As indicated by draft guidance ICH STB, two major unsolved problems for the FDA and pharmaceutical companies are how to identify effective indices for proarrhythmic activity of new therapeutics and how to integrate available data to determine the mechanism by which these drugs might contribute to risk of arrhythmia. Gene Network Sciences (GNS) will address these problems through the development of the VisualHeart, a software platform that allows ion current and action potential (AP) data to be incorporated into a mechanistic simulation of cardiac electrical activity. This platform will connect data on a drug's effect at the molecular/ion channel level to tissue-level properties to determine: 1) Proarrhythmic markers. The platform will generate proarrhythmic markers by quantifying a drug's effect on ion channels, on the AP, and on wave propagation in the ventricle. 2) Mechanism of action. The platform will help identify specific, quantitative hypotheses for the mechanism of action by which a drug may alter the cardiac AP and electrocardiogram (ECG). The platform will determine these deliverables by using ion current data to generate data-driven models of cardiac ion currents. These models will be incorporated into myocyte models that can predict the drug's effect on the cardiac AP. AP data can also be used to validate and refine the models. Finally, the myocyte models will be embedded into a model of electrical wave propagation in the ventricle that can predict a drug's effect on the ECG. This platform could resolve potential discrepancies in data, for example, a drug that exhibits HERG activity but results in little to no QT prolongation. Thus, the simulations will not replace the role of experiments in safety testing; rather, they will help to direct the collection of preclinical and clinical data and to improve the interpretation of these assays. GNS will commercialize this platform through the establishment of alliances with pharmaceutical companies and contract research organizations. The structure of these alliances will parallel alliances that GNS has established around a technology platform for oncology drug discovery and development. Phase I of the project focused on developing a software tool for exploring models of the cardiac AP. Phase II aims will focus on improving the power, flexibility, and ease of use of the single cell simulation tools in the platform, and on incorporating algorithms for studying spatially extended models and relating simulation results to cardiac risk assessment. A set of validation experiments will be used to test each step of technology development. By contributing to a better understanding of how compounds affect cardiac electrical activity, this project is relevant to the mission of the NHLBI. This research is relevant to public health because a software platform for simulating computer models of the electrical activity of the heart will help scientists and drug companies to better understand how drugs affect the heart. This improved understanding will help lead to safer and better treatments for patients. ? ? ?

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Small Business Innovation Research Grants (SBIR) - Phase II (R44)
Project #
5R44HL077938-04
Application #
7483725
Study Section
Special Emphasis Panel (ZRG1-CVS-K (10))
Program Officer
Larkin, Jennie E
Project Start
2004-09-15
Project End
2009-12-30
Budget Start
2008-07-01
Budget End
2009-12-30
Support Year
4
Fiscal Year
2008
Total Cost
$562,827
Indirect Cost
Name
Gene Network Sciences, Inc.
Department
Type
DUNS #
033322194
City
Cambridge
State
MA
Country
United States
Zip Code
02141
Zhou, Qinlian; Zygmunt, Andrew C; Cordeiro, Jonathan M et al. (2009) Identification of Ikr kinetics and drug binding in native myocytes. Ann Biomed Eng 37:1294-309
Siso-Nadal, Fernando; Otani, Niels F; Gilmour Jr, Robert F et al. (2008) Boundary-induced reentry in homogeneous excitable tissue. Phys Rev E Stat Nonlin Soft Matter Phys 78:031925
Gilmour Jr, Robert F; Gelzer, Anna R; Otani, Niels F (2007) Cardiac electrical dynamics: maximizing dynamical heterogeneity. J Electrocardiol 40:S51-5