Computational Design and Biological Testing of Highly Cytotoxic Colchicine Ring A Modifications
Bibliographic record
Abstract
Microtubules are the primary target for many anti-cancer drugs, the majority of which bind specifically to beta-tubulin. The existence of several beta-tubulin isotypes, coupled with their varied expression in normal and cancerous cells provides a platform upon which to construct selective chemotherapeutic agents. We have examined five prevalent human beta-tubulin isotypes and identified the colchicine-binding site as the most promising for drug design based on specificity. Using this binding site as a template, we have designed several colchicine derivatives and computationally probed them for affinity to the beta-tubulin isotypes. These compounds were synthesized and subjected to cytotoxicity assays to determine their effectiveness against several cancerous cell lines. We observed a correlation between computational-binding predictions and experimentally determined IC(50) values, demonstrating the utility of computational screening in the design of more effective colchicine derivatives. The most promising derivative exhibited an IC(50) approximately threefold lower than values previously reported for either colchicine or paclitaxel, demonstrating the utility of computational design and assessment of binding to tubulin.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".