SARS-CoV-2 mRNA-vaccine candidate; COReNAPCIN®, induces robust humoral and cellular immunity in mice and non-human primates
Bibliographic record
Abstract
Abstract At the forefront of biopharmaceutical industry, the messenger RNA (mRNA) technology offers a flexible and scalable platform to address the urgent need for world-wide immunization in pandemic situations. This strategic powerful platform has recently been used to immunize millions of people proving both of safety and highest level of clinical efficacy against infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Here we provide preclinical report of COReNAPCIN ® ; a vaccine candidate against SARS-CoV-2 infection. COReNAPCIN ® is a nucleoside modified mRNA-based vaccine formulated in lipid nanoparticles (LNPs) for encoding the full-length prefusion stabilized SARS-CoV-2 spike glycoprotein on the cell surface. Vaccination of C57BL/6 and BALB/c mice and rhesus macaque with COReNAPCIN ® induced strong humoral responses with high titers of virus-binding and neutralizing antibodies. Upon vaccination, a robust SARS-CoV-2 specific cellular immunity was also observed in both mice and non-human primate models. Additionally, vaccination protected rhesus macaques from symptomatic SARS-CoV-2 infection and pathological damage to the lung upon challenging the animals with high viral loads of up to 2 × 10 8 live viral particles. Overall, our data provide supporting evidence for COReNAPCIN ® as a potent vaccine candidate against SARS-CoV-2 infection for clinical studies.
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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 | 0.001 |
| 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".