Mathematical Modeling of Protein Concentration Dynamics using CAR T-Cell Therapy on Alzheimer’s Disease

Alzheimer’s disease, a progressive neurodegenerative disorder characterized by the accumulation of amyloid-beta plaques and tau proteins, poses a significant challenge in modern medicine. CAR T-cell therapy, an innovative treatment approach originally developed for cancer, shows promise in targeting these pathological proteins to potentially reduce the disease progression. Current research is being done on how engineered CAR T-cells can specifically target amyloid-beta and tau proteins, offering a new approach to treatment. Mathematical modeling plays a crucial role in this exploration by simulating the dynamics of protein concentrations and CAR T-cell interactions, providing insights into treatment efficacy and optimizing therapeutic strategies. Integrating artificial intelligence (AI) and machine learning (ML) enhances this modeling by analyzing complex data, predicting individual patient responses, and refining treatment protocols in real-world scenarios mimicking the actual biological environment. This study offers a comprehensive view of mathematical modeling for protein concentration dynamics in Alzheimer’s disease, using CAR T-cell therapy combined with AI and ML algorithms to optimize treatment strategies.