Can incorporating genotyping data into efficacy estimators improve efficiency of early phase malaria vaccine trials?

BACKGROUND: Early phase malaria vaccine field trials typically measure malaria infection by PCR or thick blood smear microscopy performed on serially sampled blood. Vaccine efficacy (VE) is the proportion reduction in an endpoint due to vaccination and is often calculated as VEHR = 1-hazard ratio or VERR = 1-risk ratio. Genotyping information can distinguish different clones and distinguish multiple infections over time, potentially increasing statistical power. This paper investigates two alternative VE endpoints incorporating genotyping information: VEmolFOI, the vaccine-induced proportion reduction in incidence of new clones acquired over time, and VEC, the vaccine-induced proportion reduction in mean number of infecting clones per exposure. METHODS: Power of VEmolFOI and VEC was compared to that of VEHR and VERR by simulations and analytic derivations, and the four VE methods were applied to three data sets: a Phase 3 trial of RTS,S malaria vaccine in 6912 African infants, a Phase 2 trial of PfSPZ Vaccine in 80 Burkina Faso adults, and a trial comparing Plasmodium vivax incidence in 466 Papua New Guinean children after receiving chloroquine + artemether lumefantrine with or without primaquine (as these VE methods can also quantify effects of other prevention measures). By destroying hibernating liver-stage P. vivax, primaquine reduces subsequent reactivations after treatment completion. RESULTS: In the trial of RTS,S vaccine, a significantly reduced number of clones at first infection was observed, but this was not the case in trials of PfSPZ Vaccine or primaquine, although the PfSPZ trial lacked power to show a reduction. Resampling smaller data sets from the large RTS,S trial to simulate phase 2 trials showed modest power gains from VEC compared to VEHR for data like those from RTS,S, but VEC is less powerful than VEHR for trials in which the number of clones at first infection is not reduced. VEmolFOI was most powerful in model-based simulations, but only the primaquine trial collected enough serial samples to precisely estimate VEmolFOI. The primaquine VEmolFOI estimate decreased after most control arm liver-stage infections reactivated (which mathematically resembles a waning vaccine), preventing VEmolFOI from improving power. CONCLUSIONS: The power gain from the genotyping methods depends on the context. Because input parameters for early phase power calculations are often uncertain, these estimators are not recommended as primary endpoints for small trials unless supported by targeted data analysis. TRIAL REGISTRATIONS: NCT00866619, NCT02663700, NCT02143934.

Can incorporating genotyping data into efficacy estimators improve efficiency of early phase malaria vaccine trials?

Background: Early phase malaria vaccine field trials typically measure malaria infection by PCR or thick blood smear microscopy performed on serially sampled blood. Vaccine efficacy (VE) is the proportion reduction in an endpoint due to vaccination and is often calculated as VEHR=1 - hazard ratio or VERR=1 - risk ratio. Genotyping information can distinguish different clones and distinguish multiple infections over time, potentially increasing statistical power. This paper investigates two alternative VE endpoints incorporating genotyping information: VEmolFOI, the vaccine-induced proportion reduction in incidence of new clones acquired over time, and VEC, the vaccine-induced proportion reduction in mean number of infecting clones per exposure. Methods: We used simulations and analytic derivations to compare power of these methods to VEHR and VERR and applied them to three data sets: a Phase 3 trial of RTS,S malaria vaccine in 6912 African infants, a Phase 2 trial of PfSPZ Vaccine in 80 Burkina Faso adults, and a trial comparing Plasmodium vivax incidence in 466 Papua New Guinean children after receiving chloroquine + artemether lumefantrine with or without primaquine (as these VE methods can also quantify effects of other prevention measures). By destroying hibernating liver-stage P. vivax, primaquine reduces subsequent reactivations after treatment completion. Results: The RTS,S vaccine significantly reduced the number of clones at first infection, but PfSPZ vaccine and primaquine did not. Resampling smaller data sets from the large RTS,S trial to simulate phase 2 trials showed modest power gains from VEC compared to VEHR for data like RTS,S, but VEC is less powerful than VEHR for vaccines which do not reduce the number of clones at first infection. VEmolFOI was most powerful in model-based simulations, but only the primaquine trial collected enough serial samples to precisely estimate VEmolFOI. The primaquine VEmolFOI estimate decreased after most control arm liver-stage infections reactivated (which mathematically resembles a waning vaccine), preventing VEmolFOI from improving power. Conclusions: The power gain from the genotyping methods depends on the context. Because input parameters for early phase power calculations are often uncertain, we recommend against these estimators as primary endpoints for small trials unless supported by targeted data analysis. Trial registrations: NCT00866619, NCT02663700, NCT02143934.

