In Vivo Neutralization of α-Cobratoxin with High-Affinity Llama Single-Domain Antibodies (VHHs) and a VHH-Fc Antibody
Bibliographic record
Abstract
Small recombinant antibody fragments (e.g. scFvs and VHHs), which are highly tissue permeable, are being investigated for antivenom production as conventional antivenoms consisting of IgG or F(ab')2 antibody fragments do not effectively neutralize venom toxins located in deep tissues. However, antivenoms composed entirely of small antibody fragments may have poor therapeutic efficacy due to their short serum half-lives. To increase serum persistence and maintain tissue penetration, we prepared low and high molecular mass antivenom antibodies. Four llama VHHs were isolated from an immune VHH-displayed phage library and were shown to have high affinity, in the low nM range, for α-cobratoxin (α-Cbtx), the most lethal component of Naja kaouthia venom. Subsequently, our highest affinity VHH (C2) was fused to a human Fc fragment to create a VHH2-Fc antibody that would offer prolonged serum persistence. After in planta (Nicotiana benthamiana) expression and purification, we show that our VHH2-Fc antibody retained high affinity binding to α-Cbtx. Mouse α-Cbtx challenge studies showed that our highest affinity VHHs (C2 and C20) and the VHH2-Fc antibody effectively neutralized lethality induced by α-Cbtx at an antibody:toxin molar ratio as low as ca. 0.75×:1. Further research towards the development of an antivenom therapeutic involving these anti-α-Cbtx VHHs and VHH2-Fc antibody molecules should involve testing them as a combination, to determine whether they maintain tissue penetration capability and low immunogenicity, and whether they exhibit improved serum persistence and therapeutic efficacy.
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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".