The nitrone compound OKN-007 delays motor neuron loss and disease progression in the G93A mouse model of amyotrophic lateral sclerosis
Bibliographic record
Abstract
Our study investigated the therapeutic potential of OKN-007 in the SOD1 G93A mouse model of amyotrophic lateral sclerosis (ALS). The impact of OKN-007, known for its antioxidant, anti-inflammatory, and neuroprotective properties, was tested at two doses (150 mg/kg and 300 mg/kg) at onset and late-stage disease. Results demonstrated a significant delay in disease progression at both doses, with treated mice showing a slower advance to early disease stages compared to untreated controls. Motor neuron counts in the lumbar spinal cord were notably higher in OKN-007 treated mice at the time of disease onset, suggesting neuroprotection. Additionally, OKN-007 reduced microglial activation and preserved reduced neuromuscular junction fragmentation, although it did not significantly alter the increase in astrocyte number or the decline in hindlimb muscle mass. MR spectroscopy (MRS) revealed improved spinal cord perfusion and normalized myo-inositol levels in treated mice, supporting reduced neuroinflammation. While the expression of several proteins associated with inflammation is increased in spinal cord extracts from G93A mice, OKN-007 dampened the expression of IL-1β, IL-1ra and IL-1α. Despite its promising effects on early-stage disease progression, in general, the beneficial effects of OKN-007 diminished over longer treatment durations. Further, we found no improvement in muscle atrophy or weakness phenotypes in OKN-007 treated G93A mice, and no effect on mitochondrial function or lifespan. Overall, our findings suggest that OKN-007 holds potential as a disease-modifying treatment for ALS, although further research is needed to optimize dosing regimens and understand its long-term effects.
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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) | 0.001 | 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".