Development of Research Foundation for Comprehensive Articulation of Drug Effects

As unexpected adverse events and successful drug repositioning have shown, drug effects are complex and include aspects not recognized by developers. How can we understand these unrecognized drug effects? Drug effects can be numerized by encompassing biological responses to drugs. For instance, the transcriptome data of cultured cells and toxicopathological images of mice treated with a compound represent the effects of the compound in vitro and in vivo, respectively. As a next step, we focused on pattern recognition, a data science framework to extract essentially important low-dimensional latent variables from high-dimensional observed data such as latent variable models. Latent variables are low-dimensional, allowing us to visualize drug effects in an easily recognizable form, such as a radar chart. This bird's-eye view of drug effects enables us to compare them with existing knowledge, potentially articulating the effects of drugs as the known knowns and known unknowns. We believe that the three-step strategy of numerization, visualization, and articulation will allow us to understand drug effects comprehensively, and we are currently verifying this approach. In this review, we will introduce these candidate studies and hope to share our interest in "pattern recognition of biological responses," the pillar of our group.