Anti-Ebola GP monoclonal antibodies protect mice and guinea pigs against lethal ebola virus challenge (105.29)
Bibliographic record
Abstract
Abstract Introduction: Ebola virus (EBOV) causes acute hemorrhagic fever in humans and nonhuman primates with mortality rates up to 90%. So far there are no effective treatments available. This study evaluates the protective efficacy of 8 monoclonal antibodies (mAbs) against Ebola glycoprotein in a mouse and guinea pig model. Methods: Healthy mice or guinea pigs were given mAbs i.p. in various doses individually or as pools of 3-4 mAbs to test their protection against a lethal dose of mouse- or guinea pig-adapted EBOV. Results: Each of the 8 mAbs (100 microgram) protected mice from a lethal EBOV challenge when administered 1 day before or after challenge. Seven mAbs were effective 2 days post-challenge (dpc), with 1 mAb demonstrating partial protection 3 dpc. Furthermore, 4 mAbs achieved 70-100% protection with a single dose of 12.5 microgram at 1 dpc. In the guinea pig model each mAb showed partial protection 1 dpc, however the mean time to death was significantly prolonged compared to the control group. Moreover, treatment with pools of 3-4 mAbs completely protected the majority of animals, while administration 2-3 dpc achieved 50-100% protection. Conclusions: Our data suggests the mAbs generated are capable of protecting both animal models against a lethal challenge of Ebola virus. These results indicate that mAbs particularly when used as an oligoclonal set are a potential therapeutic for post-exposure treatment of EBOV infection.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".