Assessment of extracellular field potential and Ca2+ transient signals for early QT/pro-arrhythmia detection using human induced pluripotent stem cell-derived cardiomyocytes.

Cardiovascular toxicity is a prominent reason for failures in drug development, resulting in the demand for assays that can predict this liability in early drug discovery. We investigated whether iCell® cardiomyocytes have utility as an early QT/TdP screen. Thirty clinical drugs with known QT/TdP outcomes were evaluated blind using label-free microelectrode array (parameters measured were beating period (BP), field potential duration (FPD), fast Na+ amplitude and slope) and live cell, fast kinetic fluorescent Ca2+ transient FLIPR® Tetra (parameters measured were peak count, width, amplitude) systems. Many FPD-altering drugs also altered BP. Correction for BP, using a Log-Log (LL) model, was required to appropriately interpret direct drug effects on FPD. In comparison with human QT effects and when drug activity was to be predicted at top test concentration (TTC), LL-corrected FPD and peak count had poor assay sensitivity and specificity values: 13%/64% and 65%/11%, respectively. If effective free therapeutic plasma concentration (EFTPC) was used instead of TTC, the values were 0%/100% and 6%/100%, respectively. When compared to LL-corrected FPD and peak count, predictive values of uncorrected FPD, BP, width and amplitude were not much different. If pro-arrhythmic risk was to be predicted using Ca2+ transient data, the values were 67%/100% and 78%/53% at EFTPC and TTC, respectively. Thus, iCell® cardiomyocytes have limited value as an integrated QT/TdP assay, highlighting the urgent need for improved experimental alternatives that may offer an accurate integrated cardiomyocyte safety model for supporting the development of new drugs without QT/TdP effects.

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.