Development of ACE2-tropic-betacoronavirus therapeutics for future pandemic preparedness
Bibliographic record
Abstract
A major challenge during viral pandemics is the ability to develop therapeutics whose efficacy can withstand viral genetic evolution. During the COVID-19 pandemic, five SARS-CoV-2 monoclonal antibody (mAb) therapeutics were rendered ineffective within a period of 2 years, leading to the U.S. FDA revoking their emergency use authorization. Here, we describe ReconnAb-multimers, a new therapeutic design that broadly and potently neutralize all tested betacoronaviruses that use host ACE2 as their receptor to enter cells. These ReconnAb-multimers have potent neutralization efficacy via avidity, enhanced breadth via a new pan-betacoronavirus-binding antibody that targets a highly conserved epitope on SARS-CoV-2 spike protein, and the potential for clinical development by using a catalytically inactive ACE2 component. We demonstrate that ReconnAb-multimers neutralize all SARS-CoV-2 pseudoviruses and authentic viral variants of concern (VOC) tested, with similar or higher potency than mAbs previously approved by the FDA; neutralize related pandemic-potential betacoronaviruses, including SARS-CoV, WIV1-CoV, PRD-0038, and merbecovirus HKU5-CoV-2; and despite a short half-life, protect female mice against authentic viral challenge with Omicron variant XBB.1.5. Our results highlight ReconnAb-multimers as a broad and highly potent therapeutic that could potentially withstand viral escape against current and future betacoronaviruses that require host ACE2 as a receptor.
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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) | 0.000 | 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) | 0.001 | 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".