A Phase 2b, multicenter, randomized, double-blind, placebo-controlled study to evaluate the efficacy and safety of intravenous prasinezumab in early-stage Parkinson's disease (PADOVA): Rationale, design, and baseline data.

Introduction: Prasinezumab was shown to potentially delay motor progression in individuals with early-stage Parkinson's disease (PD) who were either treatment-naïve or on monoamine oxidase type B inhibitor (MAO-Bi) therapy in the PASADENA study. We report the rationale, design, and baseline patient characteristics of the PADOVA study, designed to evaluate prasinezumab in an early-stage PD population receiving standard-of-care (SOC) symptomatic medications.

Methods: PADOVA (NCT04777331) is a Phase 2b, multicenter, randomized, double-blind, placebo-controlled, parallel-group study, in which individuals with early-stage PD on SOC stable symptomatic monotherapy (levodopa or MAO-Bi) receive intravenous prasinezumab 1500 mg every 4 weeks.

Staged Screening Identifies People with Biomarkers Related to Neuronal Alpha-Synuclein Disease.

Objective: Remote identification of individuals with severe hyposmia may enable scalable recruitment of participants with underlying alpha-synuclein aggregation. We evaluated the performance of a staged screening paradigm using remote smell testing to enrich for abnormal dopamine transporter single-photon emission computed tomography imaging (DAT-SPECT) and alpha-synuclein aggregation.

Methods: The Parkinson's Progression Markers Initiative (PPMI) recruited participants for the prodromal cohort who were 60-years and older without a Parkinson's disease diagnosis.

Impact of dopamine deficiency and REM sleep behavior disorder on cognition in early neuronal synuclein disease with hyposmia.

Objectives: To determine the impact of dopamine deficiency and isolated REM sleep behavior disorder (iRBD) on cognitive performance in early neuronal alpha-synuclein disease (NSD) with hyposmia.

Methods: Using Parkinson's Progression Markers Initiative baseline data, cognitive performance was assessed with a cognitive summary score (CSS) developed by applying regression-based internal norms derived from a robust healthy control (HC) group. Performance was examined for participants with hyposmia classified as NSD-Integrated Staging System (NSD-ISS) Stage 2, either Stage 2A (CSF alpha-synuclein seed amplification assay [SAA]+, SPECT dopamine transporter scan [DaTscan]-) or 2B (SAA+, DaTscan+).

Prediction, prognosis and monitoring of neurodegeneration at biobank-scale via machine learning and imaging.

Background: Alzheimer's disease and related dementias (ADRD) and Parkinson's disease (PD) are the most common neurodegenerative conditions. These central nervous system disorders impact both the structure and function of the brain and may lead to imaging changes that precede symptoms. Patients with ADRD or PD have long asymptomatic phases that exhibit significant heterogeneity.

Accelerating Parkinson's Disease drug development with federated learning approaches.

Parkinson's Disease is a progressive neurodegenerative disorder afflicting almost 12 million people. Increased understanding of its complex and heterogenous disease pathology, etiology and symptom manifestations has resulted in the need to design, capture and interrogate substantial clinical datasets. Herein we advocate how advances in the deployment of artificial intelligence models for Federated Data Analysis and Federated Learning can help spearhead coordinated and sustainable approaches to address this grand challenge.