Population-based mechanistic modeling allows for quantitative predictions of drug responses across cell types

Abstract

Quantitative mismatches between human physiology and experimental models can present serious limitations for the development of effective therapeutics. We addressed this issue, in the context of cardiac electrophysiology, through mechanistic mathematical modeling combined with statistical analyses. Physiological metrics were simulated in heterogeneous populations describing cardiac myocytes from adult ventricles and those derived from induced pluripotent stem cells (iPSC-CMs). These simulated measures were used to construct a cross-cell type regression model that predicts adult myocyte drug responses from iPSC-CM behaviors. We found that quantitatively accurate predictions of responses to selective or non-selective drugs could be generated based on iPSC-CM responses and that the method can be extended to predict drug responses in diseased as well as healthy cells. This cross-cell type model can be of great value in drug development, and the approach, which can be applied to other fields, represents an important strategy for overcoming experimental model limitations.