First-in-Man Intrathecal Application of Neurite Growth-Promoting Anti-Nogo-A Antibodies in Acute Spinal Cord Injury
Bibliographic record
Abstract
BACKGROUND: Neutralization of central nervous system neurite growth inhibitory factors, for example, Nogo-A, is a promising approach to improving recovery following spinal cord injury (SCI). In animal SCI models, intrathecal delivery of anti-Nogo-A antibodies promoted regenerative neurite growth and functional recovery. OBJECTIVE: This first-in-man study assessed the feasibility, safety, tolerability, pharmacokinetics, and preliminary efficacy of the human anti-Nogo-A antibody ATI355 following intrathecal administration in patients with acute, complete traumatic paraplegia and tetraplegia. METHODS: Patients (N = 52) started treatment 4 to 60 days postinjury. Four consecutive dose-escalation cohorts received 5 to 30 mg/2.5 mL/day continuous intrathecal ATI355 infusion over 24 hours to 28 days. Following pharmacokinetic evaluation, 2 further cohorts received a bolus regimen (6 intrathecal injections of 22.5 and 45 mg/3 mL, respectively, over 4 weeks). RESULTS: ATI355 was well tolerated up to 1-year follow-up. All patients experienced ≥1 adverse events (AEs). The 581 reported AEs were mostly mild and to be expected following acute SCI. Fifteen patients reported 16 serious AEs, none related to ATI355; one bacterial meningitis case was considered related to intrathecal administration. ATI355 serum levels showed dose-dependency, and intersubject cerebrospinal fluid levels were highly variable after infusion and bolus injection. In 1 paraplegic patient, motor scores improved by 8 points. In tetraplegic patients, mean total motor scores increased, with 3/19 gaining >10 points, and 1/19 27 points at Week 48. Conversion from complete to incomplete SCI occurred in 7/19 patients with tetraplegia. CONCLUSIONS: ATI335 was well tolerated in humans; efficacy trials using intrathecal antibody administration may be considered in acute SCI.
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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 | 0.001 | 0.001 |
| 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".