SUMMARY
# Biomarker-Guided Sequential Therapy Selection in Alzheimer's Disease
## Background and Rationale
Alzheimer's disease (AD) represents a complex neurodegenerative disorder with heterogeneous pathophysiology, necessitating personalized therapeutic approaches. Current treatment strategies employ a one-size-fits-all paradigm that fails to account for individual patient variations in disease progression, biomarker profiles, and therapeutic responsiveness. This biomarker-guided sequential therapy sel
METHODOLOGY NOTES
Phase 1 (Months 1-3): Recruit 400 AD patients (mild-moderate stages) across 8 clinical sites. Obtain comprehensive baseline biomarker profiles including CSF amyloid-beta42, phosphorylated tau, neurofilament light chain via lumbar puncture, plasma p-tau181/217 via Simoa assays, and 18F-flortaucipir PET imaging. Conduct neuropsychological assessments using ADAS-Cog13, CDR-SB, and MMSE. Phase 2 (Months 4-6): Randomize participants 1:1 to biomarker-guided therapy selection versus standard care. Apply machine learning algorithm incorporating biomarker data, APOE genotype, and cognitive profiles to assign optimal therapy: amyloid-targeting (aducanumab/lecanemab), tau-directed (semorinemab), cholinesterase inhibitors (donepezil/rivastigmine), or combination protocols. Control group receives standard cholinesterase inhibitor therapy. Phase 3 (Months 7-18): Implement assigned therapies with standardized dosing protocols. Conduct biomarker reassessments every 3 months using blood-based assays an