Optimization of an In silico Cardiac Cell Model for Proarrhythmia Risk Assessment

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.

Optimization of an In Silico Cardiac Cell Model for Proarrhythmia Risk Assessment

“…Note that this model variant has gone through a channel conductance optimization process similar to that presented in this article, as described in Dutta et al (2016), so the difference observed between this model variant ( Figure 5B) and the full optimized IKr-dyn ORd model ( Figure 5A) is mainly due to the different representation of IKr block (dynamic vs. IC50s). From Figure 5B we can see that there are two intermediate risk drugs that are not correctly categorized: cisapride that is mixed with the high risk drugs, and chlorpromazine that is mixed with the low risk drugs.…”

“…The robustness of the system could be evaluated by applying a specific perturbation with a series of strengths and measuring the range of the perturbation FIGURE 5 | qNet for the 12 CiPA training compounds for a range of doses (0.5-25x Cmax) at a pacing rate of 2,000 ms. (A) Optimized IKr-dyn ORd; (B) A model variation without the incorporation of the IKr dynamic model (note this is the same model as in Dutta et al, 2016) and; (C) A model variation without the optimized channel conductances to accurately quantify block effects of individual currents (note this is the same model as in Li et al, 2017). Different TdP risk levels are color coded (high risk in red, intermediate risk in blue and low/no risk in green).…”

Quantitative Systems Pharmacology Models for a New International Cardiac Safety Regulatory Paradigm: An Overview of the Comprehensive In Vitro Proarrhythmia Assay In Silico Modeling Approach

“…The selected metric qNet, which represents the electric charge carried by (the area under the curve of) the net current Inet (the difference between the four selected outward currents IKr, IKs, IK1, and Ito and the two selected inward currents INaL and ICaL), is not only mechanistically indicative of the distance from early afterdepolarization but also was selected based entirely on 12 training drugs by comparing with alternative metrics . Also, predetermined by the training drugs were the model structure, parameters, and any calculation/simulation methods . Together, these prespecified features effectively “froze” the model before validation and prevented it from being affected (informed) by the validation data.…”

“…The first determined was the structure, the physiological (gating) parameters, and parameterization procedure for the pharmacological (drug‐binding) parameters, of the hERG dynamic model . Following this, the other physiological parameters (ion channel conductance) of the cardiomyocyte model were adjusted and the metric qNet was selected . Building on these calibration steps, a formal uncertainty quantification process was established in which the calculation methods for pharmacodynamic parameters (drug binding and potency) and the qNet metric were updated to derive probability distributions of predicted risk .…”

“…The ISWG replaced the hERG /IKr component of the base O'Hara Rudy model with such a Markov submodel ( Figure a ), which can distinguish drugs with different binding kinetics captured by a dynamic voltage protocol ( Figure b ). Together with other model adjustments based on training data, this resulted in a prototype model CiPAORdv1.0 to be evaluated by the validation data set later …”