Improving prediction of torsadogenic risk in the CiPA in silico model by appropriately accounting for clinical exposure.

Any adverse event is reliant on three properties: the appropriate pharmacology to trigger the event, the appropriate exposure of compound, and intrinsic patient factors. Each alone is necessary but insufficient to predict the event. The Comprehensive in vitro Proarrhythmia Assessment (CiPA) initiative attempts to predict the risk of torsade de pointes (TdP) by focusing on an in-silico model with thresholds determined at modest multiples of the therapeutic exposure for the parent molecule. This emphasizes the pharmacologic properties necessary for TdP but does not account for situations where clinical exposure may be higher, or where hERG potassium channel active metabolites are involved. Could accounting for clinical worst-case scenarios and metabolites, as is already standard practice in thorough QTc studies, improve the prediction algorithm? Terfenadine, a drug classed as "Intermediate" risk by CiPA, was assessed differently in the in-silico model validation. The clinical concentration of terfenadine used for the model was the exposure in the presence of metabolic inhibition representing a 14 to 40-fold increase in exposure compared to the therapeutic plasma concentration. However, several other "Intermediate" risk compounds are also known to be sensitive to metabolic inhibition and/or to have therapeutically active major metabolites, some of which are known to block hERG. Risperidone and astemizole are relevant examples. If only parent exposure is used to calculate a therapeutic window, risperidone has a relatively large multiple between clinical exposure and the hERG potency. Using this exposure of risperidone, the drug borders the "Intermediate" and "Low/No" risk categories for the CiPA in-silico model's TdP metric. The desmethyl metabolite of astemizole likely contributes significantly to the effects on cardiac repolarization, being equipotent on hERG but circulating at much higher levels than parent. Recalculating the TdP metric and margin values for terfenadine, risperidone and astemizole using the unbound concentration normally associated with treatment and a clinical worst case changes the qNet metric to higher risk values and illustrates the potential benefit to the algorithm of consistently using a clinical high exposure scenario accounting for all "hERG-active species". This exercise suggests repeating the model qualification accounting for clinical exposures and metabolites under 'stressed' scenarios would improve prediction of the TdP risk.