Synthetic Human Monoclonal Antibodies toward Staphylococcal Enterotoxin B (SEB) Protective against Toxic Shock Syndrome
Bibliographic record
Abstract
Staphylococcal enterotoxin B (SEB) is a potent toxin that can cause toxic shock syndrome and act as a lethal and incapacitating agent when used as a bioweapon. There are currently no vaccines or immunotherapeutics available against this toxin. Using phage display technology, human antigen-binding fragments (Fabs) were selected against SEB, and proteins were produced in Escherichia coli cells and characterized for their binding affinity and their toxin neutralizing activity in vitro and in vivo. Highly protective Fabs were converted into full-length IgGs and produced in mammalian cells. Additionally, the production of anti-SEB antibodies was explored in the Nicotiana benthamiana plant expression system. Affinity maturation was performed to produce optimized lead anti-SEB antibody candidates with subnanomolar affinities. IgGs produced in N. benthamiana showed characteristics comparable with those of counterparts produced in mammalian cells. IgGs were tested for their therapeutic efficacy in the mouse toxic shock model using different challenge doses of SEB and a treatment with 200 μg of IgGs 1 h after SEB challenge. The lead candidates displayed full protection from lethal challenge over a wide range of SEB challenge doses. Furthermore, mice that were treated with anti-SEB IgG had significantly lower IFNγ and IL-2 levels in serum compared with mock-treated mice. In summary, these anti-SEB monoclonal antibodies represent excellent therapeutic candidates for further preclinical and clinical development.
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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 | 0.001 |
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".