Target screening and optimization of candidate compounds for breast cancer treatment using bioinformatics and computational chemistry approaches

Abstract

Objectives

This study aimed to identify critical therapeutic targets and design potent antitumor compounds for breast cancer treatment through an integrated bioinformatics and computational chemistry approach.

Methods

We conducted initial screening and target intersection analysis to identify potential protein targets, highlighting the adenosine A1 receptor as a key candidate. Molecular docking and molecular dynamics (MD) simulations were performed to evaluate the binding stability between selected compounds and the human adenosine A1 receptor-Gi2 protein complex (PDB ID: 7LD3). A pharmacophore model was constructed based on binding information to guide the virtual screening of additional compounds with activity. Furthermore, we designed and synthesized a novel molecule based on this model, followed by in vitro biological evaluation using MCF-7 breast cancer cells.

Results

Compound 5 exhibited stable binding to the adenosine A1 receptor, as confirmed by docking and MD simulations. Pharmacophore-based screening identified compounds 6–9 with strong binding affinities. These findings guided Molecule 10, which was rationally designed and synthesized, showing potent antitumor activity against MCF-7 cells with an IC50 value of 0.032 µM, significantly outperforming the positive control 5-FU (IC50 = 0.45 µM).

Conclusion

This study advances the understanding of molecular interactions in breast cancer therapy and demonstrates the potential of Molecule 10 as a highly effective therapeutic candidate. Integrating reverse drug screening, molecular modelling, and in vitro validation provides a robust platform for future drug discovery in breast cancer treatment.