WO2022022927A1 - Diagnostic sérologique à base d'anticorps de l'infection au sars-cov-2 - Google Patents

Diagnostic sérologique à base d'anticorps de l'infection au sars-cov-2 Download PDF

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WO2022022927A1
WO2022022927A1 PCT/EP2021/067755 EP2021067755W WO2022022927A1 WO 2022022927 A1 WO2022022927 A1 WO 2022022927A1 EP 2021067755 W EP2021067755 W EP 2021067755W WO 2022022927 A1 WO2022022927 A1 WO 2022022927A1
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sars
cov
antibody
composition
infection
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PCT/EP2021/067755
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Michael White
Ivo Mueller
Javier Jason ROSADO SANDOVAL
Stéphane PELLEAU
Marija BACKOVIC
Stéphane Petres
Tom WOUDENBERG
Timothée BRUEL
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Institut Pasteur
The Walter And Eliza Hall Institute Of Medical Research
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6854—Immunoglobulins
    • C—CHEMISTRY; METALLURGY
    • C07—ORGANIC CHEMISTRY
    • C07K—PEPTIDES
    • C07K14/00—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
    • C07K14/005—Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof from viruses
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N2770/00—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA ssRNA viruses positive-sense
    • C12N2770/00011—Details
    • C12N2770/20011—Coronaviridae
    • C12N2770/20022—New viral proteins or individual genes, new structural or functional aspects of known viral proteins or genes
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/005—Assays involving biological materials from specific organisms or of a specific nature from viruses
    • G01N2333/08—RNA viruses
    • G01N2333/165—Coronaviridae, e.g. avian infectious bronchitis virus

Definitions

  • SARS-CoV-2 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) emerged in Wuhan, China in December 2019. Since then, it has spread rapidly, with confirmed cases being recorded in nearly every country in the world.
  • the presence of viral infection can be directly detected via reverse transcriptase quantitative PCR (RT-qPCR) on samples from nasopharyngeal or throat swabs.
  • RT-qPCR reverse transcriptase quantitative PCR
  • SARS-CoV-2 virus is detectable in the first 2-3 weeks following symptom onset [1 ,2] Viral shedding is shorter in mild cases with only upper respiratory tract symptoms (1-2 weeks) [3]. For asymptomatic individuals, the duration for which SARS-CoV-2 virus can be detected is uncertain.
  • An individual is seropositive to a pathogen if they have detectable antibodies specific for that pathogen. From an immunological perspective, an individual can be defined as seropositive if they have either antibody secreting plasma cells and/or a matured memory B cell response to antigens on that pathogen.
  • serological assays are used to measure antibody responses in blood samples. Flowever, individuals who have never been infected with the target pathogen may have non-zero antibody responses due to cross-reactivity with other pathogens or background assay noise. To account for this, defining seropositivity is equivalent to determining whether the measured antibody response is greater or lower than some defined cutoff value [4] [0003] The most fundamental measure of antibody level is via concentration in a sample (e.g.
  • a range of assays can provide measurements that are positively associated with the true antibody concentration, e.g. an optical density from an enzyme-linked immunosorbent assay (ELISA), or a median fluorescent intensity (MFI) from a Luminex® microsphere assay.
  • ELISA enzyme-linked immunosorbent assay
  • MFI median fluorescent intensity
  • the antibody response generated following SARS-CoV-2 infection is diverse, consisting of multiple isotypes targeting several proteins on the virus including the spike protein (and its receptor binding domain, RBD) and the nucleoprotein [16].
  • This complexity of biomarkers provides both a challenge and an opportunity for diagnostics research.
  • the challenge lies in selecting appropriate biomarkers and choosing between the increasing number of commercial assays, many of which have not been extensively validated and may produce conflicting results.
  • the opportunity is that with multiple biomarkers, it is possible to generate a serological signature of infection that is robust to how antibody levels change over time [17-20], rather than relying on classification of seropositive individuals using a single cutoff antibody level.
  • the invention encompasses compositions and kits comprising SARS-CoV-2 trimeric Spike protein (including novel variants) and/or combinations of two or more SARS-CoV-2 antigens, and uses of these proteins and antigens in immunodiagnostic methods.
  • the invention encompasses compositions comprising a SARS-CoV-2 trimeric Spike protein or a combination of two or more SARS-CoV-2 antigens selected from SARS-CoV-2 trimeric Spike protein, S1 subunit, S2 subunit, RBD antigen (including novel variants), and nucleoprotein.
  • the SARS-CoV-2 trimeric Spike protein or a combination of two or more SARS-CoV-2 antigens is/are attached to one or more solid substrates.
  • the composition comprises at least 3, 4, 5, 6, or 7 SARS- CoV-2 antigens.
  • the solid substrate is a microplate(s) or a bead(s), preferably a magnetic bead(s).
  • the composition comprises SARS-CoV-2 trimeric Spike protein.
  • SARS-CoV-2 trimeric Spike protein 2 consists of or comprises the amino acid sequence of SEQ ID NO:5.
  • the invention encompasses a kit comprising any of the compositions of the invention.
  • the invention encompasses the use of the composition of the invention for the detection of antibodies against SARS-CoV-2 in a biological sample.
  • the invention encompasses methods for the detection of antibodies against a SARS-associated coronavirus.
  • the method comprises contacting a biological sample with a composition of the invention and visualizing the antigen-antibody complexes formed.
  • the method can be an EIA, an ELISA, a LUMINEX assay, or another multiplex assay.
  • FIG. 1 Anti-SARS-CoV-2 antibody responses.
  • B Measured IgM antibody dilutions or MFI in serum or plasma samples.
  • C Receiver Operating Characteristic (ROC) curve for IgG antibodies obtained by varying the cutoff for seropositivity. Colours correspond to those shown in part A.
  • D ROC curve for IgM antibodies obtained by varying the cutoff for seropositivity.
  • E Area under the ROC curve for individual biomarkers.
  • F Spearman correlation between measured antibody responses.
  • FIG. 2 Serological signatures of SARS-CoV-2 infection.
  • B ROC curves for multiple biomarker classifiers generated using a Random Forests algorithm.
  • C For a high specificity target (>99%), sensitivity increases with additional biomarkers. Sensitivity was estimated using a Random Forests classifier. Points and whiskers denote the median and 95% confidence intervals from repeat cross-validation.
  • FIG. 3 IgG antibody kinetics.
  • A Measured IgG antibody dilutions, shown as points, from a patient in Hopital Bichat followed longitudinally. Posterior median model predictions of IgG antibody dilution are shown as black lines, with 95% credible intervals in grey. The coloured dashed line represents the cutoff for IgG seropositivity for that antigen. IgM antibody dilutions are shown as asterisks. The black horizontal dashed lines represent the upper and lower limits of the assay.
  • B Measured IgG antibody dilutions and model predictions for the full population. Measured IgG antibody dilutions are shown as geometric mean titre (GMT) with 95% ranges.
  • C Model predicted proportion of individuals testing seropositive over time.
  • FIG. 5 Implementation of seroprevalence surveys.
  • A Receiver Operating Characteristic (ROC) analysis with cross-validated uncertainty. Solid lines represent median ROC curves and shaded regions represent 95% uncertainty intervals for specificity.
  • D Across a range of true seroprevalence, optimal values of sensitivity and specificity can be selected to minimize the expected relative error in seroprevalence surveys.
  • E The expected relative error for optimal values of sensitivity and specificity.
  • Figure 6 Quantification of uncertainty for serological classification. Results are shown for a single antigen (S tri ) IgG assay in red, and a six antigen multiplex classifier in black.
  • S tri single antigen
  • A Uncertainty estimated using Wilson’s binomial method applied to data from all samples. Uncertainty in specificity at fixed sensitivity, and variation in sensitivity at fixed specificity are shown separately.
  • B Uncertainty estimated using 1000-fold cross- validation with training (2/3 of samples) and testing (1/3 of samples) data sets. Uncertainty in specificity at fixed sensitivity, and variation in sensitivity at fixed specificity are shown separately.
  • C Cross-panel validation. The title of each plot denotes the panels that were used for testing, while the other panels were used for training.
