Highly Adaptable Dendrimer Gel Nanoparticles with Dual Targeting of uPAR and Ribonucleotide Reductase R2 for Better Retention and Improved Therapeutic Outcomes in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) accounts for approximately 15% of breast cancers and lacks estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), rendering it unresponsive to hormonal or anti-HER2 therapies. Due to its poor prognosis and limited treatment options, there is an urgent need for targeted therapies. In this study, we developed highly adaptable polyamidoamine (PAMAM) dendrimer-based gel nanoparticles with dual-targeting capabilities against urokinase-type plasminogen activator receptor (uPAR) and ribonucleotide reductase R2 (R2). These nanoparticles were designed to target both TNBC cells and cancer-associated stromal cells by leveraging uPA-uPAR interactions and delivering the antisense oligonucleotide GTI-2040 (GTI) against R2. The resulting dual-functional dendrimer gel nanoparticles, GDP-uPA/GTI, demonstrated good biocompatibility, with an average size of ∼16.45 nm. GDP-uPA/GTI enhanced GTI delivery by 3.4-fold in TNBC cells (MDA-MB-231) and by 4.8-fold in stromal cells (HCC2218) compared to GTI alone. It reduced R2 expression by 83.1% and induced ∼30% TNBC cell death. In a TNBC xenograft model, GDP-uPA/GTI significantly inhibited tumor growth by 50.5%. These findings highlight the unique design of the dual-functional dendrimer gel nanoparticles and their dual-targeting efficacy, demonstrating their potential as a promising therapeutic strategy for TNBC.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-06-02
DOI
https://doi.org/10.1021/acsami.5c03560
Citations
2
Primary Topic
Computational Drug Discovery Methods
Type
article
Field-Weighted Citation Impact
3.01
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article

Highly Adaptable Dendrimer Gel Nanoparticles with Dual Targeting of uPAR and Ribonucleotide Reductase R2 for Better Retention and Improved Therapeutic Outcomes in Triple-Negative Breast Cancer

Da Huang, Anna Chernatynskaya, Yue‐Wern Huang, Vidit Singh et al.
2 citations
ACS Applied Materials & Interfaces
Computational Drug Discovery Methods
3.01
article

Highly Adaptable Dendrimer Gel Nanoparticles with Dual Targeting of uPAR and Ribonucleotide Reductase R2 for Better Retention and Improved Therapeutic Outcomes in Triple-Negative Breast Cancer

Da Huang, Anna Chernatynskaya, Yue‐Wern Huang, Vidit Singh, Hsin-Yin Chuang, Hu Yang, Lin Qi
article en
2 citations

Abstract

Triple-negative breast cancer (TNBC) accounts for approximately 15% of breast cancers and lacks estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), rendering it unresponsive to hormonal or anti-HER2 therapies. Due to its poor prognosis and limited treatment options, there is an urgent need for targeted therapies. In this study, we developed highly adaptable polyamidoamine (PAMAM) dendrimer-based gel nanoparticles with dual-targeting capabilities against urokinase-type plasminogen activator receptor (uPAR) and ribonucleotide reductase R2 (R2). These nanoparticles were designed to target both TNBC cells and cancer-associated stromal cells by leveraging uPA-uPAR interactions and delivering the antisense oligonucleotide GTI-2040 (GTI) against R2. The resulting dual-functional dendrimer gel nanoparticles, GDP-uPA/GTI, demonstrated good biocompatibility, with an average size of ∼16.45 nm. GDP-uPA/GTI enhanced GTI delivery by 3.4-fold in TNBC cells (MDA-MB-231) and by 4.8-fold in stromal cells (HCC2218) compared to GTI alone. It reduced R2 expression by 83.1% and induced ∼30% TNBC cell death. In a TNBC xenograft model, GDP-uPA/GTI significantly inhibited tumor growth by 50.5%. These findings highlight the unique design of the dual-functional dendrimer gel nanoparticles and their dual-targeting efficacy, demonstrating their potential as a promising therapeutic strategy for TNBC.

ACS Applied Materials & InterfacesVol. 17(23)
Missouri University of Science and Technology (US), Fuzhou University (CN)
Good health and well-being
Openalex Percentile: Top 9%
Computational Drug Discovery Methods
3.01
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