Vaccine efficacy at a point in time.

Vaccine trials are generally designed to assess efficacy on clinical disease. The vaccine effect on infection, while important both as a proxy for transmission and to describe a vaccine's entire effects, requires frequent (e.g., twice a week) longitudinal sampling to capture all infections. Such sampling may not always be feasible. A logistically easy approach is to collect a sample to test for infection at a regularly scheduled visit. Such point or cross-sectional sampling does not permit estimation of classic vaccine efficacy on infection, as long duration infections are sampled with higher probability. Building on work by Rinta-Kokko and others (2009) and Lipsitch and Kahn (2021), we evaluate proxies of the vaccine effect on transmission at a point in time; the vaccine efficacy on prevalent infection and on prevalent viral load, VE$_{\rm PI}$ and VE$_{\rm PVL}$, respectively. Longer infections with higher viral loads should have more transmission potential and prevalent vaccine efficacy naturally captures this aspect. We demonstrate how these parameters obtain from an underlying proportional hazards model for infection and allow for waning efficacy on infection, duration, and viral load. We estimate these parameters based on regression models with either repeated cross-sectional sampling or frequent longitudinal sampling. We evaluate the methods by simulation and analyze a phase III vaccine trial with polymerase chain reaction (PCR) cross-sectional sampling for subclinical infection.

A note on familywise error rate for a primary and secondary endpoint.

Hung et al. (2007) considered the problem of controlling the type I error rate for a primary and secondary endpoint in a clinical trial using a gatekeeping approach in which the secondary endpoint is tested only if the primary endpoint crosses its monitoring boundary. They considered a two-look trial and showed by simulation that the naive method of testing the secondary endpoint at full level α at the time the primary endpoint reaches statistical significance does not control the familywise error rate at level α. Tamhane et al. (2010) derived analytic expressions for familywise error rate and power and confirmed the inflated error rate of the naive approach. Nonetheless, many people mistakenly believe that the closure principle can be used to prove that the naive procedure controls the familywise error rate. The purpose of this note is to explain in greater detail why there is a problem with the naive approach and show that the degree of alpha inflation can be as high as that of unadjusted monitoring of a single endpoint.

Immune correlates analysis using vaccinees from test negative designs.

Determining the effect of vaccine-induced immune response on disease risk is an important goal of vaccinology. Typically, immune correlates analyses are conducted prospectively with immune response measured shortly after vaccination and subsequent disease status regressed on immune response. In outbreaks and rare disease settings, collecting samples from all vaccinees is not feasible. The test negative design is a retrospective design used to measure vaccine efficacy where symptomatic individuals who present at a clinic are assessed for relevant disease (cases) or some other disease (controls) and vaccination status ascertained. This article proposes that test negative vaccinees have immune response to vaccine assessed both for relevant (e.g., Ebola) and irrelevant (e.g., vector) proteins. If the latter immune response is unaffected by active (Ebola) infection, and is correlated with the relevant immune response, it can serve as a proxy for the immune response of interest proximal to infection. We show that logistic regression using imputed immune response as the covariate and case disease as outcome can estimate the prospective immune response slope and detail the assumptions needed for unbiased inference. The method is evaluated by simulation under various scenarios including constant and decaying immune response. A simulated dataset motivated by ring vaccination for an ongoing Ebola outbreak is analyzed.

