key: cord-0902845-inukdtmv
authors: Sherman, Amy C.; Desjardins, Michael; Baden, Lindsey R.
title: Vaccine-induced SARS-CoV-2 Antibody Response and the Path to Accelerating Development (Determining a Correlate of Protection)
date: 2021-11-03
journal: Clin Lab Med
DOI: 10.1016/j.cll.2021.10.008
sha: 11d906397b391259cce4d904680016e4e7df37d2
doc_id: 902845
cord_uid: inukdtmv

As new public health challenges relating to COVID-19 emerge, such as variant strains, waning vaccine efficacy over time, and decreased vaccine efficacy for special populations (immunocompromised hosts), it is important to determine a correlate of protection (CoP) to allow accurate bridging studies for special populations and against variants of concern. Large-scale phase 3 clinical trials are inefficient to rapidly assess novel vaccine candidates for variant strains or special populations, because these trials are slow and costly. Defining a practical CoP will aid in efficiently conducting future assessments to further describe protection for individuals and on a population level for surveillance.

Less than 18 months after the identification of SARS-CoV-2 and its genome, thirteen authorized or approved COVID-19 vaccines are being deployed around the world [1] , and many more candidates or "correlate" of protection allows for easier monitoring and surveillance of a particular vaccine's effectiveness, which can aid in both vaccine development and licensure [2] . Markers of immune responses can also be applied to determine a population response for new variants or strains of a virus, across unique characteristics of a population (e.g. elderly, immunocompromised), and across different manufacturing or lots. Furthermore, COVID-19 vaccine boosters may be necessary, and a correlate of protection would allow for efficient measurement of persistent protection. To date, there is no control convalescent serum specimen from individuals with prior COVID-19 to compare the vaccineinduced responses to the natural infection. mRNA and vector vaccines were shown to induce bAb and nAb titers similar or higher than what is detected in convalescent sera [4, 5, 8, 9] . For inactivated vaccines, only CoronaVac and Covaxin trials reported comparison with convalescent sera, and showed respectively lower or similar nAb titers in sera from vaccinated participants compared to convalescents sera [15] . The recombinant vaccine ZF2001 showed significantly higher nAb titers in vaccinated participants than in convalescent sera [16] . However, these data must be cautiously interpreted since the serum panels differ among the different studies. Antibody titers after natural infection can vary significantly in convalescent individuals, based on hosts characteristics, severity of disease and timing from symptom onset [3, 17] .

In individuals with previous SARS-CoV-2 infection, postvaccination humoral responses differ significantly in terms of dynamics and magnitude. In those who received BNT162b2 (Pfizer, Inc) or mRNA-1273 (ModernaTx, Inc), a rapid increase of bAb is seen after the first dose, starting as early as 5-8 days [18] . The titers quickly peak at high levels between days 9 and 12 and do not significantly increase after the second dose. In comparison to those without pre-existing immunity, the titers were 10-45 times higher after the first dose, and remained 6 times higher after the second dose. Another study showed that 2 doses of BNT162b2 (Pfizer, Inc) in previously uninfected individuals induced lower nAb titers than a single dose in those with previous infection.

In the early phase 1/2 COVID-19 vaccine trials, vaccine-induced neutralizing activity was assessed by neutralization assays using pseudovirus expressing the wild-type Spike protein or using wild-J o u r n a l P r e -p r o o f type SARS-CoV-2. However, since January 2021, many different genetic variants of SARS-CoV-2 have emerged around the world. These variants have various substitutions, insertions and/or deletions in the spike protein gene that may lead to increase transmissibility or disease severity, and may also reduce vaccine-induced protection [19] . Current variants of concern according to the Centers for Disease [23] .

There are several definitions of the terms "correlate of protection" and "correlates of risk."

Plotkin defines a correlate of protection (CoP) as a "a specific immune response to a vaccine that is closely related to protection against infection, disease, or other defined end point [24] ." A correlate of protection is typically a measurable immune marker, and preferably one that is relatively easy to obtain by standard laboratory techniques, for facile scalability and reproducibility. Importantly, Plotkin argues that the correlate itself confers protection, which he distinguishes from a "surrogate," which is not itself protective but is an appropriate substitute for a different immune response that does offer protection.

When defining a correlate of protection, it is equally important to define the endpoint being described.

For example, does the immunologic parameter provide protection against infection, transmission, or hospitalization or death? Depending on the outcome measure, the threshold value of a correlate of protection may vary. The term "correlates of risk" was described by Qin et al as the statistical J o u r n a l P r e -p r o o f assessment of a correlate of protection in the context of a clinical trial. In this assessment, the clinical endpoint is the outcome measure of efficacy as predetermined in the clinical trial [25, 26] .

