A Neutralizing Human Monoclonal Antibody Protects African Green Monkeys from Hendra Virus Challenge
Bibliographic record
Abstract
Hendra virus (HeV) is a recently emerged zoonotic paramyxovirus that can cause a severe and often fatal disease in horses and humans. HeV is categorized as a biosafety level 4 agent, which has made the development of animal models and testing of potential therapeutics and vaccines challenging. Infection of African green monkeys (AGMs) with HeV was recently demonstrated, and disease mirrored fatal HeV infection in humans, manifesting as a multisystemic vasculitis with widespread virus replication in vascular tissues and severe pathologic manifestations in the lung, spleen, and brain. Here, we demonstrate that m102.4, a potent HeV-neutralizing human monoclonal antibody (hmAb), can protect AGMs from disease after infection with HeV. Fourteen AGMs were challenged intratracheally with a lethal dose of HeV, and 12 subjects were infused twice with a 100-mg dose of m102.4 beginning at either 10, 24, or 72 hours after infection and again about 48 hours later. The presence of viral RNA, infectious virus, and HeV-specific immune responses demonstrated that all subjects were infected after challenge. All 12 AGMs that received m102.4 survived infection, whereas the untreated control subjects succumbed to disease on day 8 after infection. Animals in the 72-hour treatment group exhibited neurological signs of disease, but all animals started to recover by day 16 after infection. These results represent successful post-exposure in vivo efficacy by an investigational drug against HeV and highlight the potential impact a hmAb can have on human disease.
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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".