Measuring immune correlates of disease acquisition and protection in the context of a clinical trial is a prerequisite for improved vaccine design. We analyzed binding and neutralizing antibody measurements four weeks post-vaccination as correlates of risk of moderate to severe-critical COVID-19 through 83 days post-vaccination in the phase III, double-blind placebo-controlled phase of ENSEMBLE, an international, randomized efficacy trial of a single dose of Ad26.COV2.S. We also evaluated correlates of protection in the trial cohort. Of the three antibody immune markers we measured, we found most support for 50% inhibitory dilution (ID50) neutralizing antibody titer as a correlate of risk and of protection. The outcome hazard ratio was 0.49 (95% confidence interval 0.29, 0.81; p=0.006) per 10-fold increase in ID50; vaccine efficacy was 60% (43, 72%) at nonquantifiable ID50 (< 2.7 IU50/ml) and increased to 89% (78, 96%) at ID50 = 96.3 IU50/ml. Comparison of the vaccine efficacy by ID50 titer curves for ENSEMBLE-US, the COVE trial of the mRNA-1273 vaccine, and the COV002-UK trial of the AZD1222 vaccine supported that ID50 titer is a correlate of protection across trials and vaccine types.

Background: Population-level estimates of sexual network mixing for parameterizing prediction models of pre-exposure prophylaxis (PrEP) effectiveness are needed to inform prevention of HIV transmission among men who have sex with men (MSM). Estimates obtained by egocentric sampling are vulnerable to information bias due to incomplete respondent knowledge. Methods: We estimated patterns of serosorting and PrEP sorting among MSM in the United States using data from a 2017–2019 egocentric sexual network study. Respondents served as proxies to report the HIV status and PrEP use of recent sexual partners. We contrasted results from a complete-case analysis (unknown HIV and PrEP excluded) versus a bias analysis with respondent-reported data stochastically reclassified to simulate unobserved self-reported data from sexual partners. Results: We found strong evidence of preferential partnering across analytical approaches. The bias analysis showed concordance between sexual partners of HIV diagnosis and PrEP use statuses for MSM with diagnosed HIV (39%; 95% simulation interval: 31, 46), MSM who used PrEP (32%; 21, 37), and MSM who did not use PrEP (83%; 79, 87). The fraction of partners with diagnosed HIV was higher among MSM who used PrEP (11%; 9, 14) compared to MSM who did not use PrEP (4%; 3, 5). Comparatively, across all strata of respondents, the complete-case analysis overestimated the fractions of partners with diagnosed HIV or PrEP use. Conclusion: We found evidence consistent with HIV and PrEP sorting among MSM, which may decrease the population-level effectiveness of PrEP. Bias analyses can improve mixing estimates for parameterization of transmission models.

The COVE trial randomized participants to receive two doses of mRNA-1273 vaccine or placebo on Days 1 and 29 (D1, D29). Anti-SARS-CoV-2 Spike IgG binding antibodies (bAbs), anti-receptor binding domain IgG bAbs, 50% inhibitory dilution neutralizing antibody (nAb) titers, and 80% inhibitory dilution nAb titers were measured at D29 and D57. We assessed these markers as correlates of protection (CoPs) against COVID-19 using stochastic interventional vaccine efficacy (SVE) analysis and principal surrogate (PS) analysis, frameworks not used in our previous COVE immune correlates analyses. By SVE analysis, hypothetical shifts of the D57 Spike IgG distribution from a geometric mean concentration (GMC) of 2737 binding antibody units (BAU)/mL (estimated vaccine efficacy (VE): 92.9% (95% CI: 91.7%, 93.9%)) to 274 BAU/mL or to 27,368 BAU/mL resulted in an overall estimated VE of 84.2% (79.0%, 88.1%) and 97.6% (97.4%, 97.7%), respectively. By binary marker PS analysis of Low and High subgroups (cut-point: 2094 BAU/mL), the ignorance interval (IGI) and estimated uncertainty interval (EUI) for VE were [85%, 90%] and (78%, 93%) for Low compared to [95%, 96%] and (92%, 97%) for High. By continuous marker PS analysis, the IGI and 95% EUI for VE at the 2.5th percentile (519.4 BAU/mL) vs. at the 97.5th percentile (9262.9 BAU/mL) of D57 Spike IgG concentration were [92.6%, 93.4%] and (89.2%, 95.7%) vs. [94.3%, 94.6%] and (89.7%, 97.0%). Results were similar for other D29 and D57 markers. Thus, the SVE and PS analyses additionally support all four markers at both time points as CoPs.

