The Monoamine Re-Uptake Inhibitor UWA-101 Improves Motor Fluctuations in the MPTP-Lesioned Common Marmoset
Bibliographic record
Abstract
BACKGROUND: The wearing-OFF phenomenon is a common motor complication of chronic L-3,4-dihydroxyphenylalanine (L-DOPA) therapy for Parkinson's disease. We recently described the discovery of UWA-101, a dual serotonin (SERT) and dopamine (DAT) transporter inhibitor, which increases the duration of "good quality" ON-time provided by L-DOPA in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned primate. Here, we further characterise the effects of UWA-101 on this extension of ON-time in terms of L-DOPA-induced side-effects in the MPTP-lesioned common marmoset. METHODS: Marmosets were rendered parkinsonian by MPTP injection and "primed" by repeated L-DOPA administration, to exhibit dyskinesia and psychosis-like behaviours. Animals were then administered acute challenges of L-DOPA in combination with UWA-101 (1, 3, 6 and 10 mg/kg) or vehicle. RESULTS: In combination with L-DOPA, UWA-101 (3, 6 and 10 mg/kg) significantly increased duration of ON-time (by 28%, 28%, and 33%, respectively; all P<0.05). UWA-101 (10 mg/kg) significantly extended duration of ON-time without disabling dyskinesia (by 62%, P<0.01). UWA-101 did not exacerbate the severity of dyskinesia (P>0.05). However, at the highest doses (6 and 10 mg/kg), UWA-101 increased the severity of psychosis-like behaviours (P<0.05). CONCLUSIONS: Our results demonstrate that dual SERT/ DAT inhibitors can effectively enhance L-DOPA anti-parkinsonian action, without exacerbating dyskinesia and, as such, represent a promising new therapeutic class for wearing-OFF. However, at higher doses, dual SERT/ DAT inhibitors may exacerbate dopaminergic psychosis.
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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) | 0.001 | 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".