Details

Autor(en) / Beteiligte

• Lee, William
• Windley, Monique J
• Perry, Matthew D
• Vandenberg, Jamie I
• Hill, Adam P

Titel

Protocol-Dependent Differences in IC50 Values Measured in Human Ether-Á-Go-Go-Related Gene Assays Occur in a Predictable Way and Can Be Used to Quantify State Preference of Drug Binding

Ist Teil von

• Molecular pharmacology, 2019-05, Vol.95 (5), p.537-550

Erscheinungsjahr

2019

Link zum Volltext

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• Current guidelines around preclinical screening for drug-induced arrhythmias require the measurement of the potency of block of voltage-gated potassium channel subtype 11.1 (Kv11.1) as a surrogate for risk. A shortcoming of this approach is that the measured IC50 of Kv11.1 block varies widely depending on the voltage protocol used in electrophysiological assays. In this study, we aimed to investigate the factors that contribute to these differences and to identify whether it is possible to make predictions about protocol-dependent block that might facilitate the 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 Kv11.1 block. We show for the first time that differences in IC50 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 the preference of a drug for binding to the open versus the inactivated state of Kv11.1 can also be inferred from differences in IC50 values measured between protocols. Our work therefore identifies how state preferential drug binding is a major determinant of the protocol dependence of IC50 values measured in preclinical Kv11.1 assays. It also provides a novel method for quantifying the state dependence of Kv11.1 drug binding that will facilitate the development of more complete models of drug binding to Kv11.1 and improve our understanding of proarrhythmic risk associated with compounds that block Kv11.1.