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Record W4411195516 · doi:10.1080/14728214.2025.2517582

Motor fluctuations in Parkinson disease – a mini-review of emerging drugs

2025· review· en· W4411195516 on OpenAlexaff
Priti Gros, Susan H. Fox

Bibliographic record

VenueExpert Opinion on Emerging Drugs · 2025
Typereview
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsParkinson's Clinic of Eastern Toronto & Movement Disorders CentreToronto Western HospitalPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsMedicineParkinson's diseaseDiseasePhysical medicine and rehabilitationNeuroscienceIntensive care medicineInternal medicinePsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: The symptomatic treatment of Parkinson Disease (PD) relies on levodopa. With disease progression, the response to levodopa becomes variable, leading to fluctuations in benefit on PD symptoms. These motor fluctuations are challenging to manage and negatively impact quality of life in PD. AREAS COVERED: We provide a review of experimental, non-approved pharmacological therapies in phase II and III clinical trials from 2018 to 2024 for PD motor fluctuations. EXPERT OPINION: New formulations of levodopa to improve bioavailability are in development. These include another subcutaneous infusion with efficacy in reducing motor fluctuations and an intranasal delivery with tolerability reported. An oromucosal formulation of apomorphine was safe, further studies are needed. Preliminary results of a phase III study of dopamine D1/D5 agonist tavapadon and phase II of CVN424, a GRP6 inverse agonist suggest improved ON time. Negative studies were reported with foliglurax (MGluR4 inverse agonist) and the repurposed drugs naftazone and bumetanide. Several novel targets are in early-stage development with results awaited. Overall, it is unclear whether the field is significantly further ahead, as the benefit of these emerging drugs in comparison with currently available agents for motor fluctuations needs to be clarified.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.378
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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