The humoral immune response is an essential feature of protection for many vaccine preventable diseases. Antibodies have been described as good correlates of protection for several different types of pathogens, including tetanus, pneumococcus, hepatitis A, hepatitis B, diphtheria, Haemophilus influenzae b (Hib) [27] [28] [29] . Passive immunity from transfer of antibodies can be shown to be protective. For example, antibodies transferred from maternal transmission to the fetus or antibodies provided clinically by injection can confer protection, which demonstrates a direct protective effect of the immune marker in question. Often, a discrete and quantitative Ab threshold value for protection can be described. However, it should be noted that antibody quality rather than quantity may also be important, and thus a potential limitation in identifying a simplistic quantity of Ab as being protective for a given pathogen.

The immune system is complex and also redundant. Thus, some have proposed that a correlate of protection for a given vaccine is not reflective in a single immune marker, but rather could be a series of immune markers in an immune cascade, or numerous independent immune markers. For example, a clear correlate for measles protection has been identified, with an antibody level of plaque reduction neutralization (PRN) >120 mIU/mL, as demonstrated by successful protection with maternal-fetal transmission of antibodies [30] . However, individuals who are unable to produce antibodies due to humoral deficiencies can clear measles infection, demonstrating an alternative pathway of T-cell induced immunity that confers protection [31, 32] . Therefore, multiple immune pathways may be important for generating protection depending on the pathogen and characteristics of the host, with several unique correlates of protection.

Much controversy exists in the literature regarding the meaning and utilization of immunebased correlates. A vaccine can be shown to induce a specific immune response, however this does not necessarily translate to clinical efficacy. A vaccine may also have an immune response that is statistically associated with an assessment of efficacy, however this value does not directly translate into a causal relationship between the immune marker and protection. To further refine how correlates should be described and thereby applied, several authors have suggested validation models using a combination of statistical and clinical data.

Prentice developed four criteria to evaluate endpoints for randomized controlled trials [33] . These criteria have been adapted in the context of vaccine trials, as listed below [34] :

1. Protection against the clinical endpoint is significantly related to having received the vaccine.

2. The substitute endpoint is significantly related to the vaccination status.

3. The substitute endpoint is significantly related to protection against the clinical endpoint. 4 . The full effect of the vaccine on the frequency of the clinical endpoint is explained by the substitute endpoint, as it lies on the sole causal pathway.

Although described specifically for RCTs, others have demonstrated that the Prentice criteria can also be applied for observational studies, although this was elucidated in relation to cancer research and not vaccinology research [35] .

Qin proposed a framework to statistically describe three different levels of correlates of protection, and defined the data requirements needed to systematically validate the immune marker for each level [25] . The three levels are defined as (1) "correlate of risk," which is most closely associated with protection against a clinical outcome as determined in a clinical trial, followed by (2) "Level 1 specific surrogate of protection" (further split between statistical and principal surrogates), and (3) "Level 2 general surrogate of protection." Although "correlate of risk" was initially described in the The threshold method has also been described, in which a specific level of the immune marker is identified. Individuals who have values above the threshold are considered protected against the clinical endpoint, while those with levels below the threshold are susceptible [29, 37] . Different statistical tests can estimate the threshold by either (1) comparing pre-exposure immune marker levels to disease incidence immune marker levels in observational/cohort studies or (2) examining the proportion of vaccinated and unvaccinated individuals below the threshold and calculating the immune marker-derived vaccine efficacy [38, 39] . The threshold method and variations have been used to describe specific antibody associated levels of protection for several vaccines, including the pneumococcal conjugate vaccine (PCV) [29] , meningococcal C conjugate vaccine [40] , and rubella vaccine [39] .

While the methodologies described by Prentice, Qin and others can be valuable to statistically validate a correlate of protection, the foundation rests upon the measurement of the immunological marker. Assays that have a wide degree of variability and measurement error will impact the subsequent statistical calculations used in these models. Measurement errors should be carefully considered for the SARS-CoV-2 antibody assays, which have shown varying degrees of sensitivity and specificity, with no gold standard, and with various types of assays used for different COVID-19 vaccine trials and post-EUA analyses [41, 42] .

