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Several studies show neutralizing antibody levels are an important correlate of immune protection from COVID-19 and have estimated the relationship between neutralizing antibodies and protection. However, a number of these studies appear to yield quite different estimates of the level of neutralizing antibodies required for protection. Here we show that after normalization of antibody titers current studies converge on a consistent relationship between antibody levels and protection from COVID-19.

SummarySARS-CoV-2 continues to evolve and the vaccine efficacy against variants is challenging to estimate. It is now common in phase III vaccine trials to provide vaccine to those randomized to placebo once efficacy has been demonstrated, precluding a direct assessment of placebo controlled vaccine efficacy after placebo vaccination. In this work we extend methods developed for estimating vaccine efficacy post placebo vaccination to allow variant specific time varying vaccine efficacy, where time is measured since vaccination. The key idea is to infer counterfactual strain specific placebo case counts by using surveillance data that provide the proportions of the different strains. This blending of clinical trial and observational data allows estimation of strain-specific time varying vaccine efficacy, or sieve effects, including for strains that emergent after placebo vaccination. The key requirements are that surveillance strain distribution accurately reflect the strain distribution for a placebo group, throughout follow-up after placebo group vaccination and that at least one strain is present before and after placebo vaccination. For illustration, we develop a Poisson approach for an idealized design under a rare disease assumption and then use a proportional hazards modeling to better reflect the complexities of field trials with staggered entry, crossover, and smoothly varying strain specific vaccine efficacy We evaluate these by theoretical work and simulations, and demonstrate that useful estimation of the efficacy profile is possible for strains that emerge after vaccination of the placebo group. An important principle is to incorporate sensitivity analyses to guard against mis-specfication of the strain distribution. We also provide an approach for use when genotyping of the infecting strains of the trial participants has not been done.

BackgroundIn the Coronavirus Efficacy (COVE) trial, estimated mRNA-1273 vaccine efficacy against coronavirus disease-19 (COVID-19) was 94%. SARS-CoV-2 antibody measurements were assessed as correlates of COVID-19 risk and as correlates of protection.MethodsThrough case-cohort sampling, participants were selected for measurement of four serum antibody markers at Day 1 (first dose), Day 29 (second dose), and Day 57: IgG binding antibodies (bAbs) to Spike, bAbs to Spike receptor-binding domain (RBD), and 50% and 80% inhibitory dilution pseudovirus neutralizing antibody titers calibrated to the WHO International Standard (cID50 and cID80). Participants with no evidence of previous SARS-CoV-2 infection were included. Cox regression assessed in vaccine recipients the association of each Day 29 or 57 serologic marker with COVID-19 through 126 or 100 days of follow-up, respectively, adjusting for risk factors.ResultsDay 57 Spike IgG, RBD IgG, cID50, and cID80 neutralization levels were each inversely correlated with risk of COVID-19: hazard ratios 0.66 (95% CI 0.50, 0.88; p=0.005); 0.57 (0.40, 0.82; p=0.002); 0.42 (0.27, 0.65; pConclusionsBinding and neutralizing antibodies correlated with COVID-19 risk and vaccine efficacy and likely have utility in predicting mRNA-1273 vaccine efficacy against COVID-19.Trial registration numberCOVEClinicalTrials.govnumber,NCT04470427

Broadly neutralizing antibodies are promising agents to prevent HIV infection and achieve HIV remission without antiretroviral therapy (ART). As learned from effective ART, HIV viral diversity necessitates combination antibody cocktails. Here, we demonstrate how to optimally choose the ratio within combinations based on the constraint of a total dose size. Optimization in terms of prevention efficacy outcome requires a model that synthesizes 1) antibody pharmacokinetics (PK), 2) a mapping between concentration and neutralization against a genetically diverse pathogen (e.g., distributions of viral IC50 or IC80), 3) a protection correlate to translate in vitro potency to clinical protection, and 4) an in vivo interaction model for how drugs work together. We find that there is not a general solution, and the optimal dose ratio likely will be different if antibodies cooperate versus if both products must be simultaneously present. Optimization requires trade-offs between potency and longevity; using an in silico case-study, we show a cocktail can outperform a bi-specific antibody (a single drug with 2 merged antibodies) with superior potency but worse longevity. In another practical case study, we perform a triple antibody optimization of VRC07, 3BNC117, and 10-1074 bNAb variants using empirical PK and a pre-clinical correlate of protection derived from animal challenge studies. Here, a 2:1:1 dose emphasizing VRC07 would optimally balance protection while achieving practical dosing and given conservative judgements about prior data. Our approach can be immediately applied to optimize the next generation of combination antibody prevention and cure studies.

