Dataset Information

Modeling adaptive response profiles in a vaccine clinical trial.

ABSTRACT:

Background

Vaccine clinical studies typically provide time-resolved data on adaptive response read-outs in response to the administration of that particular vaccine to a cohort of individuals. However, modeling such data is challenged by the properties of these time-resolved profiles such as non-linearity, scarcity of measurement points, scheduling of the vaccine at multiple time points. Linear Mixed Models (LMM) are often used for the analysis of longitudinal data but their use in these time-resolved immunological data is not common yet. Apart from the modeling challenges mentioned earlier, selection of the optimal model by using information-criterion-based measures is far from being straight-forward. The aim of this study is to provide guidelines for the application and selection of LMMs that deal with the challenging characteristics of the typical data sets in the field of vaccine clinical studies.

Methods

We used antibody measurements in response to Hepatitis-B vaccine with five different adjuvant formulations for demonstration purposes. We built piecewise-linear, piecewise-quadratic and cubic models with transformations of the axes with pre-selected or optimized knot locations where time is a numerical variable. We also investigated models where time is categorical and random effects are shared intercepts between different measurement points. We compared all models by using Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Deviance Information Criterion (DIC), variations of conditional AIC and by visual inspection of the model fit in the light of prior biological information.

Results

There are various ways of dealing with the challenges of the data which have their own advantages and disadvantages. We explain these in detail here. Traditional information-criteria-based measures work well for the coarse selection of the model structure and complexity, however are not efficient at fine tuning of the complexity level of the random effects.

Conclusions

We show that common statistical measures for optimal model complexity are not sufficient. Rather, explicitly accounting for model purpose and biological interpretation is needed to arrive at relevant models.

Trial registration

Clinical trial registration number for this study: NCT00805389, date of registration: December 9, 2008 (pro-active registration).

SUBMITTER: Hasdemir D

PROVIDER: S-EPMC7364493 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

Modeling adaptive response profiles in a vaccine clinical trial.

Hasdemir Dicle D van den Berg Robert A RA van Kampen Antoine A Smilde Age K AK

BMC medical research methodology 20200716 1

<h4>Background</h4>Vaccine clinical studies typically provide time-resolved data on adaptive response read-outs in response to the administration of that particular vaccine to a cohort of individuals. However, modeling such data is challenged by the properties of these time-resolved profiles such as non-linearity, scarcity of measurement points, scheduling of the vaccine at multiple time points. Linear Mixed Models (LMM) are often used for the analysis of longitudinal data but their use in these ...[more]

PMID: 32677968

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Project description:BCN02 was an exploratory, single arm study designed to evaluate a kick-and-kill therapeutic vaccine strategy in early-treated HIV infected individuals. The participants in the study received a MVA.HIVconsv vaccination before and after three cycles of infusion of romidepsin (RMD), an inhibitor of histone deacetylases that acts as a viral latency reversing agent. The inclusion of a monitored antiretroviral pause (MAP) identified 8 individuals with an early viral rebound (pVL > 2,000 HIV RNA copies/mL, < 4 weeks of MAP initiation) and 4, with a late viral rebound (pVL > 2,000 HIV RNA copies/mL, > 4 weeks of MAP initiation). In the present study, a systems biology analysis was conducted to identify the pathways modulated at transcriptomic and epigenetic level during the clinical intervention, involving vaccination and RMD, and to identify biomarkers predictive of HIV viral rebound during MAP. To this end, we studied gene expression and whole-genome DNA methylation in longitudinal PBMC samples prior and after the intervention. The main impact on host transcriptional and epigenetic signatures was observed after vaccination and RMD infusion (Vacc+RMD) with multiple pathways related to HIV infection and immune response showing changes at gene expression and DNA methylation level. Analysis of an independent cohort of individuals treated with RMD alone, showed that these changes were mainly driven by CD4 T cells. DNA methylation patterns in full PBMC obtained after full intervention (Vacc+RMD) allowed to discriminate individuals with an Early versus Late viral rebound. These individuals also showed a differential enrichment on chromatin accessibility and transcription factor binding sites. Intriguingly, 30 of the DMPs between Early and Late rebounders at Vacc+RMD were present already at baseline and were more accentuated after RMD infusion. Overall, this study provides with a deeper understanding of the mechanisms modulated during the BCN02 study and highlights the importance of DNA methylation profiles in HIV cure.

Project description:BackgroundCoronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused significant loss of life and property. In response to the serious pandemic, recently developed vaccines against SARS-CoV-2 have been administrated to the public. Nevertheless, the research on human immunization response against COVID-19 vaccines is insufficient. Although much information associated with vaccine efficacy, safety and immunogenicity has been reported by pharmaceutical companies based on laboratory studies and clinical trials, vaccine evaluation needs to be extended further to better understand the effect of COVID-19 vaccines on human beings.MethodsWe performed a comparative peptidome analysis on serum samples from 95 participants collected at four time points before and after receiving CoronaVac. The collected serum samples were analyzed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to profile the serum peptides, and also subjected to humoral and cellular immune response analyses to obtain typical immunogenicity information.ResultsSignificant difference in serum peptidome profiles by MALDI-TOF MS was observed after vaccination. By supervised statistical analysis, a total of 13 serum MALDI-TOF MS feature peaks were obtained on day 28 and day 42 of vaccination. The feature peaks were identified as component C1q receptor, CD59 glycoprotein, mannose-binding protein C, platelet basic protein, CD99 antigen, Leucine-rich alpha-2-glycoprotein, integral membrane protein 2B, platelet factor 4 and hemoglobin subunits. Combining with immunogenicity analysis, the study provided evidence for the humoral and cellular immune responses activated by CoronaVac. Furthermore, we found that it is possible to distinguish neutralizing antibody (NAbs)-positive from NAbs-negative individuals after complete vaccination using the serum peptidome profiles by MALDI-TOF MS together with machine learning methods, including random forest (RF), partial least squares-discriminant analysis (PLS-DA), linear support vector machine (SVM) and logistic regression (LR).ConclusionsThe study shows the promise of MALDI-TOF MS-based serum peptidome analysis for the assessment of immune responses activated by COVID-19 vaccination, and discovered a panel of serum peptides biomarkers for COVID-19 vaccination and for NAbs generation. The method developed in this study can help not only in the development of new vaccines, but also in the post-marketing evaluation of developed vaccines.

Dataset Information

Modeling adaptive response profiles in a vaccine clinical trial.

Background

Methods

Results

Conclusions

Trial registration

Publications

Modeling adaptive response profiles in a vaccine clinical trial.

Similar Datasets

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