Systematic in silico analysis of clinically tested drugs for reducing amyloid‐beta plaque accumulation in Alzheimer's disease
Name
alz.12312.pdf
Description
Published version
Size
3.84 MB
Format
Adobe PDF
Checksum (MD5)
cbcc28119ee7452df79911f8b12e2274
Author(s)
Madrasi, Kumpal
Das, Raibatak
Mohmmadabdul, Hafiz
Lin, Lin
Hyman, Bradley T
Lauffenburger, Douglas A
Albers, Mark W
Rissman, Robert A
Burke, John M
Apgar, Joshua F
Date Issued
2021
Journal
Alzheimer's and Dementia
Publisher
Wiley
Version
Final published version
Abstract
INTRODUCTION: Despite strong evidence linking amyloid beta (Aβ) to Alzheimer's disease, most clinical trials have shown no clinical efficacy for reasons that remain unclear. To understand why, we developed a quantitative systems pharmacology (QSP) model for seven therapeutics: aducanumab, crenezumab, solanezumab, bapineuzumab, elenbecestat, verubecestat, and semagacestat. METHODS: Ordinary differential equations were used to model the production, transport, and aggregation of Aβ; pharmacology of the drugs; and their impact on plaque. RESULTS: The calibrated model predicts that endogenous plaque turnover is slow, with an estimated half-life of 2.75 years. This is likely why beta-secretase inhibitors have a smaller effect on plaque reduction. Of the mechanisms tested, the model predicts binding to plaque and inducing antibody-dependent cellular phagocytosis is the best approach for plaque reduction. DISCUSSION: A QSP model can provide novel insights to clinical results. Our model explains the results of clinical trials and provides guidance for future therapeutic development.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1002/alz.12312
