Molecular Docking Study of Luteolin and its Derivatives for Identifying Potential ER-α Inhibitors in Breast Cancer

Breast cancer remains one of the leading causes of cancer-related mortality worldwide, with estrogen receptor alpha (ER-α) serving as a primary therapeutic target in hormone-dependent subtypes. Resistance to current endocrine therapies underscores the need for alternative compounds with improved efficacy and safety. Luteolin, a naturally occurring flavonoid, has gained attention as a potential anticancer agent, but its structural modifications may alter biological activity. This study evaluated the binding affinity and interaction profiles of luteolin and its glycosylated derivatives (luteolin 7-glucuronide and luteolin 7-O-glucoside) against ER-α using molecular docking (PDB ID: 7UJ8). The results revealed that luteolin consistently exhibited stronger binding affinity (−7.2 to −8.0 kcal/mol) and stable RMSD values compared to its derivatives, though it remained significantly weaker than the reference drug 4-hydroxytamoxifen (−8.9 to −9.4 kcal/mol). Structural analysis demonstrated that luteolin’s superiority arises from its ability to maintain extensive hydrophobic and π–π stacking interactions within the ER-α binding pocket. In contrast, glycosylation introduced bulky polar substituents that disrupted hydrophobic contacts and reduced binding affinity. These findings highlight luteolin as the most promising scaffold among the tested compounds and underscore the structural basis for why glycoside derivatization diminishes ER-α binding. Future work should focus on enhancing luteolin’s bioavailability without compromising its key hydrophobic interactions to advance its potential as a lead candidate for breast cancer therapy.

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Journal
Journal of Pharmaceutical And Sciences
Published
2025-09-27
DOI
https://doi.org/10.36490/journal-jps.com.v8i3.1022
Primary Topic
Computational Drug Discovery Methods
Type
article
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article

Molecular Docking Study of Luteolin and its Derivatives for Identifying Potential ER-α Inhibitors in Breast Cancer

Muhammad Da’i, Fasha Jamil Atwon
Journal of Pharmaceutical And Sciences
Computational Drug Discovery Methods
article

Molecular Docking Study of Luteolin and its Derivatives for Identifying Potential ER-α Inhibitors in Breast Cancer

Muhammad Da’i, Fasha Jamil Atwon
article en

Abstract

Breast cancer remains one of the leading causes of cancer-related mortality worldwide, with estrogen receptor alpha (ER-α) serving as a primary therapeutic target in hormone-dependent subtypes. Resistance to current endocrine therapies underscores the need for alternative compounds with improved efficacy and safety. Luteolin, a naturally occurring flavonoid, has gained attention as a potential anticancer agent, but its structural modifications may alter biological activity. This study evaluated the binding affinity and interaction profiles of luteolin and its glycosylated derivatives (luteolin 7-glucuronide and luteolin 7-O-glucoside) against ER-α using molecular docking (PDB ID: 7UJ8). The results revealed that luteolin consistently exhibited stronger binding affinity (−7.2 to −8.0 kcal/mol) and stable RMSD values compared to its derivatives, though it remained significantly weaker than the reference drug 4-hydroxytamoxifen (−8.9 to −9.4 kcal/mol). Structural analysis demonstrated that luteolin’s superiority arises from its ability to maintain extensive hydrophobic and π–π stacking interactions within the ER-α binding pocket. In contrast, glycosylation introduced bulky polar substituents that disrupted hydrophobic contacts and reduced binding affinity. These findings highlight luteolin as the most promising scaffold among the tested compounds and underscore the structural basis for why glycoside derivatization diminishes ER-α binding. Future work should focus on enhancing luteolin’s bioavailability without compromising its key hydrophobic interactions to advance its potential as a lead candidate for breast cancer therapy.

Journal of Pharmaceutical And Sciences
Muhammadiyah University of Surakarta (ID)
Openalex Percentile: Top 72%
Computational Drug Discovery Methods
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