NeuralCipher Research overview
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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.
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