Effect of histamine H<sub>2</sub> receptor antagonism on levodopa–induced dyskinesia in the MPTP‐macaque model of Parkinson's disease
Bibliographic record
Abstract
Levodopa-induced motor complications, including dyskinesia and wearing off, are troublesome side effects of treatment and impair quality of life in Parkinson's disease (PD) patients. The use of nondopaminergic agents as adjuncts to levodopa are potential options for managing these problems. Here, we asses the ability of the clinically available, selective histamine H(2) antagonist, famotidine (1, 3, and 30 mg/kg) to treat levodopa-induced dyskinesia and wearing off in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-macaque model of PD. Famotidine (3 mg/kg) increased peak activity, enhanced peak anti-parkinsonian action (1 and 3 mg/kg), and extended duration of action (3 mg/kg, by 38%) of a low dose of levodopa (compared to low dose levodopa alone). Enhancement of anti-parkinsonian actions of low dose levodopa by famotidine (3 mg/kg) was associated with only mild, nondisabling dystonia. Famotidine had no effect on the anti-parkinsonian actions of high dose levodopa (compared to high dose levodopa alone). However, famotidine (1, 3, and 30 mg/kg) had a significant effect on chorea, but not dystonia, induced by high dose levodopa (compared to high dose levodopa alone). Famotidine increased high dose levodopa-induced "good quality" on time, i.e., on time not associated with disabling dyskinesia, by up to 28% (compared to high dose levodopa alone). In conclusion, famotidine, a drug currently available for use in the clinic, can enhance the peak-dose anti-parkinsonian actions and extend total duration of action of a low dose of levodopa, without producing disabling dyskinesia. Furthermore, in combination with a higher dose of levodopa, famotidine can reduce peak-dose levodopa-induced chorea and improve the quality of on-time.
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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 | 0.001 | 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".