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Record W2899084046 · doi:10.3233/jpd-181471

A Proposed Roadmap for Parkinson’s Disease Proof of Concept Clinical Trials Investigating Compounds Targeting Alpha-Synuclein

2018· article· en· W2899084046 on OpenAlexafffund
Kalpana Merchant, Jesse M. Cedarbaum, Patrik Brundin, Kuldip D. Dave, Jamie L. Eberling, Alberto J. Espay, Samantha J. Hutten, Monica Javidnia, Johan Luthman, Walter Maetzler, Liliana Menalled, Alyssa Reimer, A. Jon Stoessl, David M. Weiner

Bibliographic record

VenueJournal of Parkinson s Disease · 2018
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsUniversity of British Columbia
FundersNational Institutes of HealthH. Lundbeck A/SEisaiAgence Nationale de la RechercheUniversity of CambridgeUniversity of British ColumbiaAxial BiotherapeuticsEuropean CommissionAcorda TherapeuticsWeston Brain InstituteSunovionRobert Bosch StiftungGlaxoSmithKlineTeva Pharmaceutical IndustriesMichael J. Fox Foundation for Parkinson's ResearchAmerican Academy of NeurologyAstraZeneca
KeywordsParkinson's diseaseClinical trialSynucleinopathiesAlpha-synucleinTranslational researchDiseaseMedicineDrug developmentBiomarker discoveryBiomarkerClinical study designBioinformaticsPathologyBiologyPharmacologyProteomics

Abstract

fetched live from OpenAlex

The convergence of human molecular genetics and Lewy pathology of Parkinson's disease (PD) have led to a robust, clinical-stage pipeline of alpha-synuclein (α-syn)-targeted therapies that have the potential to slow or stop the progression of PD and other synucleinopathies. To facilitate the development of these and earlier stage investigational molecules, the Michael J. Fox Foundation for Parkinson's Research convened a group of leaders in the field of PD research from academia and industry, the Alpha-Synuclein Clinical Path Working Group. This group set out to develop recommendations on preclinical and clinical research that can de-risk the development of α-syn targeting therapies. This consensus white paper provides a translational framework, from the selection of animal models and associated end-points to decision-driving biomarkers as well as considerations for the design of clinical proof-of-concept studies. It also identifies current gaps in our biomarker toolkit and the status of the discovery and validation of α-syn-associated biomarkers that could help fill these gaps. Further, it highlights the importance of the emerging digital technology to supplement the capture and monitoring of clinical outcomes. Although the development of disease-modifying therapies targeting α-syn face profound challenges, we remain optimistic that meaningful strides will be made soon toward the identification and approval of disease-modifying therapeutics targeting α-syn.

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)0.005
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.398
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.121
GPT teacher head0.404
Teacher spread0.283 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations57
Published2018
Admission routes2
Has abstractyes

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