Discovery of Dietary Plant Flavonols as Novel Potent Inhibitors Targeting DYRK1A Kinase
Bibliographic record
Abstract
DYRK1A kinase is a critical regulator in cellular signaling pathways and a promising therapeutic target for neurodegenerative diseases, diabetes and cancers. Despite its significance, the development of potent, selective and safe inhibitors remains a significant challenge. Several natural flavonoids have been reported to inhibit DYRK1A by binding in the ATP-binding pocket, exhibiting antidiabetic properties. However, a systematic screening of these structural derivatives remains lacking. In this study, we aimed to expand the pool of flavonoid-based DYRK1A inhibitor candidates for drug development against DYRK1A through targeted screening and structure-based analysis. A focused library of 13 flavonoid derivatives was screened to identify novel DYRK1A inhibitors, revealing eight new flavonol inhibitors with IC50 values ranging from 149.5 nM to 737.9 nM. Among these, fisetin demonstrated the highest potency with an IC50 of 149.5 nM, followed by kaempferol (296.3 nM), isorhamnetin (418 nM), morin (478.4 nM), myricetin (633.2 nM) and luteolin (797.8 nM), all exhibiting submicromolar inhibitory activity. Additional novel inhibitors, Apigenin and Kaempferide, also showed effective inhibition. As controls, the previously known inhibitors quercetin and curcumin were evaluated, yielding IC50 values of 737.9 nM and 2.35 μM, respectively, which validated the assay conditions. To the best of our knowledge, fisetin is the most potent known DYRK1A inhibitor among flavonoids. Cellular assays further demonstrated that the top flavonoid hits induced dose-dependent cytotoxicity and morphological changes in HeLa cells. Structure-activity relationship and molecular simulation analysis revealed that the selected flavonols interact with key residues for DYRK1A inhibition. These results highlight flavonols as a promising scaffold for DYRK1A inhibition and provide valuable natural inhibitor leads for further optimization and therapeutic development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".