Beyond Transgenic Mice: Emerging Models and Translational Strategies in Alzheimer's Disease

Paula Alexandra Lopes1,2, José L Guil-Guerrero3

  • 1CIISA-Centro de Investigação Interdisciplinar em Sanidade Animal, Faculdade de Medicina Veterinária, Universidade de Lisboa, 1300-477 Lisboa, Portugal.

Insights

Developing better Alzheimer's disease (AD) models is crucial for finding effective treatments. This review compares traditional mouse models with newer alternatives, advocating for a combined approach to accelerate therapeutic discovery for AD.

Area of Science:

  • Neuroscience
  • Genetics
  • Pharmacology

Background:

  • Alzheimer's disease (AD) is a major cause of dementia, with limited disease-modifying treatments available.
  • Current experimental models, particularly traditional transgenic mice, often fail to fully replicate the complexity of sporadic, late-onset AD.
  • This gap in translational relevance hinders the discovery of effective therapies.

Purpose of the Study:

  • To critically assess and compare existing murine and alternative models for Alzheimer's disease (AD) research.
  • To identify the strengths, limitations, and future directions for developing more translatable AD models.
  • To guide enhanced therapeutic discovery and clinical translation for AD.

Main Methods:

  • Comprehensive review and comparative analysis of traditional transgenic mouse models and alternative AD models (zebrafish, Drosophila, C. elegans, non-human primates, human brain organoids).
  • Discussion of emerging innovations including genetic engineering, neuroimaging, computational modeling, and drug repurposing.
  • Consideration of ethical implications and equitable access to diagnostics and treatments.

Main Results:

  • Traditional mouse models offer insights into amyloid-beta and tau pathologies but lack the complexity for sporadic, late-onset AD.
  • Alternative models provide complementary insights and diverse experimental advantages, showing promise for broader AD research.
  • Innovations in technology and a multi-model approach are essential for improving predictive power and accelerating clinical translation.

Conclusions:

  • A multi-model, multidisciplinary strategy integrating diverse approaches is advocated to enhance the predictive power of AD research.
  • Development of more accurate, effective, and human-relevant models is critical for combating Alzheimer's disease.
  • Emphasis on ethical considerations and equitable access is necessary for advancing AD diagnostics and therapeutics.