Fast drug action solving from cardiac action potential by model fitting in a sampled parameter space.

Model-based predictive approaches have been receiving increasing attention as a valuable tool to reduce cost in drug development. In this work, a model-fitting-based approach for solving drug actions using cardiac action potential recordings is investigated. Contribution of major ion currents in cardiac membrane excitation has been intensively studied. Cardiac cell models nowadays reproduce APs very precisely. Giving a test AP, the activities of involved ion channels can be determined by fitting the cell model to reproduce the test AP. Using experimental APs recordings both before and after drug dose, drug actions can be estimated by changes in channel activity. Due to the high computational cost in calculating cardiac models, a fast approach using only precalculated sample set is proposed. The searching strategy in the sampled space is divided into two steps: in the first step, the sample of best similarity comparing with the test AP is selected; then response surface approximation using the neighboring samples is followed and the estimation value is obtained by the approximated surface. This approach showed quite good estimation accuracy for a large number of simulation tests. Experiments using animal AP recordings from drug dose trials were also exemplified in which case the ICaL inhibition effect of nifedipine [10] was correctly discovered.