Drug Development.

Area of Science:

• Pharmacology
• Systems Biology
• Neuroscience

Background:

• Quantitative systems pharmacology (QSP) models are valuable for evaluating Alzheimer's disease (AD) therapies.
• These models integrate clinical and mechanistic insights, suiting AD's complexity for predicting outcomes and supporting regulatory processes.
• Anti-amyloid therapies, including monoclonal antibodies, have been central to recent AD drug development.

Purpose of the Study:

• To systematically review published QSP models for amyloid-targeting AD therapies.
• To inform and facilitate future QSP model development for AD.
• To identify shared and contrasting features and assess the quality of existing models.

Main Methods:

• A systematic literature review of PubMed and Web of Science databases was conducted.
• Search results were screened at title, abstract, and full-text levels using predefined inclusion/exclusion criteria.
• Nine distinct QSP models for AD were identified, and their structure, development, predictions, and quality were summarized and scored.

Main Results:

• Nine QSP models simulating anti-amyloid therapies for AD were identified from 494 candidate publications.
• Seven models used compartmental approaches for amyloid aggregation/clearance, while two employed network models.
• Model quality scores were generally low (6-9/15), with significant issues in validation and reproducibility, notably the absence of executable model code.

Conclusions:

• The review of nine QSP models provides context for refining future model development in AD.
• Enhancing focus on model quality, particularly validation and reproducibility, is crucial.
• Improved QSP models will better inform therapeutic strategies for Alzheimer's disease.