Transl Clin Pharmacol.  2019 Mar;27(1):12-18. 10.12793/tcp.2019.27.1.12.

Introduction to in silico model for proarrhythmic risk assessment under the CiPA initiative

• Park JS
• Jeon JY
• Yang JH
• Kim MG

• KMID: 2442241
• DOI: http://doi.org/10.12793/tcp.2019.27.1.12

Abstract

In 2005, the International Council for Harmonization (ICH) established cardiotoxicity assessment guidelines to identify the risk of Torsade de Pointes (TdP). It is focused on the blockade of the human ether-à-go-go-related gene (hERG) channel known to cause QT/QTc prolongation and the QT/QTc prolongation shown on the electrocardiogram. However, these biomarkers are not the direct risks of TdP with low specificity as the action potential is influenced by multiple channels along with the hERG channel. Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative emerged to address limitations of the current model. The objective of CiPA is to develop a standardized in silico model of a human ventricular cell to quantitively evaluate the cardiac response for the cardiac toxicity risk and to come up with a metric for the TdP risk assessment. In silico working group under CiPA developed a standardized and reliable in silico model and a metric that can quantitatively evaluate cellular cardiac electrophysiologic activity. The implementation mainly consists of hERG fitting, Hill fitting, and action potential simulation. In this review, we explained how the in silico model of CiPA works, and briefly summarized current overall CiPA studies. We hope this review helps clinical pharmacologists to understand the underlying estimation process of CiPA in silico modeling.

Keyword

Cardiotoxicity; CiPA; Torsade de Pointes

MeSH Terms

Action Potentials
Biomarkers
Cardiotoxicity
Computer Simulation*
Electrocardiography
Hope
Humans
In Vitro Techniques
Risk Assessment*
Sensitivity and Specificity
Torsades de Pointes
Biomarkers

Figure

• 

  Figure 1 Schematic diagram of O'Hara-Rudy human ventricular myocyte model. Among various ion currents in the model, in silico studies in CiPA focus on the seven major ion currents: IKr, IKs, ICaL, INaL, INa, Ito and IK1. IKr/hERG, rapid delayed rectifier potassium current that flows through0 the hERG channel; IKs, the slow rectifier potassium current; ICaL, the L-type calcium current; INa, the peak sodium current; INaL, the late sodium current; Ito, transient outward potassium current; IK1, inwardly rectifying potassium current. Adapted from ref. 13.

• 

  Figure 2 Structure of the hERG Markov model and equation of transition rate affected by temperature. The transition between adjacent states is a first order reaction dependent on membrane voltage, temperature and three free parameters A, B, and q. Each state transition has a different set of free parameters that are fixed and distinguished from each other by numeric suffixes. R, state transition rate; V, membrane potential (electrical field across the channel); A and B, energy barrier height in the absence and presence of electrical field, respectively; T, temperature; q, commonly used temperature extrapolating Q10 value defined as the change in rate for each 10℃ change in temperature. The hERG Markov model in CiPA in silico assay includes a saturating drug binding component (IO* and O*) and a drug trapping component (C*). The fitted drug parameters are Ku (drug unbinding rate), Kmax (maximum drug effect), n (Hill coefficient of drug binding), halfmax (EC50n, nth power of the half-maximal drug concentration), and Vhalf-trap (membrane voltage of half of drug-bound channels opening). Emax is a sigmoid model describing the concentration-response of each drug and D is the drug concentration in nM containing. The trapping rate (Kt) of channel closing with drug bound was manually fixed at 3.5×10−5 ms−1. Modified from ref. 14 and ref. 17.

• 

  Figure 3 The finalized TdP risk metric of average qNet for 1–4 × Cmax of 28 CiPA drugs. Results shown are for the 12 training drugs (a) and 16 validation drugs (b), respectively. The 95% confidence interval and median point of the torsade metric scores for each drug are shown as horizontal error bars. Two dotted lines represent the thresholds of the risk category (Threshold 2 has a value of 0.0581 and Threshold 1 has a value of 0.0671). The metric was plotted using data obtained from “hybrid” data set which combined manual data of hERG current with high-throughput data of the other three currents (INaL, INa, and ICaL). The data was provided by Dr. Zhihua Li at FDA.

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