Integrated in silico lead designing approach for azepine derivatives targeting human estrogen receptor α against breast cancer
摘要
Estrogen receptor α (ERα) is primary transcription factor for regulating gene expression, promoting cell migration and cell proliferation, leading into growth and development breast cancer (BC). Azepine derivatives were capable of modulating ERα expression in clinics and shown promise as efficient anti-BC agents. Thereby, present study aims to identify potent, safer azepine derivatives as ERα targeted anti-BC agents to overcome drug-resistance and adverse effects of current BC therapy. A rational in silico drug-discovery approach of 8 independent experiments was carried out consecutively as pharmacophore mapping and phase screening, HTVS and molecular docking, ADME, MM/GBSA, Molecular Dynamics (MD) simulations (500 ns) and MM/PBSA, DFT, PASS activity prediction and toxicity assessment, to identify potential hits. Pharmacophore-screened molecules comprising all traits of AAHHRR_1 model, undergone HTVS, SP and XP docking hierarchically, to yield 6 HITs exhibiting higher predicted binding affinities for ER-α compare to tamoxifen, toremifene and raloxifene. Analyzing drug-likeness, pharmacokinetics and binding free energies of hits and standards revealed HIT4 as most optimistic drug-candidate. MD simulations, MM-PBSA and H-bond analysis certified high predicted binding affinity and stability of HIT4 inside ERα protein. DFT calculations further validated bio-feasibility of HIT4 via HOMO-LUMO energy gap (3.238 eV) estimation. Activity and toxicity prediction study revealed anti-neoplastic and anti-estrogenic potential of HIT4 exhibiting class IV oral toxicity. Findings of pharmacophore modeling, structural analysis and binding mechanism study led us a lead framework for optimizing azepine derivatives as promising anti-estrogens. It is suggested that reducing lipophilicity of HIT4 by replacing hydrophobic alkyl-linkage with electronegative spacers may provide novel azepine-based anti-BC agents.
Graphical abstract