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The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a global initiative intended to improve drug proarrhythmia risk assessment using a new paradigm of mechanistic assays. Under the CiPA paradigm, the relative risk of drug-induced Torsade de Pointes (TdP) is assessed using an in silico model of the human ventricular action potential (AP) that integrates in vitro pharmacology data from multiple ion channels. Thus, modeling predictions of cardiac risk liability will depend critically on the variability in pharmacology data, and uncertainty quantification (UQ) must comprise an essential component of the in silico assay. This study explores UQ methods that may be incorporated into the CiPA framework. Recently, we proposed a promising in silico TdP risk metric (qNet), which is derived from AP simulations and allows separation of a set of CiPA training compounds into Low, Intermediate, and High TdP risk categories. The purpose of this study was to use UQ to evaluate the robustness of TdP risk separation by qNet. Uncertainty in the model parameters used to describe drug binding and ionic current block was estimated using the non-parametric bootstrap method and a Bayesian inference approach. Uncertainty was then propagated through AP simulations to quantify uncertainty in qNet for each drug. UQ revealed lower uncertainty and more accurate TdP risk stratification by qNet when simulations were run at concentrations below 5× the maximum therapeutic exposure (Cmax). However, when drug effects were extrapolated above 10× Cmax, UQ showed that qNet could no longer clearly separate drugs by TdP risk. This was because for most of the pharmacology data, the amount of current block measured was

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.

Background— The current proarrhythmia safety testing paradigm, although highly efficient in preventing new torsadogenic drugs from entering the market, has important limitations that can restrict the development and use of valuable new therapeutics. The CiPA (Comprehensive in vitro Proarrhythmia Assay) proposes to overcome these limitations by evaluating drug effects on multiple cardiac ion channels in vitro and using these data in a predictive in silico model of the adult human ventricular myocyte. A set of drugs with known clinical torsade de pointes risk was selected to develop and calibrate the in silico model. Methods and Results— Manual patch-clamp data assessing drug effects on expressed cardiac ion channels were integrated into the O’Hara–Rudy myocyte model modified to include dynamic drug–hERG channel (human Ether-à-go-go-Related Gene) interactions. Together with multichannel pharmacology data, this model predicts that compounds with high torsadogenic risk are more likely to be trapped within the hERG channel and show stronger reverse use dependency of action potential prolongation. Furthermore, drug-induced changes in the amount of electronic charge carried by the late sodium and L-type calcium currents was evaluated as a potential metric for assigning torsadogenic risk. Conclusions— Modeling dynamic drug–hERG channel interactions and multi-ion channel pharmacology improves the prediction of torsadogenic risk. With further development, these methods have the potential to improve the regulatory assessment of drug safety models under the CiPA paradigm.

The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a regulatory paradigm proposed to replace the ICH S7B and E14 guidelines for assessing drug-induced proarrhythmia. Under CiPA, drug effects on multiple cardiac ion channels will be measured in vitro and integrated into an in silico model of the adult human ventricular cell, based on the O'Hara-Rudy (ORd) model. However, the ORd model does not accurately represent certain ionic currents known to be critical in triggering drug-induced arrhythmias, such as the late sodium current (I NaL ). The goal of the present study is to systematically assess and improve the simulation of the main depolarizing and repolarizing ionic currents (the inward rectifying potassium currents, L-type calcium current and I NaL ) in the ORd model. We present a new model with scaled conductances calculated by fitting to O'Hara et al. in vitro human cardiomyocyte channel blocking experiments using a genetic algorithm, which improves discrepancies of the original model. The modified model particularly improves the effect of INaL block on action potential prolongation, an important determinant of proarrhythmia risk in the context of CiPA.

Introduction Current regulatory guidelines for assessing the risk of QT prolongation include in vitro assays assessing drug effects on the human ether-a-go-go-related (hERG; also known as Kv11.1) channel expressed in cell lines. These assays are typically conducted at room temperature to promote the ease and stability of recording hERG currents. However, the new Comprehensive in vitro Proarrhythmia Assay (CiPA) paradigm proposes to use an in silico model of the human ventricular myocyte to assess risk, requiring as input hERG channel pharmacology data obtained at physiological temperatures. To accommodate current industry safety pharmacology practices for measuring hERG channel activity, an in silico model of hERG channel that allows for the extrapolation of hERG assay data across different temperatures is desired. Because temperature may have an effect on both channel gating and drug binding rate, such models may need to have two components: a base model dealing with temperature-dependent gating changes without drug, and a pharmacodynamic component simulating temperature-dependent drug binding kinetics. As a first step, a base mode that can capture temperature effects on hERG channel gating without drug is needed. Methods and results To meet this need for a temperature-dependent base model, a Markov model of the hERG channel with state transition rates explicitly dependent on temperature was developed and calibrated using data from a variety of published experiments conducted over a range of temperatures. The model was able to reproduce observed temperature-dependent changes in key channel gating properties and also to predict the results obtained in independent sets of new experiments. Discussion This new temperature-sensitive model of hERG gating represents an attempt to improve the predictivity of safety pharmacology testing by enabling the translation of room temperature hERG assay data to more physiological conditions. With further development, this model can be incorporated into the CiPA paradigm and also be used as a tool for developing insights into the thermodynamics of hERG channel gating mechanisms and the temperature-dependence of hERG channel block by drugs.