Why the study?
Measured IC 50 of Kv 11.1 block varies widely depending on the electrophysiological assay voltage protocol, limiting preclinical screening for drug-induced arrhythmias.
State preferential drug binding is a major determinant of protocol-dependent IC50 differences in preclinical Kv11.1 assays, which can be predicted using machine learning to improve proarrhythmic risk assessment.
ML may reduce Kv11.1 assay variability in animals; leaves open translation to clinical proarrhythmic risk assessment.
Current guidelines around preclinical screening for drug-induced arrhythmias require the measurement of the potency of block of Kv 11.1 channels as a surrogate for risk. A shortcoming of this approach is that the measured IC 50 of Kv 11.1 block varies widely depending on the voltage protocol used in electrophysiological assays. In this study, we aimed to investigate the factors that that contribute to these differences and to identify whether it is possible to make predictions about protocol-dependent block that might facilitate comparison of potencies measured using different assays Our data demonstrate that state preferential binding, together with drug binding kinetics and trapping, is an important determinant of the protocol-dependence of Kv 11.1 block. We show for the first time that differences in IC 50 measured between protocols occurs in a predictable way, such that machine learning algorithms trained using a selection of simple voltage protocols can indeed predict protocol-dependent potency. Furthermore, we also show that a drug’s preference for binding to the open versus the inactivated state of Kv 11.1 can also be inferred from differences in IC 50 measured between protocols. Our work therefore identifies how state preferential drug binding is a major determinant of the protocol dependence of IC 50 measured in preclinical Kv 11.1 assays. It also provides a novel method for quantifying the state dependence of Kv 11.1 drug binding that will facilitate the development of more complete models of drug binding to Kv 11.1 and improve our understanding of proarrhythmic risk associated with compounds that block Kv 11.1 .
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Lee et al. (2019) studied this question.
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