Joint testing of overall and simple effects for the two-by-two factorial trial design.

BACKGROUND/AIMS: The two-by-two factorial design randomizes participants to receive treatment A alone, treatment B alone, both treatments A and B(AB), or neither treatment (C). When the combined effect of A and B is less than the sum of the A and B effects, called a subadditive interaction, there can be low power to detect the A effect using an overall test, that is, factorial analysis, which compares the A and AB groups to the C and B groups. Such an interaction may have occurred in the Action to Control Cardiovascular Risk in Diabetes blood pressure trial (ACCORD BP) which simultaneously randomized participants to receive intensive or standard blood pressure, control and intensive or standard glycemic control. For the primary outcome of major cardiovascular event, the overall test for efficacy of intensive blood pressure control was nonsignificant. In such an instance, simple effect tests of A versus C and B versus C may be useful since they are not affected by a subadditive interaction, but they can have lower power since they use half the participants of the overall trial. We investigate multiple testing procedures which exploit the overall tests' sample size advantage and the simple tests' robustness to a potential interaction. METHODS: In the time-to-event setting, we use the stratified and ordinary logrank statistics' asymptotic means to calculate the power of the overall and simple tests under various scenarios. We consider the A and B research questions to be unrelated and allocate 0.05 significance level to each. For each question, we investigate three multiple testing procedures which allocate the type 1 error in different proportions for the overall and simple effects as well as the AB effect. The Equal Allocation 3 procedure allocates equal type 1 error to each of the three effects, the Proportional Allocation 2 procedure allocates 2/3 of the type 1 error to the overall A (respectively, B) effect and the remaining type 1 error to the AB effect, and the Equal Allocation 2 procedure allocates equal amounts to the simple A (respectively, B) and AB effects. These procedures are applied to ACCORD BP. RESULTS: Across various scenarios, Equal Allocation 3 had robust power for detecting a true effect. For ACCORD BP, all three procedures would have detected a benefit of intensive glycemia control. CONCLUSIONS: When there is no interaction, Equal Allocation 3 has less power than a factorial analysis. However, Equal Allocation 3 often has greater power when there is an interaction. The R package factorial2x2 can be used to explore the power gain or loss for different scenarios.

Estimation of infection density and epidemic size of COVID-19 using the back-calculation algorithm.

The novel coronavirus (COVID-19) is continuing its spread across the world, claiming more than 160,000 lives and sickening more than 2,400,000 people as of April 21, 2020. Early research has reported a basic reproduction number (R0) between 2.2 to 3.6, implying that the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China in and outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. We also investigate the COVID-19 epidemic size in South Korea and find a similar effect of its "test, trace, isolate, and treat" strategy. Our findings are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.

Infection Density and Epidemic Size of COVID-19 in China outside the Hubei province.

The novel coronavirus (COVID-19) has spread to almost all countries in the world, claiming more than 160,000 lives and sickening more than 2,400,000 people by April 21, 2020. There has been research showing that on average, each infected person spreads the infection to more than two persons. Therefore the majority of the population is at risk of infection if no intervention measures were undertaken. The true size of the COVID-19 epidemic remains unknown, as a significant proportion of infected individuals only exhibit mild symptoms or are even asymptomatic. A timely assessment of the evolving epidemic size is crucial for resource allocation and triage decisions. In this article, we modify the back-calculation algorithm to obtain a lower bound estimate of the number of COVID-19 infected persons in China outside the Hubei province. We estimate the infection density among infected and show that the drastic control measures enforced throughout China following the lockdown of Wuhan City effectively slowed down the spread of the disease in two weeks. Our findings from China are expected to provide guidelines and enlightenment for surveillance and control activities of COVID-19 in other countries around the world.

On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value.

Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko [2016] that recommends the "D-value," which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. The abstract states "The D-value has a clear interpretation as the proportion of patients who get worse after the treatment" with similar assertions appearing later. We show these statements are incorrect because they require assumptions about the potential outcomes which are neither testable in randomized experiments nor plausible in general. The D-value will not equal the proportion of patients who get worse after treatment if (as expected) those outcomes are correlated. Independence of potential outcomes is unrealistic and eliminates any personalized treatment effects; with dependence, the D-value can even imply treatment is better than control even though most patients are harmed by the treatment. Thus, D-values are misleading for personalized medicine. To prevent misunderstandings, we advise incorporating causal models into basic statistics education.

Predictive cluster level surrogacy in the presence of interference.

Surrogate evaluation is a difficult problem that is made more so by the presence of interference. Our proposed procedure can allow for relatively easy evaluation of surrogates for indirect or spill-over clinical effects at the cluster level. Our definition of surrogacy is based on the causal-association paradigm (Joffe and Greene, 2009. Related causal frameworks for surrogate outcomes. Biometrics65, 530-538), under which surrogates are evaluated by the strength of the association between a causal treatment effect on the clinical outcome and a causal treatment effect on the candidate surrogate. Hudgens and Halloran (2008, Toward causal inference with interference. Journal of the American Statistical Association103, 832-842) introduced estimators that can be used for many of the marginal causal estimands of interest in the presence of interference. We extend these to consider surrogates for not just direct effects, but indirect and total effects at the cluster level. We suggest existing estimators that can be used to evaluate biomarkers under our proposed definition of surrogacy. In our motivating setting of a transmission blocking malaria vaccine, there is expected to be no direct protection to those vaccinated and predictive surrogates are urgently needed. We use a set of simulated data examples based on the proposed Phase IIb/III trial design of transmission blocking malaria vaccine to demonstrate how our definition, proposed criteria and procedure can be used to identify biomarkers as predictive cluster level surrogates in the presence of interference on the clinical outcome.

A unified evaluation of differential vaccine efficacy.

Many infectious diseases are well prevented by proper vaccination. However, when a vaccine is not completely efficacious, there is great interest in determining how the vaccine effect differs in subgroups conditional on measured immune responses postvaccination and also according to the type of infecting agent (eg, strain of a virus). The former is often called correlate of protection (CoP) analysis, while the latter has been called sieve analysis. We propose a unified framework for simultaneously assessing CoP and sieve effects of a vaccine in a large Phase III randomized trial. We use flexible parametric models treating times to infection from different agents as competing risks and estimated maximum likelihood to fit the models. The parametric models under competing risks allow for estimation of both cumulative incidence-based contrasts and instantaneous rates. We outline the assumptions with which we can link the observable data to the causal contrasts of interest, propose hypothesis testing procedures, and evaluate our proposed methods in an extensive simulation study.

Causal estimands and confidence intervals associated with Wilcoxon-Mann-Whitney tests in randomized experiments.

Although the P value from a Wilcoxon-Mann-Whitney test is used often with randomized experiments, it is rarely accompanied with a causal effect estimate and its confidence interval. The natural parameter for the Wilcoxon-Mann-Whitney test is the Mann-Whitney parameter, ϕ, which measures the probability that a randomly selected individual in the treatment arm will have a larger response than a randomly selected individual in the control arm (plus an adjustment for ties). We show that the Mann-Whitney parameter may be framed as a causal parameter and show that it is not equal to a closely related and nonidentifiable causal effect, ψ, the probability that a randomly selected individual will have a larger response under treatment than under control (plus an adjustment for ties). We review the paradox, first expressed by Hand, that the ψ parameter may imply that the treatment is worse (or better) than control, while the Mann-Whitney parameter shows the opposite. Unlike the Mann-Whitney parameter, ψ is nonidentifiable from a randomized experiment. We review some nonparametric assumptions that rule out Hand's paradox through bounds on ψ and use bootstrap methods to make inferences on those bounds. We explore the relationship of the proportional odds parameter to Hand's paradox, showing that the paradox may occur for proportional odds parameters between 1/9 and 9. Thus, large effects are needed to ensure that if treatment appears better by the Mann-Whitney parameter, then treatment improves responses in most individuals. We demonstrate these issues using a vaccine trial.

