Progress towards improving mouse models for preclinical testing of treatments for Alzheimer’s disease

Alzheimer’s disease (AD) research has accomplished significant strides in recent years, particularly with the first regulatory approvals of disease-modifying therapies and a comprehensive revision of disease diagnostic criteria. Both developments place new demands on preclinical mouse models used to test potential treatments. This review summarizes recent progress in optimizing mouse models for Alzheimer’s research, using the NIA-funded Model Organism Development and Evaluation for Late-onset Alzheimer’s Disease (MODEL-AD) program as a case study, with specific focus on their relevance for preclinical therapeutic testing, their genetic and molecular fidelity to the human disease, and the contribution of the MODEL-AD program. Key advancements discussed include the shift from transgenic overexpression models to knock-in and genome-replacement strategies; the development of late-onset Alzheimer’s models (LOAD), LOAD1, LOAD2, LOAD3 and related LOAD-platform mice expressing humanized APOE, APP, and MAPT; the integration of environmental risk factors such as high-fat diet and toxic metal exposure into gene x environment interaction models; detection of prodromal stages of disease using multi-omics signatures; and the use of clinically relevant biomarkers, including plasma NfL, GFAP, and p-tau217, as cross-species translational endpoints. We discuss the importance of diverse genetic backgrounds, the contribution of the AD Knowledge Portal as an open-science data-sharing resource, and the principles of rigorous preclinical study design required to maximize translational success. We also discuss the limitations of these models and the evidence still needed to establish their predictive validity for therapeutics. These advances do not yet amount to validated models of late-onset AD, but they give the field better tools and frameworks for selecting and validating mouse models for specific human mechanisms and therapeutic questions.