Review of Cytotoxic CA4 Analogues that Do Not Target Microtubules: Implications for CA4 Development
Bibliographic record
Abstract
BACKGROUND: One of the most studied anti-cancer compounds of the last several decades is the microtubule targeting agent and cis-stilbene, combretastatin A4 (CA4). Despite promising results at the pre-clinical level, future clinical use of CA4 as a monotherapy is in question due to metabolic vulnerability and conformational instability. OBJECTIVE: Thus, medicinal chemists have focused on synthesizing derivatives with improved pharmokinetic profile. One common strategy has been the incorporation of the ethylene linker into a ring system, thus preventing the isomerization of CA4 into the virtually inactive trans-isomer. Although many structurally stable and potent analogues of CA4 have been designed and synthesized, several analogues have been discovered to possess anti-proliferative properties seemingly independent of microtubule targeting. The presence of such analogues suggests that CA4 may also possess nonmicrotubule targets, which reveals the necessity for future structure activity relationship studies and optimization of any non-microtubule targeting. Furthermore, analogues of CA4 not inhibiting microtubule polymerization can no longer be assumed to be inactive. CONCLUSION: Future clinical development of the CA4 pharmacophore requires that attention should be paid to abnormal CA4 analogues, which appear to retain cytotoxicity independent of canonical microtubule inhibition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".