The protective effects of vaccines may vary depending on individual characteristics, such as age. Traditionally, such effect modification has been examined with subgroup analyses or inclusion of cross-product terms in regression frameworks. However, in many vaccine settings, effect modification may also depend on the infecting pathogen’s characteristics, which are measured post-randomization. Sieve analysis examines whether such effects are present by combining pathogen genetic sequence information with individual-level data and can generate new hypotheses on the pathways whereby vaccines provide protection. In this paper, we develop a causal framework for evaluating effect modification in the context of sieve analysis. Our approach can be used to assess the magnitude of sieve effects and, in particular, whether these effects are modified by individual-level characteristics. Our method accounts for difficulties occurring in real-world data analysis, such as competing risks, non-randomized treatments, and differential dropout. Our approach also integrates modern machine learning techniques. We demonstrate the validity and efficiency of our approach in simulation studies and apply the methodology to a malaria vaccine study.

Combination monoclonal broadly neutralizing antibody (bnAb) regimens are in clinical development for HIV prevention, necessitating additional knowledge of bnAb neutralization potency/breadth against circulating viruses. Williamson et al. (2021) described a software tool, Super LeArner Prediction of NAb Panels (SLAPNAP), with application to any HIV bnAb regimen with sufficient neutralization data against a set of viruses in the Los Alamos National Laboratory’s Compile, Neutralize, and Tally Nab Panels repository. SLAPNAP produces a proteomic antibody resistance (PAR) score for Env sequences based on predicted neutralization resistance and estimates variable importance of Env amino acid features. We apply SLAPNAP to compare HIV bnAb regimens undergoing clinical testing, finding improved power for downstream sieve analyses and increased precision for comparing neutralization potency/breadth of bnAb regimens due to the inclusion of PAR scores of Env sequences with much larger sample sizes available than for neutralization outcomes. SLAPNAP substantially improves bnAb regimen characterization, ranking, and down-selection.

Background: The literature on first generation COVID-19 vaccines show they were less effective against new SARS-CoV-2 variants of concern including Omicron (BA.1, BA.2, BA.4 and BA.5 subvariants). New vaccines developed against variant strains may provide cross-protection against emerging variants when used as boosters and facilitate vaccination across a range of countries, healthcare settings and populations. However, there are no data on such vaccines when used as a primary series. Methods: A global Phase 3, multi-stage efficacy study (NCT04904549) among adults (≥18 years) was conducted in 53 research centres in eight countries (United States, Honduras, Japan, Colombia, Kenya, India, Ghana, Nepal). Participants were randomized 1:1 to receive two intramuscular injections of a monovalent SARS-CoV-2 recombinant protein vaccine with AS03-adjuvant (10 μg of the spike (S) protein from the ancestral D614 strain) or placebo on Day 1 (D01) and Day 22 (D22). The primary efficacy endpoint was prevention of virologically confirmed SARS-CoV-2 infection with symptoms of COVID-19-like illness (CLI) ≥14 days after the second injection (post-dose 2 [PD2]) in participants who were SARS-CoV-2 naïve on D01 + D22. Safety and reactogenicity were also evaluated. Findings: Between May 26 and November 7, 2021, 10,114 participants received ≥1 study injection, and 9441 participants received both injections. 2108 (20.8%) participants were SARS-CoV-2 naïve at D01 and D22. The primary endpoint was analysed in a subset of the full analysis set (the modified full analysis set PD2 [mFAS-PD2], excluding participants who did not complete the vaccination schedule or received vaccination despite meeting one of the contraindication criteria, had onset of symptomatic COVID-19 between the first injection and before 14 days after the second injection, or participants who discontinued before 14 days after the second injection [n = 9377; vaccine, n = 4702; placebo, n = 4675]). Data were available for 2051 SARS-CoV-2 naïve and 7159 non-naïve participants. At the cut-off date (January 28, 2022), symptomatic COVID-19 was reported in 169 naïve participants (vaccine, n = 81; placebo, n = 88) ≥14 days PD2, with a vaccine efficacy (VE) of 15.3% (95% CI, −15.8; 38.2). VE regardless of D01/D22 serostatus was 32.9% (95% CI, 15.3; 47.0) and VE in non-naïve participants was 52.7% (95% CI, 31.2; 67.9). Viral genome sequencing was performed up to the data cut-off point and identified the infecting strain in 99/169 adjudicated cases in the PD2 naïve population (Delta [25], Omicron [72], other variants [3], one participant had infection with both Delta and Omicron variants and has been included in the totals for both Delta and Omicron). The vaccine was well-tolerated with an acceptable safety profile. Interpretation: In the context of changing circulating viral variants, it is challenging to induce protection in naïve individuals with a two-dose priming schedule based on the parental D614 strain. However, while the primary endpoint of this trial was not met, the results show that a monovalent D614 vaccine can still be of value in individuals previously exposed to SARS-CoV-2. Funding: This study was funded in whole or in part by Sanofi and by federal funds from the Biomedical Advanced Research and Development Authority, part of the office of the Administration for Strategic Preparedness and Response at the U.S. Department of Health and Human Services under contract number HHSO100201600005I, and in collaboration with the U.S. Department of Defense Joint Program Executive Office for Chemical, Biological, Radiological, and Nuclear Defense under contract number W15QKN-16-9-1002. The views presented here are those of the authors and do not purport to represent those of the Department of the Army, the Department of Health and Human Services, or the U.S. government.

