Development of the Sanofi Pasteur tetravalent dengue vaccine: One more step forward

Sanofi Pasteur has developed a recombinant, live-attenuated, tetravalent dengue vaccine (CYD-TDV) that is in late-stage development. The present review summarizes the different steps in the development of this dengue vaccine, with a particular focus on the clinical data from three efficacy trials, which includes one proof-of-concept phase IIb (NCT00842530) and two pivotal phase III efficacy trials (NCT01373281 and NCT01374516). Earlier studies showed that the CYD-TDV candidate had a satisfactory safety profile and was immunogenic across the four vaccine serotypes in both in vitro and in vivo preclinical tests, as well as in initial phase I to phase II clinical trials in both flavivirus-naïve and seropositive individuals. Data from the 25 months (after the first injection) active phase of the two pivotal phase III efficacy studies shows that CYD-TDV (administered at 0, 6, and 12 months) is efficacious against virologically-confirmed disease (primary endpoint) and has a good safety profile. Secondary analyses also showed efficacy against all four dengue serotypes and protection against severe disease and hospitalization. The end of the active phases in these studies completes more than a decade of development of CYD-TDV, but considerable activities and efforts remain to address outstanding scientific, clinical, and immunological questions, while preparing for the introduction and use of CYD-TDV. Additional safety observations were recently reported from the first complete year of hospital phase longer term surveillance for two phase 3 studies and the first and second completed years for one phase 2b study, demonstrating the optimal age for intervention from 9 years. Dengue is a complex disease, and both short-term and long-term safety and efficacy will continue to be addressed by ongoing long-term follow-up and future post-licensure studies.     

Serostatus cutoff levels and fold increase to define seroresponse to recombinant vesicular stomatitis virus – Zaire Ebola virus envelope glycoprotein vaccine: An evidence-based analysis

The recombinant vesicular stomatitis virus – Zaire Ebola virus envelope glycoprotein (rVSVΔG-ZEBOV-GP) vaccine is a live recombinant vesicular stomatitis virus (VSV) where the VSV G protein is replaced with ZEBOV-GP. To better understand the immune response after receiving the rVSVΔG-ZEBOV-GP vaccine, the current analyses evaluated different definitions of seroresponse that differentiate vaccine and placebo recipients enrolled in a placebo-controlled clinical trial (PREVAIL; NCT02344407) in which a subset of the study participants had elevated baseline titers. Alternative values for serostatus cutoff (SSCO; 200–500 EU/mL) and/or fold rise (two- to five-fold) were applied to compare their ability to distinguish between participants receiving rVSVΔG-ZEBOV-GP or placebo. The results indicate that an SSCO of 200 EU/mL can be used to define seropositivity at baseline (i.e. pre-vaccination). The use of dual criteria of the same SSCO (200 EU/mL) together with a two-fold rise in antibody level from baseline provided the definition of seroresponse that maximized the statistical significance between vaccine recipients and placebo recipients post-vaccination.Clinical trial registration: NCT02344407.     

A bootstrap semiparametric homogeneity test for the distributions of multigroup proportional data,with applications to analysis of quality of life outcomes in clinical trials

This article is concerned about the test for the difference in the distributions of multigroup proportional data, which is motivated by the problem of comparing the distributions of quality of life (QoL) outcomes among different treatment groups in clinical trials. The proportional data, such as QoL outcomes assessed by answers to questions on a questionnaire, are bounded in a closed interval such as [0,1] with continuous observations in (0,1) and, in addition, excess observations taking the boundary values 0 and/or 1. Common statistical procedures used in practice, such as t- and rank-based tests, may not be very powerful since they ignore the specific feature of the proportional data. In this article, we propose a three-component mixture model for the proportional data and a density ratio model for the distributions of continuous observations in (0,1). A semiparametric test statistic for the homogeneity of distributions of multigroup proportional data is derived based on the empirical likelihood ratio principle and shown to be asymptotically distributed as a chi-squared random variable under null hypothesis. A nonparametric bootstrap procedure is proposed to further improve the performance of the semiparametric test. Simulation studies are performed to evaluate the empirical type I error and power of the proposed test procedure and compare it with likelihood ratio tests (LRTs) under parametric distribution assumptions, rank-based Kruskal-Wallis test, and Wald-type test. The proposed test procedure is also applied to the analysis of QoL outcomes from a clinical trial on colorectal cancer that motivated our study.     

