Precision Neurology in Alzheimer's Disease: Genomics to Drug Optimization for Effective Therapeutic Intervention.

Alzheimer's disease (AD) exhibits high clinical and biological heterogeneity, which complicates the development of one-size-fits-all therapeutic strategies. Standard trial designs can mask subgroup-specific effects, highlighting the need to move towards precision neurology entailing multisystem genomics, biomarkers, and artificial intelligence (AI) integrative signals. The aim of the study is to critically synthesize and evaluate emerging strategies for drug optimization and individual therapeutic algorithms informed by genomic architecture, pharmacogenomics (PGx), and polygenic risk scores (PRS) with the opinion to resolve the controversy and unanswered question. Amplify the convergence into amyloid processing, lipid metabolism, and innate immunity seen from monogenic mutations and over 80 GWAS risk loci. Genotyping of CYP2D6 and CYP2C19 variants permits pharmacogenomic stratifications for the optimization of cholinesterase inhibitor and psychotropic dosing, thereby reducing adverse events. Variants of the alleles APOE ε4 and immune-related factors (e.g., FCGR3A) are significant in predicting the effects of ARIA associated with amyloid on anti-amyloid immunotherapy outcomes. PRS and deep phenotyping (including digital and fluid-based biomarkers) allow for the identification of unique biological endotypes (e.g., tau-dominant vs. vascular-inflammatory) that improve clinical trial enrichment and prognostication. This framework delineates molecular endotypes and translates CYP2D6 or APOE stratification into drug dosing and ARIA risk predictions. These biomarker-driven strategies shift AD management from a passive, reactive therapeutic scheme to an active, personalized one. Utilization of high-dimensional data streams via AI-driven learning health systems converts AD management from a reactive, symptom-based model to that which is proactive and hallmark biomarker-driven precision.