Bridging the gap between efficacy trials and model-based impact evaluation for new tuberculosis vaccines

Abstract

In Tuberculosis (TB), given the complexity of its transmission dynamics, observations of reduced epidemiological risk associated with preventive interventions can be difficult to translate into mechanistic interpretations. Specifically, in clinical trials of vaccine efficacy, a readout of protection against TB disease can be mapped to multiple dynamical mechanisms, an issue that has been overlooked so far. Here, we describe this limitation and its effect on model-based evaluations of vaccine impact. Furthermore, we propose a methodology to analyze efficacy trials that circumvents it, leveraging a combination of compartmental models and stochastic simulations. Using our approach, we can disentangle the different possible mechanisms of action underlying vaccine protection effects against TB, conditioned to trial design, size, and duration. Our results unlock a deeper interpretation of the data emanating from efficacy trials of TB vaccines, which renders them more interpretable in terms of transmission models and translates into explicit recommendations for vaccine developers.

Identifying vaccine-mechanism bias in mathematical models of vaccine impact: the case of tuberculosis

“…In order to illustrate this problem and quantify its importance, we capitalize on the same transmission model used above to infer epidemiological parameters and fractions of fast vs. slow progressors. This model [23], was later adapted to allow for the description of the effects of the introduction of new vaccines [16]. Here, we have further adapted the model to accommodate vaccine descriptions compatible with each of the seven models under analysis (see Methods & SI).…”

“…(A) Vaccine impact forecasts obtained through the comprehensive transmission model introduced in [16,23], when the vaccine is modelled according to the each of the seven descriptions here discussed. (B) Relative differences between the impacts foreseen by each model and the model with maximum Bayesian posteriors (model 6).…”

“…Impact evaluations. In this study, we make use of the transmission model introduced in [23], and generalized to describe the introduction of novel vaccines in [16]. This model constitutes a conceptual extension of the basic model sketched in figure 1A, where different types of disease are considered, along with treatment outcome dynamics and eventual relapses.…”

“…One of the reasons why such a task is difficult is the fact that the PoI or PoD efficacy readouts obtained from an RCT do not offer an unequivocal characterization of a TB vaccine, since the same risk-reduction readouts observed in a trial can be mapped onto different mechanisms of action in different vaccine candidates [14]. This is extremely important because some of these compatible mechanisms are impossible to distinguish just by interpreting the trial’s results using standard methodologies, and yet, they appear associated to significantly different impacts, as foreseen by transmission models, if applied in simulated vaccination campaigns [16]. Yet, a majority of the models in the current literature on TB vaccine modeling base their forecasts on assuming that efficacy readouts unequivocally map onto specific combinations of action mechanisms without providing a plausible justification of this important modeling choices [17].…”

Update on TB Vaccine Pipeline

“…Following the negative results of the first efficacy study which was conducted with MVA5A in infants, the priority population was changed to mainly adults. Modelling studies suggest that new effective vaccines for adolescents and adults, who are responsible for disease transmission, would have the greatest impact in halting TB incidence [71,72]. These studies suggest that although disease incidence in children under five years of age is considerable, TB transmission among this age-group is not common [73].…”