Characterization of HIV-1 Nucleoside-Modified mRNA Vaccines in Rabbits and Rhesus Macaques
Bibliographic record
Abstract
Despite the enormous effort in the development of effective vaccines against HIV-1, no vaccine candidate has elicited broadly neutralizing antibodies in humans. Thus, generation of more effective anti-HIV vaccines is critically needed. Here we characterize the immune responses induced by nucleoside-modified and purified mRNA-lipid nanoparticle (mRNA-LNP) vaccines encoding the clade C transmitted/founder HIV-1 envelope (Env) 1086C. Intradermal vaccination with nucleoside-modified 1086C Env mRNA-LNPs elicited high levels of gp120-specific antibodies in rabbits and rhesus macaques. Antibodies generated in rabbits neutralized a tier 1 virus, but no tier 2 neutralization activity could be measured. Importantly, three of six non-human primates developed antibodies that neutralized the autologous tier 2 strain. Despite stable anti-gp120 immunoglobulin G (IgG) levels, tier 2 neutralization titers started to drop 4 weeks after booster immunizations. Serum from both immunized rabbits and non-human primates demonstrated antibody-dependent cellular cytotoxicity activity. Collectively, these results are supportive of continued development of nucleoside-modified and purified mRNA-LNP vaccines for HIV. Optimization of Env immunogens and vaccination protocols are needed to increase antibody neutralization breadth and durability.
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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".