Two-antibody pan-ebolavirus cocktail confers broad therapeutic protection in ferrets and nonhuman primates
Bibliographic record
Abstract
All available experimental vaccines and immunotherapeutics 1,2 against Ebola virus (EBOV), including rVSV-ZEBOV 3 and ZMapp TM4 , lack activity against other ebolaviruses associated with human disease outbreaks. This year, two separate outbreaks of EBOV in the Democratic Republic of Congo underscored the unpredictable nature of ebolavirus reemergence in a region that has historically experienced outbreaks of the divergent ebolaviruses Sudan virus (SUDV) and Bundibugyo virus (BDBV) 5 . Here we show that MBP134 AF , a pan-ebolavirus therapeutic comprising two broadly neutralizing human antibodies (bNAbs) 6,7 (see companion manuscript, Wec et al .) could protect against lethal EBOV, SUDV, and BDBV infection in ferrets and nonhuman primates (NHPs). MBP134 AF not only not only establishes a viable therapeutic countermeasure to outbreaks caused by antigenically diverse ebolaviruses but also affords unprecedented effectiveness and potency—a single 25-mg/kg dose was fully protective in NHPs. This best-in-class antibody cocktail is the culmination of an intensive collaboration spanning academia, industry and government in response to the 2013-2016 EBOV epidemic 6,7 and provides a translational research model for the rapid development of immunotherapeutics targeting emerging infectious diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".