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Evaluating the surrogacy of multiple vaccine-induced immune response biomarkers in HIV vaccine trials.

ABSTRACT: Identifying biomarkers as surrogates for clinical endpoints in randomized vaccine trials is useful for reducing study duration and costs, relieving participants of unnecessary discomfort, and understanding vaccine-effect mechanism. In this article, we use risk models with multiple vaccine-induced immune response biomarkers to measure the causal association between a vaccine's effects on these biomarkers and that on the clinical endpoint. In this setup, our main objective is to combine and select markers with high surrogacy from a list of many candidate markers, allowing us to get a more parsimonious model which can potentially increase the predictive quality of the true markers. To address the missing "potential" biomarker value if a subject receives placebo, we utilize the baseline immunogenicity predictor design augmented with a "closeout placebo vaccination" group. We then impute the missing potential marker values and conduct marker selection through a stepwise resampling and imputation method called stability selection. We test our proposed strategy under relevant simulation settings and on (partially simulated) biomarker data from a HIV vaccine trial (RV144).

SUBMITTER: Dasgupta S

PROVIDER: S-EPMC8035998 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Evaluating the surrogacy of multiple vaccine-induced immune response biomarkers in HIV vaccine trials.

Dasgupta Sayan S Huang Ying Y

Biostatistics (Oxford, England) 20210401 2

Identifying biomarkers as surrogates for clinical endpoints in randomized vaccine trials is useful for reducing study duration and costs, relieving participants of unnecessary discomfort, and understanding vaccine-effect mechanism. In this article, we use risk models with multiple vaccine-induced immune response biomarkers to measure the causal association between a vaccine's effects on these biomarkers and that on the clinical endpoint. In this setup, our main objective is to combine and select ...[more]

PMID: 31631216

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Project description:BackgroundVaccine clinical trials should strive to recruit a racially, socioeconomically, and ethnically diverse range of participants to ensure appropriate representation that matches population characteristics. Yet, full inclusion in research is often limited.MethodsA single-center retrospective study was conducted of adults enrolled at Brigham and Women's Hospital (Boston, MA) between July 2020 and December 2021. Demographic characteristics, including age, race, ethnicity, ZIP code, and sex assigned at birth, were analyzed from both HIV and COVID-19 vaccine trials during the study period, acknowledging the limitations to representation under these parameters. We compared the educational attainment of vaccine trial participants to residents of the Massachusetts metropolitan area, geocoded participants' addresses to their census block group, and linked them to reported median household income levels from publicly available data for 2020. Frequency and quartile analyses were carried out, and spatial analyses were performed using ArcGIS Online web-based mapping software (Esri).ResultsA total of 1030 participants from four COVID-19 vaccine trials (n = 916 participants) and six HIV vaccine trials (n = 114 participants) were included in the analysis. The median age was 49 years (IQR 33-63) and 28 years (IQR 24-34) for the COVID-19 and HIV vaccine trials, respectively. Participants identifying as White were the majority group represented for both the COVID-19 (n = 598, 65.3%) and HIV vaccine trials (n = 83, 72.8%). Fewer than 25% of participants identified as Hispanic or Latin. Based on ZIP code of residence, the median household income for COVID-19 vaccine clinical trial participants (n = 846) was 102,088 USD (IQR = 81,442-126,094). For HIV vaccine clinical trial participants (n = 109), the median household income was 101,266 USD (IQR 75,052-108,832).ConclusionWe described the characteristics of participants enrolled for HIV and COVID-19 vaccine trials at a single center and found similitude in geographical distribution, median incomes, and proportion of underrepresented individuals between the two types of vaccine candidate trials. Further outreach efforts are needed to ensure the inclusion of individuals from lower educational and socioeconomic brackets. In addition, continued and sustained efforts are necessary to ensure inclusion of individuals from diverse racial and ethnic backgrounds.

