An Integrated Virtual Screening Platform to Identify Potent Co‐Assembled Nanodrugs for Cancer Treatment

Co-assembled nanodrugs provide significant advantages in cancer treatment and drug delivery, yet effective screening methods to identify molecular combinations for co-assembly are lacking. This study presents a screening strategy integrating ligand-based virtual screening (LBVS) and density functional theory (DFT) calculations to explore new molecular combinations with co-assembly capabilities. The accuracy of this screening was validated by synthesizing various co-assembled nanodrugs under mild conditions. Vinpocetine (Vin) and lenvatinib (Len) are representative co-assembly combinations that can directly co-assemble into nanoparticles (NPs) through hydrogen bonding, van der Waals forces, and π-π interactions. These NPs were further functionalized with polyethylene glycol (PEG), resulting in PEG-L/V NPs that exhibited enhanced stability and biocompatibility. In addition, PEG-L/V NPs can respond to acidic conditions and release Vin and Len, working synergistically to induce cell cycle arrest and apoptosis in tumor cells in vitro while also inhibiting xenograft tumor growth in vivo. RNA sequencing (RNA-seq) analysis revealed that the co-assembled nanodrugs exhibited mechanisms that are distinct from those of single drugs. This study demonstrates the feasibility of utilizing a computational approach combining LBVS and DFT to identify small molecules with co-assembly capabilities, leading to innovative anticancer strategies.

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Publication Details

Journal
Advanced Materials
Published
2025-02-23
DOI
https://doi.org/10.1002/adma.202414154
Citations
13
Primary Topic
Nanoparticle-Based Drug Delivery
Type
article
Field-Weighted Citation Impact
7.76

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article

An Integrated Virtual Screening Platform to Identify Potent Co‐Assembled Nanodrugs for Cancer Treatment

Hualei Chen, Fei Pan, Zhengwei Mao, Yuan Ding et al.
13 citations
Advanced Materials
Nanoparticle-Based Drug Delivery
7.76
article

An Integrated Virtual Screening Platform to Identify Potent Co‐Assembled Nanodrugs for Cancer Treatment

Hualei Chen, Fei Pan, Zhengwei Mao, Yuan Ding, Wenli Tian, Tianyu Shan, Feihe Huang, Wenjun Peng, Bo Fang
article en
13 citations

Abstract

Co-assembled nanodrugs provide significant advantages in cancer treatment and drug delivery, yet effective screening methods to identify molecular combinations for co-assembly are lacking. This study presents a screening strategy integrating ligand-based virtual screening (LBVS) and density functional theory (DFT) calculations to explore new molecular combinations with co-assembly capabilities. The accuracy of this screening was validated by synthesizing various co-assembled nanodrugs under mild conditions. Vinpocetine (Vin) and lenvatinib (Len) are representative co-assembly combinations that can directly co-assemble into nanoparticles (NPs) through hydrogen bonding, van der Waals forces, and π-π interactions. These NPs were further functionalized with polyethylene glycol (PEG), resulting in PEG-L/V NPs that exhibited enhanced stability and biocompatibility. In addition, PEG-L/V NPs can respond to acidic conditions and release Vin and Len, working synergistically to induce cell cycle arrest and apoptosis in tumor cells in vitro while also inhibiting xenograft tumor growth in vivo. RNA sequencing (RNA-seq) analysis revealed that the co-assembled nanodrugs exhibited mechanisms that are distinct from those of single drugs. This study demonstrates the feasibility of utilizing a computational approach combining LBVS and DFT to identify small molecules with co-assembly capabilities, leading to innovative anticancer strategies.

Advanced MaterialsVol. 37(13)
Ministry of Education (IR), Zhejiang Lab (CN), Blood Center of Zhejiang Province (CN), Chinese Academy of Agricultural Sciences (CN), Institute of Apiculture Research (CN), Second Affiliated Hospital of Zhejiang University (CN), Artificial Intelligence in Medicine (Canada) (CA), Zhejiang University (CN)
National Natural Science Foundation of China, Zhejiang University, National Key Research and Development Program of China, Science and Technology Department of Zhejiang Province
Openalex Percentile: Top 2%
Nanoparticle-Based Drug Delivery
7.76
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