  • Figure 7 SARS-CoV-2 antibody kinetics in Hong Kong patients. Anti- nucleoprotein (NP) and anti-receptor-binding domain (RBD) antibody responses in 22 patients with PCR confirmed SARS-CoV-2 infection admitted to hospitals in Hong Kong. Measured antibody levels in patients are depicted as points. Measured antibody levels in negative controls are depicted as crosses. Grey lines show posterior median model prediction. The uncertainty of the model predictions is presented via 95% credible intervals in Figures 7-10. The horizontal dashed line represents the cutoff for sero- positivity.
  • NP nucleoprotein
  • RBD anti-receptor-binding domain
  • Figure 8 Model fit to short-term data on anti-NP IgG antibody responses.
  • Measured antibody responses are shown as red points. Posterior median model predictions are shown as black lines, with 95% credible intervals in grey. The horizontal dashed line represents the cutoff for sero-positivity. Note that as there is no data on the long-term antibody response to SARS-CoV-2, three different sources of prior information were utilized. The half-life of the long-lived component of the antibody responses was assumed to be 200 days (short prior), 400 days (medium prior), or 800 days (long prior). Note that each of the three assumptions give near identical fits for the short-term kinetics displayed here.
  • Figure 9 Model fit to short-term data on anti-RBD IgG antibody responses.
  • Measured antibody responses are shown as red points. Posterior median model predictions are shown as black lines, with 95% credible intervals in grey. The horizontal dashed line represents the cutoff for sero-positivity. Note that as there is no data on the long-term antibody response to SARS-CoV-2, three different sources of prior information were utilized. The half-life of the long-lived component of the antibody responses was assumed to be 200 days (short prior), 400 days (medium prior), or 800 days (long prior). Note that each of the three assumptions give near identical fits for the short-term kinetics displayed here.
  • Figure 10 Model fit to short-term data on anti-NP IgM antibody responses.
  • Measured antibody responses are shown as red points. Posterior median model predictions are shown as black lines, with 95% credible intervals in grey. The horizontal dashed line represents the cutoff for sero-positivity. Note that as there is no data on the long-term antibody response to SARS-CoV-2, three different sources of prior information were utilized. The half-life of the long-lived component of the antibody responses was assumed to be 50 days (short prior), 100 days (medium prior), or 200 days (long prior). Note that each of the three assumptions give near identical fits for the short-term kinetics displayed here.
  • Figure 11 Model fit to short-term data on anti-RBD IgM antibody responses.
  • Measured antibody responses are shown as red points. Posterior median model predictions are shown as black lines, with 95% credible intervals in grey. The horizontal dashed line represents the cutoff for sero-positivity. Note that as there is no data on the long-term antibody response to SARS-CoV-2, three different sources of prior information were utilized. The half-life of the long-lived component of the antibody responses was assumed to be 50 days (short prior), 100 days (medium prior), or 200 days (long prior). Note that each of the three assumptions give near identical fits for the short-term kinetics displayed here.
  • Figure 12 Antigen Combinations. Specificity and Sensitivity with 1 , 2, 3, 4, 5, 6, or 7 antigens is shown.
  • Figure 13 Change in Avidity overtime following infection with SARS-CoV-2.
  • FIG. 14 Prediction of viral neutralization.
  • SARS-CoV-2 viral neutralization was measured in an S-Fuse assay in the Virus and Immunity Unit at Institut Pasteur. This gives a measure of functional immunity against SARS-CoV-2 in serum samples. The same samples were tested with a multiplex serological assay to measure IgG, IgM and IgA antibodies. Linear regression models and random forests algorithms were trained to allow prediction of neutralization activity given antibody measurements from a multiplex assay. This allows estimation of neutralization activity without the need for Biosafety level 3 laboratories. Detailed Description of the Invention
  • a multiplex serological assay was developed to measure IgG and IgM antibody responses to seven SARS-CoV-2 spike (including Wuhan, alpha, beta, gamma and delta variants) or nucleoprotein antigens, antigens for the Spike and nucleoproteins of the 229E, NL63, OC43 and HKU1 seasonal coronaviruses, and three non-coronavirus antigens.
  • Random forests algorithms and support vector machines were trained with the multiplex data to classify individuals with previous SARS-CoV-2 infection.
  • a mathematical model of antibody kinetics informed by prior information from other coronaviruses was used to estimate time-varying antibody responses and assess the potential sensitivity and classification performance of serological diagnostics during the first year following symptom onset.
  • a statistical estimator is presented that can provide estimates of seroprevalence in very low transmission settings.
  • Linear regression models and random forests algorithms were trained to allow prediction of neutralization activity given antibody measurements from a multiplex assay. This allows estimation of neutralization activity without the need for Biosafety level 3 laboratories.
  • the multiplex serological assay may also be used to measure IgA antibodies to the above-mentioned SARS-CoV-2 antigens.
  • the multiplex serological assay may also be used to measure the avidity of antibodies to the above-mentioned SARS-CoV-2 antigens.
  • the multiplex serological assay may be combined with the mathematical model of antibody kinetics disclosed herein to predict the duration of protection of an individual following SARS-CoV- 2 infection or vaccination or the time elapsed since a patient’s infection.
  • the multiplex serological assay may also be used in combination with linear regression models and random forests algorithms to allow prediction of neutralization activity given antibody measurements from a multiplex assay without the need for Biosafety level 3 laboratories.
  • Sensitivity and specificity were determined based on 1 , 2, 3, 4, 5, 6, or 7 antigens in a multiplex analysis of antibody responses in SARS-CoV-2 infected individuals.
  • a SARS-CoV-2 trimeric Spike protein showed substantially better sensitivity and specificity than any other single antigen tested.
  • IgG antibody responses to trimeric Spike protein identified individuals with previous RT-qPCR confirmed SARS-CoV-2 infection with 91.6% sensitivity (95% confidence interval (Cl); 87.5%, 94.5%) and 99.1% specificity (95% Cl; 97.4%, 99.7%).
  • sensitivity 95% confidence interval (Cl); 87.5%, 94.5%)
  • 99.1% specificity 95% Cl; 97.4%, 99.7%.
  • serological signature of IgG and IgM to multiple antigens, it was possible to identify infected individuals with 98.8% sensitivity (95% Cl; 96.5%, 99.6%) and 99.3% specificity (95% Cl; 97.6%, 99.8%).
  • Serological signatures based on antibody responses to multiple antigens can provide accurate and robust serological classification of individuals with previous SARS- CoV-2 infection. This provides potential solutions to two pressing challenges for SARS- CoV-2 serological surveillance: classifying individuals who were infected greater than six months ago, and measuring seroprevalence in serological surveys in very low transmission settings. In this analysis, mathematical models of antibody kinetics can be applied to serological data from the early stages of SARS-CoV-2 infection and predict the potential consequences for serological diagnostics within the first year following infection. [0033] Infection with SARS-CoV-2 induces antibodies of multiple isotypes (IgG, IgM, IgA) targeting multiple epitopes on spike proteins exposed on the virus surface, and nucleoprotein.
  • IgG, IgM, IgA isotypes targeting multiple epitopes on spike proteins exposed on the virus surface, and nucleoprotein.
  • biomarkers may exhibit distinct kinetics leading to variation in their potential diagnostic performance. There is also substantial between-individual variation in the antibody response generated following SARS-CoV-2 infection. By measuring multiple biomarkers in large numbers of individuals, it is possible to create a serological signature of previous infection [17-19] Although necessarily more complex than a single measured antibody response, such an approach has the potential of providing more accurate classification and being more stable over time.
  • IgG antibody levels to a single antigen can classify samples from individuals previously infected with SARS-CoV-2 with 91.6% sensitivity (95% Cl: 87.5%, 94.5%) and 99.1% specificity (95% Cl: 97.4%, 99.7%). Measuring additional biomarkers with a multiplex assay can improve classification performance to 98.8% sensitivity (95% Cl: 96.5%, 99.6%) and 99.3% specificity (95% Cl: 97.6%, 99.8%).
  • An advantage of continuous multiplex data is that different algorithms can be applied to the same data for different epidemiological applications.