The broadly neutralizing antibody (bnAb) VRC01 is being evaluated for its efficacy to prevent HIV-1 infection in the Antibody Mediated Prevention (AMP) trials. A secondary objective of AMP utilizes sieve analysis to investigate how VRC01 prevention efficacy (PE) varies with HIV-1 envelope (Env) amino acid (AA) sequence features. An exhaustive analysis that tests how PE depends on every AA feature with sufficient variation would have low statistical power. To design an adequately powered primary sieve analysis for AMP, we modeled VRC01 neutralization as a function of Env AA sequence features of 611 HIV-1 gp160 pseudoviruses from the CATNAP database, with objectives: (1) to develop models that best predict the neutralization readouts; and (2) to rank AA features by their predictive importance with classification and regression methods. The dataset was split in half, and machine learning algorithms were applied to each half, each analyzed separately using cross-validation and hold-out validation. We selected Super Learner, a nonparametric ensemble-based cross-validated learning method, for advancement to the primary sieve analysis. This method predicted the dichotomous resistance outcome of whether the IC50 neutralization titer of VRC01 for a given Env pseudovirus is right-censored (indicating resistance) with an average validated AUC of 0.868 across the two hold-out datasets. Quantitative log IC50 was predicted with an average validated R2 of 0.355. Features predicting neutralization sensitivity or resistance included 26 surface-accessible residues in the VRC01 and CD4 binding footprints, the length of gp120, the length of Env, the number of cysteines in gp120, the number of cysteines in Env, and 4 potential N-linked glycosylation sites; the top features will be advanced to the primary sieve analysis. This modeling framework may also inform the study of VRC01 in the treatment of HIV-infected persons.

Time is of the essence in evaluating potential drugs and biologics for the treatment and prevention of COVID-19. There are currently 876 randomized clinical trials (phase 2 and 3) of treatments for COVID-19 registered on clinicaltrials.gov. Covariate adjustment is a statistical analysis method with potential to improve precision and reduce the required sample size for a substantial number of these trials. Though covariate adjustment is recommended by the U.S. Food and Drug Administration and the European Medicines Agency, it is underutilized, especially for the types of outcomes (binary, ordinal, and time-to-event) that are common in COVID-19 trials. To demonstrate the potential value added by covariate adjustment in this context, we simulated two-arm, randomized trials comparing a hypothetical COVID-19 treatment versus standard of care, where the primary outcome is binary, ordinal, or time-to-event. Our simulated distributions are derived from two sources: longitudinal data on over 500 patients hospitalized at Weill Cornell Medicine New York Presbyterian Hospital and a Centers for Disease Control and Prevention preliminary description of 2449 cases. In simulated trials with sample sizes ranging from 100 to 1000 participants, we found substantial precision gains from using covariate adjustment–equivalent to 4–18% reductions in the required sample size to achieve a desired power. This was the case for a variety of estimands (targets of inference). From these simulations, we conclude that covariate adjustment is a low-risk, high-reward approach to streamlining COVID-19 treatment trials. We provide an R package and practical recommendations for implementation.

