Schiff Bases of Tetrahydrocurcumin as Potential Anticancer Agents
Bibliographic record
Abstract
Abstract Tetrahydrocurcumin (THC) is a metabolite of curcumin and a valuable lead structure in medicinal chemistry due to its curcumin‐induced biological effects and its derivatives can be promising antitumor agents. Thirteen Schiff base derivatives of THC ( 1 ‐ 13 ) were synthesized by direct condensation of THC with various primary amines in moderate to very good yield (45‐94%) and their structures confirmed by 1 H NMR, 13 C NMR, HR‐ESI‐MS and IR techniques. Furthermore, these compounds were screened for in vitro anticancer activity against three human cancer cell lines including human epithelial lung carcinoma (A549), human epithelial cervical cancer (HeLa) and human breast adenocarcinoma (MCF‐7). Most compounds exhibit moderate to good anticancer activity against all three tested cell lines and are significantly more active than THC. Compound 12 bears an N ‐(4‐trifluromethyl)phenylethyl group and is the most active compound with IC 50 values ranging from 4.8 to 12.7 μM. The results obtained herein are important for further structure modifications of THC and the exploitation of the therapeutic potential of THC derivatives as anticancer agents.
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 | 0.000 |
| 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 | 0.000 | 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".