Neutralization of Clostridium difficile toxin B with VHH-Fc fusions targeting the delivery and CROPs domains
Bibliographic record
Abstract
An increasing number of antibody-based therapies are being considered for controlling bacterial infections, including Clostridium difficile by targeting toxins A and B. In an effort to develop novel C. difficile immunotherapeutics, we previously isolated several single-domain antibodies (VHHs) capable of toxin A neutralization through recognition of the extreme C-terminal combined repetitive oligopeptides (CROPs) domain, but failed at identifying neutralizing VHHs that bound a similar region on toxin B. Here we report the isolation of a panel of 29 VHHs targeting at least seven unique epitopes on a toxin B immunogen composed of a portion of the central delivery domain and the entire CROPs domain. Despite monovalent affinities as high as KD = 70 pM, none of the VHHs tested were capable of toxin B neutralization; however, modest toxin B inhibition was observed with VHH-VHH dimers and to a much greater extent with VHH-Fc fusions, reaching the neutralizing potency of the recently approved anti-toxin B monoclonal antibody bezlotoxumab in in vitro assays. Epitope binning revealed that several VHH-Fcs bound toxin B at sites distinct from the region recognized by bezlotoxumab, while other VHH-Fcs partially competed with bezlotoxumab for toxin binding. Therefore, the VHHs described here are effective at toxin B neutralization when formatted as bivalent VHH-Fc fusions by targeting toxin B at regions both similar and distinct from the bezlotoxumab binding site.
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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 | 0.000 |
| 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".