Is the current use of 'positivity' thresholds meaningful for evaluating HIV-vaccine immunogenicity endpoints?

Analyzing cellular immunogenicity in vaccine clinical trials: a new statistical method including non-specific responses for accurate estimation of vaccine effect

Evaluation of immunogenicity is a key step in the clinical development of novel vaccines. T-cell responses to vaccine candidates are typically assessed by intracellular cytokine staining (ICS) using multiparametric flow cytometry. A conventional statistical approach to analyze ICS data is to compare, between vaccine regimens or between baseline and post-vaccination of the same regimen depending on the trial design, the percentages of cells producing a cytokine of interest after ex vivo stimulation of peripheral blood mononuclear cells (PBMC) with vaccine antigens, after subtracting the non-specific response (of unstimulated cells) of each sample. Subtraction of the non-specific response is aimed at capturing the specific response to the antigen, but raises methodological issues related to measurement error and statistical power. We describe here a new statistical approach to analyze ICS data from vaccine trials. We propose a bivariate linear regression model for estimating the non-specific and antigen-specific ICS responses. We benchmarked the performance of the model in terms of both bias and control of type-I and -II errors in comparison with conventional approaches, and applied it to simulated data as well as real pre- and post-vaccination data from two recent HIV vaccine trials (ANRS VRI01 in healthy volunteers and therapeutic VRI02 ANRS 149 LIGHT in HIV-infected participants). The model was as good as the conventional approaches (with or without subtraction of the non-specific response) in all simulation scenarios in terms of statistical performance, whereas the conventional approaches did not provide robust results across all scenarios. The proposed model estimated the T-cell responses to the antigens without any effect of the non-specific response on the specific response, irrespective of the correlation between the non-specific and specific responses. This novel method of analyzing T-cell immunogenicity data based on bivariate modeling is more flexible than conventional methods, and so yields more detailed results and enables accurate interpretation of vaccine-induced response.

Recent developments in clinical trial designs for HIV vaccine research

HIV vaccine strategies are expected to be a crucial component for controlling the HIV epidemic. Despite the large spectrum of potential candidate vaccines for both prophylactic and therapeutic use, the overall development process of an efficacious HIV vaccine strategy is lengthy. The design of clinical trials and the progression of a candidate strategy through the different clinical development stages remain methodologically challenging, mainly due to the lack of validated correlates of protection. In this review, we describe recent advances in clinical trial designs to increase the efficiency of the clinical development of candidate HIV vaccine strategies. The methodological aspects of the designs for early- (phase I and II) and later -stage (phase IIB and III) development are discussed, taking into account the specificities of both prophylactic and therapeutic HIV vaccine development.