Optimization of an in silico cardiac cell model for proarrhythmia risk assessment

Sara Dutta, Kelly C. Chang, Kylie A. Beattie, Jiansong Sheng, Phu N. Tran, Wendy Wu, Min Wu, David G. Strauss, Thomas Colatsky, Zhihua Li

Research output: Contribution to journalArticle

29 Citations (Scopus)

Abstract

Drug-induced Torsade-de-Pointes (TdP) has been responsible for the withdrawal of many drugs from the market and is therefore of major concern to global regulatory agencies and the pharmaceutical industry. The Comprehensive in vitro Proarrhythmia Assay (CiPA) was proposed to improve prediction of TdP risk, using in silico models and in vitro multi-channel pharmacology data as integral parts of this initiative. Previously, we reported that combining dynamic interactions between drugs and the rapid delayed rectifier potassium current (IKr) with multi-channel pharmacology is important for TdP risk classification, and we modified the original O'Hara Rudy ventricular cell mathematical model to include a Markov model of IKr to represent dynamic drug-IKr interactions (IKr-dynamic ORd model). We also developed a novel metric that could separate drugs with different TdP liabilities at high concentrations based on total electronic charge carried by the major inward ionic currents during the action potential. In this study, we further optimized the IKr-dynamic ORd model by refining model parameters using published human cardiomyocyte experimental data under control and drug block conditions. Using this optimized model and manual patch clamp data, we developed an updated version of the metric that quantifies the net electronic charge carried by major inward and outward ionic currents during the steady state action potential, which could classify the level of drug-induced TdP risk across a wide range of concentrations and pacing rates. We also established a framework to quantitatively evaluate a system's robustness against the induction of early afterdepolarizations (EADs), and demonstrated that the new metric is correlated with the cell's robustness to the pro-EAD perturbation of IKr conductance reduction. In summary, in this work we present an optimized model that is more consistent with experimental data, an improved metric that can classify drugs at concentrations both near and higher than clinical exposure, and a physiological framework to check the relationship between a metric and EAD. These findings provide a solid foundation for using in silico models for the regulatory assessment of TdP risk under the CiPA paradigm.

Original languageEnglish (US)
Article number616
JournalFrontiers in Physiology
Volume8
Issue numberAUG
DOIs
StatePublished - Aug 23 2017
Externally publishedYes

Fingerprint

Torsades de Pointes
Computer Simulation
Pharmaceutical Preparations
Drug Interactions
Action Potentials
Pharmacology
Drug and Narcotic Control
Drug Industry
Cardiac Myocytes
Potassium
Theoretical Models
In Vitro Techniques

Keywords

  • Comprehensive in vitro Proarrhythmia Assay (CiPA)
  • Drug block
  • In silico cardiac cell model
  • Model optimization
  • Proarrythmia risk
  • Rapid delayed rectifier potassium current (IKr)
  • Torsade-de-Pointes (TdP)

ASJC Scopus subject areas

  • Physiology
  • Physiology (medical)

Cite this

Dutta, S., Chang, K. C., Beattie, K. A., Sheng, J., Tran, P. N., Wu, W., ... Li, Z. (2017). Optimization of an in silico cardiac cell model for proarrhythmia risk assessment. Frontiers in Physiology, 8(AUG), [616]. https://doi.org/10.3389/fphys.2017.00616

Optimization of an in silico cardiac cell model for proarrhythmia risk assessment. / Dutta, Sara; Chang, Kelly C.; Beattie, Kylie A.; Sheng, Jiansong; Tran, Phu N.; Wu, Wendy; Wu, Min; Strauss, David G.; Colatsky, Thomas; Li, Zhihua.

In: Frontiers in Physiology, Vol. 8, No. AUG, 616, 23.08.2017.

Research output: Contribution to journalArticle

Dutta, S, Chang, KC, Beattie, KA, Sheng, J, Tran, PN, Wu, W, Wu, M, Strauss, DG, Colatsky, T & Li, Z 2017, 'Optimization of an in silico cardiac cell model for proarrhythmia risk assessment', Frontiers in Physiology, vol. 8, no. AUG, 616. https://doi.org/10.3389/fphys.2017.00616
Dutta, Sara ; Chang, Kelly C. ; Beattie, Kylie A. ; Sheng, Jiansong ; Tran, Phu N. ; Wu, Wendy ; Wu, Min ; Strauss, David G. ; Colatsky, Thomas ; Li, Zhihua. / Optimization of an in silico cardiac cell model for proarrhythmia risk assessment. In: Frontiers in Physiology. 2017 ; Vol. 8, No. AUG.
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