  • Table 2 assesses classification performance against three targets. We selected multiplex combination of antigens to optimize classification of individual samples against a target of maximizing sensitivity given a minimum specificity of 99%. However, a test that is optimal for individual-level classification is not necessarily optimal for population-level use.
  • a recommended target for serological assays for sero-surveillance surveys is to minimize the expected error in estimated seroprevalence. For scenarios where we expect low true seroprevalence ( ⁇ 10%) we show that assays with high specificity (>99%) are optimal (Figure 5).
  • the invention encompasses compositions, kits and methods for improved immunodiagnostic assays for SARS-CoV-2.
  • the invention relates to compositions, kits, statistical algorithms and methods for the immunodetection of antibodies against Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in biological samples such as human blood samples.
  • the invention encompasses compositions, kits, statistical algorithms and methods comprising SARS-CoV-2 trimeric Spike protein and combinations of SARS-CoV-2 antigens.
  • the SARS-CoV-2 trimeric Spike protein and combinations of SARS-CoV-2 antigens provide for improved detection of antibodies against Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in biological samples such as human blood or saliva samples.
  • the invention encompasses compositions and kits comprising SARS-CoV-2 trimeric Spike protein and/or combinations of two or more SARS-CoV-2 antigens.
  • the compositions and kits are for use to detect antibodies against SARS-CoV-2 in a biological sample, such as a blood sample.
  • SARS-Co-V2 refers to any isolate, strain, lineage or variant of SARS-CoV-2.
  • SARS-CoV-2 is isolate Wuhan-Hu-1 or variant thereof such as alpha, beta, gamma, delta variants and other variants.
  • SARS-CoV-2 is selected from the group consisting of: isolate Wuhan-Hu- 1 , alpha, beta, gamma, and delta variants thereof and combinations thereof.
  • the composition/kit comprises a SARS-CoV-2 trimeric Spike protein and an antigen selected from combinations that give more than 88.05% or 91 .63% sensitivity at greater than 99% target specificity, as illustrated in Figure 12, including RBDv1 , RBDv2, S1 , S2, and NR (e.g., NPv1 , and NPv2).
  • an antigen selected from combinations that give more than 88.05% or 91 .63% sensitivity at greater than 99% target specificity, as illustrated in Figure 12, including RBDv1 , RBDv2, S1 , S2, and NR (e.g., NPv1 , and NPv2).
  • composition/kit comprises SARS-CoV-2 RBDvl and RBDv2.
  • the composition/kit comprises a SARS-CoV-2 RBD and an antigen selected from combinations that give more than 88.05% or 91 .63% sensitivity at greater than 99% target specificity, as illustrated in Figure 12, including trimeric Spike protein, S2, and NP (e.g., NPv1 , and NPv2). Particularly preferred combinations are those that give more than 94% or 95% sensitivity at greater than 99% target specificity, as illustrated in Figure 12.
  • the invention encompasses compositions and kits comprising combinations of three SARS-CoV-2 antigens. In one embodiment, the invention encompasses compositions and kits comprising combinations that give more than 88.05% or 91 .63% sensitivity at greater than 99% target specificity, as illustrated in Figure 12. Particularly preferred combinations are those that give more than 94%, 95%, 96%, 97%, or 98% sensitivity at greater than 99% target specificity, as illustrated in Figure 12.
  • the invention encompasses compositions and kits comprising combinations of four SARS-CoV-2 antigens. In one embodiment, the invention encompasses compositions and kits comprising combinations that give more than 88.05% or 91 .63% sensitivity at greater than 99% target specificity, as illustrated in Figure 12. Particularly preferred combinations are those that give more than 94%, 95%, 96%, 97%, or 98% sensitivity at greater than 99% target specificity, as illustrated in Figure 12. [0051] In one embodiment, the invention encompasses compositions and kits comprising combinations of five SARS-CoV-2 antigens. In one embodiment, the invention encompasses compositions and kits comprising combinations that give 88.05% or 91.63% sensitivity at greater than 99% target specificity, as illustrated in Figure 12. Particularly preferred combinations are those that give more than 94%, 95%, 96%, 97%, or 98% sensitivity at greater than 99% target specificity, as illustrated in Figure 12.
  • the invention encompasses compositions and kits comprising combinations of six SARS-CoV-2 antigens. In one embodiment, the invention encompasses compositions and kits comprising combinations that give 88.05% or 91.63% sensitivity at greater than 99% target specificity, as illustrated in Figure 12.
  • Particularly preferred combinations are those that give more than 94%, 95%, 96%, 97%, or 98% sensitivity at greater than 99% target specificity, as illustrated in Figure 12.
  • the invention encompasses compositions and kits comprising combinations of seven SARS-CoV-2 antigens. In one embodiment, the invention encompasses compositions and kits comprising combinations that give 88.05% or 91.63% sensitivity at greater than 99% target specificity, as illustrated in Figure 12. Particularly preferred combinations are those that give more than 94%, 95%, 96%, 97%, or 98% sensitivity at greater than 99% target specificity, as illustrated in Figure 12.
  • the invention encompasses compositions and kits comprising combinations of SARS-CoV-2 antigens that give more than 89%, 90%, 91%, 92%, 93% 94%, 95%, 96%, 97%, or 98% sensitivity at greater than 94%, 95%, 96%, 97%, or 98% target specificity, as illustrated in Figure 12.
  • At least one antigen of the composition is different from the natural antigen.
  • the antigen comprises additional sequences, for example at its N-ter or C-ter compared to the natural antigen.
  • the multiplex assays can be performed as presented in the examples or by similar technique.
  • the multiplex assays of the invention show improved properties as compared to assays performed with single antigen.
  • the SARS-CoV-2 trimeric Spike protein comprises or consists of the sequence of SEQ ID NO:5.
  • the SARS-CoV-2 trimeric Spike protein comprises or consists of the sequence of amino acids 1-1208 of SEQ ID NO:5.
  • the SARS-CoV-2 trimeric Spike protein comprises the stabilizing double proline mutation (KV986-987 to PP986-987).
  • the SARS-CoV-2 trimeric Spike protein comprises the foldon domain at the C-terminus that allows the S to trimerize (YIPEAPRDGQAYVRKDGEWVLLSTFL; SEQ ID NO:2).
  • the SARS-CoV-2 trimeric Spike protein comprises a Strep tag (WSHPQFEK SEQ ID NO:3), an octa-histidine, and/or an Avi tag (GLNDIFEAQKIEWHE; SEQ ID NO:4) at the C-terminus for affinity purification.
  • the RBD antigen comprises or consists of amino acid residues 331 -519 of SEQ ID NO:5. and the entire S ectodomain (residues 1-1208).
  • the RBD antigen comprises an exogenous signal peptide of a human kappa light chain (METDTLLLWVLLLWVPGSTG; SEQ ID NO:1 ).
  • the RBD antigen is RBDvl (Native Antigen, Oxford, UK cat# REC31831-20). In one embodiment, the RBD antigen is RBDv2. In one preferred embodiment, the RBD antigen is RBDv2.
  • the S1 antigen comprises or consists of amino acids 1- 674 of SEQ ID NO:5.
  • the S2 antigen comprises or consists of amino acids 685- 1211 or 685-1208 of the S protein. In various embodiments, this comprises or doesn’t comprise the stabilizing double proline mutation (KV986-987 to PP986-987).
  • the S2 subunit has the the S2 antigen comprises or consists of amino acids 685-1211 or 685-1208 of SEQ ID NO:5, with or without the stabilizing double proline mutation (KV986-987 to PP986-987).
  • SARS-CoV-2 nucleoprotein comprises or consists of the NPv1 antigen. In various embodiments, SARS-CoV-2 nucleoprotein (NP) comprises or consists of the NPv2 antigen. In some preferred embodiments, SARS-CoV- 2 nucleoprotein (NP) comprises or consists of the NPv1 antigen.
  • SARS-CoV-2 nucleoprotein comprises or consists of an NP antigen expressed in E. coli BL21 (DE3) pDIA17 transformed with recombinant plasmid pETM11/N-nCov E. coli 3 -(His)6-Nter or pETM11/N-nCov E.
  • SARS-CoV-2 nucleoprotein comprises or consists of the NP antigen of Native Antigen, Oxford, UK: NP (cat# REC31812-100), corresponding to to NPv2 antigen.
