Abstract
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 <60%, preventing reliable estimation of IC50-values. The results of this study demonstrate that the accuracy of TdP risk prediction depends both on the intrinsic variability in ion channel pharmacology data as well as on experimental design considerations that preclude an accurate determination of drug IC50-values in vitro. Thus, we demonstrate that UQ provides valuable information about in silico modeling predictions that can inform future proarrhythmic risk evaluation of drugs under the CiPA paradigm.
Highlights
Drugs that block cardiac ion channels encoded by the humanether-à-go-go Related Gene and prolong the QT interval are associated with increased risk of Torsade de Pointes (TdP), a potentially lethal arrhythmia that caused several drugs to be withdrawn from market (Gintant et al, 2016)
Many potential sources of uncertainty exist within the Comprehensive in vitro Proarrhythmia Assay (CiPA) paradigm, the primary concern for the in silico component is uncertainty related to in vitro measurements of pharmacological effects on ionic currents
This study examined the impact of uncertainty in drug effects on simulation predictions
Summary
Drugs that block cardiac ion channels encoded by the humanether-à-go-go Related Gene (hERG) and prolong the QT interval are associated with increased risk of Torsade de Pointes (TdP), a potentially lethal arrhythmia that caused several drugs to be withdrawn from market (Gintant et al, 2016). In 2005, the International Council on Harmonisation (ICH) S7B and E14 guidelines were established to address the issue of TdP liability for new drugs. As stated in these guidelines, their intent was to be used as a screening method to identify drugs that would require more intensive electrocardiographic monitoring of patients in late phase (e.g., phase 3) clinical trials. Additional insight into TdP risk for hERG-blocking and QT-prolonging drugs can be determined by assessing whether drugs block inward currents such as, L-type calcium or late sodium (Duff et al, 1987; January and Riddle, 1989; Chézalviel-Guilbert et al, 1995; Guo et al, 2007). The Comprehensive in vitro Proarrhythmia Assay (CiPA) is a global initiative to revise the current guidelines with a new set of mechanistic assays that improve the specificity of the proarrhythmia screening process (Fermini et al, 2016)
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