Parameterization for In-Silico Modeling of Ion Channel Interactions with Drugs

• Published in: PloS one 2016-03, Vol.11 (3), p.e0150761-e0150761
• Main Authors: Moreno, Jonathan D, Lewis, Timothy J, Clancy, Colleen E
• Format: Article
• Language: English
• Subjects: Analysis; Animals; Biology and Life Sciences; Complexity; Computational biology; Computer Simulation; Constants; Drug Evaluation, Preclinical - methods; Drug interaction; Drug interactions; Drug screening; Drugs; Humans; Initial conditions; Ion channels; Ion Channels - chemistry; Kinetics; Mathematical models; Medicine and Health Sciences; Methods; Models, Molecular; Mutation; Optimization; Parameterization; Pharmaceutical Preparations - chemistry; Pharmacology; Physical Sciences; Rate constants; Research and Analysis Methods; Rodents; Sodium; Software
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0150761

Since the first Hodgkin and Huxley ion channel model was described in the 1950s, there has been an explosion in mathematical models to describe ion channel function. As experimental data has become richer, models have concomitantly been improved to better represent ion channel kinetic processes, although these improvements have generally resulted in more model complexity and an increase in the number of parameters necessary to populate the models. Models have also been developed to explicitly model drug interactions with ion channels. Recent models of drug-channel interactions account for the discrete kinetics of drug interaction with distinct ion channel state conformations, as it has become clear that such interactions underlie complex emergent kinetics such as use-dependent block. Here, we describe an approach for developing a model for ion channel drug interactions. The method describes the process of extracting rate constants from experimental electrophysiological function data to use as initial conditions for the model parameters. We then describe implementation of a parameter optimization method to refine the model rate constants describing ion channel drug kinetics. The algorithm takes advantage of readily available parallel computing tools to speed up the optimization. Finally, we describe some potential applications of the platform including the potential for gaining fundamental mechanistic insights into ion channel function and applications to in silico drug screening and development.