Abstract
Mathematical models of ionic currents are used to study the electrophysiology of the heart, brain, gut, and several other organs. Increasingly, these models are being used predictively in the clinic, for example, to predict the risks and results of genetic mutations, pharmacological treatments, or surgical procedures. These safety-critical applications depend on accurate characterization of the underlying ionic currents. Four different methods can be found in the literature to fit voltage-sensitive ion channel models to whole-cell current measurements: method 1, fitting model equations directly to time-constant, steady-state, and I-V summary curves; method 2, fitting by comparing simulated versions of these summary curves to their experimental counterparts; method 3, fitting to the current traces themselves from a range of protocols; and method 4, fitting to a single current trace from a short and rapidly fluctuating voltage-clamp protocol. We compare these methods using a set of experiments in which hERG1a current was measured in nine Chinese hamster ovary cells. In each cell, the same sequence of fitting protocols was applied, as well as an independent validation protocol. We show that methods 3 and 4 provide the best predictions on the independent validation set and that short, rapidly fluctuating protocols like that used in method 4 can replace much longer conventional protocols without loss of predictive ability. Although data for method 2 are most readily available from the literature, we find it performs poorly compared to methods 3 and 4 both in accuracy of predictions and computational efficiency. Our results demonstrate how novel experimental and computational approaches can improve the quality of model predictions in safety-critical applications.
Highlights
Computational models of ionic currents have been used to understand the formation of the cellular action potential (AP) [1,2,3], to simulate the effects of genetic mutations [4], and to study and predict the effects of pharmaceutical compounds that block various ion channels [5,6]
Models of ionic currents are routinely integrated into models of the AP and used to investigate the effects of genetic mutations, predict proarrhythmic risk in drug development, and inform clinical interventions [12,13,14]
Data from several sources have been used, including whole-cell currents [1], single-channel currents [15], AP recordings [16], dynamic-clamp experiments [17], gating currents caused by the movement of charged parts of the ion channel proteins themselves [18], and measurements of fluorophores whose visibility varies with channel conformation [19]
Summary
Computational models of ionic currents have been used to understand the formation of the cellular action potential (AP) [1,2,3], to simulate the effects of genetic mutations [4], and to study and predict the effects of pharmaceutical compounds that block various ion channels [5,6]. Models of ionic currents are routinely integrated into models of the AP and used to investigate the effects of genetic mutations, predict proarrhythmic risk in drug development, and inform clinical interventions [12,13,14]. Such safety-critical applications depend on accurate characterization of the underlying ionic currents. Data from several sources have been used, including whole-cell (aggregate) currents [1], single-channel currents [15], AP recordings [16], dynamic-clamp experiments [17], gating currents caused by the movement of charged parts of the ion channel proteins themselves [18], and measurements of fluorophores whose visibility varies with channel conformation [19]
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