  • the SARS-CoV-2 trimeric Spike protein and antigens are conjugated to a solid substrate, such as a microplate or bead(s), preferably a magnetic bead(s).
  • a solid substrate such as a microplate or bead(s)
  • the invention encompasses kits comprising the SARS-CoV-2 trimeric Spike protein and antigens conjugated to solid substrate, such as a microplate or bead(s), preferably magnetic bead(s).
  • the kit contains reagents for the detection of immune complexes formed between the antigens and human antibodies in a biological (e.g., serum) sample.
  • a biological sample e.g., serum
  • the kit can be stored at room temperature for extended period of time.
  • the invention encompasses immunodetection methods comprising SARS-CoV-2 trimeric Spike protein or combinations of SARS-CoV-2 antigens.
  • SARS-CoV-2 is isolate Wuhan-Hu-1 or variant thereof such as alpha, beta, gamma, delta variants and other variants.
  • SARS-CoV- 2 is selected from the group consisting of: isolate Wuhan-Hu-1 , alpha, beta, gamma, and delta variants thereof and combinations thereof.
  • the invention comprises a method for the detection of antibodies against a SARS-associated coronavirus, from a biological sample, which method is characterized in that it comprises contacting a biological sample, preferably from a patient infected with or suspected of being infected with a SARS-CoV-2 coronavirus or from an individual vaccinated against a SARS-CoV-2 coronavirus infection (COVID-19 disease), with SARS-CoV-2 trimeric Spike protein or combinations of SARS- CoV-2 antigens, and visualizing the antigen-antibody complexes formed.
  • the antigen-antibody complexes are visualized by EIA, ELISA, RIA, or by immunofluorescence.
  • the assay is a LUMINEX assay.
  • the biological sample is any sample obtained from an individual, in particular human, that may comprise antibodies.
  • the biological sample is a biological fluid.
  • body fluids include whole-blood, serum, plasma, urine, cerebral spinal fluid (CSF), and mucosal secretions, such as with no limitations oral and respiratory tract secretions (sputa, saliva and the like).
  • the biological sample is blood or mucosal secretion sample.
  • Blood sample includes whole-blood, serum or plasma. Mucosal secretion sample is preferably saliva sample.
  • the SARS-CoV-2 trimeric Spike protein or combinations of SARS-CoV-2 antigens is attached to an appropriate support, in particular a microplate or a bead, particularly magnetic beads.
  • the method comprises bringing a biological sample, preferably from a patient infected with or suspected of being infected with a SARS-CoV-2 coronavirus or from an individual vaccinated against a SARS-CoV-2 coronavirus infection (COVID-19 disease), into contact with a SARS-CoV-2 trimeric Spike protein or combinations of SARS-CoV-2 antigens, which is/are attached to an appropriate support, in particular a microplate or bead (e.g., magnetic), to allow binding to occur; washing the support to remove unbound antibodies; adding a detection reagent that binds to the immunoglobulins bound to the SARS-CoV-2 trimeric Spike protein or combinations of SARS-CoV-2 antigens; and detecting the SARS-CoV-2 antigen-antibody complexes formed.
  • a biological sample preferably from a patient infected with or suspected of being infected with a SARS-CoV-2 coronavirus or from an individual vaccinated against a S
  • the method for the detection of antibodies against a SARS- associated coronavirus in a biological sample comprises providing a SARS-CoV-2 trimeric Spike protein or combinations of SARS-CoV-2 antigens; providing a biological sample from a patient infected with a SARS-CoV coronavirus or vaccinated against a SARS-CoV-2 coronavirus infection (COVID-19 disease); contacting said SARS-CoV-2 trimeric Spike protein or combinations of SARS-CoV-2 antigens with said biological sample; and visualizing the antigen-antibody complexes formed.
  • the method is for the detection of IgG, IgM, and/or IgA antibodies against a SARS-associated coronavirus.
  • the method may detect either IgG, IgM or IgA antibodies against a SARS-associated coronavirus, or a mixture thereof.
  • the method may also measure avidity of IgG, IgM or IgA antibodies against a SARS- associated coronavirus, or a mixture thereof.
  • the immunodetection methods is used in combination with mathematical and/or statistical model and/or algorithm to predict the duration of protection of an individual following SARS-CoV-2 infection or vaccination or the time elapsed since a patient’s infection.
  • the immunodetection method is used in combination with mathematical and/or model and/or algorithm to predict the duration of protection of an individual following SARS-CoV-2 infection or vaccination against a range of SARS-CoV-2 variants.
  • the immunodetection methods is used in combination with the mathematical model of antibody kinetics disclosed herein and a random forests machine learning algorithm to predict the duration of protection of an individual following SARS-CoV-2 infection or vaccination or the time elapsed since a patient’s infection.
  • the immunodetection method is used in combination with the mathematical model of antibody kinetics disclosed herein and a random forests machine learning algorithm to predict the duration of protection of an individual following SARS-CoV-2 infection or vaccination against a range of SARS-CoV-2 variants.
  • the invention further relates to a method of predicting the duration of protection in a SARS-CoV-2 infected or vaccinated individual comprising:
  • the duration of protection is predicted using the antibody kinetics model disclosed herein and/or a random forests machine learning algorithm disclosed herein.
  • the prediction method is useful to determine if and when an individual should receive a dose of SARS-CoV-2 vaccine.
  • the method is used to predict the duration of protection of an individual following SARS-CoV-2 infection or vaccination against a range of SARS- CoV-2 variants.
  • the immunodetection method is used in combination with linear regression models and random forests algorithms to allow prediction of neutralization activity in a SARS-CoV-2 infected or vaccinated individual.
  • the invention further relates to a method of predicting the neutralizing activity against SARS-CoV-2 in a SARS-CoV-2 infected or vaccinated individual comprising:
  • the neutralization activity is predicted using linear regression models and random forests algorithms.
  • the method is used to predict the neutralization activity against SARS-CoV-2 of an individual following SARS-CoV-2 infection or vaccination against a range of SARS-CoV-2 variants.
  • the protein-antibody complexes are detected with an antibody or an antibody fragment that binds to human immunoglobulins.
  • the detection reagent comprises a label is selected from a chemiluminescent label, an enzyme label, a fluorescence label, and a radioactive (e.g., iodine) label.
  • the detection reagent is a labeled antibody or antibody fragment that binds to human immunoglobulins.
  • Preferred labels include a fluorescent label, such as FITC, a chromophore label, an affinity-ligand label, an enzyme label, such as alkaline phosphatase, horseradish peroxidase, or b galactosidase, an enzyme cofactor label, a hapten conjugate label, such as digoxigenin ordinitrophenyl, a Raman signal generating label, a magnetic label, a spin label, an epitope label, such as the FLAG or FIA epitope, a luminescent label, a heavy atom label, a nanoparticle label, an electrochemical label, a light scattering label, a spherical shell label, semiconductor nanocrystal label, wherein the label can allow visualization with or without a secondary detection molecule.
  • a fluorescent label such as FITC
  • a chromophore label such as alkaline phosphatase, horseradish peroxidase, or b galactosidase
  • Preferred labels include suitable enzymes such as horseradish peroxidase, alkaline phosphatase, beta-galactosidase, or acetylcholinesterase; members of a binding pair that are capable of forming complexes such as streptavidin/biotin, avidin/biotin or an antigen/antibody complex including, for example, rabbit IgG and anti-rabbit IgG; fluorophores such as umbelliferone, fluorescein, fluorescein isothiocyanate, rhodamine, tetramethyl rhodamine, eosin, green fluorescent protein, erythrosin, coumarin, methyl coumarin, pyrene, malachite green, stilbene, lucifer yellow, Cascade Blue, Texas Red, dichlorotriazinylamine fluorescein, dansyl chloride, phycoerythrin, fluorescent lanthanide complexes such as those including Europium and Ter
  • the antibody or an antibody fragment that binds to human immunoglobulins binds specifically to IgG, IgA, and IgM. In one embodiment, the antibody or an antibody fragment that binds to human immunoglobulins binds specifically to IgG, IgA, or IgM.