Immunogenicity of influenza vaccines administered to pregnant women in randomized clinical trials in Mali and South Africa

BackgroundA key consideration for expanding recommendations for influenza vaccination is a robust assessment of immunogenicity and efficiency of transplacental antibody transfer after maternal vaccination.MethodsWe pooled data from two trials of maternal influenza vaccination to analyze vaccine immunogenicity with more power than either trial had alone. We compared hemagglutination-inhibition (HAI) titers and titer factor change for women and their infants between trial arms using t-tests; maternal and infant putative seroprotective titers (HAI ≥ 1:40) within each trial arm and maternal seroconversion between trial arms using exact tests; and transplacental antibody transfer between trial arms using t-tests. We used marginal linear models and generalized estimating equations to examine the impact of time between maternal vaccination and delivery on transplacental antibody transfer, infant titers, and infant seroprotection.ResultsFor all vaccine components (A/H1N1, A/H3N2, and Type B), >80% of vaccinated women had seroprotective titers, >60% of them seroconverted, and >50% of their infants were born with seroprotective titers. These immunogenicity outcomes occurred more often in vaccine recipients and their infants than in controls. No difference in efficiency of transplacental antibody transfer was observed between vaccine recipients and controls.ConclusionsOur results provide robust support for further expansion of maternal influenza vaccination recommendations.Clinical Trials Registration: NCT01430689 and NCT01306669.     

Predictors of durable immune responses six months after the last vaccination in preventive HIV vaccine trials

BackgroundThe evaluation of durable immune responses is important in HIV vaccine research and development. The efficiency of such evaluation could be increased by incorporating predictors of the responses in the statistical analysis. In this paper, we investigated whether and how baseline demographic variables and immune responses measured two weeks after vaccination predicted durable immune responses measured six months later.MethodsWe included data from seven preventive HIV vaccine regimens evaluated in three clinical trials: a Phase 1 study of four DNA, NYVAC and/or AIDSVAX vaccine regimens (HVTN096), a Phase 2 study of two DNA and/or MVA vaccine regimens (HVTN205), and a Phase 3 study of a single ALVAC/AIDSVAX regimen (RV144). Regularized random forests and linear regression models were used to identify and evaluate predictors of the positivity and magnitude of durable immune responses.ResultsWe analyzed 201 vaccine recipients with data from 10 to 127 immune response biomarkers, and 3–5 demographic variables. The best prediction of participants’ durable response positivity based on two-week responses rendered up to close-to-perfect accuracy; the best prediction of participants’ durable response magnitude rendered correlation coefficients between the observed and predicted responses ranging up to 0.91. Though prediction performances differed among biomarkers, durable immune responses were best predicted by the two-week response level of the same biomarker. Adding demographic information and two-week response levels of different biomarkers provided little or no improvement in the predictions.ConclusionsFor some biomarkers and for the vaccines we studied, two-week post-vaccination responses can well predict durable responses six months later. Therefore, if immune response durability is only assessed in a sub-sample of vaccine recipients, statistical analyses of durable responses will have increased efficiency by incorporating two-week response data. Further research is needed to generalize the findings to other vaccine regimens and biomarkers.Clinicaltrials.gov identifiers: NCT01799954, NCT00820846, NCT00223080.     