Streptococcal group A, C and G pharyngitis in school children: a prospective cohort study in Southern India.

Diagnosing streptococcal pharyngitis in children on the basis of clinical appearance and throat culture is complicated by high colonisation rates and by the ability of other pathogens to cause clinically similar disease. To characterise the epidemiology of Lancefield Group A, C and G ß-haemolytic streptococcus (GAS, GCS and GGS, respectively) in children, we conducted a 2-year prospective study of 307 school children between 7 and 11 years old. GGS and GAS were commonly identified organisms both for silent streptococcal colonisation and symptomatic sore throat, while GCS was uncommonly found. Streptococcal culture positivity at the time of clinical pharyngitis was estimated to reflect true streptococcal pharyngitis in only 26% of instances, with the frequency varying from 54% for children rarely colonised to 1% for children frequently colonised. Numerous GAS emm types were identified, including several types previously associated with severe pharyngitis (e.g. emm types 1, 3 and 28). No severe complications were seen in any child. These data suggest that the clinical diagnosis of streptococcal pharyngitis is likely to remain difficult and that treatment decisions will remain clouded by uncertainty. There remains a need for organism-specific rapid point-of-care streptococcal diagnostic tests and tests that can distinguish between streptococcal colonisation and disease.

A single injection of crystallizable fragment domain-modified antibodies elicits durable protection from SHIV infection.

In the absence of an effective and safe vaccine against HIV-1, the administration of broadly neutralizing antibodies (bNAbs) represents a logical alternative approach to prevent virus transmission. Here, we introduced two mutations encoding amino acid substitutions (M428L and N434S, collectively referred to as 'LS') into the genes encoding the crystallizable fragment domains of the highly potent HIV-specific 3BNC117 and 10-1074 bNAbs to increase their half-lives and evaluated their efficacy in blocking infection following repeated low-dose mucosal challenges of rhesus macaques (Macaca mulatta) with the tier 2 SHIVAD8-EO. A single intravenous infusion of 10-1074-LS monoclonal antibodies markedly delayed virus acquisition for 18 to 37 weeks (median, 27 weeks), whereas the protective effect of the 3BNC117-LS bNAb was more modest (provided protection for 11-23 weeks; median, 17 weeks). Serum concentrations of the 10-1074-LS monoclonal antibody gradually declined and became undetectable in all recipients between weeks 26 and 41, whereas the 3BNC117-LS bNAb exhibited a shorter half-life. To model immunoprophylaxis against genetically diverse and/or neutralization-resistant HIV-1 strains, a combination of the 3BNC117-LS plus 10-1074-LS monoclonal antibodies was injected into macaques via the more clinically relevant subcutaneous route. Even though the administered mixture contained an amount of each bNAb that was nearly threefold less than the quantity of the single monoclonal antibody in the intravenous injections, the monoclonal antibody combination still protected macaques for a median of 20 weeks. The extended period of protection observed in macaques for the 3BNC117-LS plus 10-1074-LS combination could translate into an effective semiannual or annual immunoprophylaxis regimen for preventing HIV-1 infections in humans.

A boundary-optimized rejection region test for the two-sample binomial problem.

Testing the equality of 2 proportions for a control group versus a treatment group is a well-researched statistical problem. In some settings, there may be strong historical data that allow one to reliably expect that the control proportion is one, or nearly so. While one-sample tests or comparisons to historical controls could be used, neither can rigorously control the type I error rate in the event the true control rate changes. In this work, we propose an unconditional exact test that exploits the historical information while controlling the type I error rate. We sequentially construct a rejection region by first maximizing the rejection region in the space where all controls have an event, subject to the constraint that our type I error rate does not exceed α for any true event rate; then with any remaining α we maximize the additional rejection region in the space where one control avoids the event, and so on. When the true control event rate is one, our test is the most powerful nonrandomized test for all points in the alternative space. When the true control event rate is nearly one, we demonstrate that our test has equal or higher mean power, averaging over the alternative space, than a variety of well-known tests. For the comparison of 4 controls and 4 treated subjects, our proposed test has higher power than all comparator tests. We demonstrate the properties of our proposed test by simulation and use our method to design a malaria vaccine trial.