  • antibodies is meant to include polyclonal antibodies, monoclonal antibodies, fragments thereof, such as F(ab')2 and Fab fragments, single-chain variable fragments (scFvs), single-domain antibody fragments (VHHs or Nanobodies), bivalent antibody fragments (diabodies), as well as any recombinantly and synthetically produced binding partners.
  • the method comprises comparing the results obtained with a patient serum to positive and negative controls.
  • Positive controls can include:
  • Serum from animals e.g., rabbit, alpaca, etc.
  • animals e.g., rabbit, alpaca, etc.
  • SARS-CoV-2 trimeric Spike protein or any of the antigens described above
  • SARS-CoV-2 trimeric Spike protein or any of the antigens described above.
  • the method can comprise the use of a SARS-CoV-2 trimeric Spike protein or combinations of SARS-CoV-2 antigens to detect novel coronaviruses that do not cross- react with seasonal (non-pathogenic) coronaviruses.
  • the assay can be performed in black, 96 well, non-binding microtiter plate (cat#655090; Greiner Bio-One, Germany). Proteins can be coupled to magnetic beads as described in Longley RJ, Franga CT, White MT, Kumpitak C, Sa- Angchai P, Gruszczyk J, et al. Asymptomatic Plasmodium vivax infections induce robust IgG responses to multiple blood-stage proteins in a low-transmission region of western Thailand. Malar J. 2017; 16(1 ) : 178, which is hereby incorporated by reference.
  • mI_ of protein-conjugated magnetic beads 500/region/pL
  • 50 mI_ of diluted serum can be mixed and incubated for 30 min at room temperature on a plate shaker. All dilutions can be made in phosphate buffered saline containing 1% bovine serum albumin and 0.05% (v/v) Tween-20 (denoted as PBT), and all samples run in singlicate. Following incubation, the magnetic beads can be separated using magnetic plate separator (Luminex®) for 60 seconds and washed three times with 100 pL of PBT. The washed magnetic beads can be incubated for 15 minutes with detector secondary antibody at room temperature on a plate shaker.
  • Luminex® magnetic plate separator
  • the magnetic beads can be separated and washed three times with 100 pl_ of PBT and finally resuspended in 100 mI_ of PBT.
  • serum samples were diluted 1/200, and R-Phycoerythrin (R-PE) - conjugated Donkey Anti-Fluman IgM (cat#709-116-073; JacksonlmmunoResearch, UK) antibody can be used as secondary antibody at 1/400 dilution.
  • R-PE R-Phycoerythrin
  • R-PE R-Phycoerythrin
  • Donkey Anti-Human IgG catalog#709-116-098; JacksonlmmunoResearch, UK
  • MFI median fluorescence intensity
  • a 5-parameter logistic curve can be used to convert MFI to antibody dilution, relative to the standard curve performed on the same plate to account for inter-assay variations.
  • the multiplex immunoassay can be validated by checking that the MFI obtained are well correlated with those obtained in monoplex (only one conjugated bead type per well). For non-SARS- CoV-2 antigens, MFI data can be used for the analysis.
  • Table 1 Panels of samples. Positive control serum samples are from patients with RT- qPCR confirmed SARS-CoV-2 infection. Negative control samples are from panels of pre epidemic cohorts with ethical approval for broad antibody testing. Age is presented as median and range.
  • SARS-CoV-2 trimeric Spike ectodomain (S tri ) and its receptor binding domain (RBD) produced as recombinant proteins in mammalian cells in the Structural Virology Unit at Institut Pasteur, while S1 (cat# REC31806; comprising amino acids 1-674 of subunit 1 ) and S2 (cat# REC31807; comprising amino acids 685-1211 ) subunits were purchased from Native Antigen, Oxford, UK.
  • S tri SARS-CoV-2 trimeric Spike ectodomain
  • RBD receptor binding domain
  • RBD RBD construct
  • RBDv2 included an exogenous signal peptide of a human kappa light chain (METDTLLLWVLLLWVPGSTG; SEQ ID NO:1) to ensure efficient protein secretion into the media.
  • the S ectodomain construct was engineered, as reported before to have the stabilizing double proline mutation (KV986-987 to PP986-987) and the foldon domain at the C-terminus that allows the S to trimerize (YIPEAPRDGQAYVRKDGEWVLLSTFL; SEQ ID NO:2) resembling the native S state on the virion [24]
  • Both constructs contained a Strep (WSFIPQFEK SEQ ID NO:3), an octa-histidine, and an Avi tag (GLNDIFEAQKIEWHE; SEQ ID NO:4) at the C- terminus for affinity purification.
  • Strep tag SAWSHPQFEK (SEQ ID NO:6)
  • Protein expression was done by transient transfection of mammalian HEK293 free style cells, as already reported. Proteins were then purified from supernatants on a Streptactin column (IBA Biosciences) followed by size exclusion purification on Superdex 200 column using standard chromatography protocols.
  • Additional antigens for seasonal coronaviruses 229E NP (cat# REC31758-100) and NL63 NP (cat# REC31759- 100), influenza A (cat# FLU-H1 N1 -HA-100), adenovirus type 40 (cat# NAT41552-100) and rubella (cat# REC31651 -100) were purchased from Native Antigen. All proteins were coupled to magnetic beads as described elsewhere [25]. The mass of proteins coupled on beads were optimized to generate a log-linear standard curve with a pool of positive serum prepared from RT-qPCR-confirmed SARS-CoV-2 patients.
  • the assay was performed in black, 96 well, non-binding microtiter plate (cat#655090; Greiner Bio-One, Germany). Briefly 50 mI_ of protein-conjugated magnetic beads (500/region/pL) and 50 mI_ of diluted serum were mixed and incubated for 30 min at room temperature on a plate shaker. All dilutions were made in phosphate buffered saline containing 1% bovine serum albumin and 0.05% (v/v) Tween-20 (denoted as PBT), and all samples were run in singlicate. Following incubation, the magnetic beads were separated using magnetic plate separator (Luminex®) for 60 seconds and washed three times with 100 mI_ of PBT.
  • Luminex® magnetic plate separator
  • the washed magnetic beads were incubated for 15 minutes with detector secondary antibody at room temperature on a plate shaker.
  • the magnetic beads were separated and washed three times with 100 mI_ of PBT and finally resuspended in 100 mI_ of PBT.
  • serum samples were diluted 1/200, and R-Phycoerythrin (R-PE) -conjugated Donkey Anti-Human IgM (cat#709-116- 073; JacksonlmmunoResearch, UK) antibody was used as secondary antibody at 1/400 dilution.
  • R-PE R-Phycoerythrin
  • IgG For IgG, serum samples were diluted 1/100, and R-Phycoerythrin (R-PE) - conjugated Donkey Anti-Human IgG (cat#709-116-098; JacksonlmmunoResearch, UK) antibody was used as secondary antibody at 1/120 dilution. [0108] On each plate, two blanks (only beads, no serum) were included as well as a standard curve prepared from two-fold serial dilutions (1 :50 to 1 :25600) of a pool of positive controls. Plates were read using a Luminex® MAGPIX® system and the median fluorescence intensity (MFI) was used for analysis.
  • MFI median fluorescence intensity
  • a 5-parameter logistic curve was used to convert MFI to antibody dilution, relative to the standard curve performed on the same plate to account for inter-assay variations.
  • the multiplex immunoassay was validated by checking that the MFI obtained were well correlated with those obtained in monoplex (only one conjugated bead type per well). For non-SARS-CoV-2 antigens, MFI data was used for the analysis.
  • diagnostic sensitivity is defined as the proportion of patients with RT-qPCR confirmed SARS-CoV-2 infection with measured antibody levels above a given seropositivity cutoff.
  • diagnostic specificity is defined to be the proportion of negative controls (with no history of SARS-CoV-2 infection) with measured antibody levels below a given seropositivity cutoff. Sensitivity and specificity can be traded off against each other by varying the seropositivity cutoff. This trade-off is formally evaluated using Receiver Operating Characteristic (ROC) analysis.
  • ROC Receiver Operating Characteristic
  • Measured antibody responses to multiple antigens can be combined to identify individuals with previous SARS-CoV-2 infection using classification algorithms.
