Bispecific antibodies combine breadth, potency, and avidity of parental antibodies to neutralize sarbecoviruses
Bibliographic record
Abstract
Abstract SARS-CoV-2 mutational variants evade humoral immune responses elicited by vaccines and current monoclonal antibody (mAb) therapies. Novel antibody-based treatments will thus need to exhibit broad neutralization against different variants. Bispecific antibodies (bsAbs) combine the specificities of two distinct antibodies into one antibody taking advantage of the avidity, synergy and cooperativity provided by targeting two different epitopes. Here we used controlled Fab-arm exchange (cFAE), a versatile and straightforward method, to produce bsAbs that neutralize SARS-CoV and SARS-CoV-2 variants, including Omicron and its subvariants, by combining potent SARS-CoV-2-specific neutralizing antibodies with broader but less potent antibodies that also neutralize SARS-CoV. We demonstrate that the parental IgG’s rely on avidity for their neutralizing activity by comparing their potency to bsAbs containing one irrelevant “dead” Fab arm. We used single particle mass photometry to measure formation of antibody:spike complexes, and determined that bsAbs increase binding stoichiometry compared to corresponding cocktails, without a loss of binding affinity. The heterogeneous binding pattern of bsAbs to spike (S), observed by negative-stain electron microscopy and mass photometry provided evidence for both intra- and inter-spike crosslinking. This study highlights the utility of cross-neutralizing antibodies for designing bivalent or multivalent agents to provide a robust activity against circulating variants, as well as future SARS-like coronaviruses.
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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 | 0.000 | 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".