Uncertainty Quantification Reveals the Importance of Data Variability and Experimental Design Considerations for in Silico Proarrhythmia Risk Assessment

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.

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.…”

“…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 . This uncertainty quantification procedure also made the observation that, to achieve a low prediction error with acceptable uncertainty, one could use pharmacological data from four of the seven currents originally selected by CiPA and focus on plasma concentrations that are around the maximum free therapeutic concentration (C max ) .…”

“…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 …”

“…( c ) The use of the hERG ‐binding kinetic information, but not half‐inhibition concentrations ( IC 50s), can distinguish different torsade de pointes risk liabilities associated with dofetilide and cisapride. Left: The hERG ‐binding submodel in a was parameterized by the full data obtained from the dynamic protocol in b across multiple concentrations and then used within the O'Hara Rudy model to calculate the torsade metric scores ( qN et values averaged across 1×, 2×, 3×, and 4× maximum free therapeutic plasma concentration (C max )) . The torsade metric scores for the 12 training drugs were used by ordinal logistic regression to determine the classification thresholds.…”

“…The ISWG developed such a method to estimate the joint distributions of pharmacodynamic parameters (binding kinetics, IC50s, etc.) based on training data and then applied it to validation . In contrast, most of the other published models for TdP risk prediction did not consider variability and uncertainty .…”