article · Chemical Physics Impact
This study addressed the urgent need for new anti-breast cancer drugs due to resistance and side effects of current treatments. Researchers designed 23 novel compounds based on 1,4-quinone and quinoline derivatives. They employed integrated computational techniques, including 3D-QSAR, molecular docking, and molecular dynamics simulations, to evaluate these candidates. The 3D-QSAR models identified key molecular features influencing anti-breast cancer activity. ADMET properties were assessed, and molecular dynamics confirmed binding stability to aromatase. Ultimately, one compound, ligand 5, was identified as the most promising drug candidate for subsequent experimental testing.
Breast cancer remains a significant health challenge, with existing treatments often facing resistance or causing adverse effects. This research offers a computational approach to discover new potential drug candidates. Identifying novel compounds could lead to more effective therapies with fewer side effects, ultimately improving patient outcomes and quality of life.
This early-stage research identifies a promising compound (ligand 5) for potential development into a new anti-breast cancer drug. Pharmaceutical companies and drug developers could use these findings as a starting point for further in vitro and in vivo experimental testing, aiming to create novel therapeutic agents that address current treatment limitations.
AI-generated from the published abstract. Always read the original work before citing.
Breast cancer is the most frequent form of malignant tumor in women, and represents a major public health problem due to its high mortality rate. Although a multitude of therapeutic options exist for control of this disease, the emergence of resistance to current pharmaceutical treatments underscores the urgency of developing new anti- breast cancer drugs, with a focus on reducing the adverse effects associated with current therapeutic agents. The present study concerns a new series of (23) compounds based on 1,4-quinone and quinoline derivatives to design candidate drugs against breast cancer. For this purpose, integrated computational techniques were applied, including 3D-QSAR, molecular docking and molecular dynamics simulations (MD). CoMFA and CoMSIA were used to build a robust and highly reliable 3D-QSAR models. To validate the model's predictive capabilities, an external validation was carried out. The results of the best model (CoMSIA/SEA) revealed that electrostatic, steric and hydrogen bond acceptor fields had a significant effect on the anti-breast cancer activity of molecules studied. In addition, evaluation of ADMET properties determined whether these newly designed ligands were likely to be selected as drug-candidates. To confirm the binding stability of the selected ligands to aromatase (3S7S) and validate the molecular docking results, molecular dynamics simulations lasting 100 nanoseconds were performed by calculating RMSD, RMSF, RoG, H-bond, SASA and MM-PBSA parameters. As a result, only one designed compound (ligand 5) emerged as the most promising drug candidate for experimental in vitro and in vivo testing, due to its potential inhibition of breast cancer.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1016/j.chphi.2023.100455
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
