Chikungunya Virus Vaccines: A Review of IXCHIQ and PXVX0317 from Pre-Clinical Evaluation to Licensure
Bibliographic record
Abstract
Chikungunya virus is an emerging mosquito-borne alphavirus that causes febrile illness and arthritic disease. Chikungunya virus is endemic in 110 countries and the World Health Organization estimates that it has caused more than 2 million cases of crippling acute and chronic arthritis globally since it re-emerged in 2005. Chikungunya virus outbreaks have occurred in Africa, Asia, Indian Ocean islands, South Pacific islands, Europe, and the Americas. Until recently, no specific countermeasures to prevent or treat chikungunya disease were available. To address this need, multiple vaccines are in human trials. These vaccines use messenger RNA-lipid nanoparticles, inactivated virus, and viral vector approaches, with a live-attenuated vaccine VLA1553 and a virus-like particle PXVX0317 in phase III testing. In November 2023, the US Food and Drug Administration (FDA) approved the VLA1553 live-attenuated vaccine, which is marketed as IXCHIQ. In June 2024, Health Canada approved IXCHIQ, and in July 2024, IXCHIQ was approved by the European Commission. On August 13, 2024, the US FDA granted priority review for PXVX0317. The European Medicine Agency is considering accelerated assessment review of PXVX0317, with potential for approval by both agencies in 2025. In this review, we summarize published data from pre-clinical and clinical trials for the IXCHIQ and PXVX0317 vaccines. We also discuss unanswered questions including potential impacts of pre-existing chikungunya virus immunity on vaccine safety and immunogenicity, whether long-term immunity can be achieved, safety in children, pregnant, and immunocompromised individuals, and vaccine efficacy in people with previous exposure to other emerging alphaviruses in addition to chikungunya virus.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".