Opportunities and Challenges for the Development of MRCK Kinases Inhibitors as Potential Cancer Chemotherapeutics
Bibliographic record
Abstract
Cytoskeleton organization and dynamics are rapidly regulated by post-translational modifications of key target proteins. Acting downstream of the Cdc42 GTPase, the myotonic dystrophy-related Cdc42-binding kinases MRCKα, MRCKβ, and MRCKγ have recently emerged as important players in cytoskeleton regulation through the phosphorylation of proteins such as the regulatory myosin light chain proteins. Compared with the closely related Rho-associated coiled-coil kinases 1 and 2 (ROCK1 and ROCK2), the contributions of the MRCK kinases are less well characterized, one reason for this being that the discovery of potent and selective MRCK pharmacological inhibitors occurred many years after the discovery of ROCK inhibitors. The disclosure of inhibitors, such as BDP5290 and BDP9066, that have marked selectivity for MRCK over ROCK, as well as the dual ROCK + MRCK inhibitor DJ4, has expanded the repertoire of chemical biology tools to study MRCK function in normal and pathological conditions. Recent research has used these novel inhibitors to establish the role of MRCK signalling in epithelial polarization, phagocytosis, cytoskeleton organization, cell motility, and cancer cell invasiveness. Furthermore, pharmacological MRCK inhibition has been shown to elicit therapeutically beneficial effects in cell-based and in vivo studies of glioma, skin, and ovarian cancers.
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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".