  • classification algorithms Here we use a random forests or support vector machine algorithm [17].
  • Uncertainty in sensitivity and specificity is quantified in three ways: (i) binomial confidence intervals calculated using Wilson’s method; (ii) 1000-fold repeat cross-validation with a training set comprising 2/3 of the data and a disjoint testing set comprising 1/3 of the data; (iii) cross-panel validation with algorithms trained and tested on disjoint panels of data ( Figure 6).
  • SARS-CoV-2 antibody kinetics are described using a previously published mathematical model of the immunological processes underlying the generation and waning of antibody responses following infection or vaccination [10].
  • the existing model is adapted to account for the frequent data available in the first weeks of infection. dB
  • B B lymphocytes
  • d the rate of differentiation of B lymphocytes into antibody secreting plasma cells
  • Psde the rate of differentiation of B lymphocytes into antibody secreting plasma cells
  • P/de the long-lived plasma cells
  • p the proportion of plasma cells that are short-lived
  • g the rate of generation of antibodies (IgG or IgM) from plasma cells
  • r the rate of decay of antibody molecules.
  • the model was first fitted to data from 23 patients with RT-qPCR confirmed SARS- CoV-2 infection in Hong Kong hospitals who were followed longitudinally for up to four weeks after initial onset of symptoms [1]. Posterior estimates from this model and data were used to provide prior estimates for the parameters describing the early stages of the antibody response (Appendix Table 7).
  • a ROC curve obtained from a training data set consisting of both positive and negative samples is described by a sequence of sensitivities and specificities ⁇ se ⁇ sp .
  • N-fold cross-validation generates samples of sensitivity ⁇ se ⁇ , ..., se iN ⁇ for each sp t and samples of specificity ⁇ sp ⁇ , , sp iN ⁇ for each se t .
  • IgG and IgM antibody responses to twelve antigens were measured as median fluorescence intensity (MFI).
  • MFI median fluorescence intensity
  • the measured MFI was converted to antibody dilutions ( Figure 1 ).
  • SARS-CoV-2 biomarkers for all 14 SARS-CoV-2 biomarkers (seven antigens, IgG and IgM for both), measured responses were significantly higher in samples with RT-qPCR confirmed infection than in negative control samples ( Figure 1A-B; P value ⁇ 1 x 10 -7 ; 2 sided t test).
  • anti-S tri IgG was the best performing biomarker with 99.1% specificity (95% Cl: 97.4%, 99.7%) and 91 .6% sensitivity (95% Cl: 87.5%, 94.5%).
  • Anti-S tri IgG provided significantly better classification than all other biomarkers (Table 4; McNemar’s test P value ⁇ 10 8 ).
  • Table 4 Sensitivity and specificity targets for single biomarkers and multiplex combinations. 95% binomial confidence intervals were calculated using Wilson’s method. Antigen combinations were selected to optimize sensitivity for the high specificity target, i.e. the highest sensitivity while enforcing specificity > 99%.
  • FIG. 2A provides an overview of six pairwise comparisons of antibody responses. The data are noisy, highly correlated and high dimensional (although only two dimensions are depicted here). We refer to the pattern of multiple antibody responses in multiple dimensions as the serological signature. For all plots of SARS-CoV-2 biomarkers there are two distinct clusters: antibody responses from negative control samples in blue cluster in the bottom left, and antibody responses from serum samples from individuals with RT- qPCR confirmed SARS-CoV-2 infection cluster in the centre and top right.
  • FIG. 3A shows data from a patient from Hopital Bichat with frequent longitudinal sampling. The data and model indicate that the antibody response is in a rising phase between 5 and 30 days after symptom onset.
  • the seroconversion time depends on the seropositivity cutoff. For the cutoffs shown, seroconversion occurs for anti-S tri IgG, anti-RBD vi IgG and anti- 52 IgG, but not for anti-RBD V 2 IgG, anti-S1 IgG, anti-NP vi IgG and anti-NP V 2 IgG.
  • Figure 3B shows the model predicted IgG antibody response to SARS-CoV-2.
  • the percentage reduction in antibody level after one year was mostly determined by prior information and estimated to be 47% (95% Crl: 18%, 90%) for anti-S tri IgG antibodies, with comparable estimates for other antigens (Appendix Table 8).
  • FIG. 5A presents ROC curves for a monoplex anti-S tri IgG assay and a multiplex assay using six biomarkers from Table 2 with quantification of uncertainty via repeat cross-validation.
  • Table 3 List of antigens included in the multiplex serological assay. supplier, short expression catalog category name recombinant antigen system number
  • Ade40 HEK293 controls Hexon (capside) NAT41552-100 Rubella virus-like particles internal Native Antigen
  • Table 5 Model predicted reductions in antibody levels and sensitivity at 6 and 12 months after symptom onset. All results are shown for a high specificity target (>99%). model predicted cutoff model predicted reduction in antibody antibody sensitivity levels _ dilution _
  • NPvi IgG (2.5%, (11 .5%, (53.6%, (47.7%,
  • SARS-CoV-2 antibody kinetics There are limited available longitudinal data on SARS-CoV-2 antibody kinetics, and no data from long-term follow-up (as of June 2020). However, there are a number of published studies on the long-term antibody kinetics to other coronaviruses, most notably Severe Acute Respiratory Syndrome coronavirus (SARS-CoV). Here we review some of the available published data, and describe how this can be used to provide prior information for modelling SARS-CoV-2 antibody kinetics.
  • SARS-CoV Severe Acute Respiratory Syndrome coronavirus
  • Appendix Table 6 summarises some of the published data on the long-term antibody kinetics to a number of coronaviruses: SARS-CoV, human seasonal coronavirus 229E, and Middle East Respiratory Syndrome coronavirus (MERS-CoV). From the extracted time series, we estimated two summary statistics characterizing the long-term antibody response: the half-life of the long-lived component of the antibody response, and the percentage reduction in antibody response after one year. The half-life of the long- lived component of the antibody response was estimated by fitting a linear regression model to measurements of (log) antibody response taken greater than six months after symptom onset. The percentage reduction in antibody response after one year was estimated based on the reduction from the peak measured antibody response to the estimated antibody level at one year. Although a wide range of assays from ELISA to micro-neutralisation were used in the reviewed studies, in this simple and approximate analysis we did not attempt to account for assay dependent effects, except to subtract background antibody levels where necessary.
  • Table 6 Prior data on the duration of antibody responses to coronaviruses. Data from longitudinal studies on measured antibody levels to SARS coronavirus, seasonal coronavirus 229E, and MERS coronavirus. For each study, the time series describing the antibody kinetics was extracted. The half-life of the long-lived component of the antibody response was estimated using measurements of antibody response measured after 6 months from symptom onset - the subset of the data used for this calculation is indicated in bold below. The percentage reduction in antibodies after one year is estimated based on the reduction from the peak measured response to the estimate antibody level at year.
  • the Hong Kong based team expressed and purified recombinant proteins for receptor-binding domain (RBD) and nucleoprotein (NP).
  • RBD receptor-binding domain
  • NP nucleoprotein
  • Genes encoding the spike RBD (amino acid residues 306 to 543 of the spike protein) and full length NP of SARS-CoV-2 were codon-optimized, synthesized and cloned.
  • IgG and IgM antibody responses were quantified via the optical density (OD) from an enzyme immunoassay (EIA). Serial dilutions from 1 :100 to 1 :16,000 of a positive control serum were assayed for IgG responses. This allowed conversion of IgG antibody responses measured by EIA OD to dilutions.
  • the mean value of 93 anonymous archived serum specimens from 2018 plus 3 standard deviations was used.
  • the time to anti- RBD IgG sero-conversion was 8.6 days (IQR: 5.3, 10.4), and the time to anti-NP IgM sero-conversion was 11.6 days (IQR: 9.2, 28.6).
  • time to sero-conversion is dependent on the selection of sero-positivity cutoff, this suggests that IgM responses are not induced before IgG responses, and that both are generated at approximately the same time.
