A Multicenter Screening Trial of the Safety and Efficacy of Rasagiline in People with ALS (S36.006)
Bibliographic record
Abstract
OBJECTIVE: The primary aims are to determine whether rasagiline is safe in this patient population and if the drug has the potential to slow ALS disease progression and to determine if mitochondrial function is affected by rasagiline. BACKGROUND: Oxidative stress, mitochondrial dysfunction and apoptosis have been proposed as the cause of motor neuron death in ALS. Rasagiline, a monoamine oxidase B inhibitor, is FDA-approved for the symptomatic treatment of Parkinson disease. Rasagiline has demonstrated broad neuroprotective activities against a variety of neurotoxins in neuronal cell cultures and in the SOD mouse model of ALS. In vitro experiments indicate rasagiline stabilizes mitochondria under stress conditions. DESIGN/METHODS: Subjects are treated with rasagiline 2 mg daily for 12 months. The primary outcome measures are the number of adverse events and the change of the slope of ALS Functional Rating Scale-Revised (ALSFRS-R) over 12 months. Secondary outcome measures are the change in blood biomarkers including mitochondrial potentials in platelets, leukocytes Bcl/Bax ratio and indicators of apoptosis and oxidative stress following rasagiline administration. RESULTS: Nine centers in the Western ALS (WALS) study group are participating in this study. Total 41 patients screened, 35 were enrolled and 30 remain on treatment with four dropouts due to either side effects (4), and one patient died. There were 18 men and 17 women, with a mean age at entry of 61 years (59-82), and mean disease duration was 1.5 years (0.5-3). 16 patients have been on rasagiline 2 mg for six months. The mean ALSFRS-R for these16 patient were: 37.2 baseline, 36,4 month 1, 32.5 month 6. Mean Jc-1 lymphocyte ratio was 0.46 relative fluorescence at baseline and 0.61 relative fluorescence at six months (P<0.02). CONCLUSIONS: Rasagiline appears to be safe in this ALS population. The change in mitochondrial membrane potentials indicates target engagement of the drug. Supported by: TEVA Pharmaceutical Industries Ltd. Disclosure: Dr. Wang has received research support from Teva Neuroscience. Dr. Moore has nothing to disclose. Dr. Katz has received personal compesnation for actiities with Blue Cross and Talecris. Dr. Katz has received research support from Cytokinetics, Biogen, Neuraltis pharmaceuticals and ALS. Dr. Saperstein has received personal compensation for activities with Talecris, CSL Behring, Eli Lilly,Corinthian reference lab, as speaker, scientific advisory board and neuropathy services. Dr. Walk has nothing to disclose. Dr. Simpson has received personal compensation for activities with Grofels for speaking. Dr. Simpson has received research support from International Foundation Dr. Genge has received personal compensation for activities with Pfizer, Talecris, CSL Behring, Biogen Idec, Novartis, Purdue, and Medtronic. Dr. Bertorini has nothing to disclose. Dr. Fernandes has nothing to disclose. Dr. Swenson has nothing to disclose. Dr. Swerdlow has received personal compensation for activities with Eisai, Inc. as a consultant. Dr. Dimachkie has received personal compensation for activities with Pfizer Inc, Depomed, and Merck & Co., Inc. Dr. McVey has nothing to disclose. Dr. Herbelin has nothing to disclose. Dr. Macchi has nothing to disclose. Dr. Barohn has received personal compensation for activities with Talecris, Genzyme Corporation, and NuFactor. Dr. Barohn has received research support from Alexion Pharmaceuticals. Dr. Herbelin has nothing to disclose.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".