Advances in the delivery of treatments for Parkinson’s disease
Bibliographic record
Abstract
Innovative drug delivery in Parkinson's disease (PD) has the potential to reduce or avoid many side effects of current treatment, such as wearing-off type fluctuations, dyskinesia, on-off phenomena or bouts of motor freezing. The traditional orally administered formulations of l-dihydroxyphenylalanine combined with a peripheral aromatic acid decarboxylase inhibitor remain the mainstay of treatments for PD. However, such combination therapies have been further formulated to extend their duration of action by including a catechol-O-methyltransferase inhibitor. Preventing the breakdown of dopamine has also been achieved by monoamine oxidase-B inhibition; this approach now having been formulated for sublingual use (Zelapar, Valeant Pharmaceuticals). An alternative approach bypasses the oral route of administration and instead relies on continuous duodenal infusion (Duodopa, Solvay, NeoPharma AB) for better therapeutic effect. The clinical use of dopamine agonists as antiparkinsonian drugs now incorporates a variety of delivery techniques. For example, apomorphine, which relies on parenteral administration for maximum bioavailability, may be delivered via rectal, intranasal, sublingual and subcutaneous (e.g., Apokyn, Mylan Bertek) routes. Meanwhile, rotigotine and lisuride have both been formulated for delivery via skin patches. Finally, the authors examine more experimental delivery techniques, including the delivery of genes via viral vectors or liposomes, intracranial transplant of a variety of cells and of L-dihydroxyphenylalanine by prodrug-dispensing liposomes or pulmonary delivery (AIR, Alkermes). The advent and application of these varied technologies will help encourage patient-specific means of treatment for 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".