Self-amplifying RNA SARS-CoV-2 lipid nanoparticle vaccine induces equivalent preclinical antibody titers and viral neutralization to recovered COVID-19 patients

Pf, McKay; Hu, Kongxin; Blakney, Anna K.; Samnuan, Karnyart; Cr, Bouton; Rogers, P. S. Z.; Polra, Krunal; Lin, Peng; Barbosa, Chris; Y, Tam; Rj, Shattock

doi:10.1101/2020.04.22.055608

Cited by 23 publications

(29 citation statements)

References 18 publications

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“…The two live attenuated SARS vaccines mutated the exoribonuclease and envelop protein to reduce the virulence and/or replication capability of the SARS-CoV. Recent works also showed promising development of three types of SARS-CoV-2 vaccines, including inactivated whole virus vaccine ( 25 ), RNA vaccine ( 26 ), and virus-like particles (VLP) vaccine ( 27 ) ( Table 2 ). Overall, the whole virus vaccines can induce a strong immune response and protect against coronavirus infections.…”

Section: Resultsmentioning

confidence: 99%

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

et al. 2020

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To ultimately combat the emerging COVID-19 pandemic, it is desired to develop an effective and safe vaccine against this highly contagious disease caused by the SARS-CoV-2 coronavirus. Our literature and clinical trial survey showed that the whole virus, as well as the spike (S) protein, nucleocapsid (N) protein, and membrane (M) protein, have been tested for vaccine development against SARS and MERS. However, these vaccine candidates might lack the induction of complete protection and have safety concerns. We then applied the Vaxign and the newly developed machine learning-based Vaxign-ML reverse vaccinology tools to predict COVID-19 vaccine candidates. Our Vaxign analysis found that the SARS-CoV-2 N protein sequence is conserved with SARS-CoV and MERS-CoV but not from the other four human coronaviruses causing mild symptoms. By investigating the entire proteome of SARS-CoV-2, six proteins, including the S protein and five non-structural proteins (nsp3, 3CL-pro, and nsp8-10), were predicted to be adhesins, which are crucial to the viral adhering and host invasion. The S, nsp3, and nsp8 proteins were also predicted by Vaxign-ML to induce high protective antigenicity. Besides the commonly used S protein, the nsp3 protein has not been tested in any coronavirus vaccine studies and was selected for further investigation. The nsp3 was found to be more conserved among SARS-CoV-2, SARS-CoV, and MERS-CoV than among 15 coronaviruses infecting human and other animals. The protein was also predicted to contain promiscuous MHC-I and MHC-II T-cell epitopes, and the predicted linear B-cell epitopes were found to be localized on the surface of the protein. Our predicted vaccine targets have the potential for effective and safe COVID-19 vaccine development. We also propose that an "Sp/Nsp cocktail vaccine" containing a structural protein(s) (Sp) and a non-structural protein(s) (Nsp) would stimulate effective complementary immune responses.

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Section: Resultsmentioning

confidence: 99%

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

et al. 2020

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“…EternaFold already affords increases in predictive power for structure mapping data acquired for synthetic mRNAs that model mRNA vaccines as well as for alphaviruses, which form the basis of self-amplifying mRNA vaccines 47,48 . These observations suggest immediate applications for the packages herein and potentially rapid further improvements in EternaFold as further structural data are acquired on these new RNA molecules.…”

Section: Discussionmentioning

confidence: 99%

RNA secondary structure packages evaluated and improved by high-throughput experiments

Wayment-Steele

Kladwang

Participants

et al. 2020

Preprint

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The computer-aided study and design of RNA molecules is increasingly prevalent across a range of disciplines, yet little is known about the accuracy of commonly used structure prediction packages in real-world tasks. Here, we evaluate the performance of current packages using EternaBench, a dataset comprising 23 in vitro structure mapping and 11 riboswitch activity datasets involving 18,509 synthetic sequences from the crowdsourced RNA design project Eterna. We find that CONTRAfold and RNAsoft, packages with parameters derived through statistical learning, achieve consistently higher accuracy than more widely used packages like the ViennaRNA software, which derive parameters primarily from thermodynamic experiments.Motivated by these results, we develop a multitask-learning-based model, EternaFold, which demonstrates improved performance that generalizes to diverse external datasets, including complete viral genomes probed in vivo and synthetic designs modeling mRNA vaccines.

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“…Moreover, LNP-mRNA-based vaccinations following subcutaneous, intradermal, or intramuscular administration have demonstrated to effectively protect from viral challenge [260][261][262][263][264][265]. Given mRNA's relatively short optimization time from target identification to therapeutic, several companies including Moderna, BioNTech, and CureVac as well as universities around the globe have initiated LNP-mRNA vaccine programs to combat the recent SARS-CoV-2 pandemic [266][267][268][269]. Typical vaccine production relies on isolation and largescale virus propagation with subsequent processing to purify material (e.g.…”

Section: Future Perspectivesmentioning

confidence: 99%

Lipid nanoparticle technology for therapeutic gene regulation in the liver

Witzigmann

Kulkarni

Leung

et al. 2020

Advanced Drug Delivery Reviews

256

168

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Hereditary genetic disorders, cancer, and infectious diseases of the liver affect millions of people around the globe and are a major public health burden. Most contemporary treatments offer limited relief as they generally aim to alleviate disease symptoms. Targeting the root cause of diseases originating in the liver by regulating malfunctioning genes with nucleic acid-based drugs holds great promise as a therapeutic approach. However, employing nucleic acid therapeutics in vivo is challenging due to their unfavorable characteristics. Lipid nanoparticle (LNP) delivery technology is a revolutionary development that has enabled clinical translation of gene therapies. LNPs can deliver siRNA, mRNA, DNA, or gene-editing complexes, providing opportunities to treat hepatic diseases by silencing pathogenic genes, expressing therapeutic proteins, or correcting genetic defects. Here we discuss the state-of-the-art LNP technology for hepatic gene therapy including formulation design parameters, production methods, preclinical development and clinical translation.

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Self-amplifying RNA SARS-CoV-2 lipid nanoparticle vaccine induces equivalent preclinical antibody titers and viral neutralization to recovered COVID-19 patients

Cited by 23 publications

References 18 publications

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning

RNA secondary structure packages evaluated and improved by high-throughput experiments

Lipid nanoparticle technology for therapeutic gene regulation in the liver

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