AimsThe level of inhibition of the human Ether-à-go-go-related gene (hERG) channel is one of the earliest preclinical markers used to predict the risk of a compound causing Torsade-de-Pointes (TdP) arrhythmias. While avoiding the use of drugs with maximum therapeutic concentrations within 30-fold of their hERG inhibitory concentration 50% (IC50) values has been suggested, there are drugs that are exceptions to this rule: hERG inhibitors that do not cause TdP, and drugs that can cause TdP but are not strong hERG inhibitors. In this study, we investigate whether a simulated evaluation of multi-channel effects could be used to improve this early prediction of TdP risk.Methods and resultsWe collected multiple ion channel data (hERG, Na, l-type Ca) on 31 drugs associated with varied risks of TdP. To integrate the information on multi-channel block, we have performed simulations with a variety of mathematical models of cardiac cells (for rabbit, dog, and human ventricular myocyte models). Drug action is modelled using IC50 values, and therapeutic drug concentrations to calculate the proportion of blocked channels and the channel conductances are modified accordingly. Various pacing protocols are simulated, and classification analysis is performed to evaluate the predictive power of the models for TdP risk. We find that simulation of action potential duration prolongation, at therapeutic concentrations, provides improved prediction of the TdP risk associated with a compound, above that provided by existing markers.ConclusionThe suggested calculations improve the reliability of early cardiac safety assessments, beyond those based solely on a hERG block effect.

Given that cardiovascular safety liabilities remain a major cause of drug attrition during preclinical and clinical development, adverse drug reactions, and post‐approval withdrawal of medicines, the Medical Research Council Centre for Drug Safety Science hosted a workshop to discuss current challenges in determining, understanding and addressing ‘Cardiovascular Toxicity of Medicines’. This article summarizes the key discussions from the workshop that aimed to address three major questions: (i) what are the key cardiovascular safety liabilities in drug discovery, drug development and clinical practice? (ii) how good are preclinical and clinical strategies for detecting cardiovascular liabilities? and (iii) do we have a mechanistic understanding of these liabilities? It was concluded that in order to understand, address and ultimately reduce cardiovascular safety liabilities of new therapeutic agents there is an urgent need to: Fully characterize the incidence, prevalence and impact of drug‐induced cardiovascular issues at all stages of the drug development process. Ascertain the predictive value of existing non‐clinical models and assays towards the clinical outcome. Understand the mechanistic basis of cardiovascular liabilities; by addressing areas where it is currently not possible to predict clinical outcome based on preclinical safety data. Provide scientists in all disciplines with additional skills to enable them to better integrate preclinical and clinical data and to better understand the biological and clinical significance of observed changes. Develop more appropriate, highly relevant and predictive tools and assays to identify and wherever feasible to eliminate cardiovascular safety liabilities from molecules and wherever appropriate to develop clinically relevant and reliable safety biomarkers.

It is widely accepted that more needs to be done to bring new, safe, and efficacious drugs to the market. Cardiovascular toxicity detected both in early drug discovery as well as in the clinic, is a major contributor to the high failure rate of new molecules. The growth of translational safety offers a promising approach to improve the probability of success for new molecules. Here we describe a cross-company initiative to determine the concordance between the conscious telemetered dog and phase I outcome for 3 cardiovascular parameters. The data indicate that, in the context of the methods applied in this analysis, the ability to detect compounds that affect the corrected QT interval (QTc) was good within the 10-30x exposure range but the predictive or detective value for heart rate and diastolic blood pressure was poor. These findings may highlight opportunities to refine both the animal and the clinical study designs, as well as refocusing the assessment of value of dog cardiovascular assessments beyond phase 1. This investigation has also highlighted key considerations for cross-company data sharing and presents a unique learning opportunity to improve future translational projects.