A general procedure to select calibration drugs for lab-specific validation and calibration of proarrhythmia risk prediction models: An illustrative example using the CiPA model

INTRODUCTION

In response to the ongoing shift of the regulatory cardiac safety paradigm, a recent White Paper proposed general principles for developing and implementing proarrhythmia risk prediction models. These principles included development strategies to validate models, and implementation strategies to ensure a model developed by one lab can be used by other labs in a consistent manner in the presence of lab-to-lab experimental variability. While the development strategies were illustrated through the validation of the model under the Comprehensive In vitro Proarrhythmia Assay (CiPA), the implementation strategies have not been adopted yet.

METHODS

The proposed implementation strategies were applied to the CiPA model by performing a sensitivity analysis to identify a subset of calibration drugs that were most critical in determining the classification thresholds for proarrhythmia risk prediction.

RESULTS

The selected calibration drugs were able to recapitulate classification thresholds close to those calculated from the full list of CiPA drugs. Using an illustrative dataset it was shown that a new lab could use these calibration drugs to establish its own classification thresholds (lab-specific calibration), and verify that the model prediction accuracy in the new lab is comparable to that in the original lab where the model was developed (lab-specific validation).

DISCUSSION

This work used the CiPA model as an example to illustrate how to adopt the proposed model implementation strategies to select calibration drugs and perform lab-specific calibration and lab-specific validation. Generic in nature, these strategies could be generally applied to different proarrhythmia risk prediction models using various experimental systems under the new paradigm.

A systematic strategy for estimating hERG block potency and its implications in a new cardiac safety paradigm

Introduction

hERG block potency is widely used to calculate a drug's safety margin against its torsadogenic potential. Previous studies are confounded by use of different patch clamp electrophysiology protocols and a lack of statistical quantification of experimental variability. Since the new cardiac safety paradigm being discussed by the International Council for Harmonisation promotes a tighter integration of nonclinical and clinical data for torsadogenic risk assessment, a more systematic approach to estimate the hERG block potency and safety margin is needed.

Methods

A cross-industry study was performed to collect hERG data on 28 drugs with known torsadogenic risk using a standardized experimental protocol. A Bayesian hierarchical modeling (BHM) approach was used to assess the hERG block potency of these drugs by quantifying both the inter-site and intra-site variability. A modeling and simulation study was also done to evaluate protocol-dependent changes in hERG potency estimates.

Results

A systematic approach to estimate hERG block potency is established. The impact of choosing a safety margin threshold on torsadogenic risk evaluation is explored based on the posterior distributions of hERG potency estimated by this method. The modeling and simulation results suggest any potency estimate is specific to the protocol used.

Discussion

This methodology can estimate hERG block potency specific to a given voltage protocol. The relationship between safety margin thresholds and torsadogenic risk predictivity suggests the threshold should be tailored to each specific context of use, and safety margin evaluation may need to be integrated with other information to form a more comprehensive risk assessment.

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hERG potency/safety margin is a widely used nonclinical cardiac safety strategy.

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A new regulatory paradigm promotes the integration of nonclinical and clinical data.

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Lack of uncertainty quantification hindered using hERG potency in the new paradigm.

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A systematic method was established to address this limitation.

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Analysis supports using different safety margin thresholds in different context.

General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy

This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.

Assessment of an In Silico Mechanistic Model for Proarrhythmia Risk Prediction Under the CiPA Initiative

The International Council on Harmonization (ICH) S7B and E14 regulatory guidelines are sensitive but not specific for predicting which drugs are pro‐arrhythmic. In response, the Comprehensive In Vitro Proarrhythmia Assay (CiPA) was proposed that integrates multi‐ion channel pharmacology data in vitro into a human cardiomyocyte model in silico for proarrhythmia risk assessment. Previously, we reported the model optimization and proarrhythmia metric selection based on CiPA training drugs. In this study, we report the application of the prespecified model and metric to independent CiPA validation drugs. Over two validation datasets, the CiPA model performance meets all pre‐specified measures for ranking and classifying validation drugs, and outperforms alternatives, despite some in vitro data differences between the two datasets due to different experimental conditions and quality control procedures. This suggests that the current CiPA model/metric may be fit for regulatory use, and standardization of experimental protocols and quality control criteria could increase the model prediction accuracy even further.