Linking Neuroproteomics to Predictive Brain Drug Delivery: A Physicochemically-Informed Modeling Framework for Silver Nanocarriers in Alzheimer’s and Parkinson’s Disease

Neurodegenerative disorders such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are characterized by region-specific molecular dysregulation, progressive neuronal loss, and impaired brain clearance mechanisms, collectively limiting the efficacy of regenerative therapeutics. In parallel, biomaterial-based drug delivery systems face critical challenges in achieving stable, targeted, and safe transport across the blood–brain barrier (BBB). Here, we propose a physicochemically informed computational framework that systematically links neuroproteomics to predictive brain drug delivery using silver nanocarriers. Public cerebrospinal fluid (CSF) proteomics and brain-region transcriptomics datasets are integrated to derive disease-specific molecular signatures, enabling classification of AD versus PD and prioritization of affected brain regions and signaling pathways associated with neurodegeneration and impaired clearance. These biological outputs are coupled to a reduced pharmacokinetic model incorporating blood–brain transport, interstitial exposure, and a parameterized ISF–CSF exchange term serving as a proxy for glymphatic function. This structure enables scenario-based simulations reflecting preserved versus impaired clearance dynamics. To ensure translational relevance, experimentally determined physicochemical properties of ready-made silver nanoparticles—including hydrodynamic size, polydispersity index, ζ-potential, optical stability, and stability in physiological media—are embedded as feasibility constraints within the modeling space. This integration enables stability-aware optimization of dose and release kinetics for a model regenerative factor under predefined safety boundaries. The resulting framework establishes a quantitative bridge between disease-specific molecular dysregulation and rational nanocarrier design, providing a scalable and data-driven blueprint for optimizing brain-directed regenerative therapies in AD and PD.