mRNA-LNP expressing PfCSP and Pfs25 vaccine candidates targeting infection and transmission of Plasmodium falciparum
Bibliographic record
Abstract
Malaria is a deadly disease responsible for between 550,000 and 627,000 deaths annually. There is a pressing need to develop vaccines focused on malaria elimination. The complex lifecycle of Plasmodium falciparum provides opportunities not only to target the infectious sporozoite stage, introduced by anopheline mosquitoes, but also the sexual stages, which are ingested by mosquitoes during blood feeding, leading to parasite transmission. It is widely recognized that a vaccine targeting multiple stages would induce efficacious transmission reducing immunity. Technological advancements offer new vaccine platforms, such as mRNA-LNPs, which can be used to develop highly effective malarial vaccines. We evaluated the immunogenicity of two leading P. falciparum vaccine candidates, Pfs25 and PfCSP, delivered as mRNA-LNP vaccines. Both vaccines induced extremely potent immune responses when administered alone or in combination, which were superior to Pfs25 and PfCSP DNA vaccine formulations. Purified IgGs from Pfs25 mRNA-LNPs immunized mice were highly potent in reducing malaria transmission to mosquitoes. Additionally, mice after three and four immunizations with PfCSP mRNA-LNP provided evidence for varying degrees of protection against sporozoite challenge. The comparison of immune responses and stage-specific functional activity induced by each mRNA-LNP vaccine, administered alone or in combination, also supports the development of an effective combination vaccine without any risk of immune interference for targeting malaria parasites at various life cycle stages. A combination of vaccines targeting both the infective stage and sexual/midgut stages is expected to interrupt malaria transmission, which is critical for achieving elimination goals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".