study aimed to identify novel PDEδ inhibitors through an in silico computer-aided design approach to block the oncogenic signaling of KRas in colorectal cancer. Methods: An integrated computational strategy was used, including pharmacophore modeling based on the crystal structure of PDEδ complexed with an inhibitor, virtual screening of chemical libraries, and drug-likeness filtering according to Lipinski and Veber rules. Selected compounds underwent molecular docking, ADME-Tox prediction, bioavailability assessment, and molecular dynamics simulations (GROMACS) to evaluate stability and binding behavior. Results: The identified hit compound showed strong binding affinity and stable hydrogen interactions with PDEδ. It met all Lipinski and Veber criteria, suggesting good pharmacokinetic potential and oral bioavailability. ADMET analysis revealed a favorable safety profile, and molecular dynamics simulations confirmed its greater stability compared to the co-crystallized ligand. Conclusion:This study identified a promising PDEδ inhibitor capable of interfering with KRas oncogenic signaling in colorectal cancer. These findings provide a solid foundation for the development of new targeted therapies, with future perspectives involving in vitro, in vivo, and clinical validation. Keywords: PDEδ, KRas, Colorectal cancer, Pharmacophore modeling, Virtual screening, Molecular docking, In silico approach, ADMET.
Institution: University of Hassan II Casablanca
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4416247345