  • Table 7 Parameter estimates for antibody kinetics model fitted to Hong Kong data. Parameters of the antibody kinetics model are presented as posterior medians with 95% credible intervals. The model is fitted in a mixed-effects framework, so for every parameter we estimate the distribution within the entire population rather than a fixed value. We present the mean and standard deviation as summary statistics for the estimated distributions half-life of long- di 100 104 103 lived ASCs (76, 142) (68, (66,
  • Table 8 Parameter estimates for antibody kinetics model fitted to France data. Parameters of the antibody kinetics model are presented as posterior medians with 95% credible intervals. The model is fitted in a mixed-effects framework, so for every parameter we estimate the distribution within the entire population rather than a fixed value.
  • SARS-CoV-2 diagnostics performance data. The Foundation for innovative New Diagnostics (FIND). https://www. finddx. org/covid-19/dx-data/ Guo L, Ren L, Yang S, etal. Profiling Early Humoral Response to Diagnose Novel Coronavirus Disease (COVID-19). Clin Inf Dis. 2020; https://doi.Org/10.1093/cid/ciaa310 Okba NMA, Muller MA, Li W, et al. SARS-CoV-2 specific antibody responses in

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Abstract

L'invention concerne des compositions, des kits, des algorithmes statistiques et des procédés pour l'immunodétection d'anticorps contre le coronavirus 2 du syndrome respiratoire aigu sévère (SARS-CoV-2) dans des échantillons biologiques tels que des échantillons de sang humain. L'invention concerne des compositions, des kits, des algorithmes statistiques et des procédés comprenant une protéine de spicule trimérique de SARS-CoV-2 et des combinaisons d'antigènes du SARS-CoV-2. La protéine de spicule trimérique de SARS-CoV-2 et des combinaisons d'antigènes du SARS-CoV-2 permettent une détection améliorée d'anticorps contre le coronavirus 2 du syndrome respiratoire aigu sévère (SARS-CoV-2) dans des échantillons biologiques tels que des échantillons de sang humain.
PCT/EP2021/067755 2020-07-28 2021-06-28 Diagnostic sérologique à base d'anticorps de l'infection au sars-cov-2 WO2022022927A1 (fr)

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Non-Patent Citations (50)

* Cited by examiner, † Cited by third party
Title
ADAMS ERANAND RANDERSSON MIAUCKLAND KBAILLIE JKBARNES E ET AL.: "Evaluation of antibody testing for SARS-Cov-2 using ELISA and lateral flow immunoassays", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.04.15.20066407>
AMANAT FSTADLBAUER DSTROHMEIER SNGUYEN TCHROMIKOVA VMCMAHON M ET AL.: "A serological assay to detect SARS-CoV-2 seroconversion in humans", NATURE MED, 2020
ANDRAUD MLEJEUNE OMUSORO ET AL.: "Living on three time scales: the dynamics of plasma cell and antibody populations illustrated for hepatitis A virus", PLOS COMPUT BIOL, vol. 8, 2012, pages e1002418
AZMAN ASLESSLER JLUQUERO FJBHUIYAN TR ET AL.: "Estimating cholera incidence with cross-sectional serology", SCI TRANSL MED, vol. 11, 2019, pages 480
BIN JU ET AL: "Potent human neutralizing antibodies elicited by SARS-CoV-2 infection", 26 March 2020 (2020-03-26), XP055737104, Retrieved from the Internet <URL:https://www.biorxiv.org/content/10.1101/2020.03.21.990770v2.full.pdf> [retrieved on 20201006], DOI: 10.1101/2020.03.21.990770 *
BRYANT JEAZMAN ASFERRARI MJARNOLD BFBONI MFBOUM Y ET AL.: "Serology for SARS-CoV-2: Apprehensions, opportunities, and the path forward", SCIENCE IMMUNOL, vol. 5, 2020, pages 47
CALLOW KAPARRY HFSERGEANT MTYRRELL DA: "The time course of the immune response to experimental coronavirus infection of man", EPIDEMIOL INFECT, vol. 105, no. 2, 1990, pages 435 - 46
CAO WCLIU WZHANG PHZHANG FRICHARDUS JH: "Disappearance of antibodies to SARS-associated coronavirus after recovery", N ENGL J MED, vol. 357, no. 11, 2007, pages 1162 - 3
CHOE PGPERERA RAPMPARK WBSONG KHBANG JHKIM ES ET AL.: "MERS-CoV antibody responses 1 year after symptom onset, South Korea, 2015", EMERG INFECT DIS., vol. 23, no. 7, 2017, pages 1079 - 1084
DIGGLE PJ: "Estimating prevalence using an imperfect test", EPI RES INTERNATIONAL, 2011, pages 608719
FAFI-KREMER SBRUEL TMADEC YGRANT RTONDEUR LGRZELAK L ET AL.: "Serologic responses to SARS-CoV-2 infection among hospital staff with mild disease in eastern France", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.05.19.20101832>
GELMAN A: "Carpenter B. Bayesian analysis of tests with unknown specificity and sensitivity", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.05.22.20108944>
GRZELAK LTEMMAM SPLANCHAIS CDEMERET CHUON CGUIVEL F ET AL.: "SARS-CoV-2 serological analysis of COVID-19 hospitalized patients, pauci-symptomatic individuals and blood donors", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.04.21.20068858>
GUO LREN LYANG S ET AL.: "Profiling Early Humoral Response to Diagnose Novel Coronavirus Disease (COVID-19", CLIN INF DIS, 2020, Retrieved from the Internet <URL:https://doi.org/10.1093/cid/ciaa310>
HACHIM AKAVIAN NCOHEN CACHIN AWHCHU DKWKA PUN MOK C ET AL.: "Beyond the Spike: identification of viral targets of the antibody response to SARS-CoV-2 in COVID-19 patients", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.04.30.20085670>
HADJADJ JYATIM NBARNABEI LCORNEAU ABOUSSIER JPERE H ET AL.: "Impaired type I interferon activity and exacerbated inflammatory responses in severe Covid-19 patients", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.04.19.20068015>
HANSEN JOHANNA ET AL: "Studies in humanized mice and convalescent humans yield a SARS-CoV-2 antibody cocktail", SCIENCE (AMERICAN ASSOCIATION FOR THE ADVANCEMENT OF SCIENCE), 15 June 2020 (2020-06-15), United States, pages 1010 - 1014, XP055834409, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7299284/pdf/369_1010.pdf> [retrieved on 20210824], DOI: 10.1126/science.abd0827 *
HANSEN JOHANNA ET AL: "Supplementary Materials:Studies in humanized mice and convalescent humans yield a SARS-CoV-2 antibody cocktail;", SCIENCE, vol. 369, no. 6506, 15 June 2020 (2020-06-15), US, pages 1 - 30, XP055780760, ISSN: 0036-8075, DOI: 10.1126/science.abd0827 *
HELB DATETTEH KKFEIGNER PLSKINNER JHUBBARD AARINAITWE E ET AL.: "Novel serologic biomarkers provide accurate estimates of recent Plasmodium falciparum exposure for individuals and communities", PROC NATL ACAD SCI USA., vol. 112, no. 32, 2015, pages E4438 - 47
HUANG ATGARCIA-CARRERAS BHITCHINGS MDTYANG BKATZELNICK LRATTIGAN SM ET AL.: "A systematic review of antibody mediated immunity to coronaviruses: antibody kinetics, correlates of protection, and association of antibody responses with severity of disease", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.04.14.20065771>
KIRCHDOERFER RNWANG NPALLESEN JWRAPP DTURNER HLCOTTRELL CA ET AL.: "Stabilized coronavirus spikes are resistant to conformational changes induced by receptor recognition or proteolysis", SCI REP, vol. 8, no. 1, 2018, pages 15701, XP055734535, DOI: 10.1038/s41598-018-34171-7
