Research

A deep understanding of disease mechanisms provides the foundation for effective identification of biomarkers and therapeutic targets in neurological and psychiatric disorders. New technologies, datasets and computational methods have vastly improved our ability to analyze samples beginning with identification of genetic associations, causal DNA variants, directionality of effect and genes linked to these variants.

Next, it will be important to understand the functional effects of disease-associated genes and sequence variants in relevant cells and tissues, in addition to animal models. Such cells and tissues should include model systems with human genetic backgrounds, such as induced pluripotent stem cells (iPSCs) reprogrammed into relevant cell types, human brain organoids and xenotransplantation of human cellular models into the brains of model organisms.

When integrated with data from single cell and other multiomics technologies, genetic information also enables identification of cell types and cell states involved in disease pathophysiology. Such studies generate large amounts of data, the integration of which requires sophisticated analytical approaches, including machine learning. Analyses designed to identify disease mechanisms should be validated in patients and patient samples. Importantly, such analyses should be used to nominate biomarkers to improve stratification of patients and clinical trial design and to identify genetically or mechanistically validated therapeutic targets.

These approaches and emerging tools should facilitate demonstration of target engagement in clinical trials, as described below in an adaptation from Krainc et al, Science Translational Medicine, 2023.