Last Updated: 26/09/2025

Identifying correlates of protection to accelerate vaccine trials: systems evaluation of two models of experimentally-induced immunity to malaria (SysMalVac)

Objectives

To apply an analytical method of mapping the human immune response to malaria immunisation strategies of malaria vaccination and allow a predictive artificial intelligence model to identify a biomarker signature correlating to protection in the RTS,S and CPS immunisation strategies. Common or generalizable in vitro immune correlates of protection will be refined and validated in an experimental CPS animal model and additional samples from human immunisation trials. The final validated set of transcriptional biomarkers will be bundled as a proposed surrogate of protection, which will be an actionable item for partners to develop a product work package.

Rationale and Abstract

Vaccine development is an empirical process (trial and error) and involves a long, expensive clinical development pipeline to license an efficacious vaccine candidate. Better tools for vaccine evaluation are needed to adapt to a rising number of candidate vaccines entering clinical trials for many diseases.

Surrogate biomarkers of immunity offer the possibility of expediting the clinical development by eliminating non-viable candidates earlier in the pipeline, shortening vaccine trial timeframes by giving a proxy measurement for efficacy and by guiding future vaccine design.

In the case of malaria and other complex diseases, a surrogate biomarker of immunity has been difficult to achieve with classical immunological assays. We propose, using a systems biology analytical approach in two efficacious malaria vaccination models, to identify combinatorial biomarkers of protection.

First, newly generated cellular transcriptome profiles and previously generated immunological read-outs common to both trials will be integrated into a database for this analysis. An already developed artificial intelligence-based analytical tool that generates biological network maps, transforms experimental data to the map and discriminates transcriptional gene signatures to physiological states (protection or susceptibility) will be applied in both vaccination models. The aim is to determine malaria signatures of protection that will then be refined and validated in an experimentally induced immunity non-human primate model. The optimized model will be further validated on additional samples from the two protective human trials. The identified biomarkers of protection will be used to produce a customised Immunome Chip, which together with traditional immunological read-outs will be used to evaluate vaccine efficacy, shortening times and costs of clinical trials. This strategy may also prove useful for other diseases and support the systems medicine approach.

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