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image of Computational Optimization and In silico Analysis for the Discovery of New HER2 and CDK4/6 Drug Candidates for Breast Cancer

Abstract

Background

Breast cancer is an abnormal cell growth that develops in the breast and spreads throughout the body. Despite cancer being the second leading cause of death, survival rates are increasing as a result of progress in cancer screening and therapy. Breast cancer is the most frequently diagnosed cancer type among women, but in most cases, there are no obvious symptoms. Screening mammograms can be used for early detection of cancer. The size of the tumor and the extent of cancer spread determine the type of needed treatment. There are different forms of treatment, where targeted therapy is generally the least harmful. It targets specific characteristics of cancer cells, such as human epidermal growth factor receptor 2 (HER2). Tyrosine kinase inhibitors are effective targeted treatment of HER2 positive breast cancer. A newer class has emerged, cyclin dependent kinase (CDK4/6), which is used to treat metastatic breast cancer.

Objectives

Although CDK4/6 inhibitors class of therapy has revolutionized the treatment of metastatic breast cancer, some patients showed resistance and decreased efficacy. This study is the first to propose innovative computational strategies to improve the effectiveness and pharmacokinetic properties of existing HER2/CDK4/6 inhibitors anti-cancer agents. Through computer-aided drug design, the activity of existing breast cancer drug candidates has been tested. Structural modifications have been applied for in-silico optimization of their biological activity.

Methods

In this research, twenty-two analogues of the tested compounds have been proposed. Their biological activity and pharmacokinetic properties (ADMET) have been tested using BIOVIA Discovery Studio software.

Results

Out of the designed analogous compounds, seven proposed structures demonstrated superior efficacy compared to the original drugs. The research study docking studies revealed that modifications to lapatinib and tucatinib improved binding affinity to HER2 by 15-25%, with docking scores of -18.34 kcal/mol and -1.04 kcal/mol, respectively. Similarly, CDK4/6 inhibitors exhibited enhanced selectivity, with abemaciclib showing the highest binding energy of -13.2 kcal/mol. ADMET predictions suggested improved solubility and reduced toxicity risks compared to the original drugs.

Conclusion

The research study results demonstrate that the synthesis of more lipophilic analogues of lapatinib or tucatinib and, likewise designing of fluorinated derivatives of CDK4/6 inhibitors play a crucial role in improving the efficacy of these anti-cancer agents. These findings highlight the potential of the proposed modifications as promising candidates for further pharmacological and and clinical validation.

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2025-05-13
2025-09-27

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Computational Optimization and In silico Analysis for the Discovery of New HER2 and CDK4/6 Drug Candidates for Breast Cancer
Bentham Science Publishers ; https://doi.org/10.2174/0118715206382065250507114908
/content/journals/acamc/10.2174/0118715206382065250507114908
/content/journals/acamc/10.2174/0118715206382065250507114908
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  • Article Type:     Research Article
Keywords: in-silico modeling ; targeted therapy ; drug optimization ; female mortality ; drug design ; Breast cancer

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