The emergence and establishment of SARS-CoV-2 variants of interest (VOI) and variants of concern (VOC) highlight the importance of genomic surveillance. We propose a statistical learning strategy (SLS) for identifying and spatiotemporally tracking potentially relevant Spike protein mutations. We analyzed 167,893 Spike protein sequences from US COVID-19 cases (excluding 21,391 sequences from VOI/VOC strains) deposited at GISAID from January 19, 2020 to March 15, 2021. Alignment against the reference Spike protein sequence led to the identification of viral residue variants (VRVs), i.e., residues harboring a substitution compared to the reference strain. Next, generalized additive models were applied to model VRV temporal dynamics, to identify VRVs with significant and substantial dynamics (false discovery rate q-value 10% on at least one day).Unsupervised learning was then applied to hierarchically organize VRVs by spatiotemporal patterns and identify VRV-haplotypes. Finally, homology modelling was performed to gain insight into potential impact of VRVs on Spike protein structure. We identified 90 VRVs, 71 of which have not previously been observed in a VOI/VOC, and 35 of which have emerged recently and are durably present. Our analysis identifies 17 VRVs ∼91 days earlier than their first corresponding VOI/VOC publication. Unsupervised learning revealed eight VRV-haplotypes of 4 VRVs or more, suggesting two emerging strains (B1.1.222 and B.1.234). Structural modeling supported potential functional impact of the D1118H and L452R mutations. The SLS approach equally monitors all Spike residues over time, independently of existing phylogenic classifications, and is complementary to existing genomic surveillance methods.

A gene signature previously correlated with mosaic adenovirus 26 vaccine protection in simian immunodeficiency virus (SIV) and SHIV challenge models in non-human primates (NHP). In this report we investigated presence of this signature as a correlate of reduced risk in human clinical trials and potential mechanism for protection. The absence of this gene signature in the DNA/rAd5 human vaccine trial which did not show efficacy, strengthens our hypothesis that this signature is only enriched in studies that demonstrated protection. This gene signature was enriched in the partially effective RV144 human trial that administered the ALVAC/protein vaccine, and we find that the signature associates with both decreased risk of HIV-1 acquisition and increased vaccine efficacy. Total RNA-seq in a clinical trial that used the same vaccine regimen as the RV144 HIV vaccine implicated antibody-dependent cellular phagocytosis (ADCP) as a potential mechanism of vaccine protection. CITE-seq profiling of 53 surface markers and transcriptomes of 53,777 single cells from the same trial, showed that genes in this signature were primarily expressed in cells belonging to the myeloid lineage including monocytes, which are major effector cells for ADCP. The consistent association of this transcriptome signature with vaccine efficacy represents a tool both to identify potential mechanisms, as with ADCP here, and to screen novel approaches to accelerate development of new vaccine candidates.

Although interim results from several large, placebo-controlled, phase 3 trials demonstrated high vaccine efficacy (VE) against symptomatic coronavirus disease 2019 (COVID-19), it is unknown how effective the vaccines are in preventing people from becoming asymptomatically infected and potentially spreading the virus unwittingly. It is more difficult to evaluate VE against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than against symptomatic COVID-19 because infection is not observed directly but rather is known to occur between 2 antibody or reverse-transcription polymerase chain reaction (RT-PCR) tests. Additional challenges arise as community transmission changes over time and as participants are vaccinated on different dates because of staggered enrollment of participants or crossover of placebo recipients to the vaccine arm before the end of the study. Here, we provide valid and efficient statistical methods for estimating potentially waning VE against SARS-CoV-2 infection with blood or nasal samples under time-varying community transmission, staggered enrollment, and blinded or unblinded crossover. We demonstrate the usefulness of the proposed methods through numerical studies that mimic the BNT162b2 phase 3 trial and the Prevent COVID U study. In addition, we assess how crossover and the frequency of diagnostic tests affect the precision of VE estimates., We show how to estimate potentially waning efficacy of coronavirus disease 2019 vaccines against severe acute respiratory syndrome coronavirus 2 infection using blood or nasal samples collected periodically from clinical trials with staggered enrollment of participants and crossover of placebo recipients.

