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Communication | Open Access

Designing a Novel Nano-Vaccine against SARS-CoV-2

Ang Gao1,2,#, Hui Liang2,#, Qi Shen2, Cheng Zhou2, Xiao Min Chen2, Jing Tian2, Xueling Li2, Zexi Liu2, Jian Ni1( ), Daxiang Cui1,2( )
Institute of Nano Biomedicine and Engineering, Shanghai Engineering Research Centre for Intelligent Diagnosis and Treatment Instrument, Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dong Chuan RD, Shanghai 200240, PR China
National Engineering Research Center for Nanotechnology, 28 East Jiang Chuan Road, Shanghai 200241, China

# These authors contributed equally to this work.

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Abstract

The new coronavirus SARS-CoV-2 has become a global pandemic, which has had a huge impact on the lives of people around the world and has caused huge impacts and losses on global economic development. To now, there is still no effective drug or therapy against coronavirus. A large number of studies have shown that vaccines are the ultimate weapon to eliminate major infectious diseases. The development of new vaccines against new coronaviruses is the best way to prevent new coronavirus infections. In this study, we developed a new vaccine against the new coronavirus by combining our self-developed nano adjuvant loaded with carnosine graphene oxide adjuvant with loaded with CpG molecule and RBD protein antigen. Our results showed that this vaccine can produce high titer anti-SARS-CoV-2 RBD antibody neutralizing SARS-CoV-2 in mice within 2 weeks.

Keywords

VaccineCOVID-1Neutralizing antibodie

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Nano Biomedicine and Engineering
Volume 12 Issue 4,
December 2020
Pages 321-324
DOI: 10.5101/nbe.v12i4.p321-324
Cite this article:
Gao A, Liang H, Shen Q, et al. Designing a Novel Nano-Vaccine against SARS-CoV-2. Nano Biomedicine and Engineering, 2020, 12(4): 321-324. https://doi.org/10.5101/nbe.v12i4.p321-324

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Received: 24 August 2020
Accepted: 27 October 2020
Published: 28 October 2020
© Ang Gao, Hui Liang, Qi Shen, Cheng Zhou, Xiao Min Chen, Jing Tian, Xueling Li, Zexi Liu, Jian Ni, and Daxiang Cui.

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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