Modeling the effects of ß1-adrenergic receptor blockers and polymorphisms on cardiac myocyte Ca2+ handling.

ß-Adrenergic receptor blockers (ß-blockers) are commonly used to treat heart failure, but the biologic mechanisms governing their efficacy are still poorly understood. The complexity of ß-adrenergic signaling coupled with the influence of receptor polymorphisms makes it difficult to intuit the effect of ß-blockers on cardiac physiology. While some studies indicate that ß-blockers are efficacious by inhibiting ß-adrenergic signaling, other studies suggest that they work by maintaining ß-adrenergic responsiveness. Here, we use a systems pharmacology approach to test the hypothesis that in ventricular myocytes, these two apparently conflicting mechanisms for ß-blocker efficacy can occur concurrently. We extended a computational model of the ß(1)-adrenergic pathway and excitation-contraction coupling to include detailed receptor interactions for 19 ligands. Model predictions, validated with Ca(2+) and Förster resonance energy transfer imaging of adult rat ventricular myocytes, surprisingly suggest that ß-blockers can both inhibit and maintain signaling depending on the magnitude of receptor stimulation. The balance of inhibition and maintenance of ß(1)-adrenergic signaling is predicted to depend on the specific ß-blocker (with greater responsiveness for metoprolol than carvedilol) and ß(1)-adrenergic receptor Arg389Gly polymorphisms.

Cardiac models in drug discovery and development: a review.

Cardiovascular diseases are among the leading causes of death in the developed world. Developing novel therapies for diseases like heart failure is crucial, but this is hampered by the high attrition rate in drug development. The withdrawal of drugs at the final hurdle of approval is mostly because of their unpredictable effects on normal cardiac rhythm. The advent of cardiac computational modeling in the last 5 decades has aided the understanding of heart function significantly. Recently, these models increasingly have been applied toward designing and understanding therapies for cardiac disease. This article will discuss how cellular models of electrophysiology, cell signaling, and metabolism have been used to investigate pharmacologic therapies for cardiac diseases including arrhythmia, ischemia, and heart failure.

Automated image analysis of cardiac myocyte Ca2+ dynamics.

Intracellular Ca(2+) dynamics act as a key link between the electrical and mechanical activity of the heart. Here we present a method for high-throughput measurement, automated cell segmentation and signal analysis of Ca(2+) transients in isolated adult ventricular myocytes. In addition to increasing experimental throughput ~10-fold compared to conventional approaches, this approach allows the study of individual cell-cell variability and relationships between Ca(2+) signaling and cell morphology.

Systems analysis of small signaling modules relevant to eight human diseases.

Using eight newly generated models relevant to addiction, Alzheimer's disease, cancer, diabetes, HIV, heart disease, malaria, and tuberculosis, we show that systems analysis of small (4-25 species), bounded protein signaling modules rapidly generates new quantitative knowledge from published experimental research. For example, our models show that tumor sclerosis complex (TSC) inhibitors may be more effective than the rapamycin (mTOR) inhibitors currently used to treat cancer, that HIV infection could be more effectively blocked by increasing production of the human innate immune response protein APOBEC3G, rather than targeting HIV's viral infectivity factor (Vif), and how peroxisome proliferator-activated receptor alpha (PPARα) agonists used to treat dyslipidemia would most effectively stimulate PPARα signaling if drug design were to increase agonist nucleoplasmic concentration, as opposed to increasing agonist binding affinity for PPARα. Comparative analysis of system-level properties for all eight modules showed that a significantly higher proportion of concentration parameters fall in the top 15th percentile sensitivity ranking than binding affinity parameters. In infectious disease modules, host networks were significantly more sensitive to virulence factor concentration parameters compared to all other concentration parameters. This work supports the future use of this approach for informing the next generation of experimental roadmaps for known diseases.