Though eleven novel COVID-19 vaccines have demonstrated efficacy, additional affordable, scalable, and deliverable vaccines are needed to meet unprecedented global demand. With placebo-controlled efficacy trials becoming infeasible due to the roll out of licensed vaccines, a correlate of protection is urgently needed to provide a path for regulatory approval of novel vaccines. To assess whether antibody titers may reasonably predict efficacy, we evaluated the relationship between efficacy and in vitro neutralizing and binding antibodies of 7 vaccines for which sufficient data have been generated. Once calibrated to titers of human convalescent sera reported in each study, a robust correlation was seen between neutralizing titer and efficacy (ρ= 0.79) and binding antibody titer and efficacy (ρ = 0.93), despite geographically diverse study populations subject to different forces of infection and circulating variants, and use of different endpoints, assays, convalescent sera panels and manufacturing platforms. This correlation is strengthened by substituting post-hoc analyses for efficacy against the ancestral strain (D614G), where available. Together with an accumulating body of evidence from natural history studies and animal models, these results support the use of post-immunization antibody titers as the basis for establishing a correlate of protection for COVID-19 vaccines.

Ongoing SARS-CoV-2 vaccine trials assess vaccine efficacy against disease (VEDIS), the ability of a vaccine to block symptomatic COVID-19. They will only partially discriminate whether VEDIS is mediated by preventing infection as defined by the detection of virus in the airways (vaccine efficacy against infection defined as VESUSC), or by preventing symptoms despite breakthrough infection (vaccine efficacy against symptoms or VESYMP). Vaccine efficacy against infectiousness (VEINF), defined as the decrease in secondary transmissions from infected vaccine recipients versus from infected placebo recipients, is also not being measured. Using mathematical modeling of data from King County Washington, we demonstrate that if the Moderna and Pfizer vaccines, which have observed VEDIS>90%, mediate VEDIS predominately by complete protection against infection, then prevention of a fourth epidemic wave in the spring of 2021, and associated reduction of subsequent cases and deaths by 60%, is likely to occur assuming rapid enough vaccine roll out. If high VEDIS is explained primarily by reduction in symptoms, then VEINF>50% will be necessary to prevent or limit the extent of this fourth epidemic wave. The potential added benefits of high VEINF would be evident regardless of vaccine allocation strategy and would be enhanced if vaccine roll out rate is low or if available vaccines demonstrate waning immunity. Finally, we demonstrate that a 1.0 log vaccine-mediated reduction in average peak viral load might be sufficient to achieve VEINF=60% and that human challenge studies with 104 infected participants, or clinical trials in a university student population could estimate VESUSC, VESYMP and VEINF using viral load metrics.

Continued follow-up of participants in COVID-19 vaccine trials after vaccination of the placebo group permits determination of vaccine durability and allows the incorporation of a booster trial if waning vaccine efficacy is observed, while ensuring the ethical treatment of participants., Multiple candidate vaccines to prevent COVID-19 have entered large-scale phase 3 placebo-controlled randomized clinical trials, and several have demonstrated substantial short-term efficacy. At some point after demonstration of substantial efficacy, placebo recipients should be offered the efficacious vaccine from their trial, which will occur before longer-term efficacy and safety are known. The absence of a placebo group could compromise assessment of longer-term vaccine effects. However, by continuing follow-up after vaccination of the placebo group, this study shows that placebo-controlled vaccine efficacy can be mathematically derived by assuming that the benefit of vaccination over time has the same profile for the original vaccine recipients and the original placebo recipients after their vaccination. Although this derivation provides less precise estimates than would be obtained by a standard trial where the placebo group remains unvaccinated, this proposed approach allows estimation of longer-term effect, including durability of vaccine efficacy and whether the vaccine eventually becomes harmful for some. Deferred vaccination, if done open-label, may lead to riskier behavior in the unblinded original vaccine group, confounding estimates of long-term vaccine efficacy. Hence, deferred vaccination via blinded crossover, where the vaccine group receives placebo and vice versa, would be the preferred way to assess vaccine durability and potential delayed harm. Deferred vaccination allows placebo recipients timely access to the vaccine when it would no longer be proper to maintain them on placebo, yet still allows important insights about immunologic and clinical effectiveness over time.

