A multimodal approach to accelerate drug discovery and development in Alzheimer’s disease

Alzheimer’s disease (AD) is a major societal challenge, impacting up to one third of the population over 85-years old.

The European Commission and various international bodies have repeatedly fostered research initiatives to prevent the development or stem the progression of the disease.

Several recent phase 3 clinical trials have failed to show any efficacy in slowing disease progression, calling into question the current drug targets. This project will use genetic data to identify new AD-relevant molecular pathways and their associated drug targets.

Given the increased risk of AD in women, we will apply our innovative analyses not only to autosomal variants but also to X-chromosome variants as well.

The mechanistic effects of genetic variants on pathogenesis will be delineated using gene expression from post-mortem brain tissue and AD biomarkers derived from multimodal brain imaging studies.

These in-vivo PET and MRI biomarkers are essential for enrolling patients correctly in clinical trials and assessing the effect of treatments on disease progression.

We will assess whether a polygenic risk score, based on thousands of genetic variants, will be useful in predicting an individual’s clinical and biomarker progression over time.

This project is markedly interdisciplinary in nature between its analysis of multimodal brain imaging and genomics data, and the combination of big data analysis guided by expert medical knowledge of the pathogenesis.

Results have the potential to (i) identify new drug targets and (ii) strengthen clinical trial design, thereby speeding the development of drugs for the prevention and treatment of AD.

This fellowship represents a unique opportunity to transfer knowledge and analysis methods of next generation genomics AD data from one of the leading US groups in integrating multimodal imaging and genomics data to the European AD research community.