Safety and immunogenicity of an optimized self-replicating RNA platform for low dose or single dose vaccine applications: a randomized, open label Phase I study in healthy volunteers
Bibliographic record
Abstract
Self-replicating RNA (srRNA) technology, in comparison to mRNA vaccines, has shown dose-sparing by approximately 10-fold and more durable immune responses. However, no improvements are observed in the adverse events profile. Here, we develop an srRNA vaccine platform with optimized non-coding regions and demonstrate immunogenicity and safety in preclinical and clinical development. Optimized srRNA vaccines generate protective immunity (according to the WHO defined thresholds) at doses up to 1,000,000-fold lower than mRNA in female mouse models of influenza and rabies. Clinically, safety and immunogenicity of RBI-4000, an srRNA vector encoding the rabies glycoprotein, was evaluated in a Phase I study (NCT06048770). RBI-4000 was able to elicit de novo protective immunity in the majority of healthy participants when administered at a dose of 0.1, 1, or 10 microgram (71%, 94%, 100%, respectively) in a prime-boost schedule. Similarly, we observe immunity above the WHO benchmark of protection following a single administration in most participants at both 1 and 10 microgram doses. There are no serious adverse events reported across all cohorts. These data establish the high therapeutic index of optimized srRNA vectors, demonstrating feasibility of both low dose and single dose approaches for vaccine applications. Here the authors report an optimized self-replicating RNA (srRNA) vaccine approach that generates protective immunity at much lower doses than mRNA vaccines in mice. In a Phase 1 study using rabies glycoprotein as antigen, they show robust immune responses at doses as low as 0.1 µg, with a favorable safety profile.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".