Abstract
On the tenth anniversary of two key International Conference on Harmonisation (ICH) guidelines relating to cardiac proarrhythmic safety, an initiative aims to consider the implementation of a new paradigm that combines in vitro and in silico technologies to improve risk assessment. The Comprehensive In Vitro Proarrhythmia Assay (CiPA) initiative (co-sponsored by the Cardiac Safety Research Consortium, Health and Environmental Sciences Institute, Safety Pharmacology Society and FDA) is a bold and welcome step in using computational tools for regulatory decision making. This review compares and contrasts the state-of-the-art tools from empirical to mechanistic models of cardiac electrophysiology, and how they can and should be used in combination with experimental tests for compound decision making.
Highlights
Introduction to cardiac modelsThe groundbreaking work of Hodgkin & Huxley on squid giant axon published in 1952 [22] laid the core foundation for the mechanistic modelling of electrophysiology
This study suggests gene expression profiles can be used as a surrogate for hERG inhibition based on the premise that, hERG inhibition is independent of structurally diverse chemicals, it is dependent on a conserved cell physiological response that can be independent of chemical diversity
We show an example of linking PK with a cardiac model to simulate two related drug formulations with different clearance rates. It exemplifies the importance of applying a Quantitative systems pharmacology (QSP) approach, which looks to integrate our understanding of drug PK profile over time to these mechanism-based cardiac models
Summary
Scientific models, only reflecting simplified reality, help us to integrate our knowledge, to quantify a phenomenon and to predict outcomes; these models can facilitate evidencebased decision making. Used to contrast to, for example, in vitro (cellbased) models or in vivo (animal-based) models Markov state model an alternative method to Hodgkin– Huxley approaches that represents the open, closing and inactivation of ion channels as a sequence of dependent states and transitions, which can be dependent upon drug binding and ion concentrations, voltage or time Model calibration the process by which all or a subset of parameters or components of the model are adjusted or modified to best fit with a set of previously measured outcomes Model validation the process of testing the performance of an in silico model against a set of measurements that are (usually) not part of the model calibration process Monodomain equations differential reaction–diffusion equation representing the time and space changes in charge throughout a single spatial ‘domain’ – the inside of cells – and the contributions that the transmembrane currents make to this Parameter set this term is used to describe the set of values that form components of the algorithm behind the in silico model that are fit to characterise the system. Rather than being seen as competing and isolated approaches, there is a need for a more dialectic approach with increased iteration and crosstalk between these modelling methods; we can (and should) see this as a race towards more-productive models
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