Open-label titration of apomorphine sublingual film in patients with Parkinson's disease and “OFF” episodes
Bibliographic record
Abstract
INTRODUCTION: The efficacy and safety of apomorphine sublingual film (APL-130277; APL) for the on-demand treatment of "OFF" episodes associated with Parkinson's disease (PD) was demonstrated in a double-blind trial. Herein we describe the ability of patients to receive effective and tolerable APL dose titration during the open-label titration phase. METHODS: Adult patients with levodopa-responsive PD and "OFF" episodes were enrolled. In practically defined "OFF," patients were observed for a FULL "ON" after their usual morning carbidopa/levodopa (CD/LD) dose and then after titration with APL following each increasing dose (10-35 mg). Antiemetic medication was administered for 3 days before initiation of titration and was continued throughout titration. Motor responses were evaluated predose and postdose using Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) Part III score. Safety outcomes were evaluated. RESULTS: Among 141 patients who enrolled in the study and received APL during open-label titration, 109 (77.3%) achieved a FULL "ON" (66.1% at 10-20 mg) and 10 did not. Patients who successfully completed APL dose titration tended to be younger, had a longer mean time since PD diagnosis, and had lower levodopa requirements than those who discontinued during titration for any reason. Change in MDS-UPDRS Part III scores from predose to 30 min postdose after titration with the effective dose of APL (n = 109) was similar across all dose groups. In a post hoc analysis, the magnitude of motor response with APL was ~2-fold higher than with CD/LD 15 min postdose, and the observed peak response occurred earlier with APL than with the trend seen for CD/LD (45 vs 90 min, respectively). Overall, the most common (≥10%) treatment-emergent adverse events (TEAEs) during APL dose titration were nausea (20.6%), yawning (12.1%), dizziness (11.3%), and somnolence (11.3%). Twelve patients discontinued due to TEAEs during APL dose titration, most commonly (≥2%) because of dizziness (2.8%), nausea (2.1%), and somnolence (2.1%). CONCLUSION: Among eligible patients with PD and "OFF" episodes who had their APL dose successfully titrated to an effective and tolerable level, most were able to do so within the first 3 titrated doses but some required further dose escalations. The use of APL can provide benefit for the treatment of "OFF" episodes.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".