Data-Driven Modeling of Amyloid-beta Targeted Antibodies for Alzheimer's Disease

Abstract: Alzheimer's disease (AD) is driven by the accumulation of amyloid-beta (Abeta) proteins in the brain, leading to memory loss and cognitive decline. While monoclonal antibodies targeting Abetahave been approved, optimizing their use to maximize benefits while minimizing side effects remains a challenge. This study develops a mathematical model to describe Abeta aggregation, capturing its progression from monomers to toxic oligomers, protofibrils, and fibrils using mass-action kinetics and coarse-grained modeling. The model is calibrated with experimental data, incorporating parameter estimation and sensitivity analysis to ensure accuracy. An optimal control framework is introduced to determine the best drug dosing strategy that reduces toxic Abeta aggregates while minimizing adverse effects, such as amyloid-related imaging abnormalities (ARIA). Results indicate that Donanemab achieves the greatest reduction in fibrils. This work provides a quantitative framework for optimizing AD treatment strategies, offering insights into balancing therapeutic efficacy and safety.