Parkinson's disease between old drugs and new digital therapies: A pharmacological review

Abstract

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the progressive loss of dopaminergic neurons in the substantia nigra and the accumulation of α-synuclein aggregates. Current pharmacological treatments, including levodopa and dopamine agonists, mainly offer symptomatic relief but do not modify disease progression. The heterogeneity of clinical manifestations and underlying molecular mechanisms has prompted a shift toward personalized medicine and innovative therapeutic strategies. Among emerging approaches, cell-based therapies — such as stem cell transplantation and induced pluripotent stem cells — aim to restore dopaminergic function and are under active investigation in preclinical and early clinical settings. In parallel, digital therapeutics and wearable technologies offer real-time, objective monitoring of motor and non-motor symptoms, enabling more accurate disease tracking and treatment optimization. These digital tools also facilitate remote patient management and adherence to therapy. Artificial intelligence (AI) has become increasingly integrated into PD research and care, with machine learning algorithms demonstrating high accuracy in early disease detection, symptom monitoring, and risk prediction. Notably, AI-based analyses of data from wearable sensors and digital biomarkers have identified prodromal signs of PD years before clinical diagnosis, offering promising avenues for preventive strategies. While no disease-modifying therapies are currently available, combining pharmacological treatments with advanced technologies and a deeper understanding of disease biology may pave the way for more effective and individualized interventions. The integration of AI, digital health, and cell-based therapies is a paradigm shift in the management and future treatment landscape of Parkinson's disease.