Efficacy of Ebola Glycoprotein-Specific Equine Polyclonal Antibody Product Against Lethal Ebola Virus Infection in Guinea Pigs
Bibliographic record
Abstract
Background: Filoviruses including Ebola, Sudan, and other species are emerging zoonotic pathogens representing a significant public health concern with high outbreak potential, and they remain a potential bioterrorism-related threat. We have developed a despeciated equine Ebola polyclonal antibody (E-EIG) postexposure treatment against Ebola virus (EBOV) and evaluated its efficacy in the guinea pig model of EBOV infection. Methods: Guinea pigs were infected with guinea pig-adapted EBOV (Mayinga strain) and treated with various dose levels of E-EIG (20-100 mg/kg) twice daily for 6 days starting at 24 h postinfection. The E-EIG was also assessed for neutralization activity against related filoviruses including EBOV strains Mayinga, Kikwit, and Makona and the Bundibugyo and Taï Forest ebolavirus species. Results: Treatment with E-EIG conferred 83% to 100% protection in guinea pigs. The results demonstrated a comparable neutralization activity (range, 1:512-1:896) of E-EIG against all tested strains, suggesting the potential for cross-protection with the polyclonal antibody therapeutic. Conclusions: This study showed that equine-derived polyclonal antibodies are efficacious against lethal EBOV disease in a relevant animal model. Furthermore, the studies support the utility of the equine antibody platform for the rapid production of a therapeutic product in the event of an outbreak by a filovirus or other zoonotic pathogen.
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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".