Fusogenics: A Recombinant Immunotoxin-Based Screening Platform to Select Internalizing Tumor-Specific Antibody Fragments
Bibliographic record
Abstract
Antibody-based therapeutics play a vital role in the treatment of certain cancers; however, despite commercial success, various strategies are being pursued to increase their potency and hence improve patient outcomes. The use of antibodies to deliver a cytotoxic payload offers a promising alternative for more efficacious therapies. Immunotoxins are composed of an internalizing antibody fragment linked to a bacterial or plant toxin. Once internalized, the payload, such as Pseudomonas exotoxin A (PE), blocks protein synthesis and induces apoptosis. Typically, immunotoxins are developed by first isolating a tumor-specific antibody, which is then either chemically linked to a toxin or reengineered as a fusion protein. Here, the authors describe the development of Fusogenics, an immunotoxin-based screening method that selects internalizing tumor-specific antibodies using a functional assay. Selected immune library clones were characterized and shown to be selective against normal tissues and specific to tumor tissues. In summary, the Fusogenics immunotoxin platform represents a unique, single-step selection approach combining specificity and functionality to isolate novel internalizing tumor-specific antibody fragments with potential for direct clinical application in the treatment of cancer.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".