Impact of Intravenous Immunoglobulin on Survival in Necrotizing Fasciitis With Vasopressor-Dependent Shock: A Propensity Score-Matched Analysis From 130 US Hospitals.

BACKGROUND: Shock frequently complicates necrotizing fasciitis (NF) caused by group A Streptococcus (GAS) or Staphylococcus aureus. Intravenous immunoglobulin (IVIG) is sometimes administered for presumptive toxic shock syndrome (TSS), but its frequency of use and efficacy are unclear. METHODS: Adult patients with NF and vasopressor-dependent shock undergoing surgical debridement from 2010 to 2014 were identified at 130 US hospitals. IVIG cases were propensity-matched and risk-adjusted. The primary outcome was in-hospital mortality and the secondary outcome was median length of stay (LOS). RESULTS: Of 4127 cases of debrided NF with shock at 121 centers, only 164 patients (4%) at 61 centers received IVIG. IVIG subjects were younger with lower comorbidity indices, but higher illness severity. Clindamycin and vasopressor intensity were higher among IVIG cases, as was coding for TSS and GAS. In-hospital mortality did not differ between matched IVIG and non-IVIG groups (crude mortality, 27.3% vs 23.6%; adjusted odds ratio, 1.00 [95% confidence interval, .55-1.83]; P = .99). Early IVIG (≤2 days) did not alter this effect (P = .99). Among patients coded for TSS, GAS, and/or S. aureus, IVIG use was still unusual (59/868 [6.8%]) and lacked benefit (P = .63). Median LOS was similar between IVIG and non-IVIG groups (26 [13-49] vs 26 [11-43]; P = .84). Positive predictive values for identifying true NF and debridement among IVIG cases using our algorithms were 97% and 89%, respectively, based on records review at 4 hospitals. CONCLUSIONS: Adjunctive IVIG was administered infrequently in NF with shock and had no apparent impact on mortality or hospital LOS beyond that achieved with debridement and antibiotics.

Non-inferiority tests for anti-infective drugs using control group quantiles.

BACKGROUND/AIMS: In testing for non-inferiority of anti-infective drugs, the primary endpoint is often the difference in the proportion of failures between the test and control group at a landmark time. The landmark time is chosen to approximately correspond to the qth historic quantile of the control group, and the non-inferiority margin is selected to be reasonable for the target level q. For designing these studies, a troubling issue is that the landmark time must be pre-specified, but there is no guarantee that the proportion of control failures at the landmark time will be close to the target level q. If the landmark time is far from the target control quantile, then the pre-specified non-inferiority margin may not longer be reasonable. Exact variable margin tests have been developed by Röhmel and Kieser to address this problem, but these tests can have poor power if the observed control failure rate at the landmark time is far from its historic value. METHODS: We develop a new variable margin non-inferiority test where we continue sampling until a pre-specified proportion of failures, q, have occurred in the control group, where q is the target quantile level. The test does not require any assumptions on the failure time distributions, and hence, no knowledge of the true [Formula: see text] control quantile for the study is needed. RESULTS: Our new test is exact and has power comparable to (or greater than) its competitors when the true control quantile from the study equals (or differs moderately from) its historic value. Our nivm R package performs the test and gives confidence intervals on the difference in failure rates at the true target control quantile. The tests can be applied to time to cure or other numeric variables as well. CONCLUSION: A substantial proportion of new anti-infective drugs being developed use non-inferiority tests in their development, and typically, a pre-specified landmark time and its associated difference margin are set at the design stage to match a specific target control quantile. If through changing standard of care or selection of a different population the target quantile for the control group changes from its historic value, then the appropriateness of the pre-specified margin at the landmark time may be questionable. Our proposed test avoids this problem by sampling until a pre-specified proportion of the controls have failed.

