Development of experimental and early investigational drugs for the treatment of Ebola virus infections
Bibliographic record
Abstract
INTRODUCTION: Ebola virus (EBOV) causes severe hemorrhagic fever in humans, and due to the aggressive nature of infection it has been difficult to develop effective medical countermeasures. Total casualties from past outbreaks numbered fewer than 1500 cases, but EBOV unexpectedly emerged from Guinea in late 2013 and infected over 25,000 people in nine countries spanning Africa, Europe and North America. Concern among the public and authorities helped spark an unprecedented push to fast-track experimental drugs for clinical use. AREAS COVERED: The authors provide a historical timeline of the progress in developing a licensed post-exposure EBOV drug for use in humans. Furthermore, they summarize and discuss the published data with different in light of their potential to play a role during outbreak times. EXPERT OPINION: Monoclonal antibody-based therapy is able to reverse advanced EBOV disease, but the outbreak of an antigenically divergent filovirus would require the reformulation and possibly redevelopment of the most promising candidates. Immunocompetent small animal models have not yet been developed for screening drugs against other filoviruses aside from Ravn and Marburg virus, and thus the number of prophylactic and therapeutic candidates lag behind that of EBOV. There is an urgent need for the proactive development of drugs against other neglected pathogens before the next major outbreak.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".