Effect of glycine transporter 1 inhibition with bitopertin on parkinsonism and L-DOPA induced dyskinesia in the 6-OHDA-lesioned rat
Bibliographic record
Abstract
Dyskinesia remains an unmet need in Parkinson's disease (PD). We have previously demonstrated that glycine transporter 1 (GlyT1) inhibition with ALX-5407 reduces dyskinesia and slightly improves parkinsonism in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-lesioned marmoset. Here, we sought to determine the effect of bitopertin, a clinically-tested GlyT1 inhibitor, on parkinsonism and dyskinesia in the 6-hydroxydopamine (6-OHDA)-lesioned rat. To do so, we assessed the effect of bitopertin on parkinsonism as monotherapy and as adjunct to a low dose of L-3,4-dihydroxyphenylalanine (L-DOPA). We then assessed the efficacy of bitopertin on dyskinesia in the context of acute challenge and chronic administration studies. Lastly, we evaluated whether de novo treatment with bitopertin, started concurrently with L-DOPA, would diminish the development of dyskinesia. We discovered that bitopertin (0.3 mg/kg), when administered alone, reduced the severity of parkinsonism by 35% (P < 0.01). As adjunct to a low dose of L-DOPA, bitopertin (3 mg/kg) enhanced the anti-parkinsonian effect of L-DOPA by 36% (P < 0.05). Moreover, the acute addition of bitopertin (0.03 mg/kg) to L-DOPA reduced dyskinesia by 27% (P < 0.001), and there was no tolerance to the anti-dyskinetic benefit after 4 weeks of daily administration. Lastly, bitopertin (0.03 mg/kg) started concurrently with L-DOPA, also attenuated the development of dyskinesia, by 33% (P < 0.01), when compared to L-DOPA alone. Our results suggest that GlyT1 inhibition may simultaneously reduce parkinsonism and L-DOPA-induced dyskinesia and represents a novel approach to treat, possibly prevent, motor complications in 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) | 0.001 | 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".