Synthesis and Biological Evaluation of Novel Triple-Modified Colchicine Derivatives as Potent Tubulin-Targeting Anticancer Agents
Bibliographic record
Abstract
Specific modifications of colchicine followed by synthesis of its analogues have been tested in vitro with the objective of lowering colchicine toxicity. Our previous studies have clearly shown the anticancer potential of double-modified colchicine derivatives in C-7 and C-10 positions. Here, a series of novel triple-modified colchicine derivatives is reported. They have been obtained following a four-step strategy. In vitro cytotoxicity of these compounds has been evaluated against four human tumor cell lines (A549, MCF-7, LoVo, and LoVo/DX). Additionally, the mode of binding of the synthesized compounds was evaluated in silico using molecular docking to a 3D structure of β-tubulin based on crystallographic data from the Protein Data Bank and homology methodology. Binding free energy estimates, binding poses, and MlogP values of the compounds were obtained. All triple-modified colchicine derivatives were shown to be active at nanomolar concentrations against three of the investigated cancer cell lines (A549, MCF-7, LoVo). Four of them also showed higher potency against tumor cells over normal cells as confirmed by their high selectivity index values. A vast majority of the synthesized derivatives exhibited several times higher cytotoxicity than colchicine, doxorubicin, and cisplatin.
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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".