Development of cardiac safety translational tools for QT prolongation and torsade de pointes

Abstract

OBJECTIVE: A regulatory science priority at the Food and Drug Administration (FDA) is to promote the development of new innovative tools such as reliable and validated computational (in silico) models. This FDA Critical Path Initiative project involved the development of predictive clinical computational models for decision-support in CDER evaluations of QT/QTc interval prolongation and proarrhythmic potential for non-antiarrhythmic drugs. METHODS: Several classification models were built using predictive technologies of quantitative structure-activity relationship analysis using clinical in-house and public data on induction of QT prolongation and torsade de pointes (TdP) in humans. Specific models were geared toward prediction of high-risk drugs with attention to outcomes from thorough QT studies and TdP risk based on clinical in-house data. Models used were independent of non-clinical data or known molecular mechanisms. The positive predictive performance of the in silico models was validated using cross-validation and independent external validation test sets. RESULTS: Optimal performance was observed with high sensitivity (87%) and high specificity (88%) for predicting QT interval prolongation using in-house data, and 77% sensitivity in predicting drugs withdrawn from the market. Furthermore, the article describes alerting substructural features based on drugs tested in the clinical trials. CONCLUSIONS: The in silico models provide evidence of a structure-based explanation for these cardiac safety endpoints. The models will be made publically available and are under continual prospective external validation testing and updating at CDER using TQT study outcomes.