In vivo expressed biologics for infectious disease prophylaxis: rapid delivery of DNA-based antiviral antibodies
Bibliographic record
Abstract
With increasing frequency, humans are facing outbreaks of emerging infectious diseases (EIDs) with the potential to cause significant morbidity and mortality. In the most extreme instances, such outbreaks can become pandemics, as we are now witnessing with COVID-19. According to the World Health Organization, this new disease, caused by the novel coronavirus SARS-CoV-2, has already infected more than 10 million people worldwide and led to 499,913 deaths as of 29 June, 2020. How high these numbers will eventually go depends on many factors, including policies on travel and movement, availability of medical support, and, because there is no vaccine or highly effective treatment, the pace of biomedical research. Other than an approved antiviral drug that can be repurposed, monoclonal antibodies (mAbs) hold the most promise for providing a stopgap measure to lessen the impact of an outbreak while vaccines are in development. Technical advances in mAb identification, combined with the flexibility and clinical experience of mAbs in general, make them ideal candidates for rapid deployment. Furthermore, the development of mAb cocktails can provide a faster route to developing a robust medical intervention than searching for a single, outstanding mAb. In addition, mAbs are well-suited for integration into platform technologies for delivery, in which minimal components need to be changed in order to be redirected against a novel pathogen. In particular, utilizing the manufacturing and logistical benefits of DNA-based platform technologies in order to deliver one or more antiviral mAbs has the potential to revolutionize EID responses.
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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 | 0.001 | 0.001 |
| 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".