Virus inoculation and treatment regimens for evaluating anti-filovirus monoclonal antibody efficacy in vivo
Bibliographic record
Abstract
The development of monoclonal antibodies to treat disease caused by filoviruses, particularly Ebola virus, has risen steeply in recent years thanks to several key studies demonstrating their remarkable therapeutic potential. The increased drive to develop new and better monoclonal antibodies has necessarily seen an increase in animal model efficacy testing, which is critical to the pre-clinical development of any novel countermeasure. Primary and secondary efficacy testing against filoviruses typically makes use of one or more rodent models (mice, guinea pigs, and occasionally hamsters) or the more recently described ferret model, although the exact choice of model depends on the specific filovirus being evaluated. Indeed, no single small animal model exists for all filoviruses, and the use of any given model must consider the nature of that model as well as the nature of the therapeutic and the experimental objectives. Confirmatory evaluation, on the other hand, is performed in nonhuman primates (rhesus or cynomolgus macaques) regardless of the filovirus. In light of the number of different animal models that are currently used in monoclonal antibody efficacy testing, we sought to better understand how these efficacy tests are being performed by numerous different laboratories around the world. To this end, we review the animal models that are being used for antibody efficacy testing against filoviruses, and we highlight the challenge doses and routes of infection that are used. We also describe the various antibody treatment regimens, including antibody dose, route, and schedule of administration, that are used in these model systems. We do not identify any single best model or treatment regimen, and we do not advocate for field-wide protocol standardization. Instead, we hope to provide a comprehensive resource that will facilitate and enhance the continued pre-clinical development of novel monoclonal antibody therapeutics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".