Use of predictive markers of HIV disease progression in vaccine trials

Generating broadly neutralizing antibodies with candidate vaccines has remained an elusive goal. Consequently, vaccine candidates developed have aimed at eliciting cell-mediated immune effector activities (CMI) that could delay disease progression, and maybe also limit secondary transmission, by controlling virus replication. There is considerable discussion about what types of endpoints would constitute definable standardized clinical benefit to the individual that would result in licensure of these candidate vaccines. Identifying biomarkers that can be used as surrogates for clinical endpoints in randomized clinical trials would be useful, because it would shorten studies and reduce costs. Biological markers associated with disease progression and secondary transmission and that may be used as prognosis markers and surrogate endpoints in HIV vaccine trials have emerged from analyses of data from studies on natural history of HIV infection. Extensive literature is cited to support the use of plasma viral load as a primary endpoint for supporting licensure decisions. Overall, a significant result on viral load in a vaccine trial should be considered as a significant breakthrough for vaccines and be aggressively pursued with the caveat that such a result should rapidly be followed by well-defined studies to verify durable virological and immunological vaccine benefit, as well as ultimate clinical benefit. The review also provides perspectives on magnitude of viral load reduction, durability of viral load reduction for reduced progression of HIV disease.     

Methods for incorporating covariate adjustment, subgroup analysis and between-centre differences into cost-effectiveness evaluations

BACKGROUND: Overall assessments of cost-effectiveness are now commonplace in informing medical policy decision making. It is often important, however, also to investigate how cost-effectiveness varies between patient subgroups. Yet such analyses are rarely undertaken, because appropriate methods have not been sufficiently developed. METHODS: We propose a coherent set of Bayesian methods to extend cost-effectiveness analyses to adjust for baseline covariates, to investigate differences between subgroups, and to allow for differences between centres in a multicentre study using a hierarchical model. These methods consider costs and effects jointly, and allow for the typically skewed distribution of cost data. The results are presented as inferences on the cost-effectiveness plane, and as cost-effectiveness acceptability curves. RESULTS: In applying these methods to a randomised trial of case management of psychotic patients, we show that overall cost-effectiveness can be affected by ignoring the skewness of cost data, but that it may be difficult to gain substantial precision by adjusting for baseline covariates. While analyses of overall cost-effectiveness can mask important subgroup differences, crude differences between centres may provide an unrealistic indication of the true differences between them. CONCLUSIONS: The methods developed allow a flexible choice for the distributions used for cost data, and have a wide range of applicability--to both randomised trials and observational studies. Experience needs to be gained in applying these methods in practice, and using their results in decision making.     

Assessing different measures of population-level vaccine protection using a case–control study

BackgroundCase–control studies have not been examined for their utility in assessing population-level vaccine protection in individually randomized trials.MethodsWe used the data of a randomized, placebo-controlled trial of a cholera vaccine to compare the results of case–control analyses with those of cohort analyses. Cases of cholera were selected from the trial population followed for three years following dosing. For each case, we selected 4 age-matched controls who had not developed cholera. For each case and control, GIS was used to calculate vaccine coverage of individuals in a surrounding “virtual” cluster. Specific selection strategies were used to evaluate the vaccine protective effects.Results66,900 out of 108,389 individuals received two doses of the assigned regimen. For direct protection among subjects in low vaccine coverage clusters, we observed 78% (95% CI: 47–91%) protection in a cohort analysis and 84% (95% CI: 60–94%) in case–control analysis after adjusting for confounding factors. Using our GIS-based approach, estimated indirect protection was 52% (95% CI: 10–74%) in cohort and 76% (95% CI: 47–89%) in case control analysis. Estimates of total and overall effectiveness were similar for cohort and case–control analyses.ConclusionThe findings show that case–control analyses of individually randomized vaccine trials may be used to evaluate direct as well as population-level vaccine protection.     

Association of vaccine hesitancy and immunization coverage rates in the European Union

While previous studies have validated vaccine hesitancy scales with uptake behavior at the individual level, the conditions under which aggregated survey data are useful are less clear. We show that vaccine public opinion data aggregated at the subnational level can serve as a valid indicator of aggregate vaccine behaviour. We use a public opinion survey (Eurobarometer EB 91.2) with data on vaccine hesitancy for the EU in 2019. We link this information to (subnational) regional immunization coverage rates for childhood vaccines – DTP3, MCV1, and MCV2 -- obtained from the WHO for 2019. We conduct multilevel regression analyses with data for 177 regions in 20 countries. Given the variation in vaccine hesitancy and immunization rates between countries and within countries, we affirm the valuable role that surveys can play as a public health surveillance tool when it comes to vaccine behavior. We find statistically significantly lower regional vaccine immunization rates in regions where vaccine hesitancy is more pronounced. Our results suggest that different uptake rates across subnational regions are due, at least in part, to differences in attitudes towards vaccines and vaccination. The results are robust to several alternative specifications.     