LARREMORE DBFOSDICK BKBUBAR KMZHANG SKISSLER SMMETCALF CJE ET AL.: "Estimating SARS-CoV-2 seroprevalence and epidemiological parameters with uncertainty from serological surveys", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.04.15.20067066>
LASSAUNIERE RFRISCHE AHARBOE ZBNIELSEN ACYFOMSGAARD AKROGFELT KAJORGENSEN CS: "Evaluation of nine commercial SARS-CoV-2 immunoassays", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.04.09.20056325>
LESCURE FXBOUADMA LNGUYEN DPARISEY MWICKY PHBEHILLIL S ET AL.: "Clinical and virological data of the first cases of COVID-19 in Europe: a case series", LANCET INF DIS., no. 20, 2020, pages 1473 - 3099
LIU WFONTANET AZHANG PHZHAN LXIN ZTBARIL L ET AL.: "Two-year prospective study of the humoral immune response of patients with severe acute respiratory syndrome", J INFECT DIS, vol. 193, 2006, pages 792 - 5
LONG QDENG HCHEN J ET AL.: "Antibody responses to SARS-CoV-2 in COVID-19 patients: the perspective application of serological tests in clinical practice", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.03.18.20038018>
LONG QXTANG XJSHI QLLI QDENG HJYUAN J ET AL.: "Clinical and immunological assessment of asymptomatic SARS-CoV-2 infections", NATURE MED, 2020, Retrieved from the Internet <URL:https://doi.org/10.1038/s41591-020-0965-6>
LONGLEY RJFRANGA CTWHITE MTKUMPITAK CSA-ANGCHAI PGRUSZCZYK J ET AL.: "Asymptomatic Plasmodium vivax infections induce robust IgG responses to multiple blood-stage proteins in a low-transmission region of western Thailand", MALAR J, vol. 16, no. 1, 2017, pages 178
LONGLEY RJWHITE MTTAKASHIMA EBREWSTER JMORITA MHARBERS M ET AL.: "Development and validation of serological markers for detecting recent exposure to Plasmodium vivax infection", NATURE MED, vol. 26, 2020, pages 741 - 749
MAIA NORMAN ET AL: "Ultra-Sensitive High-Resolution Profiling of Anti-SARS-CoV-2 Antibodies for Detecting Early Seroconversion in COVID-19 Patients", MEDRXIV, 2 May 2020 (2020-05-02), XP055769481, Retrieved from the Internet <URL:https://www.medrxiv.org/content/10.1101/2020.04.28.20083691v1.full.pdf> DOI: 10.1101/2020.04.28.20083691 *
MIGCHELSEN SJMARTIN DLSOUTHISOMBATH KTURYAGUMA PHEGGEN ARUBANGAKENE PP ET AL.: "Defining Seropositivity Thresholds for Use in Trachoma Elimination Studies", PLOS NEGL TROP DIS, vol. 11, no. 1, 2017, pages e0005230
MO HZENG GREN XLI HKE CTAN Y ET AL.: "Longitudinal profile of antibodies against SARS-coronavirus in SARS patients and their clinical significance", RESPIROLOGY, vol. 11, no. 1, 2006, pages 49 - 53
OKBA NMAMULLER MALI W ET AL.: "SARS-CoV-2 specific antibody responses in COVID-19 patients", MEDRXIV, 2020, Retrieved from the Internet <URL:https://doi.org/10.1101/2020.03.18.20038059>
PERRAUT RVARELA MLLOUCOUBAR CNIASS OSIDIBE ATALL A ET AL.: "Serological signatures of declining exposure following intensification of integrated malaria control in two rural Senegalese communities", PLOS ONE, vol. 12, no. 6, 2017, pages e0179146
PRENTICE RL: "Surrogate endpoints in clinical-trials-definition and operational criteria", STAT MED, vol. 8, 1989, pages 431 - 40
QIN LGILBERT PBCOREY LMCELRATH MJSELF SG: "A framework for assessing immunological correlates of protection in vaccine trials", J INFECT DIS, vol. 196, 2007, pages 1304 - 12, XP002546592, DOI: 10.1086/522428
RANDAD PRANAY R. ET AL: "COVID-19 serology at population scale: SARS-CoV-2-specific antibody responses in saliva", MEDRXIV, 26 May 2020 (2020-05-26), XP055779351, Retrieved from the Internet <URL:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7273305/pdf/nihpp-2020.05.24.20112300.pdf> [retrieved on 20210224], DOI: 10.1101/2020.05.24.20112300 *
ROSADO JASON ET AL: "Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study", THE LANCET MICROBE, vol. 2, no. 2, 21 December 2020 (2020-12-21), pages e60 - e69, XP055834323, Retrieved from the Internet <URL:https://reader.elsevier.com/reader/sd/pii/S266652472030197X?token=8AD75105B56C7811C22BC82593F951C74C779FCB46B6259482632BD2A4F0E601F77A3738093D3C301D381B4CF18D7E0C&originRegion=eu-west-1&originCreation=20210824085103> *
ROSADO JASON ET AL: "Multiplex assays for the identification of serological signatures of SARS-CoV-2 infection: an antibody-based diagnostic and machine learning study; Supplementary appendix", THE LANCET MICROBE, vol. 2, no. 2, 21 December 2020 (2020-12-21), pages 1 - 32, XP055834337, ISSN: 2666-5247, Retrieved from the Internet <URL:https://ars.els-cdn.com/content/image/1-s2.0-S266652472030197X-mmc1.pdf> DOI: 10.1016/S2666-5247(20)30197-X *
SIORDIA JA JR: "Epidemiology and clinical features of COVID-19: A review of current literature", J CLIN VIROL, vol. 127, 2020, pages 104357, XP055737696, DOI: 10.1016/j.jcv.2020.104357
TAKAHASHI SGREENHOUSE BRODRIGUEZ-BARRAQUER I: "Are SARS-CoV-2 seroprevalence estimates biased?", OSF PREPRINTS, 2020, Retrieved from the Internet <URL:https://doi.org/10.31219/osf.io/y3fxt>
TEUNIS PFVAN EIJKEREN JCDE GRAAF WFMARINOVIC ABKRETZSCHMAR ME: "Linking the seroresponse to infection to within-host heterogeneity in antibody production", EPIDEMICS, vol. 16, 2016, pages 33 - 39, XP029755930, DOI: 10.1016/j.epidem.2016.04.001
TO KKTSANG OTLEUNG WSTAM ARWU TCLUNG DC ET AL.: "Temporal profiles of viral load in posterior oropharyngeal saliva samples and serum antibody responses during infection by SARS-CoV-2: an observational cohort study", LANCET INFECT DIS, vol. 20, 2020, pages 1473 - 3099
WHITE MTBEJON POLOTU A ET AL.: "A combined analysis of immunogenicity, antibody kinetics and vaccine efficacy from phase 2 trials of the RTS,S malaria vaccine", BMC MED, vol. 12, 2014, pages 117, XP021192612, DOI: 10.1186/s12916-014-0117-2
WHITE MTGRIFFIN JTAKPOGHENETA O ET AL.: "Dynamics of the antibody response to Plasmodium falciparum infection in African children", J INFECT DIS, vol. 210, 2014, pages 1115 - 22
WHITE MTIDOKO OSOW SDIALLO AKAMPMANN BBORROW RTROTTER C: "Antibody kinetics following vaccination with MenAfriVac and implications for the duration of protection: an analysis of serological data", LANCET INF DIS, vol. 19, no. 3, 2019, pages 327 - 336
WHITE MTVERITY RGRIFFIN JT ET AL.: "Immunogenicity of the RTS,S/AS01 malaria vaccine and implications for duration of vaccine efficacy: secondary analysis of data from a phase 3 randomised controlled trial", LANCET INFECT DIS, vol. 15, 2015, pages 1450 - 58, XP055509706, DOI: 10.1016/S1473-3099(15)00239-X
WOLFEL RCORMAN VMGUGGEMOS WSEILMAIER MZANGE SMULLER MA ET AL.: "Virological assessment of hospitalized patients with COVID-2019", NATURE, 2020
WU LPWANG NCCHANG YHTIAN XYNA DYZHANG LYZHENG LLAN TWANG LFLIANG GD: "Duration of antibody responses after severe acute respiratory syndrome", EMERG INFECT DIS, vol. 13, no. 10, 2007, pages 1562 - 4
YMAN VWHITE MTASGHAR MSUNDLING CSONDEN KDRAPER SJ ET AL.: "Antibody responses to merozoite antigens after natural Plasmodium falciparum infection: kinetics and longevity in absence of re-exposure", BMC MED, vol. 17, no. 1, 2019, pages 22

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