title: A new classifier-based strategy for in-silico ion-channel cardiac drug safety assessment.
abstract: There is currently a strong interest in using high-throughput in-vitro ion-channel screening data to make predictions regarding the cardiac toxicity potential of a new compound in both animal and human studies. A recent FDA think tank encourages the use of biophysical mathematical models of cardiac myocytes for this prediction task. However, it remains unclear whether this approach is the most appropriate. Here we examine five literature data-sets that have been used to support the use of four different biophysical models and one statistical model for predicting cardiac toxicity in numerous species using various endpoints. We propose a simple model that represents the balance between repolarisation and depolarisation forces and compare the predictive power of the model against the original results (leave-one-out cross-validation). Our model showed equivalent performance when compared to the four biophysical models and one statistical model. We therefore conclude that this approach should be further investigated in the context of early cardiac safety screening when in-vitro potency data is generated.
authors: Manchester Pharmacy School, University of Manchester Manchester, UK.
authors: Hitesh B
authors: HB
authors: Mistry
authors: QT-Informatics Limited Macclesfield, UK.
authors: Mark R
authors: MR
authors: Davies
authors: Cancer Research UK Cambridge Institute, University of Cambridge Cambridge, UK.
authors: Giovanni Y
authors: GY
authors: Di Veroli
keywords: biostatistics; cardiac toxicity; ion-channel pharmacology; mathematical model; predictive pharmacology