Development of recombinant COVID-19 vaccine based on CHO-produced,prefusion spike trimer and alum/CpG adjuvants

COVID-19 pandemic has severely impacted the public health and social economy worldwide. A safe, effective, and affordable vaccine against SARS-CoV-2 infections/diseases is urgently needed. We have been developing a recombinant vaccine based on a prefusion-stabilized spike trimer of SARS-CoV-2 and formulated with aluminium hydroxide and CpG 7909. The spike protein was expressed in Chinese hamster ovary (CHO) cells, purified, and prepared as a stable formulation with the dual adjuvant. Immunogenicity studies showed that candidate vaccines elicited robust neutralizing antibody responses and substantial CD4⁺ T cell responses in both mice and non-human primates. And vaccine-induced neutralizing antibodies persisted at high level for at least 6 months. Challenge studies demonstrated that candidate vaccine reduced the viral loads and inflammation in the lungs of SARS-CoV-2 infected golden Syrian hamsters significantly. In addition, the vaccine-induced antibodies showed cross-neutralization activity against B.1.1.7 and B.1.351 variants. These data suggest candidate vaccine is efficacious in preventing SARS-CoV-2 infections and associated pneumonia, thereby justifying ongoing phase I/II clinical studies in China (NCT04982068 and NCT04990544).     

A practical guide to dose-response analyses and risk assessment in occupational epidemiology

Dose-response modeling in occupational epidemiology is usually motivated by questions of causal inference (eg, is there a monotonic increase of risk with increasing exposure?) or risk assessment (eg, how much excess risk exists at any given level of exposure?). We focus on several approaches to dose-response in occupational cohort studies. Categorical analyses are useful for detecting the shape of dose-response. However, they depend on the number and location of cutpoints and result in step functions rather than smooth curves. Restricted cubic splines and penalized splines are useful parametric techniques that provide smooth curves. Although splines can complement categorical analyses, they do not provide interpretable parameters. The shapes of these curves will depend on the degree of "smoothing" chosen by the analyst. We recommend combining categorical analyses and some type of smoother, with the goal of developing a reasonably simple parametric model. A simple parametric model should serve as the goal of dose-response analyses because (1) most "true" exposure response curves in nature may be reasonably simple, (2) a simple parametric model is easily communicated and used by others, and (3) a simple parametric model is the best tool for risk assessors and regulators seeking to estimate individual excess risks per unit of exposure. We discuss these issues and others, including whether the best model is always the one that fits the best, reasons to prefer a linear model for risk in the low-exposure region when conducting risk assessment, and common methods of calculating excess lifetime risk at a given exposure from epidemiologic results (eg, from rate ratios). Points are illustrated using data from a study of dioxin and cancer.     

THE USE OF THE ‘BINORMAL’ MODEL FOR PARAMETRIC ROC ANALYSIS OF QUANTITATIVE DIAGNOSTIC TESTS

The binormal model is widely used for parametric receiver operating characteristic (ROC) analyses of data concerning the accuracy of medical diagnostic tests. Empirical evaluation of the performance of this model in the face of departures from binormality has been limited to interpretations of radiology-type examinations recorded on a rating scale. This paper extends the investigation to the performance of the model with biochemical and other tests recorded on an interval scale. In order to describe non-binormal pairs of distributions, a useful standardized graphical display is developed; this display also illustrates several features of ROC curves. We consider non-binormal pairs of distributions with or without a monotone likelihood ratio and show that by transformation of the underlying scale, one can make many such pairs resemble closely the binormal model. These findings justify Metz's use of the binormal model in the ‘LABROC’ software for ROC analyses of laboratory type data even when the raw data may ‘look’ decidedly non-Gaussian.     

