Antimyeloma Potential of Caffeic Acid Phenethyl Ester and Its Analogues through Sp1 Mediated Downregulation of IKZF1-IRF4-MYC Axis
Bibliographic record
Abstract
Abstract Caffeic acid phenethyl ester (CAPE, 2), a natural compound from propolis, is a well-documented antitumor agent with nuclear factor kappa B (NF-κB) inhibitory activity. Key transcription factors regulated by NF-κB, namely, interferon regulatory factor-4 (IRF4) and octameric binding protein-2 (OCT2), are implicated in the tumorigenesis of multiple myeloma (MM), an incurable bone marrow cancer. Adverse effects and resistance to current chemotherapeutics pose a great challenge for MM treatment. Hence, the structure–activity relationships of CAPE (2) and 21 of its analogues were evaluated for their antimyeloma potential. Preclinical evaluation revealed that CAPE (2) and the 3-phenylpropyl (4), 2,5-dihydroxycinnamic acid 3-phenylpropyl ester (17), and 3,4-dihydroxycinnamic ether (22) analogues inhibited human myeloma cell growth. Analogue 4 surpassed CAPE (2) and lenalidomide in showing strong apoptotic effects with a remarkable decrease in IRF4 levels. The analogue 17 exhibited the most potent anti-MM activity. The downregulation of specificity protein 1 (Sp1) and the IKZF1-IRF4-MYC axis by CAPE (2) analogues 4 and 17 revealed their novel mechanism of action. The analogues showed no adverse cytotoxic effects on normal human cells and exhibited appropriate in silico pharmacokinetic properties and drug-likeness. These findings suggest the promising application of CAPE (2) analogues to target Ikaros (IKZF1)/IRF4 addiction, the so-called Achilles heel of myeloma, for better treatment outcomes.
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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) | 0.002 | 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".