In Silico Design of Dual Estrogen Receptor and Hsp90 Inhibitors for ER-Positive Breast Cancer Through a Mixed Ligand/Structure-Based Approach

Breast cancer remains one of the most prevalent and lethal malignancies in women, particularly the estrogen receptor-positive (ER+) subtype, which accounts for approximately 70% of cases. Traditional endocrine therapies, including aromatase inhibitors, selective estrogen receptor degraders/antagonists (SERDs), and selective estrogen receptor modulators (SERMs), have improved outcomes for metastatic ER+ breast cancer. However, resistance to these agents presents a significant challenge. This study explores a novel therapeutic strategy involving the simultaneous inhibition of the estrogen receptor (ER) and the chaperone protein Hsp90, which is crucial for the stabilization of various oncoproteins, including ER itself. We employed a hybrid, hierarchical in silico virtual screening approach to identify new dual ER/Hsp90 inhibitors, utilizing the Biotarget Predictor Tool (BPT) for efficient multitarget screening of a large compound library. Subsequent structure-based studies, including molecular docking analyses, were conducted to further evaluate the interaction of the top candidates with both ER and Hsp90. Supporting this, molecular dynamics simulations demonstrate the high stability of the multitarget inhibitor <b>755435</b> in complex with ER and Hsp90. Our findings suggest that several small molecules, particularly compound <b>755435</b>, exhibit promising potential as dual inhibitors, representing a new avenue to overcome resistance in ER+ breast cancer.

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Publication Details

Journal
Molecules
Published
2024-12-21
DOI
https://doi.org/10.3390/molecules29246040
Citations
1
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
0.36

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article

In Silico Design of Dual Estrogen Receptor and Hsp90 Inhibitors for ER-Positive Breast Cancer Through a Mixed Ligand/Structure-Based Approach

Gabriele La Monica, Annamaria Martorana, Antonino Lauria, Alessia Bono et al.
1 citations
Molecules
Computational Drug Discovery Methods
0.36
article

In Silico Design of Dual Estrogen Receptor and Hsp90 Inhibitors for ER-Positive Breast Cancer Through a Mixed Ligand/Structure-Based Approach

Gabriele La Monica, Annamaria Martorana, Antonino Lauria, Alessia Bono, Francesco Mingoia, Federica Alamia
article en
1 citations

Abstract

Breast cancer remains one of the most prevalent and lethal malignancies in women, particularly the estrogen receptor-positive (ER+) subtype, which accounts for approximately 70% of cases. Traditional endocrine therapies, including aromatase inhibitors, selective estrogen receptor degraders/antagonists (SERDs), and selective estrogen receptor modulators (SERMs), have improved outcomes for metastatic ER+ breast cancer. However, resistance to these agents presents a significant challenge. This study explores a novel therapeutic strategy involving the simultaneous inhibition of the estrogen receptor (ER) and the chaperone protein Hsp90, which is crucial for the stabilization of various oncoproteins, including ER itself. We employed a hybrid, hierarchical in silico virtual screening approach to identify new dual ER/Hsp90 inhibitors, utilizing the Biotarget Predictor Tool (BPT) for efficient multitarget screening of a large compound library. Subsequent structure-based studies, including molecular docking analyses, were conducted to further evaluate the interaction of the top candidates with both ER and Hsp90. Supporting this, molecular dynamics simulations demonstrate the high stability of the multitarget inhibitor <b>755435</b> in complex with ER and Hsp90. Our findings suggest that several small molecules, particularly compound <b>755435</b>, exhibit promising potential as dual inhibitors, representing a new avenue to overcome resistance in ER+ breast cancer.

MoleculesVol. 29(24)
Institute of Nanostructured Materials (IT), University of Palermo (IT)
Università degli Studi di Palermo
Good health and well-being
Openalex Percentile: Top 33%
Computational Drug Discovery Methods
0.36
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