Summary
Heart failure is defined as the inability of cardiac output to meet demand. Moreover, heart failure causes
significant morbidity and mortality, affecting over 6 million Americans, with ~50% mortality at five years despite
current pharmacologic and device-based therapies. 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. Thus, intervening early during cardiac hypertrophy has the potential to improve the health and
outcomes of patients. For decades, investigations have characterized individual intracellular molecular
regulators of cardiac hypertrophy; however, effective clinical therapies specifically targeting cardiac hypertrophy
remain elusive. 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.