SummaryThe phase 2b AMP trials are testing whether the broadly neutralizing antibody VRC01 prevents HIV-1 infection in two cohorts: women in sub-Saharan Africa, and men and transgender persons who have sex with men (MSM/TG) in the Americas and Switzerland. We used nonlinear mixed effects modeling of longitudinal serum VRC01 concentrations to characterize pharmacokinetics and predict HIV-1 neutralization coverage. We found that body weight significantly influenced clearance, and that the mean peripheral volume of distribution, steady state volume of distribution, elimination half-life, and accumulation ratio were significantly higher in MSM/TG than in women. Neutralization coverage was predicted to be higher in the first (versus second) half of a given 8-week infusion interval, and appeared to be higher in MSM/TG than in women overall. Study cohort differences in pharmacokinetics and neutralization coverage provide insights for interpreting the AMP results and for investigating how VRC01 concentration and neutralization correlate with HIV incidence.

In HIV prevention trials, precise identification of infection time is critical to quantify drug efficacy but difficult to estimate as trials may have relatively sparse visit schedules. The last negative visit does not guarantee a boundary on infection time because viral nucleic acid is not present in the blood during early infection. Here, we developed a framework that combines stochastic and deterministic within-host mathematical modeling of viral dynamics accounting for the early unobservable viral load phase until it reaches a high chronic set point. The infection time estimation is based on a population non-linear mixed effects (pNLME) framework that includes the with-in host modeling. We applied this framework to viral load data from the RV217 trial and found a parsimonious model capable of recapitulating the viral loads. When adding the stochastic and deterministic portion of the best model, the estimated infection time for the RV217 data had an average of 2 weeks between infecting exposure and first positive. We assessed the sensitivity of the infection time estimation by conducting in silico studies with varying viral load sampling schemes before and after infection. pNLME accurately estimates infection times for a daily sampling scheme and is fairly robust to sparser schemes. For a monthly sampling scheme before and after first positive bias increases to -7 days. For pragmatic trial design, we found sampling weekly before and monthly after first positive allows accurate pNLME estimation. Our estimates can be used in parallel with other approaches that rely on viral sequencing, and because the model is mechanistic, it is primed for future application to infection timing for specific interventions.

SummarySingle broadly neutralizing antibody (bnAb) regimens are currently being evaluated in randomized trials for prevention efficacy against HIV-1 infection. Subsequent trials will evaluate combination bnAb regimens (e.g., cocktails, multi-specific antibodies), which demonstrate higher potency and breadth in vitro compared to single bnAbs. Given the large number of potential regimens in the research pipeline, methods for down-selecting these regimens into efficacy trials are of great interest. To aid the down-selection process, we developed Super LeArner Prediction of NAb Panels (SLAPNAP), a software tool for training and evaluating machine learning models that predict in vitro neutralization resistance of HIV Envelope pseudoviruses to a given single or combination bnAb regimen, based on Envelope amino acid sequence features. SLAPNAP also provides measures of variable importance of sequence features. These results can rank bnAb regimens by their potential prevention efficacy and aid assessments of how prevention efficacy depends on sequence features.Availability and ImplementationSLAPNAP is a freely available docker image that can be downloaded from DockerHub (https://hub.docker.com/r/slapnap/slapnap). Source code and documentation are available at GitHub (respectively,https://github.com/benkeser/slapnapandhttps://benkeser.github.io/slapnap/).ContactDavid Benkeser,benkeser@emory.edu