Computationally guided design and synthesis of dual‐drug loaded polymeric nanoparticles for combination therapy
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(Published version)
Date
2024
Authors
Jin, S.
Lan, Z.
Yang, G.
Li, X.
Shi, J.Q.
Liu, Y.
Zhao, C.
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Journal article
Citation
Aggregate, 2024; 5(5):e606-1-e606-10
Statement of Responsibility
Song Jin, Zhenwei Lan, Guangze Yang, Xinyu Li, Javen Qinfeng Shi, Yun Liu, Chun-Xia Zhao
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Abstract
Single-drug therapies or monotherapies are often inadequate, particularly in the case of life-threatening diseases like cancer. Consequently, combination therapies emerge as an attractive strategy. Cancer nanomedicines have many benefits in addressing the challenges faced by small molecule therapeutic drugs, such as low water solubility and bioavailability, high toxicity, etc. However, it remains a significant challenge in encapsulating two drugs in a nanoparticle. To address this issue, computational methodologies are employed to guide the rational design and synthesis of dual-drugloaded polymer nanoparticles while achieving precise control over drug loading. Based on the sequential nanoprecipitation technology, five factors are identified that affect the formulation of drug candidates into dual-drug loaded nanoparticles, and then screened 176 formulations under different experimental conditions. Based on these experimental data, machine learning methods are applied to pin down the key factors. The implementation of this methodology holds the potential to significantly mitigate the complexities associated with the synthesis of dual-drug loaded nanoparticles, and the co-assembly of these compounds into nanoparticulate systems demonstrates a promising avenue for combination therapy. This approach provides a new strategy for enabling the streamlined, high-throughput screening and synthesis of new nanoscale drug-loaded entities.
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First published: 03 June 2024
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© 2024 The Author(s). Aggregate published by SCUT, AIEI and John Wiley & Sons Australia, Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.