J o u r n a l P r e -p r o o f Determining a CoP for SARS-CoV-2 is essential to determine both individual and population level immunity, and to describe protection both after natural infection and post-vaccination. Furthermore, as new variants emerge and current vaccines are adapted, a defined correlate of protection will be useful to efficiently generate and implement vaccination programs and identify novel vaccines for use in specific populations. As described above, an important factor in describing a CoP is defining and harmonizing the clinical or efficacy endpoint. A uniform endpoint for SARS-CoV-2 has not been clearly defined, with heterogeneous outcome measures described across clinical trials and other COVID-19 studies [43] . The current literature describes the insights gained from passive immunization of monoclonal antibodies in humans, as well as possible correlates of protection as shown in animal models and cohort studies (summarized in Table 2 ). Randomized controlled trials, large population observational studies, and challenge trials may also aid in identifying CoPs for SARS-CoV-2.

Furthermore, as new SARS-CoV-2 variants emerge, sieve analyses may be used to better understand the mechanism behind vaccine protection by using genetic and statistical approaches to measure dissimilarity between virus strains in vaccinated individuals as compared to virus strains in placebo recipients [44] . Similar approaches have been utilized in the field of HIV-1 vaccines and prevention [45] .

As described earlier, a true correlate of protection is an immune component that is responsible for protection against a disease endpoint, and can be demonstrated by passive transfer from an immune individual to a naïve individual. For SARS-CoV-2, monoclonal antibodies (mAbs) have been developed that validate the role of neutralization antibodies as a mechanism of protection against disease [46] . 

The authors next investigated the use of IgG transfer from convalescent macaque sera to naïve macaques who were subsequently challenged with SARS-CoV-2, as well as depletion of CD8+ T-cells in convalescent macaques to identify a correlate of protection [50] . The macaques who received the purified IgG were protected against the challenge infection in a dose-dependent manner. Using logistic regression models, antibody thresholds above 50 for pseudovirus nAb titers, 100 for RBD ELISA titers, and 400 for S ELISA titers were demonstrated to be protective. In the CD8+ T-cell depleted group, some breakthrough infections occurred, suggesting that protection is not independently related to T-cell function, but that cellular immunity likely plays a role, especially in the setting of low antibody titers. The same macaque model was then utilized to assess for vaccine-induced protection with DNA vaccine candidates and adenovirus serotype 26 (Ad26) vector vaccines [51, 51] . Viral replication in BAL fluid and nasal secretions were measured for the endpoint analyses. Due to variability in the outcomes based on the different vaccine constructs administered, the authors were able to evaluate for immune

CoPs. An inverse correlation was described between neutralizing antibodies (both pseudovirus and live virus neutralizing antibody titers) and RNA levels from BAL and nasal secretions, suggesting nAbs as an immune correlate of protection, with nAb titers between 100-250 offering complete protection.

Nonhuman primate challenge models have also been used to evaluate immune responses and determine CoP post-vaccination. To evaluate CoP in the context of mRNA-1273 administration, nonhuman primates were challenged with intratracheal and intranasal SARS-CoV-2 4 weeks after the second vaccination with mRNA-1273 [52] . The endpoint assessment was quantification of SARS-CoV-2 RNA in BAL fluid and nasal secretions. mRNA-1273 induced serum neutralization activity was then correlated with RNA from BAL and nasal secretions and was found to be negatively correlated. Given this finding, in combination with the rapid reduction in viral replication 24-48 hours after challenge, the authors speculated that antibodies do serve as the primary mechanism of protection. However, a J o u r n a l P r e -p r o o f specific threshold could not be determined, since the vaccine induced immune response offered high protection and with limited variation in viral replication.

A limitation of animal models is the inability to entirely recapitulate human pathogenesis and disease. The concentration and inoculation of virus for the challenge in animals may not reflect true transmission dynamics in humans.

Cohort and observational studies can provide information about CoP through epidemiological analyses. Several cohort studies have examined rates of re-infection within distinct populations, which can also provide clues regarding CoP [53] [54] [55] . For example, a large, prospective cohort study in the UK, the SARS-CoV-2 Immunity and Reinfection Evaluation (SIREN) study, enrolled over 30,000 healthcare workers and documented SARS-CoV-2 PCR and antibody testing every 2-4 weeks [56] . The authors describe that the seropositive participants (those with a prior history of SARS-CoV-2 infection) had an 84% lower risk of reinfection (adjusted incidence rate ratio 0.159; 95% CI 0. 13-0.19) . The data provide evidence that antibodies are protective against reinfection, although the authors did not correlate specific antibody thresholds with protection [57] .

