Drug-induced torsadogenicity prediction model: An explainable machine learning-driven quantitative structure-toxicity relationship approach

• Published in: Computers in biology and medicine 2024-11, Vol.182, p.109209, Article 109209
• Main Authors: Kelleci Çelik, Feyza, Doğan, Seyyide, Karaduman, Gül
• Format: Article
• Language: English
• Subjects: Algorithms; Animal research; Biomarkers; Cardiotoxicity; Correlation analysis; Data collection; Datasets; Drug development; Drug screening; Drug-Related Side Effects and Adverse Reactions; Drugs; Feature selection; Humans; Laboratory animals; Machine Learning; Models, Biological; Molecular structure; Pharmaceutical industry; Pharmaceuticals; Pharmacovigilance; Physicochemical properties; Prediction models; Predictions; QSAR; QSTR; Quantitative Structure-Activity Relationship; Research facilities; SHapley additive exPlanations; Support Vector Machine; Support vector machines; Tachyarrhythmia; Torsade de pointes; Torsades de pointes; Torsades de Pointes - chemically induced; Toxicity; Toxicology
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.109209

Drug-induced Torsade de Pointes (TdP), a life-threatening polymorphic ventricular tachyarrhythmia, emerges due to the cardiotoxic effects of pharmaceuticals. The need for precise mechanisms and clinical biomarkers to detect this adverse effect presents substantial challenges in drug safety assessment. In this study, we propose that analyzing the physicochemical properties of pharmaceuticals can provide valuable insights into their potential for torsadogenic cardiotoxicity. Our research centers on estimating TdP risk based on the molecular structure of drugs. We introduce a novel quantitative structure-toxicity relationship (QSTR) prediction model that leverages an in silico approach developed by adopting the 4R rule in laboratory animals. This approach eliminates the need for animal testing, saves time, and reduces cost. Our algorithm has successfully predicted the torsadogenic risks of various pharmaceutical compounds. To develop this model, we employed Support Vector Machine (SVM) and ensemble techniques, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost). We enhanced the model's predictive accuracy through a rigorous two-step feature selection process. Furthermore, we utilized the SHapley Additive exPlanations (SHAP) technique to explain the prediction of torsadogenic risk, particularly within the RF model. This study represents a significant step towards creating a robust QSTR model, which can serve as an early screening tool for assessing the torsadogenic potential of pharmaceutical candidates or existing drugs. By incorporating molecular structure-based insights, we aim to enhance drug safety evaluation and minimize the risks of drug-induced TdP, ultimately benefiting both patients and the pharmaceutical industry. [Display omitted] •Prediction of drug-induced Torsade de Pointes using quantitative structure-toxicity relationship model.•Identification of key structural descriptors for toxicological risk assessment.•Ethical, cost-effective, and time-saving alternative to animal testing.•Insights into suspected molecular descriptors behind drug-induced torsadogenicity.•Potential for specific molecule modifications to prevent the drugs' Torsade de Pointes effect.