Abstract
Torsade de Pointes (TdP), a rare but lethal ventricular arrhythmia, is a potential cardiac side effect of drugs. To assess TdP risk, safety regulatory guidelines require to quantify the effects of new therapeutic compounds on hERG channel block in vitro and QT interval prolongation in vivo. Unfortunately, these have proven to be poor predictors of torsadogenic risk, and are likely to have prevented safe compounds from reaching the clinical phase. 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) male-based classifiers perform more poorly when applied to female data; 3) female-based classifier performances are 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 life-threatening consequences for the female population.
Competing Interest Statement
The authors have declared no competing interest.