Human antibody cocktail deploys multiple functions to confer pan-ebolavirus protection
Bibliographic record
Abstract
During the unprecedented 2013–2016 Ebola virus disease (EVD) epidemic in Western Africa and in its aftermath, the passive administration of monoclonal antibodies (mAbs) emerged as a promising treatment approach 1–7 . However, all antibody-based therapeutics currently in advanced development are specific for a single member of the Ebolavirus genus, Ebola virus (EBOV), and ineffective against divergent outbreak-causing ebolaviruses, including Bundibugyo virus (BDBV) and Sudan virus (SUDV) 2,3,5,7 . Here we advance MBP134, a cocktail of two broadly neutralizing human mAbs targeting the filovirus surface glycoprotein, GP, as a candidate pan-ebolavirus therapeutic. One component of this cocktail is a pan-ebolavirus neutralizing mAb, ADI-15878, isolated from a human EVD survivor 8,9 . The second, ADI-23774, was derived by affinity maturation of a human mAb 8,9 via yeast display to enhance its potency against SUDV. MBP134 afforded exceptionally potent pan-ebolavirus neutralization in vitro and demonstrated greater protective efficacy than ADI-15878 alone in the guinea pig model of lethal EBOV challenge. A second-generation cocktail, MBP134 AF , engineered to effectively harness natural killer (NK) cells afforded additional, unprecedented improvements in protective efficacy against EBOV and SUDV in guinea pigs relative to both its precursor and to any mAbs or mAb cocktails tested previously. MBP134 AF is a best-in-class mAb cocktail suitable for evaluation as a pan-ebolavirus therapeutic in nonhuman primates.
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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 | 0.000 | 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".