A Phase 2A Trial of the Novel mGluR5‐Negative Allosteric Modulator Dipraglurant for Levodopa‐Induced Dyskinesia in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: The metabotropic glutamate receptor 5-negative allosteric modulator dipraglurant reduces levodopa-induced dyskinesia in the MPTP-macaque model. The objective of this study was to assess the safety, tolerability (primary objective), and efficacy (secondary objective) of dipraglurant on levodopa-induced dyskinesia in Parkinson's disease (PD). METHODS: The study was a phase 2A double-blind, placebo-controlled, randomized (2:1), 4-week, parallel-group, multicenter dose-escalation (from 50 mg once daily to 100 mg 3 times daily) clinical trial involving 76 PD subjects with moderate to severe levodopa-induced dyskinesia. Safety and tolerability were assessed based on clinical and biological examination and adverse events recording. Secondary efficacy outcome measures included the modified Abnormal Involuntary Movement Scale, UPDRS, and diaries. Pharmacokinetics were measured at 3 visits following a single dose. RESULTS: Fifty-two patients were exposed to dipraglurant and 24 to placebo. There were no major safety concerns. Two subjects did not complete the study because of adverse events. Most frequent adverse events included dyskinesia, dizziness, nausea, and fatigue. Dipraglurant significantly reduced peak dose dyskinesia (modified Abnormal Involuntary Movement Scale) on day 1 (50 mg, 20%; P = 0.04) and on day 14 (100 mg, 32%; P =0 .04) and across a 3-hour postdose period on day 14 (P = 0.04). There was no evidence of worsening of parkinsonism. Dipraglurant was rapidly absorbed (tmax = 1 hour). The 100-mg dose led to a mean Cmax of 1844 ng/mL on day 28. CONCLUSIONS: Dipraglurant proved to be safe and well tolerated in its first administration to PD patients. Its efficacy in reversing levodopa-induced dyskinesia warrants further investigations in a larger number of patients. © 2016 International Parkinson and Movement Disorder Society.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".