A major factor plaguing drug development is that there is no preclinical drug screen that can accurately predict unintended drug induced cardiac arrhythmias. The current approaches rely on substitute markers such as QT interval prolongation on the ECG. Unfortunately, QT prolongation is neither specific nor sensitive to indicate likelihood of arrhythmias. There is an urgent need to identify a new approach that can predict actual proarrhythmia rather than surrogate indicators. Mathematical modeling and simulation constitutes one of the most promising methodologies to reveal fundamental biological principles and mechanisms, model effects of interactions between system components and predict emergent drug effects. Thus, we propose the development of a novel multiscale approach based on drug-channel structural interactions and kinetics intended to predict drug induced cardiotoxicity in the context of: 1) preclinical drug screening, 2) drug rehabilitation, and 3) prediction of the intersection of drug effects and coexistent risk factors. Our underlying hypothesis is that the fundamental mode of drug interaction derived from each drug?s unique structure activity relationship determines the resultant effects on cardiac electrical activity in cells and tissue. By capturing these complex drug channel interactions in a model, we expect to be able to predict drug safety or electro-toxicity in the heart. We have brought together an expert team to assemble and test a new multiscale model framework that connects detailed mathematical models to predict atomic scale interactions of drugs on the promiscuous hERG potassium channel to functional scale predictions at the level of the channel, cell and tissue. Predictions from the atomic structure simulations will be used to inform the kinetic parameters of models that capture the complex dynamical interactions of drugs and ion channels. The computational components will then be studied in predictive models at the channel, cell and tissue scales to expose fundamental mechanisms and complex interactions underlying emergent behaviors. Experiments in mammalian cells and tissues will be undertaken to validate model predictions. Drug properties will be perturbed in models to rehabilitate dangerous drugs and reduce their potential toxicity. The multiscale model for prediction of cardiopharmacology that we will develop in this application will be applied to projects demonstrating its usefulness for efficacy or toxicity of drug treatments in the complex physiological system of the heart.

Public Health Relevance

Cardiotoxicity in the form of deadly abnormal rhythms is one of the most common and dangerous risks for drugs in development. There is an urgent need for new approaches to screen and predict the effects of drugs on cardiac rhythms. Our team proposes a new computer based model framework to predict drug effects from the level of the receptor interaction to the cardiac rhythm.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
Project #
Application #
Study Section
Electrical Signaling, Ion Transport, and Arrhythmias Study Section (ESTA)
Program Officer
Tjurmina, Olga A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of California Davis
Schools of Medicine
United States
Zip Code
Sepela, Rebecka J; Sack, Jon T (2018) Taming unruly chloride channel inhibitors with rational design. Proc Natl Acad Sci U S A 115:5311-5313
Wacker, Soren; Noskov, Sergei Yu (2018) Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel. Comput Toxicol 6:55-63
Agarwal, Shailesh R; Gratwohl, Jackson; Cozad, Mia et al. (2018) Compartmentalized cAMP Signaling Associated With Lipid Raft and Non-raft Membrane Domains in Adult Ventricular Myocytes. Front Pharmacol 9:332
Vorobyov, Igor; Clancy, Colleen E (2018) Sex, drugs, and funky rhythms. Heart Rhythm 15:485-486
Cranford, Jonathan P; O'Hara, Thomas J; Villongco, Christopher T et al. (2018) Efficient Computational Modeling of Human Ventricular Activation and Its Electrocardiographic Representation: A Sensitivity Study. Cardiovasc Eng Technol 9:447-467
DeMarco, Kevin R; Bekker, Slava; Clancy, Colleen E et al. (2018) Digging into Lipid Membrane Permeation for Cardiac Ion Channel Blocker d-Sotalol with All-Atom Simulations. Front Pharmacol 9:26
Perissinotti, Laura L; De Biase, Pablo M; Guo, Jiqing et al. (2018) Determinants of Isoform-Specific Gating Kinetics of hERG1 Channel: Combined Experimental and Simulation Study. Front Physiol 9:207
Yang, Pei-Chi; Perissinotti, Laura L; López-Redondo, Fernando et al. (2017) A multiscale computational modelling approach predicts mechanisms of female sex risk in the setting of arousal-induced arrhythmias. J Physiol 595:4695-4723
Miranda, Williams E; Ngo, Van A; Perissinotti, Laura L et al. (2017) Computational membrane biophysics: From ion channel interactions with drugs to cellular function. Biochim Biophys Acta Proteins Proteom 1865:1643-1653
Chiamvimonvat, Nipavan; Chen-Izu, Ye; Clancy, Colleen E et al. (2017) Potassium currents in the heart: functional roles in repolarization, arrhythmia and therapeutics. J Physiol 595:2229-2252

Showing the most recent 10 out of 17 publications