Venom and Toxin as Targeted Therapy
Bibliographic record
Abstract
Targeted therapy has developed significantly in the last one and half decades, prescribing specific medications for treatment of particular diseases, such as cancer, diabetes, and heart disease. One of the most exciting recent developments in targeted therapies was the isolation of disease-specific molecules from natural resources, such as animal venoms and plant metabolites/toxins, for use as templates for new drug motif designs. In addition, the study of venom proteins/peptides and toxins naturally targeted mammalian receptors and demonstrated high specificity and selectivity towards defined ion channels of cell membranes. Research has also focsed intensely on receptors. The focus of this Special Issue of Toxins addressed the most recent advances using animal venoms, such as frog secretions, bee/ant venoms and plant/fungi toxins, as medicinal therapy. Recent advances in venom/toxin/immunotoxins for targeted cancer therapy and immunotherapy, along with using novel disease-specific venom-based protein/peptide/toxin and currently available FDA-approved drugs for combinationtreatments will be discussed. Finally, we included an overview of select promising toad/snake venom-based peptides/toxins potentially able to address the forthcoming challenges in this field. Both research and review articles proposing novelties or overviews, respectively, were published in this Special Issue after rigorous evaluation and revision by expert peer reviewers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.019 |
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".