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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".