Project description:Developing adjuvant vaccines to combat rising multidrug-resistant (MDR) Acinetobacter baumannii (A. baumannii) infections is a promising and cost-effective approach. The aim of this analysis was to construct a pDNA-CPG C274-adjuvant nano-vaccine and investigate its immunogenicity and protection in BALB/c mice. The CPG ODN C274 adjuvant was chemically synthesized and cloned into pcDNA3.1( +), and the cloning was verified using PCR and BamHI/EcoRV restriction enzyme digestion. Then, utilizing a complex coacervation approach, pDNA-CPG C274 was encapsulated by chitosan (CS) nanoparticles (NPs). TEM and DLS are used to explore the properties of the pDNA/CSNP complex. TLR-9 pathway activation was investigated in human HEK-293 and RAW 264.7 mouse cells. The vaccine's immunogenicity and immune-protective effectiveness were investigated in BALB/c mice. The pDNA-CPG C274/CSNPs were small (mean size 79.21 ± 0.23 nm), positively charged (+ 38.87 mV), and appeared to be spherical. A continuous slow release pattern was achieved. TLR-9 activation was greatest in the mouse model with CpG ODN (C274) at concentrations of 5 and 10 μg/ml with 56% and 55%, respectively (**P < 0.01). However, in HEK-293 human cells, by increasing the concentration of CpG ODN (C274) from 1 to 50 μg/ml, the activation rate of TLR-9 also increased, so that the highest activation rate (81%) was obtained at the concentration of 50 μg/ml (***P < 0.001). pDNA-CPG C274/CSNPs immunized BALB/c mice produced increased amounts of total-IgG, as well as IFN-γ and IL-1B in serum samples, compared to non-encapsulated pDNA-CPG C274. Furthermore, liver and lung injuries, as well as bacterial loads in the liver, lung, and blood, were reduced, and BALB/c mice immunized with pDNA-CPG C274/CSNPs showed potent protection (50-75%) against acute fatal Intraperitoneal A. baumannii challenge. pDNA-CPG C274/CSNPs evoked total-IgG antibodies, Th1 cellular immunity, and the TLR-9 pathway, as well as protection against an acute fatal A. baumannii challenge. Our findings suggest that this nano-vaccine is a promising approach for avoiding A. baumannii infection when used as a powerful adjuvant.

Project description:Identifying effective and valid surrogate markers to make inference about a treatment effect on long-term outcomes is an important step in improving the efficiency of clinical trials. Replacing a long-term outcome with short-term and/or cheaper surrogate markers can potentially shorten study duration and reduce trial costs. There is sizable statistical literature on methods to quantify the effectiveness of a single surrogate marker. Both parametric and nonparametric approaches have been well developed for different outcome types. However, when there are multiple markers available, methods for combining markers to construct a composite marker with improved surrogacy remain limited. In this paper, building on top of the optimal transformation framework of Wang et al. (2020), we propose a novel calibrated model fusion approach to optimally combine multiple markers to improve surrogacy. Specifically, we obtain two initial estimates of optimal composite scores of the markers based on two sets of models with one set approximating the underlying data distribution and the other directly approximating the optimal transformation function. We then estimate an optimal calibrated combination of the two estimated scores which ensures both validity of the final combined score and optimality with respect to the proportion of treatment effect explained by the final combined score. This approach is unique in that it identifies an optimal combination of the multiple surrogates without strictly relying on parametric assumptions while borrowing modeling strategies to avoid fully nonparametric estimation which is subject to the curse of dimensionality. Our identified optimal transformation can also be used to directly quantify the surrogacy of this identified combined score. Theoretical properties of the proposed estimators are derived, and the finite sample performance of the proposed method is evaluated through simulation studies. We further illustrate the proposed method using data from the Diabetes Prevention Program study.

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Evaluating the surrogacy of multiple vaccine-induced immune response biomarkers in HIV vaccine trials.

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Evaluating the surrogacy of multiple vaccine-induced immune response biomarkers in HIV vaccine trials.

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