In Silico and In Vitro Perspectives on the Potential Anticancer Activity and Toxicity of Anticancer Drug Modified with Carbohydrates Containing Novel Triazole Compounds
Bibliographic record
Abstract
Abstract 5‐Fluorouracil (5‐FU) is one of the first‐line chemotherapeutic agents used in systemic therapy of solid tumors. However, several challenges restrict the use of 5‐FU such as serious side effects and short plasma half‐life. Because carbohydrates and 1,2,3‐triazoles have various biological activities, they have been extensively used in medicine to obtain more effective anticancer drugs in recent years. The aim of this study is to modify 5‐FU with carbohydrates containing 1,2,3‐triazole compounds to reduce its toxic effect, and to reveal the anticancer properties of the obtained 5‐FU derivatives. These derivatives (5‐FU‐I, 5‐FU‐II, and 5‐FU‐III) revealed dose‐dependent cytotoxic effects on CaCo‐2, PANC‐1, and A549 cancer cells. It was determined that cytotoxic effects of the 5‐FUs change dependent on used carbohydrate types, cell lines, and administered doses. These derivatives showed apoptotic and necrotic cell deaths which used to destroy the cancer cells. 5‐FUs showed no genotoxic effect in the bacterial reverse mutasyon assay. They demonstrated strong antiangiogenic properties in the HET‐CAM test. In silico study results demonstrated that carbohydrate modification can increase half‐life and clearance, also decrease side effects of 5‐FU. In silico data supported the in vitro findings and results demonstrated 5‐FU derivatives were better drug candidates. Our results reveal that 5‐FUs derivatives modified carbohydrates containing 1,2,3‐triazole compounds have potential in cancer therapy.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".