Challenges in detecting disease modification in Parkinson's disease clinical trials. (November 2016)

• Record Type: Journal Article
• Authors: Athauda, Dilan; Foltynie, Thomas
• Is Part Of: Parkinsonism & related disorders. Volume 32(2016)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-11
• Subjects: Parkinson's disease -- Periodicals; Movement disorders -- Periodicals; Movement Disorders -- Periodicals; Nerve Degeneration -- Periodicals; Nervous System Diseases -- Periodicals; Parkinson Disease -- Periodicals; Tremor -- Periodicals; Parkinson, Maladie de -- Périodiques; Parkinson's disease; 616.833
• DOI: 10.1016/j.parkreldis.2016.07.019
• Languages: English
• ISSNs: 1353-8020

Abstract

Abstract: Despite the wealth of encouraging data from numerous compounds that demonstrate "neuroprotection" in pre-clinical studies of Parkinson's disease, and despite numerous clinical trials, to date, no intervention has been demonstrated to able to modify the course of disease progression. While this "failure to translate" is likely due to numerous factors including our incomplete understanding of the pathogenic mechanisms underlying PD together with excessive reliance on data from the toxin-based animal models of PD, here we will discuss the "structural issues" pertaining to inadequate clinical trial design, selection of inappropriate endpoints and poor patient selection which are often not addressed following failed disease modification trials. Future directions to overcome these challenges such as reducing the heterogeneity of patient cohorts, identifying and utilising a pre-diagnostic population, embracing a personalised medicine approach and utilising novel trial designs may be required to ultimately fulfil the goal of conclusively demonstrating evidence of disease modification. Highlights: No disease modifying trials conducted to date have conclusively demonstrated evidence of disease modification. Improvements in trial design may increase the chance of detecting true disease modifying effects. Stratifying patient populations and embracing precision medicine may reduce patient heterogeneity. Mathematical disease modelling and improved statistical techniques can help reduce the inherent variability of data collection. Novel technology to remotely collect data in a continuous and quantitative manner allows more objective data collection.