Synthesis and biological evaluation of retinoyl and docosahexaenoyl derivatives of 5-Fluoro-2' -deoxyuridine as anticancer prodrugs
Bibliographic record
Abstract
According to the survey (Gaudett et a!. 1998, 1996), cancer is the number one disease that causes death in Canada and U.S. Many approaches have been used to treat cancer. Chemotherapy has played and will continue to play an important role in cancer treatment. Although many anticancer drugs are available, there are serious problems associated with cancer chemotherapy including toxicity and development of drug resistance. Retinoids such as all-trans retinoic acid, omega-3 polyunsaturated fatty acids such as cis-4,7,10,13,16,19-docosahexaenoic acid (DHA) and fluoropyrimidines such as 5-fluoro-2'-deoxyuridine (FUdR) have potent distinct anticancer mechanisms. Since many cancer cells are known to overexpress low density lipoprotein (WL) receptors, LDL has been proposed as a cancer specific carrier. In this study, LDL was investigated as a drug carrier to enhance the drug delivery to cancer cells (Hela, MCF7, MB231 and HepG2 cell lines). Four derivatives of FUdR (3' -0-retinoyl-FUdR, 3' -0-docosahexaenoyl-FUdR, 5'- 0-retinoyl-FUdR and 3', 5' -di-0-retinoyl-FUdR) were synthesized as prodrugs of FUdR. The prodrugs were incorporated into LDL. The results showed that the cytotoxicity of the respective prodrugs was increased compared with parent drug FUdR. The prodrug/LDL complex was more effective than the prodrug without LDL as a carrier in Hela cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".