Network Approach to Drug Target Identification and Drug Combinations: Implications for cGMP-based Therapeutics

The complex nature of polygenic diseases encompasses a broad range of underlying pathologies arising from heterogeneous molecular mechanisms and exhibiting a diversity of disease phenotypes. This complexity can be formalized using molecular interaction networks (e.g., the protein-protein interaction network) and analyzed using the basic tenets of graph theory. Among the features of these network that can be discerned are included subnetworks that are specific to a particular disease (i.e., a disease module), unique mechanisms or pathways that govern the disease phenotype, and the identification of potential drug targets that exist within or near the disease module. In this presentation, I will discuss the computational approaches used to identify disease modules and drug targets, including approaches to drug repositioning or repurposing; experimental approaches to characterizing the dynamic interactions of drugs and their targets in real time in single cells; and efficient single-cell strategies for assessing combinations of drugs. These principles will be illustrated with examples from specific diseases, and their implications for cell signaling pathways, including cGMP signaling, considered. The strategies presented will ultimately contribute to and guide the ongoing evolution of precision medicine.