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Enhancing the immunogenicity of lipid-nanoparticle mRNA vaccines by adjuvanting the ionizable lipid and the mRNA

Nature Biomedical Engineering (2023)Cite this article

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Abstract

To elicit optimal immune responses, messenger RNA vaccines require intracellular delivery of the mRNA and the careful use of adjuvants. Here we report a multiply adjuvanted mRNA vaccine consisting of lipid nanoparticles encapsulating an mRNA-encoded antigen, optimized for efficient mRNA delivery and for the enhanced activation of innate and adaptive responses. We optimized the vaccine by screening a library of 480 biodegradable ionizable lipids with headgroups adjuvanted with cyclic amines and by adjuvanting the mRNA-encoded antigen by fusing it with a natural adjuvant derived from the C3 complement protein. In mice, intramuscular or intranasal administration of nanoparticles with the lead ionizable lipid and with mRNA encoding for the fusion protein (either the spike protein or the receptor-binding domain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)) increased the titres of antibodies against SARS-CoV-2 tenfold with respect to the vaccine encoding for the unadjuvanted antigen. Multiply adjuvanted mRNA vaccines may improve the efficacy, safety and ease of administration of mRNA-based immunization.

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Fig. 1: High-throughput synthesis and screening of biodegradable cyclic ionizable lipids synthesized by Ugi-3CR.
Fig. 2: Immunogenicity of immunostimulatory LNPs.
Fig. 3: Immunogenicity study of C3d fusion SARS-CoV-2 mRNA.
Fig. 4: Synergistic effect of immunostimulatory LNPs and C3d fusion mRNA following intramuscular and intranasal administration.
Fig. 5: Comparative analysis of vaccine-induced antibody coverage and FcR binding across SARS-CoV-2 variants of concern.

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Data availability

The main data supporting the findings of this study are available within this paper and its Supplementary Information. The raw and analysed datasets are too large to readily share publicly yet are available for research purposes from the corresponding author on reasonable request.

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Acknowledgements

This work was supported by the National Institutes of Health (NIH) (R61AI161805) and Translate Bio. B.L. was also supported by the Leslie Dan Faculty of Pharmacy startup fund, the Princess Margaret Cancer Centre operating fund, the Connaught Fund (514681), the J. P. Bickell Foundation (515159), the Canada Research Chairs Program (CRC-2022-00575), Canadian Institutes of Health Research (PJH-185722), Natural Sciences and Engineering Research Council of Canada (RGPIN-2023-05124) and the Canada Foundation for Innovation—John R. Evans Leaders Fund (43711). A.Y.J. was also supported by the NIH (UG3HL147367 and UH3HL147367). The Systems Serology Laboratory is supported by the generous gifts of M. and L. Schwartz, T. and S. Ragon and the Samana Kay Research Scholars award. The Systems Serology Lab also receives funding from the Massachusetts Consortium on Pathogen Readiness (MassCPR), the Gates Global Health Vaccine Accelerator Platform and the NIH (1P01AI165072-01, 3R37AI080289-11S1, U19AI42790-01, U19AI135995-02 and U19AI42790-01). T.S. was supported by the Marble Centre for Cancer Nanomedicine. S.B. is a Howard Hughes Medical Institute investigator. J.W. was supported by the Cystic Fibrosis Foundation (WITTEN19XX0) and the NIH (R01 HL162564-02). Y.X. was supported by a Postdoc Fellowship from the PRiME-UHN Clinical Catalyst Program at the University of Toronto. We thank the Koch Institute Swanson Biotechnology Centre for technical support, specifically the Animal Imaging and Preclinical Testing, Flow Cytometry, High Throughput Sciences, Histology and Nanotechnology Materials Core Facilities. We also thank R. Bronson and E. Calle for assistance with the histology, and M. Gentili and N. Hacohen for providing the reagents for the pseudotype neutralization assay. Figures 2a and 4e were created with Biorender.com.

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Author notes

  1. These authors contributed equally: Bowen Li, Allen Yujie Jiang, Idris Raji.

Authors and Affiliations

  1. David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Bowen Li, Allen Yujie Jiang, Idris Raji, Theresa M. Raimondo, Akiva G. R. Gordon, Luke H. Rhym, Tahoura Samad, Corina MacIsaac, Jacob Witten, Haseeb Mughal, Sangeeta Bhatia, Robert Langer & Daniel G. Anderson

  2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Bowen Li, Allen Yujie Jiang, Idris Raji, Theresa M. Raimondo, Akiva G. R. Gordon, Luke H. Rhym, Jacob Witten, Robert Langer & Daniel G. Anderson

  3. Department of Pharmaceutical Sciences, Leslie Dan Faculty of Pharmacy, University of Toronto, Toronto, Ontario, Canada

    Bowen Li & Yue Xu

  4. Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada

    Bowen Li

  5. Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada

    Bowen Li

  6. Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA

    Caroline Atyeo, Taras M. Chicz, Ryan P. McNamara, Galit Alter & Daniel G. Anderson

  7. Division of Medical Sciences, Harvard University, Boston, MA, USA

    Caroline Atyeo

  8. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Tahoura Samad, Sangeeta Bhatia, Robert Langer & Daniel G. Anderson

  9. Harvard–MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Corina MacIsaac, Sangeeta Bhatia, Robert Langer & Daniel G. Anderson

