Targeted Diphtheria Toxin-Based Therapy: A Review Article
Bibliographic record
Abstract
Cancer remains one of the leading causes of death worldwide. Conventional therapeutic strategies usually offer limited specificity resulting in severe side effects and toxicity to normal tissues. Targeted cancer therapy, on the other hand, can improve the therapeutic potential of anti-cancer agents and decrease unwanted side effects. Targeted applications of cytolethal bacterial toxins have been found to be especially useful for the specific eradication of cancer cells. Targeting is either mediated by peptides or protein targeting moieties, such as antibodies, antibody fragments, cell-penetrating peptides, growth factors or cytokines. Together with a toxin domain, these molecules are more commonly referred to as immunotoxins. Targeting can also be achieved through gene delivery and cell-specific expression of a toxin. Of the available cytolethal toxins, diphtheria toxin (DT) is one of the most frequently used for these strategies. Of the many DT-based therapeutic strategies investigated to date, two immunotoxins, Ontak™ and Tagraxofusp™, have gained FDA approval for clinical applications. Despite some success with immunotoxins, suicide gene therapy strategies are gaining prominence, whereby controlled tumor specific expression of DT is used for the eradication of malignant cells. The first part of this review focuses on DT-based immunotoxins, followed by recent developments in tumor specific expression of DT.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".