The outbreak that occurred on a fishery boat departing from Seattle was essential in determining that nAbs were protective against SARS-CoV-2. 103 out of 117 individuals were seronegative prior to departure and were subsequently infected. Three members of the crew were seropositive with high neutralizing antibodies (1:174, 1:161, and 1:3,082) prior to departure, and did not develop infection as evidenced by negative SARS-CoV-2 PCR from nasopharyngeal swabs and lack of clinical symptoms [58] . Thus, high nAbs were associated with protection, but no exact threshold could be determined from this observational study.

Human challenge studies involve the direct and controlled infection of healthy human volunteers and have been used to investigate novel vaccine candidates. Unlike randomized controlled trials or large population-based studies, controlled human challenge studies are faster and require fewer participants to measure efficacy and immune responses. These designs have been used to study other respiratory viral pathogens like influenza [59] and HCoV-229E, and have been proposed to evaluate SARS-CoV-2 [60, 61] . Challenge models are attractive designs to determine immune CoP, since the exact timing of natural infection and/or immunization and dose can be tightly controlled, allowing for high resolution assessment of correlations between immune markers and efficacy endpoints. [62] . The trials are currently ongoing; no data has been released yet regarding early findings. Later stages may offer insight in discerning CoP.

Randomized controlled trials (RCTs) are well suited to define CoP, since clear clinical endpoints are established and measures of both vaccine efficacy and immune markers are documented at defined intervals. Using the threshold method and other statistical calculations, the vaccine efficacy can be correlated with an immune marker level to determine a CoP. Current evaluation of the Phase 3 data is on-going to determine a CoP, which may be vary for different vaccine constructs.

Based on correlates of protection for other infectious diseases, other important factors must be considered when defining immunological markers of protection after COVID-19 vaccination. This J o u r n a l P r e -p r o o f section will review some of these considerations, such as host factors, the vaccine platform and target antigen, and other important immunological aspects of the immune response to vaccination.

Host factors, such as age, chronic medical conditions and the use of immunosuppressive therapies, have been shown to impact the antibody responses to COVID-19 vaccines. These factors may also impact definitions of COVID-19 postvaccination correlates or surrogates of protection.

Age is an important factor influencing humoral vaccine responses. Most of the COVID-19 vaccine phase 1/2 trials showed that the magnitude of the vaccine-induced antibody responses in older individuals is generally lower than the Ab magnitude produced by younger individuals. For example, mRNA vaccines were shown to produce lower titers of binding antibodies (bAb) and lower or similar titers of neutralizing antibodies (nAb) in participants over 55-65 years of age [5, 9] . The same tendency was shown with vector vaccines, except for AZD1222 which showed similar bAb and nAb titers in all age groups [4, 8, 10] . BBIP-CorV, an inactivated vaccine, led to lower nAb production in those aged 60 and older [63] .

The components of the immune response postvaccination that best correlate with protection may differ quantitively and qualitatively due to immunosenescence [64] . For example, in adults up to 50 years old, serum influenza hemagglutination inhibition (HAI) levels of about 1:40 correlate well with protection [24] . However, higher post-vaccination titers ≥ 1:40 are common among older individuals who develop influenza, suggesting that this threshold is not protective for older individuals [65] . In older individuals, T-cell responses may be a better correlate of vaccine protection against influenza [66] .

The effect of age on COVID-19 vaccine immune correlates is currently unknown. The correlation of bAb and nAb titers after Ad26.CoV2.S was stronger in younger individuals than in those 65 years and J o u r n a l P r e -p r o o f older [4] . This suggests a variation in the immune response phenotype in older individuals, which could influence the definition of immune correlates in this population.

Data are emerging regarding other hosts factors that are associated with lower humoral responses to COVID-19 vaccines, such as chronic comorbidities and immunocompromised states. For example, patients undergoing maintenance hemodialysis showed significant lower bAb than controls after 2 doses of BNT162b2 [67] . Individuals with chronic inflammatory disease treated with immunosuppressive therapies, in particular those receiving B-cell depletion therapy of corticosteroids, exhibit significantly lower bAb and nAb titers after mRNA vaccines [68] . Solid organ transplant recipients were shown to have poor humoral responses after mRNA vaccines [69, 70] , with older individuals and those receiving anti-metabolite therapy having some of the poorest humoral responses.

Immunocompromised individuals have a significantly reduced humoral response to COVID-19

vaccines. CoP in this population may be different than in the general population. For example, patients treated with B-cell depletion therapy (anti-CD20) are usually unable to mount strong humoral immune responses to COVID-19 vaccines or SARS-CoV-2 infection [71, 72] . However, infected individuals on such therapy still have the ability to clear the virus, which suggest that the cellular immune response or other arms of the immune system may have an important role.

