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Visual Abstract, Highlights • Antiarrhythmic therapies remain suboptimal due to our inability to predict how drug interactions with ion channels will affect the ability of the tissue to initiate and sustain an arrhythmia. • We built a computational framework that allows for in silico design of precision-targeted therapeutic agents that simultaneously assesses antiarrhythmic markers of success and failure at multiple spatial and time scales. Using this framework, a novel in silico mexiletine “booster” was designed that may dramatically improve the efficacy of mexiletine in suppression of arrhythmia triggers. • These results provide a roadmap for the design of novel molecular-based therapy to treat myriad arrhythmia syndromes, including ventricular tachycardia, heart failure arrhythmias, and inherited arrhythmia syndromes. • In summary, computational modeling approaches to drug discovery represent a novel tool to design and test precision-targeted therapeutic agents. By exploiting nontraditional ion channel drug targets, an entirely new dimension can be added to the wide parameter space of traditional antiarrhythmic drugs to develop more precision-targeted and potent Class I therapeutic agents., Summary Antiarrhythmic treatment strategies remain suboptimal due to our inability to predict how drug interactions with ion channels will affect the ability of the tissues to initiate and sustain an arrhythmia. We built a multiscale molecular model of the Na+ channel domain III (domain III voltage-sensing domain) to highlight the molecular underpinnings responsible for mexiletine drug efficacy. This model predicts that a hyperpolarizing shift in the domain III voltage-sensing domain is critical for drug efficacy and may be leveraged to design more potent Class I molecules. The model was therefore used to design, in silico, a theoretical mexiletine booster that can dramatically rescue a mutant resistant to the potent antiarrhythmic effects of mexiletine. Our framework provides a strategy for in silico design of precision-targeted therapeutic agents that simultaneously assesses antiarrhythmic markers of success and failure at multiple spatial and time scales. This approach provides a roadmap for the design of novel molecular-based therapy to treat myriad arrhythmia syndromes, including ventricular tachycardia, heart failure arrhythmias, and inherited arrhythmia syndromes.

Markov models of ion channel dynamics have evolved as experimental advances have improved our understanding of channel function. Past studies have examined limited sets of various topologies for Markov models of channel dynamics. We present a systematic method for identification of all possible Markov model topologies using experimental data for two types of native voltage-gated ion channel currents: mouse atrial sodium currents and human left ventricular fast transient outward potassium currents. Successful models identified with this approach have certain characteristics in common, suggesting that aspects of the model topology are determined by the experimental data. Incorporating these channel models into cell and tissue simulations to assess model performance within protocols that were not used for training provided validation and further narrowing of the number of acceptable models. The success of this approach suggests a channel model creation pipeline may be feasible where the structure of the model is not specified a priori., Author summary Markov models of ion channel dynamics have evolved as experimental advances have improved our understanding of channel function. Past studies have examined limited sets of various structures for Markov models of channel dynamics. Here, we present a computational routine designed to thoroughly search for Markov model topologies for simulating whole-cell currents. We tested this method on two distinct types of voltage-gated cardiac ion channels and found the number of states and connectivity required to recapitulate experimentally observed kinetics. Successful models identified with this approach have certain characteristics in common, suggesting that model structures are determined by the experimental data. Incorporation of these models into higher scale action potential and cable (an approximation of one-dimensional action potential propagation) simulations, identified key channel phenomena that were required for proper function. These methods provide a route to create functional channel models that can be used for action potential simulation without pre-defining their structure ahead of time.