# Quantitative Systems Pharmacology Models of Anti‐Amyloid Treatments for Alzheimer's Disease: A Systematic Review

## TL;DR

This study reviews existing models used to study Alzheimer's disease treatments targeting amyloid, finding that while useful, these models need improvements in quality and reproducibility.

## Contribution

The study systematically reviews and evaluates the quality of published QSP models for anti-amyloid Alzheimer's therapies, highlighting reproducibility issues and opportunities for improvement.

## Key findings

- Seven published QSP models targeting amyloid in Alzheimer's were identified and analyzed.
- Model quality scores were low, ranging from 40% to 53% based on best practice criteria.
- None of the models provided executable code, highlighting reproducibility issues.

## Abstract

Quantitative systems pharmacology (QSP) models have emerged as useful tools for evaluating the efficacy of Alzheimer's disease (AD) therapies. Bringing together a clinical focus with the mechanistic detail of systems biology, QSP models are well suited to the complexity of AD and have been used to predict treatment outcomes and support regulatory submissions. Therapies targeting the amyloid pathway are prominent in the AD clinical trial landscape, with anti‐amyloid monoclonal antibodies representing the first approved disease‐modifying therapies. To inform and facilitate future QSP model development, a systematic review of published QSP models focused on amyloid‐targeting therapies for AD was completed. The PubMed and Web of Science databases were searched on February 1, 2025, identifying 540 candidate publications. Predefined exclusion and inclusion criteria were applied to identify seven published AD QSP models used to simulate treatment effects for one or more anti‐amyloid therapies. The structure, development, and predictions of the models were summarized. Shared and contrasting model features were identified across included models. A set of model quality features was scored against a checklist of 15 criteria adapted from “best practice” guidelines for QSP. Model quality scores were generally low, ranging from 40% to 53%. Key quality issues related to model validation and reproducibility were identified; in particular, none of the seven papers provided executable model code. This systematic review provides useful context to support ongoing efforts to develop and refine QSP models such that they may better inform therapeutic strategies for the treatment of AD.

What is the current knowledge on the topic?
○Quantitative systems pharmacology (QSP) models are valuable tools for evaluating Alzheimer's disease (AD) therapies, particularly anti‐amyloid treatments, and have been used to predict treatment outcomes and support regulatory submissions.
What question did this study address?
○This study systematically reviewed published QSP models focused on amyloid‐targeting AD therapies to ask to what extent they adhere to best practice guidelines, and what can be learned from their structural and predictive characteristics.
What does this study add to our knowledge?
○The review identified seven relevant QSP models and highlighted the wide range of model applications as well as key opportunities for improving model development, especially regarding reproducibility and extending model scope.
How might this change drug discovery, development, and/or therapeutics?
○By identifying strengths and weaknesses in current QSP models, the results of this systematic review can help inform future model development to enhance reliability, thereby improving the use of QSP in guiding AD therapeutic strategies.

What is the current knowledge on the topic?
○Quantitative systems pharmacology (QSP) models are valuable tools for evaluating Alzheimer's disease (AD) therapies, particularly anti‐amyloid treatments, and have been used to predict treatment outcomes and support regulatory submissions.

Quantitative systems pharmacology (QSP) models are valuable tools for evaluating Alzheimer's disease (AD) therapies, particularly anti‐amyloid treatments, and have been used to predict treatment outcomes and support regulatory submissions.

What question did this study address?
○This study systematically reviewed published QSP models focused on amyloid‐targeting AD therapies to ask to what extent they adhere to best practice guidelines, and what can be learned from their structural and predictive characteristics.

This study systematically reviewed published QSP models focused on amyloid‐targeting AD therapies to ask to what extent they adhere to best practice guidelines, and what can be learned from their structural and predictive characteristics.

What does this study add to our knowledge?
○The review identified seven relevant QSP models and highlighted the wide range of model applications as well as key opportunities for improving model development, especially regarding reproducibility and extending model scope.

The review identified seven relevant QSP models and highlighted the wide range of model applications as well as key opportunities for improving model development, especially regarding reproducibility and extending model scope.

How might this change drug discovery, development, and/or therapeutics?
○By identifying strengths and weaknesses in current QSP models, the results of this systematic review can help inform future model development to enhance reliability, thereby improving the use of QSP in guiding AD therapeutic strategies.

By identifying strengths and weaknesses in current QSP models, the results of this systematic review can help inform future model development to enhance reliability, thereby improving the use of QSP in guiding AD therapeutic strategies.