Application of the remaining vaccine vial monitor life calculation to field temperature monitoring data to improve visibility into cold chain equipment performance

Vaccine Vial Monitors (VVM) are used to estimate if a vaccine has been exposed to excessive hot temperatures. This endpoint measurement is useful in determining if a vaccine is safe to be administered to a patient, but it does not pinpoint where in the cold chain a vaccine was exposed to excessive heat. With the expansion and technological advancement of cold chain equipment temperature monitoring, it is now possible to remotely estimate VVM status as a vaccine moves through the cold chain. In the present study, we examine the application of the mathematical principles backing VVMs on real, continuous, temperature monitoring data in Africa. Results suggest that exposure to short bursts of hot temperature or long power outages may still allow for safe distribution of affected vaccines. The remaining VVM life calculation could improve managerial visibility into cold chain equipment performance allowing for better data-driven planning and maintenance decisions.     

Guideline for collection,analysis and presentation of safety data in clinical trials of vaccines in pregnant women

Vaccination during pregnancy is increasingly being used as an effective approach for protecting both young infants and their mothers from serious infections. Drawing conclusions from published studies in this area can be difficult because of the inability to compare vaccine trial results across different studies and settings due to the heterogeneity in the definitions of terms used to assess the safety of vaccines in pregnancy and the data collected in such studies.The guidelines proposed in this document have been developed to harmonize safety data collection in all phases of clinical trials of vaccines in pregnant women and apply to data from the mother, fetus and infant. Guidelines on the prioritization of the data to be collected is also provided to allow applicability in various geographic, cultural and resource settings, including high, middle and low-income countries.     

Studies reporting ROC curves of diagnostic and prediction data can be incorporated into meta-analyses using corresponding odds ratios

OBJECTIVE: To develop an approach by which studies describing the accuracy of diagnostic tests or clinical predictions can be combined in a meta-analysis, even though studies may report their results using different summary measures. STUDY DESIGN: A method is proposed to allow algebraic and numerical conversion of values of the Receiver Operating Characteristic Area Under the Curve (AUC) summary statistic into corresponding odds ratios (OR). A similar conversion is demonstrated for the standard errors (SEs) of these summary statistics. RESULTS: The conversion of the AUC values into OR values was achieved using a logit-threshold model. The delta method was used to convert the associated SEs. An example concerning predictions of mortality in the intensive care unit illustrates the calculations. CONCLUSION: This paper provides an accessible method that permits the meta-analyst to overcome some of the difficulties implied by incomplete and inconsistent reporting of research studies in this area. It allows all studies to be included on the same metric, which in turn more easily permits exploration of issues such as heterogeneity. The method can readily be used for meta-analyses of diagnostic or screening tests, or for prediction data.     

The Instrumental Variable Method to Study Self-Selection Mechanism: A Case of Influenza Vaccination

OBJECTIVE: To assess whether estimates of the effectiveness of influenza vaccination in reducing rates of hospitalizations and all-cause mortality derived from cross-sectional data could be improved by applying the instrumental variable (IV) method to data representing the community-dwelling elderly population in the United States in order to adjust for self-selection bias. METHODS: Secondary data analysis, using the 1996-97 Medicare Current Beneficiary Survey data. First, using single-equation probit regressions this study analyzed influenza-related hospitalization and death due to all causes predicted by vaccination status, which was measured by claims or survey data. Second, to adjust for potential self-selection of the vaccine receipt, for example, higher vaccination rates among high-risk individuals, bivariate probit (BVP) models and two-stage least squares (2SLS) models were employed. The IV was having either arthritis or gout. RESULTS: In single-equation probit models, vaccination appeared to be ineffective or even to increase the probability of adverse outcomes. Based on BVP and 2SLS models, vaccination was demonstrated to be effective in reducing influenza-related hospitalization by at least 31%. The BVP model results implied significant self-selection in the single-equation probit models. CONCLUSIONS: Adjusting for self-selection, BVP analyses yielded vaccine effectiveness estimates for a nationally representative cross-sectional sample of the community-dwelling elderly population that are consistent with previous estimates based on randomized controlled trials, prospective cohort studies, and meta-analyses. This result suggests that analyses with 2SLS and BVP in particular may be useful for the analysis of observational data regarding prevention in which self-selection is an important potential source of bias.     

