Investigational drugs for the treatment of spinal cord injury: review of preclinical studies and evaluation of clinical trials from Phase I to II
Bibliographic record
Abstract
INTRODUCTION: Efforts in basic research have clarified mechanisms involved in spinal cord injury (SCI), and resulted in positive findings using experimental treatments including cell transplantation and drug administration preclinically. Based on accumulated results, various clinical trials have begun for human SCI. AREAS COVERED: In this review, the authors focus on five investigational drugs: riluzole, minocycline, Rho protein antagonist, magnesium chloride in polyethylene glycol formulation, and basic fibroblast growth factor. All drugs have established safety and tolerability from Phase I clinical trials, and are now in Phase II. They have been proven to have neuroprotective and/or neuroregenerative effects in animal models of SCI. EXPERT OPINION: To date, diverse drugs have been translated into clinical trials, but none have reached clinical application. A key gap was the lack of reliable biomarkers for SCI to fast-track Phase I/II trials. Furthermore, problems were often due to lack of adequate outcome assessments for both animal models and SCI patients. In order to advance clinical trials more quickly and with greater success, more clinically relevant animal models should be used in basic research. Clinically, it is indispensable to use appropriate outcome measurements and to construct a wide network among clinical centers to validate the efficacy of drugs.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".