Pseudomonas Exotoxin-Based Immunotoxins: Over Three Decades of Efforts on Targeting Cancer Cells With the Toxin
Bibliographic record
Abstract
Cancer is one of the prominent causes of death worldwide. Despite the existence of various modalities for cancer treatment, many types of cancer remain uncured or develop resistance to therapeutic strategies. Furthermore, almost all chemotherapeutics cause a range of side effects because they affect normal cells in addition to malignant cells. Therefore, the development of novel therapeutic agents that are targeted specifically toward cancer cells is indispensable. Immunotoxins (ITs) are a class of tumor cell-targeted fusion proteins consisting of both a targeting moiety and a toxic moiety. The targeting moiety is usually an antibody/antibody fragment or a ligand of the immune system that can bind an antigen or receptor that is only expressed or overexpressed by cancer cells but not normal cells. The toxic moiety is usually a protein toxin (or derivative) of animal, plant, insect, or bacterial origin. To date, three ITs have gained Food and Drug Administration (FDA) approval for human use, including denileukin diftitox (FDA approval: 1999), tagraxofusp (FDA approval: 2018), and moxetumomab pasudotox (FDA approval: 2018). All of these ITs take advantage of bacterial protein toxins. The toxic moiety of the first two ITs is a truncated form of diphtheria toxin, and the third is a derivative of Pseudomonas exotoxin (PE). There is a growing list of ITs using PE, or its derivatives, being evaluated preclinically or clinically. Here, we will review these ITs to highlight the advances in PE-based anticancer strategies, as well as review the targeting moieties that are used to reduce the non-specific destruction of non-cancerous cells. Although we tried to be as comprehensive as possible, we have limited our review to those ITs that have proceeded to clinical trials and are still under active clinical evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".