Riluzole as a pharmacological therapy for spinal cord injury: where does this therapy stand?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Spinal cord injury (SCI) remains a disabling condition associated with long term neurological impairment, functional disability, and reduced quality of life. Despite decades of research, pharmacological interventions with proven clinical efficacy remain limited. This review critically evaluates the current evidence supporting riluzole as a neuroprotective agent for acute traumatic and nontraumatic SCI. We synthesize findings from preclinical and clinical studies, assess the progress towards clinical translation, and outline key challenges and research opportunities for future implementation. RECENT FINDINGS: Riluzole, an FDA-approved agent for amyotrophic lateral sclerosis (ALS), inhibits voltage-gated sodium channels and modulates glutaminergic transmission, two mechanisms central to the pathogenesis of secondary injury in SCI and in nerve cell degeneration in nontraumatic forms of SCI, including degenerative cervical myelopathy (DCM). Preclinical studies consistently demonstrate functional and histopathological improvements following riluzole administration. Phase I/II trials have provided evidence for its safety and tolerability in acute SCI patients, while the RISCIS and CSM-PROTECT trials, two landmark multicenter randomized controlled studies, along with their secondary analyses, revealed promising multidomain improvements in motor function, independence, and quality of life indices. Sub-studies have also established pharmacokinetic and pharmacodynamic frameworks for individualized dosing, and early biomarker analysis suggests potential for predictive stratification. SUMMARY: Riluzole represents a promising candidate for neuroprotection in traumatic and nontraumatic SCI. The consistency of favorable trends across multiple domains and strong support from preclinical studies highlight riluzole's value in orphan diseases such as SCI. Future directions should focus on refining the therapeutic window, optimizing PK/PD modeling, and identifying patient subgroups most likely to benefit. Its implementation in a multimodal treatment paradigm for acute SCI will be crucial for optimizing management protocols in this highly disabling condition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".