An enhanced intracellular delivery platform based on a distant diphtheria toxin homolog that evades pre-existing antitoxin antibodies
Bibliographic record
Abstract
Targeted intracellular delivery of therapeutic proteins remains a significant unmet challenge in biotechnology. A promising approach is to leverage the intrinsic capabilities of bacterial toxins like diphtheria toxin (DT) to deliver a potent cytotoxic enzyme into cells with an associated membrane translocation moiety. Despite showing promising clinical efficacy, widespread deployment of DT-based therapeutics is complicated by the prevalence of pre-existing antibodies in the general population arising from childhood DT toxoid vaccinations, which impact the exposure, efficacy, and safety of these potent molecules. Here, we describe the discovery and characterization of a distant DT homolog from the ancient reptile pathogen Austwickia chelonae that we have dubbed chelona toxin (ACT). We show that ACT is comparable to DT structure and function in all respects except that it is not recognized by pre-existing anti-DT antibodies circulating in human sera. Furthermore, we demonstrate that ACT delivers heterologous therapeutic cargos into target cells more efficiently than DT. Our findings highlight ACT as a promising new chassis for building next-generation immunotoxins and targeted delivery platforms with improved pharmacokinetic and pharmacodynamic properties.
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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".