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      Neutralization titer biomarker for antibody-mediated prevention of HIV-1 acquisition

      research-article
      1 , 2 , , 1 , 3 , 1 , 1 , 1 , 1 , 4 , 24 , 4 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 5 , 6 , 7 , 8 , 7 , 8 , 7 , 8 , 7 , 8 , 25 , 7 , 8 , 7 , 8 , 9 , 9 , 9 , 9 , 9 , 10 , 10 , 10 , 10 , 10 , 11 , 1 , 1 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 3 , 20 , 21 , 1 , 22 , 1 , 21 , 23 , 9 , 7 , 8 , 13
      Nature Medicine
      Nature Publishing Group US
      HIV infections, Antibodies, Predictive markers

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          Abstract

          The Antibody Mediated Prevention trials showed that the broadly neutralizing antibody (bnAb) VRC01 prevented acquisition of human immunodeficiency virus-1 (HIV-1) sensitive to VRC01. Using AMP trial data, here we show that the predicted serum neutralization 80% inhibitory dilution titer (PT 80) biomarker—which quantifies the neutralization potency of antibodies in an individual’s serum against an HIV-1 isolate—can be used to predict HIV-1 prevention efficacy. Similar to the results of nonhuman primate studies, an average PT 80 of 200 (meaning a bnAb concentration 200-fold higher than that required to reduce infection by 80% in vitro) against a population of probable exposing viruses was estimated to be required for 90% prevention efficacy against acquisition of these viruses. Based on this result, we suggest that the goal of sustained PT 80 <200 against 90% of circulating viruses can be achieved by promising bnAb regimens engineered for long half-lives. We propose the PT 80 biomarker as a surrogate endpoint for evaluatinon of bnAb regimens, and as a tool for benchmarking candidate bnAb-inducing vaccines.

          Abstract

          By integrating the serum concentration of a broadly neutralizing antibody (bNAb) with its in vitro 80% inhibitory concentration, the PT 80 biomarker may be used to guide target levels of bNAbs for effective prevention of HIV-1 acquisition.

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          Neutralizing antibody levels are highly predictive of immune protection from symptomatic SARS-CoV-2 infection

          Predictive models of immune protection from COVID-19 are urgently needed to identify correlates of protection to assist in the future deployment of vaccines. To address this, we analyzed the relationship between in vitro neutralization levels and the observed protection from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using data from seven current vaccines and from convalescent cohorts. We estimated the neutralization level for 50% protection against detectable SARS-CoV-2 infection to be 20.2% of the mean convalescent level (95% confidence interval (CI) = 14.4-28.4%). The estimated neutralization level required for 50% protection from severe infection was significantly lower (3% of the mean convalescent level; 95% CI = 0.7-13%, P = 0.0004). Modeling of the decay of the neutralization titer over the first 250 d after immunization predicts that a significant loss in protection from SARS-CoV-2 infection will occur, although protection from severe disease should be largely retained. Neutralization titers against some SARS-CoV-2 variants of concern are reduced compared with the vaccine strain, and our model predicts the relationship between neutralization and efficacy against viral variants. Here, we show that neutralization level is highly predictive of immune protection, and provide an evidence-based model of SARS-CoV-2 immune protection that will assist in developing vaccine strategies to control the future trajectory of the pandemic.
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            A Concordance Correlation Coefficient to Evaluate Reproducibility

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              Immune correlates analysis of the mRNA-1273 COVID-19 vaccine efficacy clinical trial

              Symptomatic COVID-19 infection can be prevented by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines. A “correlate of protection” is a molecular biomarker to measure how much immunity is needed to fight infection and is key for successful global immunization programs. Gilbert et al . determined that antibodies are the correlate of protection in vaccinated individuals enrolled in the Moderna COVE phase 3 clinical trial (see the Perspective by Openshaw). By measuring binding and neutralizing antibodies against the viral spike protein, the authors found that the levels of both antibodies correlated with the degree of vaccine efficacy. The higher the antibody level, the greater the protection afforded by the messenger RNA (mRNA) vaccine. Antibody levels that predict mRNA vaccine efficacy can therefore be used to guide vaccine regimen modifications and support regulatory approvals for a broader spectrum of the population. —PNK SARS-CoV-2 binding and neutralizing antibodies correlate with the degree of vaccine efficacy and protection for the Moderna mRNA COVID-19 vaccine. In the coronavirus efficacy (COVE) phase 3 clinical trial, vaccine recipients were assessed for neutralizing and binding antibodies as correlates of risk for COVID-19 disease and as correlates of protection. These immune markers were measured at the time of second vaccination and 4 weeks later, with values reported in standardized World Health Organization international units. All markers were inversely associated with COVID-19 risk and directly associated with vaccine efficacy. Vaccine recipients with postvaccination 50% neutralization titers 10, 100, and 1000 had estimated vaccine efficacies of 78% (95% confidence interval, 54 to 89%), 91% (87 to 94%), and 96% (94 to 98%), respectively. These results help define immune marker correlates of protection and may guide approval decisions for messenger RNA (mRNA) COVID-19 vaccines and other COVID-19 vaccines.
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                Author and article information

