Prediction of Thorough QT study results using action potential simulations based on ion channel screens.

INTRODUCTION: Detection of drug-induced pro-arrhythmic risk is a primary concern for pharmaceutical companies and regulators. Increased risk is linked to prolongation of the QT interval on the body surface ECG. Recent studies have shown that multiple ion channel interactions can be required to predict changes in ventricular repolarisation and therefore QT intervals. In this study we attempt to predict the result of the human clinical Thorough QT (TQT) study, using multiple ion channel screening which is available early in drug development. METHODS: Ion current reduction was measured, in the presence of marketed drugs which have had a TQT study, for channels encoded by hERG, CaV1.2, NaV1.5, KCNQ1/MinK, and Kv4.3/KChIP2.2. The screen was performed on two platforms - IonWorks Quattro (all 5 channels, 34 compounds), and IonWorks Barracuda (hERG & CaV1.2, 26 compounds). Concentration-effect curves were fitted to the resulting data, and used to calculate a percentage reduction in each current at a given concentration. Action potential simulations were then performed using the ten Tusscher and Panfilov (2006), Grandi et al. (2010) and O'Hara et al. (2011) human ventricular action potential models, pacing at 1Hz and running to steady state, for a range of concentrations. RESULTS: We compared simulated action potential duration predictions with the QT prolongation observed in the TQT studies. At the estimated concentrations, simulations tended to underestimate any observed QT prolongation. When considering a wider range of concentrations, and conventional patch clamp rather than screening data for hERG, prolongation of ≥5ms was predicted with up to 79% sensitivity and 100% specificity. DISCUSSION: This study provides a proof-of-principle for the prediction of human TQT study results using data available early in drug development. We highlight a number of areas that need refinement to improve the method's predictive power, but the results suggest that such approaches will provide a useful tool in cardiac safety assessment.

A correlation between the in vitro drug toxicity of drugs to cell lines that express human P450s and their propensity to cause liver injury in humans.

Drug toxicity to T-antigen-immortalized human liver epithelial (THLE) cells stably transfected with plasmid vectors that encoded human cytochrome P450s 1A2, 2C9, 2C19, 2D6, or 3A4, or an empty plasmid vector (THLE-Null), was investigated. An automated screening platform, which included 1% dimethyl sulfoxide (DMSO) vehicle, 2.7% bovine serum in the culture medium, and assessed 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetrazolium reduction, was used to evaluate the cytotoxicity of 103 drugs after 24h. Twenty-two drugs caused cytotoxicity to THLE-Null cells, with EC50 ≤ 200 µM; 21 of these drugs (95%) have been reported to cause human liver injury. Eleven drugs exhibited lower EC50 values in cells transfected with CYP3A4 (THLE-3A4 cells) than in THLE-Null cells; 10 of these drugs (91%) caused human liver injury. An additional 8 drugs, all of which caused human liver injury, exhibited potentiated THLE-3A4 cell toxicity when evaluated using a manual protocol that included 0.2% or 1% DMSO, but not bovine serum. Fourteen of the drugs that exhibited potentiated THLE-3A4 cell toxicity are known to be metabolized by P450s to reactive intermediates. These drugs included troglitazone, which was shown to undergo metabolic bioactivation and covalent binding to proteins in THLE-3A4 cells. A single drug (rimonabant) exhibited marked THLE cell toxicity but did not cause human liver injury; this drug had very low reported plasma exposure. These results indicate that evaluation of toxicity to THLE-Null and THLE-3A4 cell lines during drug discovery may aid selection of drugs with reduced propensity to cause drug-induced liver injury and that consideration of human exposure is required to enhance data interpretation.

Prospective Prediction of Antitarget Activity by Matched Molecular Pairs Analysis.

Matched molecular pairs analysis (MMPA)1,2 is an inverse quantitative structure activity relationship (QSAR) technique that is rapidly gaining popularity in the retrospective analysis of large experimental datasets.3,4 While much of the recent focus has been on the differences in properties between structurally related groups of existing compounds, attempts to extend this methodology to the de-novo design of novel structures have been limited. To our knowledge the aggregate effect of multiple transformations, all suggesting the same molecular structure, has only ever being considered within a very limited dataset.5 We therefore sought to test this exciting new approach to the design (and absolute property prediction - effectively QSAR-by-MMPA) of novel chemical entities based on a larger, more diverse dataset, and couple these designs to MMPA-based predictions of antitarget activity.