AAV-monoclonal antibody expression protects mice from Ebola virus without impeding the endogenous antibody response to heterologous challenge
Bibliographic record
Abstract
Filoviruses cause severe hemorrhagic fever with case fatality rates as high as 90%. Filovirus-specific monoclonal antibodies (mAbs) confer protection in nonhuman primates as late as 5 days after challenge, and FDA-approved mAbs REGN-EB3 and mAb114 have demonstrated efficacy against Ebola virus (EBOV) infection in humans. Vectorized antibody expression mediated by adeno-associated virus (AAV) can generate protective and sustained concentrations of therapeutic mAbs in animal models for a variety of infectious diseases, including EBOV. Here we demonstrate that AAV6.2FF-mediated expression of murine IgG2a EBOV mAbs, 2G4 and 5D2, protects from mouse-adapted (MA)-EBOV infection with none of the surviving mice developing anti-VP40 antibodies above background. Protective serum concentrations of AAV6.2FF-2G4/AAV6.2FF-5D2 did not alter endogenous antibody responses to heterologous virus infection. AAV-mediated expression of EBOV mAbs 100 and 114, and pan-ebolavirus mAbs, FVM04, ADI-15878, and CA45, as human IgG1 antibodies conferred protection against MA-EBOV at low serum concentrations, with minimum protective serum levels as low as 2 μg/mL. Vectorized expression of murine IgG2a or human IgG1 mAbs led to sustained expression in the serum of mice for >400 days or for the lifetime of the animal, respectively. AAV6.2FF-mediated mAb expression offers an alternative to recombinant antibody administration in scenarios where long-term protection is preferable to passive immunization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".