SUMMARY OF THE FUNDED PARENT GRANT Heart failure is defined as the inability of cardiac output to meet demand. Cardiac hypertrophy, defined as an increase in cardiomyocyte size and heart muscle mass, leads to maladaptive remodeling and is a significant precursor of heart failure. We aim to overcome the past obstacles of focusing on a single signaling molecule by employing a systems approach that considers the more extensive network of signaling interactions and FDA-approved drugs that are viable candidates for drug repurposing. Our overall goal is to identify drugs and network mechanisms as therapeutic targets to control cardiac hypertrophy. To achieve this goal, we will test the overall hypothesis that a systems pharmacology network model can accurately predict the context-dependent effects of drugs on cardiomyocyte hypertrophy in vitro and in vivo. In Specific Aim 1, we will apply a systems pharmacology model to predict drugs and drug combinations that cause context-dependent regulation of cardiomyocyte hypertrophy. We will develop a computational model that integrates the cardiomyocyte signaling network with the pharmacologic mechanisms of FDA-approved drugs. We will then use this model to predict the drug combinations and network mechanisms that inhibit cardiomyocyte hypertrophy under distinct environmental contexts. In Specific Aim 2, we will validate our model predictions of candidate drugs using cultured rat and human cardiomyocytes to test the context-dependent inhibition of cardiomyocyte hypertrophy. In Specific Aim 3, we will translate the model and cell- based experimental data to in vivo mouse models of cardiac hypertrophy and determine whether the modeling accurately predicts the effects of drugs in a context-dependent manner. Overall, these studies will establish a systems pharmacology model, new computational insights into how drugs modulate cardiac hypertrophy, and a wealth of new experimental data that will validate these predictions.