We propose that blood-based biomarker discovery and development should be an inherent part of drug discovery and development programs throughout all stages from preclinical to clinical in a consequent co-development process that allows objective go/no-go decisions. Traditionally, this has not been achieved with very negative consequences for decision making within Alzheimer’s Disease drug development programs.
The APMI is currently focused on investigating the existence of a characteristic molecular “Alzheimer’s Disease profile” (or molecular “signature”), i.e., a specific set of fluid (CSF and blood) molecules that may serve at several context-of-use.
Neurochemical fluid biomarker-based research is focused on the effort to in-vivo track the pathophysiological mechanisms occurring in Alzheimer’s Disease, such as reflected by inflammatory markers (such as YKL-40, TNF receptor complex, the IL-6 receptor complex, ferritin), axonal and neurodegeneration markers such as neurofilament light chain (NFL) protein, neurogranin, alpha-synuclein, total and phosphorylated tau protein (ser199, thr181, thr231), amyloid beta-antibodies and amyloid beta-oligomers (correlation with cognitive decline), TACE and BACE1 functional proteins, microvascular damage and endothelial markers (in brain, CSF and plasma). Validation of biomarkers against post-mortem histochemical findings (e.g., p-tau thr231 correlation with regional brain tangle density).
Novel structural MR-imaging biomarkers have been introduced and systematically validated from mono-center methodological studies to multi-site validation. Structural brain changes induced by genetic stratification in healthy adults and normal aging (e.g., APOE effects on hippocampal atrophy). Multivariate analysis tools were developed to track white matter changes, cortical thickness analysis, voxel-based DTI analysis of the whole, demonstrating a specific Alzheimer’s Disease pattern of fractional anisotropy and identifying anatomical neural networks using advanced tractography within the brain. Application of machine learning algorithms, voxel- and deformation-based morphometry. Regarding the functional MRI assessment, we developed connectivity-related approaches that showed brain changes in subjects at risk of Alzheimer’s Disease before the onset of dementia. In addition, metabolic imaging focused on studies at rest and activation using FDG-, Amyloid-, and Tau-PET tracers. It is planned to expand studies using ligands to track inflammation and other mechanisms. Current neuroimaging research focuses on the understanding how the brain constructs structural and functional networks of interacting regions to perform cognitive tasks, especially those associated with memory, attention and language, and how these networks are altered during aging and neurodegenerative brain diseases.
The development of high-throughput technologies such as next generation sequencing (NGS) combined with highly sophisticated bioinformatics software for data analysis/visualization allowed investigating whole genomes, transcriptomes, proteomes, and metabolomes in unprecedented detail. Genome-wide association studies (GWAS) enabled to map more than 20 common genetic variants associated with Alzheimer’s Disease. Moreover, recent NGS-based studies led to identify several rare variants exerting large effects on Alzheimer’s Disease risk, thus indicating that not only common variants with small effect sizes, but also rare or low-frequency coding variants with moderate-to-large effect sizes contribute to Alzheimer’s Disease risk. These findings certainly point to specific pathophysiological pathways, namely Aβ and tau pathology, immune response and inflammation, cell migration, microglial/myeloid cell function, hippocampal synaptic function, cytoskeletal function and axonal transport, gene expression regulation, and post-translational modifications. Exploring these definite molecular pathophysiological pathways as major mechanisms involved in Alzheimer’s Disease etiology provides substantial information for elucidating the pathophysiology of the disease and helps identifying targets for prevention and treatment.