Recurrent event data analysis with intermittently observed time-varying covariates.

Although recurrent event data analysis is a rapidly evolving area of research, rigorous studies on estimation of the effects of intermittently observed time-varying covariates on the risk of recurrent events have been lacking. Existing methods for analyzing recurrent event data usually require that the covariate processes are observed throughout the entire follow-up period. However, covariates are often observed periodically rather than continuously. We propose a novel semiparametric estimator for the regression parameters in the popular proportional rate model. The proposed estimator is based on an estimated score function where we kernel smooth the mean covariate process. We show that the proposed semiparametric estimator is asymptotically unbiased, normally distributed, and derives the asymptotic variance. Simulation studies are conducted to compare the performance of the proposed estimator and the simple methods carrying forward the last covariates. The different methods are applied to an observational study designed to assess the effect of group A streptococcus on pharyngitis among school children in India. Copyright © 2016 John Wiley & Sons, Ltd.

Implementation of an Ebola virus disease vaccine clinical trial during the Ebola epidemic in Liberia: Design, procedures, and challenges.

The index case of the Ebola virus disease epidemic in West Africa is believed to have originated in Guinea. By June 2014, Guinea, Liberia, and Sierra Leone were in the midst of a full-blown and complex global health emergency. The devastating effects of this Ebola epidemic in West Africa put the global health response in acute focus for urgent international interventions. Accordingly, in October 2014, a World Health Organization high-level meeting endorsed the concept of a phase 2/3 clinical trial in Liberia to study Ebola vaccines. As a follow-up to the global response, in November 2014, the Government of Liberia and the US Government signed an agreement to form a research partnership to investigate Ebola and to assess intervention strategies for treating, controlling, and preventing the disease in Liberia. This agreement led to the establishment of the Joint Liberia-US Partnership for Research on Ebola Virus in Liberia as the beginning of a long-term collaborative partnership in clinical research between the two countries. In this article, we discuss the methodology and related challenges associated with the implementation of the Ebola vaccines clinical trial, based on a double-blinded randomized controlled trial, in Liberia.

CD8(+) T-cell Cytotoxic Capacity Associated with Human Immunodeficiency Virus-1 Control Can Be Mediated through Various Epitopes and Human Leukocyte Antigen Types.

Understanding natural immunologic control over Human Immunodeficiency Virus (HIV)-1 replication, as occurs in rare long-term nonprogressors/elite controllers (LTNP/EC), should inform the design of efficacious HIV vaccines and immunotherapies. Durable control in LTNP/EC is likely mediated by highly functional virus-specific CD8(+) T-cells. Protective Human Leukocyte Antigen (HLA) class I alleles, like B*27 and B*57, are present in most, but not all LTNP/EC, providing an opportunity to investigate features shared by their HIV-specific immune responses. To better understand the contribution of epitope targeting and conservation to immune control, we compared the CD8(+) T-cell specificity and function of B*27/57(neg) LTNP/EC (n = 23), B*27/57(pos) LTNP/EC (n = 23) and B*27/57(neg) progressors (n = 13). Fine mapping revealed 11 previously unreported immunodominant responses. Although B*27/57(neg) LTNP/EC did not target more highly conserved epitopes, their CD8(+) T-cell cytotoxic capacity was significantly higher than progressors. Similar to B*27/57(pos) LTNP/EC, this superior cytotoxicity was mediated by preferential expansion of immunodominant responses and lysis through the predicted HLA. These findings suggest that increased CD8(+) T-cell cytotoxic capacity is a common mechanism of control in most LTNP/EC regardless of HLA type. They also suggest that potent cytotoxicity can be mediated through various epitopes and HLA molecules and could, in theory, be induced in most people.