Mathematical Biosciences and Engineering, 2011, 8(3): 861-873. doi: 10.3934/mbe.2011.8.861.

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Defining candidate drug characteristics for Long-QT (LQT3) syndrome

1. Center for Biomedical Computing, Simula Research Laboratory, P.O. Box 134, Lysaker 1325
2. Cardiac Bioelectricity & Arrhythmia Center, Washington University, St. Louis, MO 63130-4899
3. Department of Bioengineering, University of California San Diego

Mutations of the SCN5A gene can significantly alter the function of cardiac myocyte sodium channels leading to increased risk of ventricular arrhythmia. Over the past decade, detailed Markov models of the action potential of cardiac cells have been developed. In such models, the effects of a drug can be treated as alterations in on- and off rates between open and inactivated states on one hand, and blocked states on the other hand. Our aim is to compute the rates specifying a drug in order to: (a) restore the steady-state open probability of the mutant channel to that of normal wild type channels; and (b) minimize the difference between whole cell currents in drugged mutant and wild type cells. The difference in the electrochemical state vector of the cell can be measured in a norm taking all components and their dynamical properties into account. Measured with this norm, the difference between the state of the mutant and wild-type cell was reduced by a factor of 36 after the drug was introduced and by factors of 4 over mexitiline and 25 over lidocaine. The results suggest the potential to synthesize more effective drugs based on mechanisms of action of existing compounds.
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Keywords optimization.; markov models; Cardiac arrhythmia; drug design

Citation: Aslak Tveito, Glenn T. Lines, Pan Li, Andrew McCulloch. Defining candidate drug characteristics for Long-QT (LQT3) syndrome. Mathematical Biosciences and Engineering, 2011, 8(3): 861-873. doi: 10.3934/mbe.2011.8.861

 

This article has been cited by

  • 1. Aslak Tveito, Glenn T. Lines, , Computing Characterizations of Drugs for Ion Channels and Receptors Using Markov Models, 2016, Chapter 1, 1, 10.1007/978-3-319-30030-6_1
  • 2. Karoline Horgmo Jæger, Verena Charwat, Bérénice Charrez, Henrik Finsberg, Mary M. Maleckar, Samuel Wall, Kevin E. Healy, Aslak Tveito, Improved Computational Identification of Drug Response Using Optical Measurements of Human Stem Cell Derived Cardiomyocytes in Microphysiological Systems, Frontiers in Pharmacology, 2020, 10, 10.3389/fphar.2019.01648

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