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Review
. 2025 Apr 27:1877718X251330839.
doi: 10.1177/1877718X251330839. Online ahead of print.

From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease

Cen Cong  1 , Madison Milne-Ives  1   2 , Ananya Ananthakrishnan  1 , Walter Maetzler  3 , Edward Meinert  1   4
Affiliations
Free article
Review

From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease

Cen Cong et al. J Parkinsons Dis. .
Free article

Abstract

Recent years have seen successes in symptomatic drugs for Parkinson's disease, but the development of treatments for stopping disease progression continues to fail in clinical drug trials, largely due to the lack of clinical efficacy of drugs. This may be related to limited understanding of disease mechanisms, data heterogeneity, poor target screening and candidate selection, challenges in determining optimal dosage levels, reliance on animal models, insufficient patient participation, and lack of drug adherence in trials. Most of the recent applications of digital health technologies and artificial intelligence (AI)-based tools focused mainly on stages before clinical drug trials. Recent applications used AI-based algorithms or models to discover novel targets, inhibitors and indications, recommend drug candidates and drug dosage, and promote remote data collection. This paper reviews the state of the literature and highlights strengths and limitations in digital approaches to drug discovery and development for Parkinson's disease from 2021 to 2024, and offers recommendations for future research and practice for the success of drug clinical trials.

Keywords: Parkinson's disease; clinical trial; drug discovery; technology.

Plain language summary

In recent years, there have been successes in developing drugs that can help people with Parkinson's disease manage their symptoms but developing drugs that can actually stop the disease from getting worse has been a continuing challenge. Artificial intelligence (AI) has shown promise in improving the success of drug discovery and development for Parkinson's disease. This paper provided an overview of the existing challenges to successful drug trials for Parkinson's disease, recent digital approaches to improve trial success, and remaining challenges that need to be addressed in the future. We found that digital health technologies and AI-based tools were useful for improving the success of clinical drug trials by finding new drug targets and suitable candidates, improving efficiency in pre-clinical evaluation, and facilitating more convenient drug monitoring. While they are helpful in driving innovation and efficiency, drug clinical trials still continue to fail. This may be related to several factors, such as a limited understanding of disease mechanisms, a lack of animal models that can mimic Parkinson's disease and limited real-world patient data for algorithm training. The use of these technologies in clinical drug trials is in its early stages. More research is still needed better to understand the root causes of Parkinson's disease. Researchers are also encouraged to explore how to use existing digital tools to help with drug development at later stages, like large-scale clinical trials and monitoring of drugs after they are approved.

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