Hitchings Matt D T, Cummings Derek A T, Grais Rebecca F, Isanaka Sheila
Department of Biology, University of Florida, United States; Emerging Pathogens Institute, University of Florida, United States.
Department of Biology, University of Florida, United States; Emerging Pathogens Institute, University of Florida, United States.
Vaccine. 2020 Dec 3;38(51):8161-8166. doi: 10.1016/j.vaccine.2020.10.079. Epub 2020 Nov 6.
Analysis of immunogenicity data is a critical component of vaccine development, providing a biological basis to support any observed protection from vaccination. Conventional methods for analyzing immunogenicity data use either post-vaccination titer or change in titer, often defined as a binary variable using a threshold. These methods are simple to implement but can be limited especially in populations experiencing natural exposure to the pathogen. A mixture model can overcome the limitations of the conventional approaches by jointly modeling the probability of an immune response and the level of the immune marker among those who respond. We apply a mixture model to analyze the immunogenicity of an oral, pentavalent rotavirus vaccine in a cohort of children enrolled into a placebo-controlled vaccine efficacy trial in Niger. Among children with undetectable immunoglobulin A (IgA) at baseline, vaccinated children had 5.2-fold (95% credible interval (CrI) 3.7, 8.3) higher odds of having an IgA response than placebo children, but the mean log IgA among vaccinated responders was 0.9-log lower (95% CrI 0.6, 1.3) than among placebo responders. This result implies that the IgA response generated by vaccination is weaker than that generated by natural infection. Multivariate logistic regression of seroconversion defined by ≥ 3-fold rise in IgA similarly found increased seroconversion among vaccinated children, but could not demonstrate lower IgA among those who seroresponded. In addition, we found that the vaccine was less immunogenic among children with detectable IgA pre-vaccination, and that pre-vaccination infant serum IgG and mother's breast milk IgA modified the vaccine immunogenicity. Increased maternal antibodies were associated with weaker IgA response in placebo and vaccinated children, with the association being stronger among vaccinated children. The mixture model is a powerful and flexible method for analyzing immunogenicity data and identifying modifiers of vaccine response and independent predictors of immune response.
免疫原性数据分析是疫苗研发的关键组成部分，为支持任何观察到的疫苗接种保护作用提供生物学依据。分析免疫原性数据的传统方法使用接种后滴度或滴度变化，通常使用阈值将其定义为二元变量。这些方法易于实施，但可能存在局限性，尤其是在自然接触病原体的人群中。混合模型可以通过联合建模免疫反应的概率和有反应者的免疫标志物水平来克服传统方法的局限性。我们应用混合模型分析了一种口服五价轮状病毒疫苗在参与尼日尔安慰剂对照疫苗效力试验的一组儿童中的免疫原性。在基线时免疫球蛋白A（IgA）检测不到的儿童中，接种疫苗的儿童产生IgA反应的几率比安慰剂组儿童高5.2倍（95%可信区间（CrI）3.7, 8.3），但接种疫苗有反应者的平均对数IgA比安慰剂有反应者低0.9个对数（95% CrI 0.6, 1.3）。这一结果表明，疫苗接种产生的IgA反应比自然感染产生的反应弱。由IgA升高≥3倍定义的血清转化的多变量逻辑回归同样发现接种疫苗的儿童血清转化增加，但无法证明有血清反应者的IgA较低。此外，我们发现该疫苗在接种前可检测到IgA的儿童中免疫原性较低，并且接种前婴儿血清IgG和母亲母乳中的IgA会改变疫苗免疫原性。母体抗体增加与安慰剂组和接种疫苗儿童中较弱的IgA反应相关，在接种疫苗儿童中这种关联更强。混合模型是分析免疫原性数据以及识别疫苗反应修饰因素和免疫反应独立预测因素的一种强大且灵活的方法。
