Current cancer drug targets · 2026
Computational Identification of Progesterone Receptor Modulators for Breast Cancer through 3D Pharmacophore Screening and MD Simulation of the ChEMBL Database.
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Mane Shiom, Udugade Babaso, Patil Sachinkumar, Sankpal Poournima, Udugade Swati, Jawaid Talha, Yasmeen Ayesha, Pathak Rashmi
Аннотация
INTRODUCTION: Breast cancer remains a major global health concern, creating an urgent need for safer and more effective treatments. This study aimed to identify novel progesterone receptor modulators as potential candidates for targeted breast cancer therapy using an integrated in silico approach.
MATERIALS AND METHODS: The X-ray-validated three-dimensional structure of the human progesterone receptor (PDB ID: 7AXK) was refined using PDB-REDO. Ulipristal acetate, aso-prisnil, and mifepristone were selected as reference ligands. Additional structurally similar compounds were identified through SwissSimilarity using the canonical SMILES of ulipristal acetate. A three-dimensional pharmacophore model describing the essential chemical features required for receptor binding was applied to screen compounds from the ChEMBL database. The selected molecules underwent molecular docking to determine their binding affinities and interactions with important receptor residues. ADME-Tox analysis was subsequently conducted to evaluate their drug-likeness, pharmacokinetic properties, and potential toxicity.
RESULTS: PDB-REDO refinement improved the overall structural quality of the 7AXK protein, as assessed using Kleywegt's methodology. Docking and pharmacophore screening identified several compounds with strong predicted binding affinities and favourable interactions within the progesterone receptor's binding site. Selected compounds also demonstrated acceptable drug-like properties, promising pharmacokinetic behaviour, and comparatively favourable predicted safety profiles.
DISCUSSION: The combined computational strategy effectively identified promising progesterone receptor modulators. These candidates show potential for developing targeted breast cancer treatments; however, molecular dynamics simulations and experimental validation are required.
CONCLUSION: Several potential progesterone receptor modulators with favourable binding and ADME-Tox characteristics were identified. These findings demonstrate the value of computational methods in accelerating early-stage breast cancer drug discovery.
MATERIALS AND METHODS: The X-ray-validated three-dimensional structure of the human progesterone receptor (PDB ID: 7AXK) was refined using PDB-REDO. Ulipristal acetate, aso-prisnil, and mifepristone were selected as reference ligands. Additional structurally similar compounds were identified through SwissSimilarity using the canonical SMILES of ulipristal acetate. A three-dimensional pharmacophore model describing the essential chemical features required for receptor binding was applied to screen compounds from the ChEMBL database. The selected molecules underwent molecular docking to determine their binding affinities and interactions with important receptor residues. ADME-Tox analysis was subsequently conducted to evaluate their drug-likeness, pharmacokinetic properties, and potential toxicity.
RESULTS: PDB-REDO refinement improved the overall structural quality of the 7AXK protein, as assessed using Kleywegt's methodology. Docking and pharmacophore screening identified several compounds with strong predicted binding affinities and favourable interactions within the progesterone receptor's binding site. Selected compounds also demonstrated acceptable drug-like properties, promising pharmacokinetic behaviour, and comparatively favourable predicted safety profiles.
DISCUSSION: The combined computational strategy effectively identified promising progesterone receptor modulators. These candidates show potential for developing targeted breast cancer treatments; however, molecular dynamics simulations and experimental validation are required.
CONCLUSION: Several potential progesterone receptor modulators with favourable binding and ADME-Tox characteristics were identified. These findings demonstrate the value of computational methods in accelerating early-stage breast cancer drug discovery.