Antiproliferative Activity and Molecular Docking of Novel Double-Modified Colchicine Derivatives
Bibliographic record
Abstract
Microtubules are tubulin polymer structures, which are indispensable for cell growth and division. Its constituent protein β-tubulin has been a common drug target for various diseases including cancer. Colchicine has been used to treat gout, but it has also been an investigational anticancer agent with a known antimitotic effect on cells. However, the use of colchicine as well as many of its derivatives in long-term treatment is hampered by their high toxicity. To create more potent anticancer agents, three novel double-modified colchicine derivatives have been obtained by structural modifications in C-4 and C-10 positions. The binding affinities of these derivatives of colchicine with respect to eight different isotypes of human β-tubulin have been calculated using docking methods. In vitro cytotoxicity has been evaluated against four human tumor cell lines (A549, MCF-7, LoVo and LoVo/DX). Computer simulations predicted the binding modes of these compounds and hence the key residues involved in the interactions between tubulin and the colchicine derivatives. Two of the obtained derivatives, 4-bromothiocolchicine and 4-iodothiocolchicine, were shown to be active against three of the investigated cancer cell lines (A549, MCF-7, LoVo) with potency at nanomolar concentrations and a higher relative affinity to tumor cells over normal 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".