Abstract

Introduction: Cardiomyocyte (CM) hypertrophy is predictive of heart failure, however there are no clinical therapies that target its intracellular pathways. Hypertrophy is a complex process involving numerous neurohormonal and cytokine inputs, resulting in context-dependent responses that determine CM growth. In the face of this complexity, it is critical that computational models are developed. Accurate predictions of drug activity in CM hypertrophy will require a pharmacological model that is developed with and validated against experimental data. Hypothesis: We test the hypothesis that our in silico pharmacological model accurately predicts drugs that inhibit cardiac hypertrophy as well as the context-dependent mechanisms by which they work. Methods: Here we employ a previously published computational model of cardiac hypertrophy signaling. This model utilizes logic-based ordinary differential equations to simulate a network of 106 nodes. Using the DrugBank database, we constructed a pipeline for simulating FDA-approved drugs within this hypertrophy network under multiple environmental contexts. The predicted outcomes of the model were then compared to measured phenotypes from experimental findings in literature. Results: Predicted outcomes of our model were successfully validated against 29 out of 36 distinct experiments described in literature. These simulations identify the optimal drug types that inhibit hypertrophy for each of 17 different stimuli. Sensitivity analyses performed by simulating knockdowns in our model reveals context-dependent mechanisms predicted for 51 drug types. These predictions confirmed, for example, the role of celecoxib in inhibiting CM hypertrophy induced by isoproterenol. Mechanistic analysis suggests celecoxib prevents protein kinase B (Akt) inhibition of glycogen synthase kinase 3 beta (GSK3β), consistent with literature. Conclusions: Our pharmacological model accurately predicts FDA-approved drugs that show in vitro inhibition of CM hypertrophy.

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