Aggregate data meta-analysis with time-to-event outcomes

In a meta-analysis of randomized controlled trials with time-to-event outcomes, an aggregate data approach may be required for some or all included studies. Variation in the reporting of survival analyses in journals suggests that no single method for extracting the log(hazard ratio) estimate will suffice. Methods are described which improve upon a previously proposed method for estimating the log(HR) from survival curves. These methods extend to life-tables. In the situation where the treatment effect varies over time and the trials in the meta-analysis have different lengths of follow-up, heterogeneity may be evident. In order to assess whether the hazard ratio changes with time, several tests are proposed and compared. A cohort study comparing life expectancy of males and females with cerebral palsy and a systematic review of five trials comparing two anti-epileptic drugs, carbamazepine and sodium valproate, are used for illustration.     

Effectiveness of the 10-valent pneumococcal conjugate vaccine among girls,boys, preterm and low-birth-weight infants – Results from a randomized,double-blind vaccine trial

BackgroundSeveral studies have shown differences in susceptibility to infections and immune response to vaccines by sex. Prematurely born infants are at higher risk for pneumococcal diseases, with lower effectiveness for some vaccines compared to term infants. We have reported the effectiveness of the 10-valent pneumococcal non-typeable Haemophilus influenzae protein D conjugate vaccine (PHiD-CV10) on several endpoints in the Finnish Invasive Pneumococcal disease (FinIP) vaccine trial. Now, we present the results of a post-hoc analysis evaluating PHiD-CV10 effectiveness in subgroups by sex, gestational age, and birth weight.MethodsThe FinIP trial was a phase III/IV cluster-randomized, double-blind trial. Infants enrolled < 7 months of age received PHiD-CV10 in two thirds of clusters (3 + 1 or 2 + 1 schedule) and hepatitis B vaccine as control in remaining third. Outcome data included invasive pneumococcal disease, pneumonia, tympanostomy tube placements, and antimicrobial purchases collected through national, routinely used health registers. Negative binomial model was used in the incidence and vaccine effectiveness estimation, and differences in incidences between subgroups were tested among control children.ResultsOf the 30,527 infants enrolled 51% were boys. The incidences of hospital-diagnosed pneumonia and otitis-related outcomes were higher among boys in control groups. There were no significant sex differences in the vaccine effectiveness estimates.Altogether, 1519 (5%) infants were born before 37th gestational week. The incidences of pneumonia outcomes were higher among premature infants when compared to term infants.The vaccine effectiveness estimates among preterm infants were not statistically significant except for antimicrobial purchases, but all point estimates were at the same level among preterm infants as among term infants. There was no significant difference between 2 + 1 and 3 + 1 schedules in any of the subgroups analysed.ConclusionPHiD-CV10 had a similar effectiveness in both sexes, and seemed to be protective in preterm infants.Trial registration: ClinicalTrials.gov NCT00861380 and NCT00839254     

Using economic and social data to improve veterinary vaccine development: Learning lessons from human vaccinology

The drivers of vaccine development are many and varied. They include, for example, recognition of the burden of a vaccine-targeted disease, prioritisation of the multiple problems associated with a disease, consideration of the differing socio-economic situations under which vaccines are used, the influence of advocacy groups, and assessment of the feasibility of large-scale vaccine manufacture and distribution. In the field of human health, data-driven development of vaccines is becoming increasingly common through the availability of reliable information on the Global Burden of Disease (GBD) and stringent evaluations of vaccination programmes utilising empirical data on costing and effectiveness, and standardised cost-effectiveness thresholds. The data generated from such analyses allow policymakers, implementing partners, industries and researchers to make decisions based on the best, and most contextually relevant, available evidence. In this paper, we wish to explore the current use of economic and social data for the development of veterinary vaccines. Through comparison with the development of human vaccines, we will look for opportunities in animal health sciences to better integrate socio-economic data and analyses into the process of veterinary vaccine selection, development, and field implementation. We believe that more robust animal health impact assessments could add value to veterinary vaccine development by improving resource allocation and animal disease management.