                Contributors
                pgilbert@fredhutch.org
                Journal
                Nat Med
                Nat Med
                Nature Medicine
                Nature Publishing Group US (New York )
                1078-8956
                1546-170X
                22 August 2022
                22 August 2022
                2022
                : 28
                : 9
                : 1924-1932
                Affiliations
                [1 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Vaccine and Infectious Disease Division, , Fred Hutchinson Cancer Center, ; Seattle, WA USA
                [2 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Biostatistics, , University of Washington, ; Seattle, WA USA
                [3 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Global Health, , University of Washington, ; Seattle, WA USA
                [4 ]GRID grid.148313.c, ISNI 0000 0004 0428 3079, Los Alamos National Laboratory, ; Los Alamos, NM USA
                [5 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Center for Virology and Vaccine Research, , Beth Israel Deaconess Medical Center, ; Boston, MA USA
                [6 ]GRID grid.32224.35, ISNI 0000 0004 0386 9924, Ragon Institute of Massachusetts General Hospital, Massachusetts Institute of Technology and Harvard, ; Cambridge, MA USA
                [7 ]GRID grid.416657.7, ISNI 0000 0004 0630 4574, National Institute for Communicable Diseases, , National Health Laboratory Service, ; Johannesburg, South Africa
                [8 ]GRID grid.11951.3d, ISNI 0000 0004 1937 1135, Antibody Immunity Research Unit, Faculty of Health Sciences, , University of the Witwatersrand, ; Johannesburg, South Africa
                [9 ]GRID grid.189509.c, ISNI 0000000100241216, Department of Surgery, , Duke University Medical Center, ; Durham, NC USA
                [10 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Duke University Departments of Surgery, Immunology, Molecular Genetics and Micobiology, , Duke Center for Human Systems Immunology, ; Durham, NC USA
                [11 ]GRID grid.245835.d, ISNI 0000 0001 0300 5112, Family Health International, ; Durham, NC USA
                [12 ]GRID grid.21729.3f, ISNI 0000000419368729, Division of Infectious Diseases, Department of Medicine, , Columbia University Irving Medical Center, ; New York, NY USA
                [13 ]GRID grid.16463.36, ISNI 0000 0001 0723 4123, Centre for the AIDS Programme of Research in South Africa, , University of KwaZulu-Natal, ; Durban, South Africa
                [14 ]GRID grid.16463.36, ISNI 0000 0001 0723 4123, Discipline of Public Health Medicine, School of Nursing and Public Health, , University of KwaZulu-Natal, ; Durban, South Africa
                [15 ]GRID grid.10800.39, ISNI 0000 0001 2107 4576, Centro de Investigaciones Tecnológicas, Biomédicas y Medioambientales, , Universidad Nacional Mayor de San Marcos, ; Lima, Peru
                [16 ]GRID grid.10698.36, ISNI 0000000122483208, Division of Infectious Diseases, , The University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                [17 ]Botswana-Harvard AIDS Initiative Partnership for HIV Research and Education, Gaborone, Botswana
                [18 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Division of Infectious Disease, , Beth Israel Deaconess Medical Center, ; Boston, MA USA
                [19 ]GRID grid.7836.a, ISNI 0000 0004 1937 1151, Division of Medical Virology, Faculty of Health Sciences, , University of Cape Town, ; Cape Town, South Africa
                [20 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Microbiology, , University of Washington, ; Seattle, WA USA
                [21 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Medicine, , University of Washington, ; Seattle, WA USA
                [22 ]GRID grid.10698.36, ISNI 0000000122483208, Institute of Global Health and Infectious Diseases, , The University of North Carolina at Chapel Hill, ; Chapel Hill, NC USA
                [23 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Laboratory Medicine, , University of Washington, ; Seattle, WA USA
                [24 ]GRID grid.270240.3, ISNI 0000 0001 2180 1622, Present Address: Vaccine and Infectious Disease Division, , Fred Hutchinson Cancer Center, ; Seattle, WA USA
                [25 ]GRID grid.26009.3d, ISNI 0000 0004 1936 7961, Present Address: Duke Center for Human Systems Immunology, , Duke University Departments of Surgery, Immunology, Molecular Genetics and Microbiology, ; Durham, NC USA
                Author information
                http://orcid.org/0000-0002-2662-9427
                http://orcid.org/0000-0003-1546-6172
                http://orcid.org/0000-0001-5946-9733
                http://orcid.org/0000-0002-5056-2664
                http://orcid.org/0000-0002-2026-5757
                http://orcid.org/0000-0002-8393-8103
                http://orcid.org/0000-0002-0920-2915
                http://orcid.org/0000-0003-0333-5925
                http://orcid.org/0000-0001-5127-4659
                http://orcid.org/0000-0003-3064-2947
                http://orcid.org/0000-0002-2009-3270
                http://orcid.org/0000-0003-2258-5276
                http://orcid.org/0000-0001-5684-9538
                http://orcid.org/0000-0002-4530-234X
                http://orcid.org/0000-0003-0125-1226
                http://orcid.org/0000-0002-2179-2436
                http://orcid.org/0000-0003-0856-6319
                http://orcid.org/0000-0003-3961-7828
                Article
                1953
                10.1038/s41591-022-01953-6
                9499869
                35995954
                8052b258-f0c0-47de-8643-9ee5c09f5dd8
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 November 2021
                : 14 July 2022
                Funding
                Funded by: National Institutes of Health, UM1 AI068635, 4 R37 AI054165-21
                Funded by: FundRef https://doi.org/10.13039/100000865, Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation);
                Award ID: OPP1146996
                Award ID: INV-036842
                Award Recipient :
                Funded by: National Institutes of Health UM1 AI068614
                Funded by: National Institutes of Health UM1 AI068614, P30 AI064518
                Funded by: National Institutes of Health K25 AI155224
                Funded by: National Institutes of Health P30 AI027757
                Funded by: National Institutes of Health UM1 AI068619
                Funded by: South African Medical Research Council (SAMRC)
                Funded by: National Institutes of Health, UM1 AI068635
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                © The Author(s), under exclusive licence to Springer Nature America, Inc. 2022

                Medicine
                hiv infections,antibodies,predictive markers
                Medicine
                hiv infections, antibodies, predictive markers

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