Leveraging computational approaches in identifying novel HER-2 + breast cancer potential therapeutics: integrating virtual screening and molecular dynamics simulation

Abstract

Background

Breast cancer, particularly the human epidermal growth factor receptor 2 positive subtype, presents a significant global health challenge due to its high prevalence and mortality rates. This study delves into the molecular intricacies of HER-2 positive breast cancer, with an emphasis on the role of the HER-2 oncoprotein and its associated signaling pathways in cell growth, differentiation, and survival. In our pursuit of overcoming the limitations of one of the leading therapeutic options, Lapatinib, such as its inhibition of hERG, we embarked on a comprehensive research journey.

Result

This study involved dual-stage molecular docking, initially with a library of PubChem-curated compounds, revealing Compound 90196902 as the best of the set. This was followed by the docking of DataWarrior-generated structural analogs of Compound 90196902, using various docking protocols such as standard precision, extra precision, and induced fit docking. Through this rigorous screening protocol, three promising drug candidates (Compound_56, Compound_81, and Compound_339) were identified, showing excellent interaction with the target. Additionally, binding free energy calculations, ADME and toxicity profiling, and molecular dynamics simulations presented these compounds as lead-like.

Conclusion

Compound_56 showed the most promising pharmacodynamic and pharmacokinetic properties, coupled with substantial structural stability. While immensely promising, further optimization and pre-clinical investigation are imperative to validate this compound as a viable alternative to existing therapies for HER-2 positive breast cancer.

Graphical abstract