Shifting the selectivity of pyrido[2,3-d]pyrimidin-7(8 <i>H</i> )-one inhibitors towards the salt-inducible kinase (SIK) subfamily
Bibliographic record
Abstract
ABSTRACT Salt-inducible kinases 1-3 (SIK1-3) are key regulators of the LKB1-AMPK pathway and play an important role in cellular homeostasis. Dysregulation of any of the three isoforms has been associated with tumorigenesis in liver, breast, and ovarian cancers. We have recently developed the dual pan-SIK/group I p21-activated kinase (PAK) chemical probe MRIA9. However, inhibition of p21-activated kinases has been associated with cardiotoxicity in vivo , which complicates the use of MRIA9 as a tool compound. Here, we present a structure-based approach involving the back-pocket and gatekeeper residues, for narrowing the selectivity of pyrido[2,3-d]pyrimidin-7(8 H )-one-based inhibitors towards SIK kinases, eliminating PAK activity. Optimization was guided by high-resolution crystal structure analysis and computational methods, resulting in a pan-SIK inhibitor, MR22, which no longer exhibited activity on STE group kinases and displayed excellent selectivity in a representative kinase panel. MR22-dependent SIK inhibition led to centrosome dissociation and subsequent cell-cycle arrest in ovarian cancer cells, as observed with MRIA9, conclusively linking these phenotypic effects to SIK inhibition. Taken together, MR22 represents a valuable tool compound for studying SIK kinase function in cells.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".