The novel ROCK2 selective inhibitor NRL-1049 preserves the blood-brain barrier after acute injury
Bibliographic record
Abstract
Endothelial blood-brain barrier (BBB) dysfunction is critical in the pathophysiology of brain injury. Rho-associated protein kinase (ROCK) activation disrupts BBB integrity in the injured brain. We aimed to test the efficacy of a novel ROCK2 inhibitor in preserving the BBB after acute brain injury. We characterized the molecular structure and pharmacodynamic and pharmacokinetic properties of a novel selective ROCK2 inhibitor, NRL-1049, and its first metabolite, 1-hydroxy-NRL-1049 (referred to as NRL-2017 hereon) and tested the efficacy of NRL-1049 on the BBB integrity in rodent models of acute brain injury. Our data show that NRL-1049 and NRL-2017 both inhibit ROCK activity and are 44-fold and 17-fold more selective towards ROCK2 than ROCK1, respectively. When tested in a mouse model of cortical cryoinjury, NRL-1049 significantly attenuated the increase in water content. Interestingly, 60% of the mice in the vehicle arm developed seizures within 2 hours after cryoinjury versus none in the NRL-1049 arm. In spontaneously hypertensive rats, NRL-1049 attenuated the dramatic surge in Evans Blue extravasation compared with the vehicle arm after transient middle cerebral artery occlusion. Hemorrhagic transformation was also reduced. We show that NRL-1049, a selective ROCK2 inhibitor, is a promising drug candidate to preserve the BBB after brain injury.
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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 | 0.001 |
| 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".