  10. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

    Haseeb Mughal & Sangeeta Bhatia

  11. Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Sangeeta Bhatia

  12. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Sangeeta Bhatia

  13. Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA

    Sangeeta Bhatia

  14. Wyss Institute at Harvard, Cambridge, MA, USA

    Sangeeta Bhatia

  15. Howard Hughes Medical Institute, Cambridge, MA, USA

    Sangeeta Bhatia

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  1. Bowen Li
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  2. Allen Yujie Jiang
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B.L. and A.Y.J. conceived the project and wrote the paper, with input from all authors. B.L. and I.R. designed the combinatorial lipid library. B.L., A.Y.J., I.R., C.A, T.M.R., A.G.R.G, L.H.R., T.S., C.M., J.W., H.M., T.M.C., Y.X. and R.P.M. performed experiments and analysed data. B.L., A.Y.J., R.L. and D.G.A. discussed the results and edited the paper. B.L., S.B., G.A., R.L. and D.G.A. acquired funding and supervised the project.

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Correspondence to Daniel G. Anderson.

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Competing interests

B.L., A.Y.J. and D.G.A. have filed a patent for the use of C3d in mRNA vaccines. D.G.A receives research funding from Sanofi/Translate Bio and is a co-founder of Orna Therapeutics. R.L. co-founded Moderna and serves on its board. He has been an advisor for Hopewell Therapeutics and Combined Therapeutics. For a list of entities with which R.L. is or has been recently involved, compensated or uncompensated, refer to the ‘Competing Interests’ section of the supplementary information (accurate as of July 2023). The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Comparison of LNP formulations containing MC3, Lipid 331, ALC-0315 or A18-Iso5-2DC18.

a, Transfection of mFFL LNPs in THP-1 cells (50 ng mRNA per well, n = 3). b, Transfection of mFFL LNPs containing either MC3, Lipid 331, ALC-0315, or A18-Iso5-2DC18 (A18) at the i.m. injection site in mice (0.2 mg/kg mRNA, n = 3 biologically independent mice per group). c. Representative IVIS imaging results from IM injection. d. The abundance of residual ionizable lipids at the injection site three days post-injection (n = 5 mice per group). Statistical significance was analysed using a one-way ANOVA with Dunnett’s correction (a) or one-way ANOVA with post-hoc Tukey test (b) or a two-tailed Student’s t-test (d). Data are presented as mean ± SD.

Extended Data Fig. 2 Characterization of anti-RBDDelta IgG1, IgG2b and IgG2c following the vaccination of RBDDelta, mRNA or RBDDelta/C3d mRNA mixture, or RBDDelta-C3d fusion mRNA.

a, MFIs of IgG subclasses obtained from Luminex assay measuring serological antibody binding against the RBD antigen from the Delta variant of SARS-CoV-2. Data related to Fig. 3i. b, Ratio of IgG2c to IgG1 levels as a surrogate of Th1-Th2 bias. Ratios were calculated as log10(MFIIgG2c)/log10(MFIIgG1). n = 5, statistical significance was analysed using a one-way ANOVA with post-hoc Tukey test. Data are presented as mean ± SD.

Extended Data Fig. 3 Pseudovirus neutralization titers following IM or IN vaccination with MC3 or Lipid 331 LNPs encapsulating either mRBDDelta or mRBDDelta-C3d.

NT50 of sera collected from vaccinated C57BL/6J mice two weeks post (a) IM or (b) IN boost vaccination. Mice were vaccinated with either mRBDDelta in MC3 or Lipid 331 LNPs (MC3 and 331, respectively), mRBDDelta-C3d in MC3 or Lipid 331 LNPs (MC3 + C3d and 331 + C3d, respectively), or PBS. A pseudovirus neutralization assay was used to determine NT50 values. Statistical significance was determined using a Kruskal-Wallis one-way ANOVA. Data are presented as geometric mean titre± geometric SD.

Extended Data Fig. 4 Polar plots of antibody features elicited by IM or IN vaccination with MC3 or Lipid 331 LNPs encapsulating either mRBDDelta or mRBDDelta-C3d.

The polar plots show the mean percentile rank for each antibody feature against RBD from the Delta variant of SARS-CoV-2 in serum collected from mice two weeks post-boost vaccination. Data related to vaccination study in Figs. 4 and 5.

Extended Data Fig. 5 Characterization of anti-RBDDelta antibody subclasses and Fc receptor binding following IM or IN vaccination with MC3 or Lipid 331 LNPs encapsulating either mRBDDelta or mRBDDelta-C3d.

a, MFIs of antibody features obtained from Luminex assay measuring serological antibody binding against the RBD antigen from the Delta variant of SARS-CoV-2. Serum was collected from mice vaccinated with either mRBDDelta in MC3 or Lipid 331 LNPs (MC3 and 331, respectively), mRBDDelta-C3d in MC3 or Lipid 331 LNPs (MC3 + C3d and 331 + C3d, respectively), or PBS. Data related to vaccination study in Fig. 4. b, Ratio of IgG2c to IgG1 levels as a surrogate of Th1-Th2 bias. Ratios were calculated as log10(MFIIgG2c)/log10(MFIIgG1). n = 5, statistical significance was analysed using a one-way ANOVA with post-hoc Tukey test. Data are presented as mean ± SD.

Supplementary information

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Supplementary methods and figures, and detailed competing interests for R.L.

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Li, B., Jiang, A.Y., Raji, I. et al. Enhancing the immunogenicity of lipid-nanoparticle mRNA vaccines by adjuvanting the ionizable lipid and the mRNA. Nat. Biomed. Eng (2023). https://doi.org/10.1038/s41551-023-01082-6

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