AAV Vectored Immunoprophylaxis for Filovirus Infections
Bibliographic record
Abstract
Filoviruses are among the deadliest infectious agents known to man, causing severe hemorrhagic fever, with up to 90% fatality rates. The 2014 Ebola outbreak in West Africa resulted in over 28,000 infections, demonstrating the large-scale human health and economic impact generated by filoviruses. Zaire ebolavirus is responsible for the greatest number of deaths to date and consequently there is now an approved vaccine, Ervebo, while other filovirus species have similar epidemic potential and remain without effective vaccines. Recent clinical success of REGN-EB3 and mAb-114 monoclonal antibody (mAb)-based therapies supports further investigation of this treatment approach for other filoviruses. While efficacious, protection from passive mAb therapies is short-lived, requiring repeat dosing to maintain therapeutic concentrations. An alternative strategy is vectored immunoprophylaxis (VIP), which utilizes an adeno-associated virus (AAV) vector to generate sustained expression of selected mAbs directly in vivo. This approach takes advantage of validated mAb development and enables vectorization of the top candidates to provide long-term immunity. In this review, we summarize the history of filovirus outbreaks, mAb-based therapeutics, and highlight promising AAV vectorized approaches to providing immunity against filoviruses where vaccines are not yet available.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".