The main finding of this work is that providing a relatively low cell concentration is used in IonWorks HT, the potency information generated correlates well with that determined using conventional electrophysiology. The effect on potency of increasing cell concentration may relate to a reduced free concentration of test compound owing to partitioning into cell membranes. In summary, the IonWorks HT hERG assay can generate pIC50 values based on a direct assessment of channel function in a timeframe short enough to influence chemical design.
Owing to its association with Torsades de Pointes, drug-induced QT interval prolongation has been and remains a significant hurdle to the development of safe, effective medicines. Genetic and pharmacological evidence highlighting the pivotal role the human ether-a-go-go-related gene (hERG) channel was a critical step in understanding how to start addressing this issue. It led to the development of hERG assays with the rapid throughput needed for the short timescales required in early drug discovery. The resulting volume of hERG data has fostered in silico models to help chemists design compounds with reduced hERG potency. In early drug discovery, a pragmatic approach based on exceeding a given potency value has been required to decide when a compound is likely to carry a low QT risk, to support its progression to late-stage discovery. At this point, the in vivo efficacy and metabolism characteristics of the potential drug are generally defined, as well its safety profile, which includes usually a dog study to assess QT interval prolongation risk. The hERG and in vivo QT data, combined with the likely indication and the estimated free drug level for efficacy, are put together to assess the risk that the potential drug will prolong QT in man. Further data may be required to refine the risk assessment before making the major investment decisions for full development. The non-clinical data are essential to inform decisions about compound progression and to optimize the design of clinical QT studies. Redfern et al., 2003). Given the cost of bringing a new drug to the market [estimated in the year 2000 at approximately US$800m (DiMasi et al., 2003)], the proportion of drugs withdrawn owing to QT prolongation/TdP was a significant concern for pharmaceutical companies. The headline drugs withdrawn from sale were just the tip of the iceberg, however, as the development of many more potential drugs was halted following evidence of a QT prolongation risk and some drugs remain on sale despite carrying a QT risk but cannot be prescribed to certain patient groups. Such was the level of concern in the pharmaceutical industry that the 'QT issue' was jokingly dubbed 'Pharmageddon' (WS Redfern, pers. comm.). All in all this was, and remains, a significant hurdle to the development of effective but safe medicines in an industry needing to focus on improving its productivity by reducing safety-related attrition. This overview aims to introduce the topic and summarize non-clinical strategies to assess and reduce QT interval prolongation risk. It builds on a previous review of the topic and complements more specific case studies described by Valentin et al. (2010) and is considered from a broader perspective in the commentary by Guth and Rast (2010).
British Journal of Pharmacology
The voltage-gated potassium channel encoded by hERG carries a delayed rectifying potassium current (IKr) underlying repolarization of the cardiac action potential. Pharmacological blockade of the hERG channel results in slowed repolarization and therefore prolongation of action potential duration and an increase in the QT interval as measured on an electrocardiogram. Those are possible to cause sudden death, leading to the withdrawals of many drugs, which is the reason for hERG screening. Computational in silico prediction models provide a rapid, economic way to screen compounds during early drug discovery. In this review, hERG prediction models are classified as 2D and 3D quantitative structure-activity relationship models, pharmacophore models, classification models, and structure based models (using homology models of hERG).
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