A Nanoparticle Platform for Improved Potency, Stability, and Adjuvanticity of Poly(I:C)
Bibliographic record
Abstract
Abstract Cancer immunotherapies and prophylactic vaccines against infectious diseases often exploit adjuvants such as toll‐like receptor agonists (TLRa) to drive potent and directed immune responses. Unfortunately, a promising class of TLRa based on nucleic acid derivatives is susceptible to degradation by nucleases, cause life‐threatening systemic toxicities, and is difficult to target to specific cell populations or tissues within the body. In this study a library of cationic polymeric nanoparticles (NP) is developed for encapsulation and delivery of the double‐stranded RNA structural mimic, poly(I:C) (pIC), to address these limitations. Using a combinatorial library screening approach, pIC/poly(β‐amino ester) (PBAE) NPs are identified that skew activation resulting in enhanced potency (13‐fold increase in type I interferon [IFN] production) and negligible toxicity. These highly potent adjuvant NPs increase the magnitude, duration, and affinity maturation of antigen‐specific antibodies following vaccination with a model subunit vaccine. This NP platform provides an opportunity to alter the immune response to pIC, creating a potent type I IFN‐producing adjuvant capable of driving stronger humoral responses to immunization and improving affinity maturation more than 14‐fold. This platform can be applied generally to develop more effective vaccines and immunotherapies.
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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".