Sex‐Specific Classification of Drug‐Induced Torsade de Pointes Susceptibility Using Cardiac Simulations and Machine Learning

Abstract

Torsade de Pointes (TdP), a rare but lethal ventricular arrhythmia, is a toxic side effect of many drugs. To assess TdP risk, safety regulatory guidelines require quantification of hERG channel block in vitro and QT interval prolongation in vivo for all new therapeutic compounds. Unfortunately, these have proven to be poor predictors of torsadogenic risk, and are likely to have prevented safe compounds from reaching clinical phases. While this has stimulated numerous efforts to define new paradigms for cardiac safety, none of the recently developed strategies accounts for patient conditions. In particular, despite being a well-established independent risk factor for TdP, female sex is vastly underrepresented in both basic research and clinical studies, and thus current TdP metrics are likely biased toward the male sex. Here, we apply statistical learning to synthetic data, generated by simulating drug effects on cardiac myocyte models capturing male and female electrophysiology, to develop new sex-specific classification frameworks for TdP risk. We show that (1) TdP classifiers require different features in females vs. males; (2) malebased classifiers perform more poorly when applied to female data; (3) female-based classifier performance is largely unaffected by acute effects of hormones (i.e., during various phases of the menstrual cycle). Notably, when predicting TdP risk of intermediate drugs on female simulated data, male-biased predictive models consistently underestimate TdP risk in women. Therefore, we conclude that pipelines for preclinical cardiotoxicity risk assessment should consider sex as a key variable to avoid potentially lifethreatening consequences for the female population..

Sex Differences in Drug-Induced Arrhythmogenesis

“…These in silico augmented biomarkers showcase improved sensitivity and specificity with respect to the gold standard human ether-à-gogo and QT prolongation guidelines (Passini et al, 2017 ; Li et al, 2019 ). Currently, these in silico arrhythmogenicity biomarkers focus mainly on lower-fidelity isolated cardiac cell models (Lancaster and Sobie, 2016 ; Britton et al, 2017 ; Fogli Iseppe et al, 2021 ) or simplified cable simulations (Polak et al, 2018 ; Romero et al, 2018 ; Yang et al, 2020 ). The underlying motivation for such an approach is the role of cellular early afterdepolarizations and repolarization failures in providing a trigger for the development of arrhythmia.…”

“…This study sought to take female sex into account as an independent biological variable by developing two sex-specific in silico augmented multiscale arrhythmogenic risk classifiers. To accomplish this, we first extended the multiscale envelope of studying sex-differences in cardiac electrophysiology beyond the cell or tissue level (Yang et al, 2017 ; Fogli Iseppe et al, 2021 ) up to the organ scale. Next, we used the developed framework to delineate male vs. female arrhythmogenic sensitivity to drugs.…”

“…Based on our sex-specific arrhythmia risk classifiers in which the male arrhythmogenic {β CaL , β Kr } risk zone ⊂ the female arrhythmogenic {β CaL , β Kr } risk zone, our classifiers do not predict any drug to have a higher risk for women than for men. Interestingly though, the alternative approach identified specific drugs that are safer for women than for men (Fogli Iseppe et al, 2021 ). This disagreement might be associated with the current ambiguity on functional sex differences in Ca 2+ handling (Parks and Howlett, 2013 ; Parks et al, 2014 ) which led the authors to disregard the genomic sex differences in sodium potassium I NaK pump and Ca 2+ uptake channel activities and relatively upscale the female sodium calcium exchange currents we deduced in Table 1 .…”

Machine Learning as a Novel Method to Support Therapeutic Drug Management and Precision Dosing

“…16 Other recent examples of promising studies documenting the applicability of machine learning and artificial intelligence include machine learning modeling of free text data and adverse drug reactions coding for automatic identification of adverse drug reactions from unstructured electronic health record data and by integrating machine learning approaches in preclinical cardiotoxicity risk assessment and contributing patient factors analysis. 17,18 Language processing and text mining software tools can support data collection from electronic health records and provide important insights into real-world drug treatment outcomes. 19 CPT's sister journals are also publishing on machine learning, including two articles highlighted in this issue of CPT.…”

Diversity in Clinical Pharmacology and Therapeutics

“…In addition, a range of new translational and quantitative tools are now available to underpin new inclusive clinical development strategies 26 . For example, a recent study published in this journal showed how a combination of mechanistic quantitative systems pharmacology modeling and machine learning provided new insights into sex‐specific classification of drug‐induced Torsade de Pointes (TdP) susceptibility 27 . Notably, it was demonstrated that male‐biased predictive models consistently underestimate TdP risk in women, which suggests that current standard preclinical and clinical safety assessment paradigms are inadequate.…”