•Stochastic interventional vaccine efficacy (SVE) analysis for clinical audience.•SVE analysis estimates how vaccine efficacy (VE) would change under hypothetical immune marker shifts.•First ...application of SVE to immune correlates based on phase 3 VE trial dataset.•SVE analysis of the COVE messenger RNA-1273 trial supports post dose 2 D614G titer as a CoP.•Predicting variant-specific VE proved difficult due to various reasons.
Stochastic interventional vaccine efficacy (SVE) analysis is a new approach to correlate of protection (CoP) analysis of a phase III trial that estimates how vaccine efficacy (VE) would change under hypothetical shifts of an immune marker.
We applied nonparametric SVE methodology to the COVE trial of messenger RNA-1273 vs placebo to evaluate post-dose 2 pseudovirus neutralizing antibody (nAb) titer against the D614G strain as a CoP against COVID-19. Secondly, we evaluated the ability of these results to predict VE against variants based on shifts of geometric mean titers to variants vs D614G. Prediction accuracy was evaluated by 13 validation studies, including 12 test-negative designs.
SVE analysis of COVE supported post-dose 2 D614G titer as a CoP: estimated VE ranged from 66.9% (95% confidence interval: 36.2, 82.8%) to 99.3% (99.1, 99.4%) at 10-fold decreased or increased titer shifts, respectively. The SVE estimates only weakly predicted variant-specific VE estimates (concordance correlation coefficient 0.062 for post 2-dose VE).
SVE analysis of COVE supports nAb titer as a CoP for messenger RNA vaccines. Predicting variant-specific VE proved difficult due to many limitations. Greater anti-Omicron titers may be needed for high-level protection against Omicron vs anti-D614G titers needed for high-level protection against pre-Omicron COVID-19.
Classification accuracy is the ability of a marker or diagnostic test to discriminate between two groups of individuals, cases and controls, and is commonly summarized by using the receiver operating ...characteristic (roc) curve. In studies of classification accuracy, there are often covariates that should be incorporated into the ROC analysis. We describe three ways of using covariate information. For factors that affect marker observations among controls, we present a method for covariate adjustment. For factors that affect discrimination (i.e., the ROC curve), we describe methods for modeling the ROC curve as a function of covariates. Finally, for factors that contribute to discrimination, we propose combining the marker and covariate information, and we ask how much discriminatory accuracy improves (in incremental value) with the addition of the marker to the covariates. These methods follow naturally when representing the ROC curve as a summary of the distribution of case marker observations, standardized with respect to the control distribution.
Treatment selection markers, sometimes called predictive markers, are factors that help clinicians select therapies that maximize good outcomes and minimize adverse outcomes for patients. Existing ...statistical methods for evaluating a treatment selection marker include assessing its prognostic value, evaluating treatment effects in patients with a restricted range of marker values, and testing for a statistical interaction between marker value and treatment. These methods are inadequate, because they give misleading measures of performance that do not answer key clinical questions about how the marker might help patients choose treatment, how treatment decisions should be made on the basis of a continuous marker measurement, what effect using the marker to select treatment would have on the population, or what proportion of patients would have treatment changes on the basis of marker measurement. Marker-by-treatment predictiveness curves are proposed as a more useful aid to answering these clinically relevant questions, because they illustrate treatment effects as a function of marker value, outcomes when using or not using the marker to select treatment, and the proportion of patients for whom treatment recommendations change after marker measurement. Randomized therapeutic clinical trials, in which entry criteria and treatment regimens are not restricted by the marker, are also proposed as the basis for constructing the curves and evaluating and comparing markers.
Background. It is important to identify vaccine-induced immune responses that predict the preventative efficacy of a human immunodeficiency virus (HIV)–1 vaccine. We assessed T-cell response markers ...as correlates of risk in the HIV Vaccine Trials Network (HVTN) 505 HIV-1 vaccine efficacy trial. Methods. 2504 participants were randomized to DNA/rAd5 vaccine or placebo, administered at weeks 0, 4, 8, and 24. Peripheral blood mononuclear cells were obtained at week 26 from all 25 primary endpoint vaccine cases and 125 matched vaccine controls, and stimulated with vaccine-insert-matched peptides. Primary variables were total HIV-1-specific CD4+ T-cell magnitude and Envspecific CD4+ polyfunctionality. Four secondary variables were also assessed. Immune responses were evaluated as predictors of HIV-1 infection among vaccinees using Cox proportional hazards models. Machine learning analyses identified immune response combinations best predicting HIV-1 infection. Results. We observed an unexpectedly strong inverse correlation between Env-specific CD8+ immune response magnitude and HIV-1 infection risk (hazard ratio HR = 0.18 per SD increment; P = .04) and between Env-specific CD8+ polyfunctionality and infection risk (HR = 0.34 per SD increment; P < .01). Conclusions. Further research is needed to determine if these immune responses are predictors of vaccine efficacy or markers of natural resistance to HIV-1 infection.
HIV vaccine trials routinely measure multiple vaccine-elicited immune responses to compare regimens and study their potential associations with protection. Here we employ unsupervised learning tools ...facilitated by a bidirectional power transformation to explore the multivariate binding antibody and T-cell response patterns of immune responses elicited by two pox-protein HIV vaccine regimens. Both regimens utilized a recombinant canarypox vector (ALVAC-HIV) prime and a bivalent recombinant HIV-1 Envelope glycoprotein 120 subunit boost. We hypothesized that within each trial, there were participant subgroups sharing similar immune responses and that their frequencies differed across trials.
