Humanization and Characterization of an Anti-Ricin Neutralization Monoclonal Antibody
Bibliographic record
Abstract
Ricin is regarded as a high terrorist risk for the public due to its high toxicity and ease of production. Currently, there is no therapeutic or vaccine available against ricin. D9, a murine monoclonal antibody developed previously in our laboratory, can strongly neutralize ricin and is therefore a good candidate for humanization. Humanization of D9 variable regions was achieved by a complementarity-determining region grafting approach. The humanized D9 (hD9) variable regions were further grafted onto human heavy and light chain constant regions to assemble the complete antibody gene. A foot-and-mouth-disease virus-derived 2A self-processing sequence was introduced between heavy and light chain DNA sequences to cleave the recombinant protein into a functional full-length antibody molecule from a single open reading frame driven by a single promoter in an adenoviral vector. After expression in mammalian cells and purification, the hD9 was demonstrated to have equimolar expression of the full-length antibody heavy and light chains. More importantly, the hD9 exhibited high affinity to ricin with K(D) of 1.63 nM, comparable to its parental murine D9 (2.55 nM). In a mouse model, intraperitoneal (i.p.) administration of hD9, at a low dose of 5 µg per mouse, 4 hours after the i.p. challenge with 5×LD50 ricin was found to rescue 100% of the mice. In addition, administered 6 hours post-challenge, hD9 could still rescue 50% of the mice. The hD9 has the potential to be used for prophylactic or therapeutic purposes against ricin poisoning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".