Deep learning to identify and predict cardiotoxicities of anticancer drugs.

Abstract

e15012 Background: Recent advances in cancer treatment have revolutionized patient outcomes. However, toxicities associated with anti-cancer drugs remain a concern with many anti-cancer drugs now implicated in cardiotoxicity. The complete spectrum of cardiotoxicity associated with anti-cancer drugs is only evident post-approval of drugs. Deep Learning methods can identify novel and emerging safety signals in “real-world” clinical settings. Methods: We used AERS Mine, an open-source data mining platform to identify drug toxicity signatures in the FDA’s Adverse Event Reporting System of 16 million patients. We identified 1.3 million patients on traditional and targeted anti-cancer therapy to analyze therapy-specific cardiotoxicity patterns. Cardiotoxicity training dataset contained 1571 molecules characterized with bioassay against hERG potassium channel and included 350 toxic compounds with an IC50 of < 1μM. We implemented a Deep Belief Network to extract a deep hierarchical representation of the training data, and the Extra Tree Classifier to predict the toxicity of drug candidates. Drugs were encoded using 1024-bit Morgan fingerprint representation using SMILES with search radius of 7 atoms. Pharmacovigilance metrics (Relative Risks and safety signals) were used to establish statistical correlation. Results: This analysis identified signatures of arrhythmias and conduction abnormalities associated with common anti-cancer drugs (e.g. atrial fibrillation with ibrutinib, alkylating agents, immunomodulatory drugs; sinus bradycardia with 5FU, paclitaxel, thalidomide; sinus tachycardia with anthracyclines). Our analysis also identified myositis/myocarditis association with newer immune checkpoint inhibitors (e.g., atezolizumab, durvalumab, cemiplimab, avelumab) paralleling earlier signals for pembrolizumab, nivolumab, and ipilimumab. Deep Learning identified signatures of chemical moieties linked to cardiotoxicity, including common motifs in drugs associated with arrhythmias and conduction abnormalities with an accuracy of 89%. Conclusions: Deep Learning provides a comprehensive insight into emerging cardiotoxicity patterns of approved and investigational drugs, allows detection of ‘rogue’ chemical moieties, and shows promise for novel drug discovery and development.