The selective mu‐opioid receptor antagonist adl5510 reduces levodopa‐induced dyskinesia without affecting antiparkinsonian action in mptp‐lesioned macaque model of Parkinson's disease
Bibliographic record
Abstract
In Parkinson's disease (PD), dyskinesia develops following long-term treatment with 3,4-dihydroxyphenylalanine (L-dopa). Given the prominent role of the opioid system in basal ganglia function, nonselective opioid receptor antagonists have been tested for antidyskinetic efficacy in the clinic (naltrexone and naloxone), although without success. In the current study, ADL5510, a novel, orally active opioid antagonist with mu opioid receptor selectivity, was examined in L-dopa-treated 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) macaques. Antidyskinetic effects were compared with those of naltrexone. Parkinsonian monkeys with established L-dopa-induced dyskinesia (LID) received acute challenges with L-dopa (subcutaneously) in combination with either vehicle, ADL5510 (0.1, 1, 3 or 10 mg/kg by mouth), or naltrexone (1, 3, or 10 mg/kg subcutaneously). Following treatments, behavior was monitored for 6 hours. Parameters assessed were total activity, parkinsonism, and dyskinesia. ADL5510 (1, 3, and 10 mg/kg) reduced activity and LID (chorea and dystonia) without affecting the antiparkinsonian benefits of L-dopa. The antidyskinetic effect of ADL5510 showed a U-shaped dose-response. It was inactive at 0.1 mg/kg, efficacious at 1 and 3 mg/kg (72% and 40% reductions, respectively), and then less effective at 10 mg/kg. The quality of ON time produced by L-dopa was improved, as indicated by a reduction in the percentage of ON time spent experiencing disabling dyskinesia (70% and 61% reductions with 1 and 3 mg/kg, respectively, compared with L-dopa). Naltrexone, in contrast, did not alleviate LID or affect the antiparkinsonian actions of L-dopa. Mu-selective opioid antagonists have the potential to form the basis of novel antidyskinetic therapies for PD.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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".