K252a and CEP1347 Are Neuroprotective Compounds That Inhibit Mixed-lineage Kinase-3 and Induce Activation of Akt and ERK
Bibliographic record
Abstract
K252a is best known as a Trk inhibitor, but is also a neuroprotective compound. CEP1347, a K252a derivative, retains neuroprotective properties, but does not inhibit TrkA. CEP1347 has recently been shown to directly inhibit MAPKKKs, including MLK3, but the effect of K252a on MAPKKKs remains unknown. K252a and CEP1347 not only prevent death, but also facilitate neurite outgrowth and maintenance, somal hypertrophy, and neurotransmitter synthesis. The biochemical basis for these trophic effects remains unknown. We have compared the effects of CEP1347 and K252a on MLK and JNK signaling and on neurotrophic pathways that support survival and growth. Our data show that K252a is a potent inhibitor of MLK3 activity in vivo and in vitro (IC(50) approximately 5 nm). However, we also found that K252a and CEP1347 activate Akt and ERK and show that blockade of phosphatidylinositol 3-kinase or MEK activity ablates the effect of K252a and CEP1347 on cell survival. Activation of Akt and ERK occurs through an MLK-independent pathway that may involve c-Src. Together, these data show that the neuroprotective and neurotrophic effects of K252a and CEP1347 involve activation of several neurotrophic signaling pathways.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".