We analyzed data from three trials-RV144 (NCT00223080), HVTN 097 (NCT02109354), and HVTN 100 (NCT02404311), the latter of which was pivotal in advancing the tested pox-protein HIV vaccine regimen to the HVTN 702 Phase 2b/3 efficacy trial. We found that bivariate CD4+ T-cell and anti-V1V2 IgG/IgG3 antibody response patterns were similar by age, sex-at-birth, and body mass index, but differed for the pox-protein clade AE/B alum-adjuvanted regimen studied in RV144 and HVTN 097 (PAE/B/alum) compared to the pox-protein clade C/C MF59-adjuvanted regimen studied in HVTN 100 (PC/MF59). Specifically, more PAE/B/alum recipients had low CD4+ T-cell and high anti-V1V2 IgG/IgG3 responses, and more PC/MF59 recipients had broad responses of both types. Analyses limited to "vaccine-matched" antigens suggested that some of the differences in responses between the regimens could have been due to antigens in the assays that did not match the vaccine immunogens. Our approach was also useful in identifying subgroups with unusually absent or high co-responses across assay types, flagging individuals for further characterization by functional assays. We also found that co-responses of anti-V1V2 IgG/IgG3 and CD4+ T cells had broad variability. As additional immune response assays are standardized and validated, we anticipate our framework will be increasingly valuable for multivariate analysis.
Our approach can be used to advance vaccine development objectives, including the characterization and comparison of candidate vaccine multivariate immune responses and improved design of studies to identify correlates of protection. For instance, results suggested that HVTN 702 will have adequate power to interrogate immune correlates involving anti-V1V2 IgG/IgG3 and CD4+ T-cell co-readouts, but will have lower power to study anti-gp120/gp140 IgG/IgG3 due to their lower dynamic ranges. The findings also generate hypotheses for future testing in experimental and computational analyses aimed at achieving a mechanistic understanding of vaccine-elicited immune response heterogeneity.
While new vaccines for SARS-CoV-2 are authorized based on neutralizing antibody (nAb) titer against emerging variants of concern, an analogous pathway does not exist for preventative monoclonal ...antibodies. In this work, nAb titers were assessed as correlates of protection against COVID-19 in the casirivimab + imdevimab monoclonal antibody (mAb) prevention trial (ClinicalTrials.gov #NCT4452318) and in the mRNA-1273 vaccine trial (ClinicalTrials.gov #NCT04470427). In the mAb trial, protective efficacy of 92% (95% confidence interval (CI): 84%, 98%) is associated with a nAb titer of 1000 IU50/ml, with lower efficacy at lower nAb titers. In the vaccine trial, protective efficacies of 93% 95% CI: 91%, 95% and 97% (95% CI: 95%, 98%) are associated with nAb titers of 100 and 1000 IU50/ml, respectively. These data quantitate a nAb titer correlate of protection for mAbs benchmarked alongside vaccine induced nAb titers and support nAb titer as a surrogate endpoint for authorizing new mAbs.
Abstract Background Although the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines are highly efficacious at preventing severe disease in the general population, current data are ...lacking regarding vaccine efficacy (VE) for individuals with mild immunocompromising conditions. Methods A post hoc, cross-protocol analysis of participant-level data from the blinded phase of four randomized, placebo-controlled, coronavirus disease 2019 (COVID-19) vaccine phase 3 trials (Moderna, AstraZeneca, Janssen, and Novavax) was performed. We defined a “tempered immune system” (TIS) variable via a consensus panel based on medical history and medications to determine VE against symptomatic and severe COVID-19 cases in TIS participants versus non-TIS individuals starting at 14 days after completion of the primary series through the blinded phase for each of the 4 trials. An analysis of participants living with well-controlled human immunodeficiency virus was conducted using the same methods. Results A total of 3852/30 351 (12.7%) Moderna participants, 3088/29 868 (10.3%) Novavax participants, 3549/32 380 (11.0%) AstraZeneca participants, and 5047/43 788 (11.5%) Janssen participants were identified as having a TIS. Most TIS conditions (73.9%) were due to metabolism and nutritional disorders. Vaccination (vs placebo) significantly reduced the likelihood of symptomatic and severe COVID-19 for all participants for each trial. VE was not significantly different for TIS participants versus non-TIS for either symptomatic or severe COVID-19 for each trial, nor was VE significantly different in the symptomatic endpoint for participants with human immunodeficiency virus. Conclusions For individuals with mildly immunocompromising conditions, there is no evidence of differences in VE against symptomatic or severe COVID-19 compared with those with non-TIS in the 4 COVID-19 vaccine randomized controlled efficacy trials.
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.
The best assay or marker to define mRNA-1273 vaccine-induced antibodies as a correlate of protection (CoP) is unclear. In the COVE trial, participants received two doses of the mRNA-1273 COVID-19 ...vaccine or placebo. We previously assessed IgG binding antibodies to the spike protein (spike IgG) or receptor binding domain (RBD IgG) and pseudovirus neutralizing antibody 50 or 80% inhibitory dilution titer measured on day 29 or day 57, as correlates of risk (CoRs) and CoPs against symptomatic COVID-19 over 4 months after dose. Here, we assessed a new marker, live virus 50% microneutralization titer (LV-MN
), and compared and combined markers in multivariable analyses. LV-MN
was an inverse CoR, with a hazard ratio of 0.39 (95% confidence interval, 0.19 to 0.83) at day 29 and 0.51 (95% confidence interval, 0.25 to 1.04) at day 57 per 10-fold increase. In multivariable analyses, pseudovirus neutralization titers and anti-spike binding antibodies performed best as CoRs; combining antibody markers did not improve correlates. Pseudovirus neutralization titer was the strongest independent correlate in a multivariable model. Overall, these results supported pseudovirus neutralizing and binding antibody assays as CoRs and CoPs, with the live virus assay as a weaker correlate in this sample set. Day 29 markers performed as well as day 57 markers as CoPs, which could accelerate immunogenicity and immunobridging studies.
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