Neurodegenerative diseases (NDs), including Alzheimer’s, Parkinson’s, Huntington’s, amyotrophic lateral sclerosis, and multiple sclerosis, share a common pathogenic signature: disrupted proteostasis driven by impaired autophagy and maladaptive exosome dynamics. Under normal conditions, autophagy maintains neuronal homeostasis by clearing misfolded proteins and damaged organelles, while exosomes mediate neuroglial communication. When autophagic flux is impaired or lysosomal function is compromised, intracellular cargo handling can shift toward secretion, and undegraded cargo may be redirected into exosomes/EVs, which disseminate pathogenic proteins such as amyloid-β, tau, α-synuclein, and TDP-43, a phenomenon reported in several experimental models and proposed to contribute to intercellular spread of pathology. This dual dysregulation amplifies neuroinflammation, demyelination, and progressive neuronal loss. Pharmacological strategies aimed at restoring the autophagy exosome axis are gaining traction. Agents such as rapamycin and resveratrol enhance autophagic flux, whereas engineered or stem-cell-derived exosomes delivering siRNAs, neurotrophic factors, or anti-inflammatory microRNAs show promise in preclinical neuroprotection and immune modulation. However, translational barriers remain, including safety, biodistribution, dosing, and standardization. Emerging artificial intelligence (AI) and machine learning (ML) frameworks can accelerate translation by integrating multi-omics and exosomal biomarker datasets for early diagnosis, patient stratification, and therapy optimization. Deep learning and generative modeling may further enable rational drug design to fine-tune autophagy and engineer targeted exosome delivery to the brain. Collectively, these advances position the autophagy–exosome axis as an integrative framework linking intracellular clearance with intercellular signaling, with emerging diagnostic and therapeutic implications for neurodegenerative disorders.
Sedighi et al. (Sun,) studied this question.