Delivery vehicle and route of administration influences self-amplifying RNA biodistribution, expression kinetics, and reactogenicity
Bibliographic record
Abstract
Self-amplifying RNA (saRNA) is a next-generation RNA platform derived from an alphavirus that enables replication in host cytosol, offering a promising shift from traditional messenger RNA (mRNA) therapies by enabling sustained protein production from minimal dosages. The approval of saRNA-based vaccines, such as the ARCT-154 for COVID-19 in Japan, underscores its potential for diverse therapeutic applications, including vaccine development, cancer immunotherapy, and gene therapy. This study investigates the role of delivery vehicle and administration route on saRNA expression kinetics and reactogenicity. Employing ionizable lipid-based nanoparticles (LNPs) and polymeric nanoparticles, we administered saRNA encoding firefly luciferase to BALB/c mice through six routes (intramuscular (IM), intradermal (ID), intraperitoneal (IP), intranasal (IN), intravenous (IV), and subcutaneous (SC)), and observed persistent saRNA expression over a month. Our findings reveal that while LNPs enable broad route applicability and stability, pABOL (poly (cystamine bisacrylamide-co-4-amino-1-butanol)) formulations significantly amplify protein expression via intramuscular delivery. Notably, the disparity between RNA biodistribution and protein expression highlight the nuanced interplay between administration routes, delivery vehicles, and therapeutic outcomes. Additionally, our research unveiled distinct biodistribution profiles and inflammatory responses contingent upon the chosen delivery formulation and route. This research illuminates the intricate dynamics governing saRNA delivery, biodistribution and reactogenicity, offering essential insights for optimizing therapeutic strategies and advancing the clinical and commercial viability of saRNA technologies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".