# Mechanistic modeling of amyloid dynamics relating to Alzheimer's disease progression

## TL;DR

This paper presents a detailed model of Alzheimer's disease progression that could help in early diagnosis and personalized treatment.

## Contribution

The novel contribution is an integrated mechanistic model of Alzheimer's progression combining amyloid dynamics and biomarker kinetics.

## Key findings

- The model integrates APP processing, Aβ peptide generation, aggregation, transport, and clearance mechanisms.
- It provides a framework for personalized precision neurology and pre-symptomatic diagnosis of Alzheimer's disease.
- The model supports the development of optimal therapeutic interventions for Alzheimer's.

## Abstract

The use of mechanistic models to support personalized medicine and precision diagnostics offers transformative potential for neurology. In this study, we developed a mechanistic model of Alzheimer's Disease progression (mAD) that integrates amyloid precursor protein (APP) processing, Aβ peptide generation, Aβ aggregation pathway modeling, Aβ transport, and whole-body biomarker kinetics (BxK) of Aβ40 and Aβ42 peptides, including enzymatic and microglial clearance mechanisms. The purpose of this work was to formulate an integrated, multiscale quantitative systems pharmacology (QSP) mechanistic model of Alzheimer's progression to advance neuroscience QSP frameworks. The model described in this work provides a basis for personalized precision neurology with the potential to facilitate pre-symptomatic AD diagnosis, thereby establishing early prevention strategies, and accelerating identification of optimal therapeutic interventions.