Enhanced delivery of protein therapeutics with a diphtheria toxin-like platform that evades pre-existing neutralizing immunity
Bibliographic record
Abstract
ABSTRACT Targeted intracellular delivery of therapeutic peptides and proteins remains an important but unresolved goal in biotechnology. A promising approach is to engineer bacterial exotoxins that deliver their cytotoxic enzymes into cells and can be engineered to target cancer cells as is the case with immunotoxins. The well-studied diphtheria toxin translocation domain is ideally suited as a delivery platform as it has been shown to be capable of delivering a wide range of macromolecular cargo. Widespread deployment of DT-based therapeutics in humans, however, is complicated by the prevalence of pre-existing anti-DT antibodies from childhood vaccinations that reduce the exposure, efficacy and safety of this important class of protein drugs. Thus, there is a great need for delivery platforms with no pre-existing immunity in humans. Here, we describe the discovery and characterization of a distant diphtheria toxin homolog from the ancient reptile pathogen Austwickia chelonae that we have named Chelona Toxin (CT). We show that CT is comparable to DT structure and function in all respects except that it is not recognized by pre-existing anti-DT antibodies present in human sera. Moreover, we demonstrate that the CT translocase is superior to the DT translocase at delivering therapeutic protein cargo into target cells. These findings highlight CT as a potentially class-enabling new chassis for developing safer and more efficacious immunotoxins and intracellular protein delivery platforms for cancer therapy.
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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".