Pyrrole as an Important Scaffold of Anticancer Drugs: Recent Advances
Bibliographic record
Abstract
With the significant increase of patients suffering from different types of cancer, it is evident that prompt measures in the development of novel and effective agents need to be taken. Pyrrole moiety has been found in various active compounds with anti-inflammatory, antiseptic, antibacterial, lipid-lowering and anticancer properties. Recent advances in the exploration of highly active and selective cytotoxic structures containing pyrrole motifs have shown promising data for future investigations. Accordingly, this review presents an overview of recent developments in the pyrrole derivatives as anticancer agents, with a main focus towards the key moieties required for the anti-tumor activities. Pyrrole molecules comprising prominent targeting capacities against microtubule polymerization, tyrosine kinases, cytochrome p450 family 1, histone deacetylase and bcl-2 proteins were reported. In addition, several mechanisms of action, such as apoptosis, cell cycle arrest, inhibiting kinases, angiogenesis, disruption of cell migration, modulation of nuclear receptor responsiveness and others were analyzed. Furthermore, in most of the discussed cases we provided synthesis schemes of the mentioned molecules. Overall, the utilization of pyrrole scaffold for the design and synthesis of novel anticancer drugs could be a promising approach for future investigations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".