Redefining Parkinson's Care: The Promise of Nanotechnology and Artificial Intelligence

Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by the depletion of dopaminergic neurons and the buildup of α-synuclein aggregates, resulting in damaging motor and non-motor symptoms. Conventional therapies, comprising levodopa and dopamine agonists, give symptomatic relief but fail to terminate disease progression and are associated with long-term complications.

Relevant review papers and articles from the past were investigated. Certain factors, including early disease diagnosis, therapeutic efficacy in PD models, and predictive modeling of drug-nanoparticle interactions, were considered during the conduct of this research. This literature review is a comprehensive narrative of research articles obtained from various platforms, namely Scopus, PubMed, Google Scholar, and Research Gate. Inclusion and exclusion criteria were applied to filter out the suitable materials.

Artificial intelligence (AI) is emerging as a complementary tool, facilitating design-optimized nanocarriers and predicting drug interactions while emphasizing liposomes and metallic nanoparticles as important platforms for dopamine replacement, gene therapy, and neuroinflammation modulation.

Despite the progress made so far, clinical translation still has considerable challenges to overcome, including nanoparticle toxicity, scalability, long-term safety, and variability in AI model performance. Integration of AI with biologically relevant PK/PD models and personalized nanomedicine strategies should overcome such existing gaps and enhance the therapeutic reliability of nanomedicine.

This review summarizes current advances in nanomedicine and AI-driven approaches for PD, discussing their mechanisms, therapeutic targets, and future perspectives in achieving disease-modifying interventions.