Socioeconomic status, usually closely related to other factors such as nutritional status, risk and frequency of exposure, has been shown to impact immune correlates for other diseases. For example, the Ab titers associated with protection against pneumococcal infection has been shown to be higher among infants who live in low-resource settings [29, 73] . The impact of socioeconomic status of environmental factors on correlates of protection from SARS-CoV-2 vaccination is unknown. However, since lower socioeconomic status has been already recognized as a risk factor for disease incidence and mortality [74, 75] , it may as well be an important factor to consider when defining immune correlates post-vaccination.

Vaccines using different technological platforms and antigen targets may induce different qualitative and quantitative antibodies, which is another important factor to consider when establishing immune correlates for COVID-19 vaccines. This concept has been well described with other vaccines such as those against Haemophilus influenzae type b (polysaccharide vs conjugated vaccine) and

Bordetella pertussis (whole cell vs acellular vaccine) [76, 77] , where different platforms were shown to yield different immune repertoire. COVID-19 vaccines use different technologies (mRNA, vector, subunit, inactivated) and different antigen targets (full spike, pre-fusion stabilized spike protein, RBD, inactivated virus), which may lead to different immune response quality and repertoire. Inactivated vaccines have the unique characteristic of presenting the whole virus to the immune system, which leads to the production of antibodies other than anti-spike, such as anti-nucleocapsid [15] . Even if the main target of nAb against SARS-CoV-2 appears to be the spike protein [78] , the antibody repertoire and diversity produced by inactivated vaccines may have immunological significance against SARS-CoV-2 and the circulating variants that possess critical spike protein mutations [79, 80] .

The immune mechanisms leading to protection are complex and usually involve a combination of both humoral and cellular responses [81] . The impact of the relative importance of these two branches of the adaptive immune system for protection against SARS-CoV-2 is still unknown. Many studies have shown that antibodies are associated with protection against re-infection [56] , but few have evaluated the implication of cellular immune response on re-infection. COVID-19 vaccines have been shown to induce strong humoral immunity, but T-cell responses were also elicited after vaccination [4, 5] . In a non-humate primate study using an adenovirus based vaccine (Ad26-S.PP), T-cell J o u r n a l P r e -p r o o f responses did not seem to correlate with protection [51] . It is still unknown if the cellular response contributes to protection in humans, however there are clues that cellular responses are important. For example, the clinical protection from BNT162 against COVID-19 may start as soon as 12 days after the first dose [82] . However, nAb titers within the first 21 days post-vaccination are low or undetectable [9] .

Researchers showed that three weeks after the first BNT162b2 dose, nAb were not detected, but strong responses of RBD and spike antibodies with Fc-mediated effector functions and cellular responses largely by CD4+ T cell responses were seen [83] .

Mucosal immunity is another possible key component of COVID-19 protection, as SARS-CoV-2 initially infects the respiratory mucosal surfaces [84] . However, the mucosal immunity that results from COVID-19 natural infection and vaccination and its implication in defining COVID-19 correlates of protection remains largely unknown.

The vaccine-induced correlate of protection for SARS-CoV-2 has yet to be defined. When establishing a CoP, it will be essential not only to identify the appropriate immune marker, but also to properly define the endpoint measure (e.g. clinical disease especially severe illness, transmission, SARS-CoV-2 PCR positivity) and understand the nuances of CoP in terms of host and antigen characteristics.

Furthermore, standardized assays for the chosen immune marker(s) must be established in order to ensure comparability between disparate vaccine platforms and conditions of use. Ideally, these assays should be a test that is relatively easy to perform and does not require specialized equipment or reagents to promote easy scalability across the globe. Much of the focus has been to determine a humoral CoP, in part due to the ease of collection and evaluation, though cellular responses are also likely to be important.

As new public health challenges relating to COVID-19 emerge, such as variant strains, waning vaccine efficacy over time, and decreased vaccine efficacy for special populations (such as J o u r n a l P r e -p r o o f immunocompromised hosts), it is important to determine a CoP to allow accurate bridging studies for special populations and against variants of concern In the context of a global pandemic with dynamic threats to public health, large scale Phase 3 clinical trials are inefficient to rapidly assess novel vaccine candidates for variant strains or for special populations, since these trials are slow and costly. Defining a practical CoP will aid in efficiently conducting future assessments to further describe protection for individuals and on a population-level for surveillance.

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