Combining Systems Pharmacology Modeling With Machine Learning To Identify Sub-Populations At Risk Of Drug-Induced Torsades de Pointes

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Project Summary Torsades de Pointes, a lethal ventricular arrhythmia, is a side effect of several commonly used antiarrhythmics, antibiotics, antipsychotics, antihistamines and other ‘non-cardiovascular’ therapies. Though this adverse event is rare, it can lead to ventricular fibrillation and sudden cardiac death. The ignorance about the underlying differences between those at high risk versus low risk of forming this drug-induced arrhythmia halts any considerable progress in preventing it. Rather than simply removing these drugs from the market, a closer examination of the physiological and clinical traits of patients who benefited from the treatment and those who formed the arrhythmia needs to be performed. This highlights the idea of precision medicine and the importance of identifying relevant sub-groups of patients likely to benefit from a treatment versus those who are highly susceptible to a drug-induced adverse event. The current standards for predicting risk, a lengthened action potential (AP) duration of cells and a prolonged QT interval on an echocardiogram (ECG) have proven ineffective. Thus, there is a need to extract pertinent information from the cellular and tissue levels before administration of the therapeutic to detect patterns only apparent in the high-risk population. To analyze this concept, I plan to (1) explain at a mechanistic level the differences between the healthy and at-risk patients, (2) identify important AP and ECG signatures that can predict risk early on, and (3) connect the physiological and clinical findings to improve the profile and description of the high-risk population. I will combine two complementary computational techniques: (1) simulations with mechanistic quantitative systems pharmacology models of heart cells and tissues; and (2) advanced machine learning approaches that can identify hidden patterns. Thus, this project aims to develop an algorithm which will improve risk prediction and upgrade the current imperfect and unreliable standards for prescribing proarrhythmic therapies.