Purpose: Given incomplete data reporting by race, we used data on COVID-19 cases and deaths in U.S. counties to describe racial disparities in COVID-19 disease and death and associated determinants. Methods: Using publicly available data (accessed April 13, 2020), predictors of COVID-19 cases and deaths were compared between disproportionately (≥13%) black and all other (<13% black) counties. Rate ratios were calculated, and population attributable fractions were estimated using COVID-19 cases and deaths via zero-inflated negative binomial regression model. National maps with county-level data and an interactive scatterplot of COVID-19 cases were generated. Results: Nearly 90% of disproportionately black counties (656/677) reported a case and 49% (330/677) reported a death versus 81% (1987/2465) and 28% (684/2465), respectively, for all other counties. Counties with higher proportions of black people have higher prevalence of comorbidities and greater air pollution. Counties with higher proportions of black residents had more COVID-19 diagnoses (Rate Ratio (RR): 1.24, 95% confidence interval: 1.17–1.33) and deaths (RR: 1.18, 95% confidence interval: 1.00–1.40), after adjusting for county-level characteristics such as age, poverty, comorbidities, and epidemic duration. COVID-19 deaths were higher in disproportionally black rural and small metro counties. The population attributable fraction of COVID-19 diagnosis due to lack of health insurance was 3.3% for counties with less than 13% black residents and 4.2% for counties with greater than or equal to 13% black residents. Conclusions: Nearly 20% of U.S. counties are disproportionately black, and they accounted for 52% of COVID-19 diagnoses and 58% of COVID-19 deaths nationally. County-level comparisons can both inform COVID-19 responses and identify epidemic hot spots. Social conditions, structural racism, and other factors elevate risk for COVID-19 diagnoses and deaths in black communities.

Background Transgender youth demonstrate significantly higher rates of engagement in sexual risk behaviors relative to their cisgender or gender-conforming counterparts, including high rates of condomless anal intercourse and engagement in sex work. In addition, transgender youth experience increased physical or sexual abuse, victimization, substance use, mental health disorders, incarceration, and homelessness. Owing to these syndemic health disparities, transgender youth are at substantially increased risk of HIV infection. Objective This protocol aims to describe a randomized controlled trial (RCT), Adolescent Medicine Trials Network 160 TechStep (N=250), which assesses the differential immediate and sustained effects of each of 3 conditions (text messaging, WebApp, or information-only control) for reducing sexual risk behaviors and increasing pre-exposure prophylaxis (PrEP) uptake among high-risk, HIV-negative transgender youth and young adults (aged 15-24 years). Methods Participants will be recruited through web-based (targeted social media sites and apps) and offline (print ads and flyers) advertisements, peer and clinic referrals, and street- and venue-based outreach, and by contacting potential participants who have requested contact for future studies. Participants will be randomized into 1 of the 3 conditions: (1) text messaging, (2) WebApp, or (3) information-only control for 6 months. Assessments will occur at baseline and at 3, 6, and 9 months. Participants who do not show improvements in sexual risk or PrEP uptake at the 3-month assessment will be rerandomized to receive weekly electronic coaching (eCoaching) sessions in addition to their assigned text messaging or WebApp intervention, or remain in the original text messaging or WebApp intervention using a 2:1 ratio. Participants originally assigned to the information-only condition are not eligible for rerandomization. Results Funding for TechStep was awarded in June 2017. Phase 1 was approved by the Institutional Review Board (IRB) in April 2018. Recruitment began in November 2018 for phase 1, the formative phase. Initial phase 2 IRB approval came in June 2019. The data collection for phase 2, the RCT, is expected to be completed in April 2021. As of March 2020, 54 participants have been enrolled in TechStep. The final results are anticipated in May 2021. Conclusions By providing culturally responsive, technology-based interventions, TechStep aims to improve sexual health outcomes among HIV-negative transgender youth and young adults at high risk of HIV. TechStep will evaluate the efficacy of technology-based interventions for reducing HIV sexual risk behaviors and increasing PrEP initiation, adherence, and persistence. The suite of technology-based interventions developed in TechStep, and assessed for efficacy in a 3-condition RCT, represents an important advancement in intervention science toward developing tailored and scalable interventions for transgender youth and young adults. Trial Registration ClinicalTrials.gov NCT04000724; http://clinicaltrials.gov/ct2/show/NCT04000724 International Registered Report Identifier (IRRID) DERR1-10.2196/18326