Computing threshold antibody levels of protection in vaccine clinical trials: An assessment of methodological bias

Abstract

In the development of new vaccines, understanding the level of vaccine-induced antibody that is sufficient to protect against disease can simplify and expedite the development and licensing process. If there is an accepted threshold antibody level that is indicative of protection, then smaller trials measuring antibody concentration alone can be conducted to test new vaccines, instead of large efficacy studies powered on clinical outcomes. Commonly, threshold levels of protective antibody are determined from clinical efficacy trials in which clinical endpoints are measured on everyone and a small subset of participants have antibody concentrations measured. The proportion of participants with antibody below a threshold in each group in the immunogenicity subset can be compared to the proportions with disease in each group in the larger trial to find an appropriate threshold. Mathematically, this method seeks to compute an absolute threshold whereby antibody above the threshold provides complete, sterilizing immunity. However, in practice it is often understood that such thresholds may only be indicative of a relative degree of protection rather than an absolute one. Although this approach is common, the accuracy of such methods when the underlying mathematical assumptions do not hold true, has never been tested. We simulated data from clinical trial scenarios under varying assumptions of vaccine efficacy and calculated antibody thresholds of protection. We estimated the bias in the calculated thresholds derived from each scenario and showed that in many situations this method produces inflated estimates of thresholds, particularly if a vaccine induces high levels of antibody or when the underlying assumption of sterilizing immunity is violated.

Influenza A virus vaccine research conducted in swine from 1990 to May 2018: A scoping review

“…2) The seven categories used to chart outcome data were insufficient to convey the diversity of reported outcomes measures. 3) Outcome diversity is predominantly and importantly a function of the researchers' differing conceptual definitions of IAV-S vaccine-induced protection in swine, and a function of the quickly evolving technical understanding of IAV-S in swine [15,70,74,[100][101][102][103][104][105]. 4) Research is likely insufficient to support systematic review of a narrowly defined outcome given the diversity of viruses involved, and the importance of strain specificity for immunologic responses.…”

Bayesian Meta-Analysis to Validate Correlate of Protection for High Vaccine Efficacy Clinical Trials

“…The sampler adapts its behaviour to maximize their efficiency after every 1000 iterations. The Trace plots, Figures (1) and (2) reveal the stability and proper mixing of the parameters R 2 and variance-covariance matrix across the 3 parallel chains.…”

“…To evaluate a vaccine's efficacy, it is generally useful to identify the level of an immune marker above which vaccinees have a defined probability of being protected. It is called the protective threshold, and is used to calculate the protective response rate [1]. During clinical development, if such a protective threshold (otherwise known as correlate of protection) is known, one can then define a level (a titer, a concentration or a fold change) above which the vaccinated subject has responded to the vaccine, that is, the vaccine response threshold, and it's used to calculate the response rate [1].…”

“…It is called the protective threshold, and is used to calculate the protective response rate [1]. During clinical development, if such a protective threshold (otherwise known as correlate of protection) is known, one can then define a level (a titer, a concentration or a fold change) above which the vaccinated subject has responded to the vaccine, that is, the vaccine response threshold, and it's used to calculate the response rate [1]. However, during clinical development, vaccine correlate of protection is generally unknown [2].…”

Mathematical Modelling for Optimal Vaccine Dose Finding: Maximising Efficacy and Minimising Toxicity

“…Although immunological data may be continuous in nature, a predictive model between immunological readout and probability of efficacy is often unknown [ 22 ]. Hence, it is common to define a threshold and consider individuals with immune response in excess of that threshold to have experienced an efficacious response [ 23 ]. Therefore, the actual desired endpoint (e.g., protection/survival) is likely binary in nature, and surrogates are often also binary.…”