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High DNA-Binding Affinity and Gene-Transfection Efficacy of Bioreducible Cationic Nanomicelles

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Gene Delivery

Part of the book series: Biomaterial Engineering ((BIOENG))

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Abstract

Cationic polymers have become one of the most promising nonviral vectors for gene delivery. However, complex formation of anionic nucleic acid molecules and cationic polymers are unstable because of their weak electrostatic interactions, resulting in polymer/nucleic acid polyplexes with poor serum resistance and a short circulation time in vivo. Furthermore, most polymer/nucleic acid polyplexes mixture exhibit high toxicity because an excess of high molecular weight cationic polymers that are typically required for complete gene condensation. This chapter introduces the preparation and characterizations of a class of bioreducible cationic nanomicelles endowed with high DNA-binding affinity, allowing for efficient DNA condensation and high transfection efficiency at a low nitrogen to phosphorus (N/P) ratio. These cationic nanomicelles hold promising potential as a high efficiency nonviral gene delivery vector for clinical use.

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Authors and Affiliations

  1. CAS Key Laboratory of Soft Matter Chemistry, Department of Polymer Science and Engineering, University of Science and Technology of China, Hefei, Anhui, China

    Long-Hai Wang & Ye-Zi You

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  1. Long-Hai Wang
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  2. Ye-Zi You
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Correspondence to Ye-Zi You .

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Editors and Affiliations

  1. Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, China

    Huayu Tian

  2. Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, China

    Xuesi Chen

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Wang, LH., You, YZ. (2021). High DNA-Binding Affinity and Gene-Transfection Efficacy of Bioreducible Cationic Nanomicelles. In: Tian, H., Chen, X. (eds) Gene Delivery. Biomaterial Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-33-6198-0_15-1

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  • DOI: https://doi.org/10.1007/978-981-33-6198-0_15-1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-6198-0

  • Online ISBN: 978-981-33-6198-0

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

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