Can non-clinical repolarization assays predict the results of clinical thorough QT studies? Results from a research consortium.

BACKGROUND AND PURPOSE: Translation of non-clinical markers of delayed ventricular repolarization to clinical prolongation of the QT interval corrected for heart rate (QTc) (a biomarker for torsades de pointes proarrhythmia) remains an issue in drug discovery and regulatory evaluations. We retrospectively analysed 150 drug applications in a US Food and Drug Administration database to determine the utility of established non-clinical in vitro IKr current human ether-à-go-go-related gene (hERG), action potential duration (APD) and in vivo (QTc) repolarization assays to detect and predict clinical QTc prolongation. EXPERIMENTAL APPROACH: The predictive performance of three non-clinical assays was compared with clinical thorough QT study outcomes based on free clinical plasma drug concentrations using sensitivity and specificity, receiver operating characteristic (ROC) curves, positive (PPVs) and negative predictive values (NPVs) and likelihood ratios (LRs). KEY RESULTS: Non-clinical assays demonstrated robust specificity (high true negative rate) but poor sensitivity (low true positive rate) for clinical QTc prolongation at low-intermediate (1×-30×) clinical exposure multiples. The QTc assay provided the most robust PPVs and NPVs (ability to predict clinical QTc prolongation). ROC curves (overall test accuracy) and LRs (ability to influence post-test probabilities) demonstrated overall marginal performance for hERG and QTc assays (best at 30× exposures), while the APD assay demonstrated minimal value. CONCLUSIONS AND IMPLICATIONS: The predictive value of hERG, APD and QTc assays varies, with drug concentrations strongly affecting translational performance. While useful in guiding preclinical candidates without clinical QT prolongation, hERG and QTc repolarization assays provide greater value compared with the APD assay.

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

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.