A Venomics Approach to the Identification and Characterization of Bioactive Peptides From Animal Venoms for Colorectal Cancer Therapy: Protocol for a Proof-of-Concept Study
Bibliographic record
Abstract
BACKGROUND: Cancer is the third leading cause of death in the United Arab Emirates (UAE), after cardiovascular diseases and accidents. In the UAE, colorectal cancer (CRC) is the first and fourth most common cancer in males and females, respectively. Several treatment modalities have been employed for cancer treatment, such as surgery, radiotherapy, chemotherapy, hormone replacement therapy, and immunotherapy. These treatment modalities often elicit adverse effects on normal cells, causing toxic side effects. To circumvent these toxicities, there has been an increased impetus towards the identification of alternate treatment strategies. Animal venoms are rich sources of pharmacologically active polypeptides and proteins. OBJECTIVE: In this proof-of-concept study, we will apply a high-throughput venomics strategy to identify and characterize anticancer bioactive peptides (BAPs) from 20 different animal venoms, specifically targeting CRC. We chose to focus on CRC because it is one of the foremost health issues in the UAE. METHODS: In the initial study, we will screen 2500 different peptides derived from 20 different animal venoms for anticancer activity specifically directed against 3 CRC cell lines and two control cell lines employing the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) colorimetric assay for cytotoxicity. Of the 20 venoms, 3 that exhibit specific and potent anticancer activity directed against the 3 CRC cell lines will be selected; and from these 3 venoms, the specific peptides with anti-CRC activity will be isolated and characterized. RESULTS: This study is at the protocol development stage only, and as such, no results are available. However, we have initiated the groundwork required to disseminate the proposed study, which includes culturing of colorectal cancer cell lines and preparation of venom screens. CONCLUSIONS: In summary, the proposed study will generate therapeutic leads to manage and treat one of the leading health issues in the UAE, namely, CRC. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/31128.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".