Toward Digital Twin Technology for Precision Pharmacology

Abstract

The authors demonstrate the feasibility of technological innovation for personalized medicine in the context of drug-induced arrhythmia. The authors use atomistic-scale structural models to predict rates of drug interaction with ion channels and make predictions of their effects in digital twins of induced pluripotent stem cell-derived cardiac myocytes. The authors construct a simplified multilayer, 1-dimensional ring model with sufficient path length to enable the prediction of arrhythmogenic dispersion of repolarization. Finally, the authors validate the computational pipeline prediction of drug effects with data and quantify drug-induced propensity to repolarization abnormalities in cardiac tissue. The technology is high throughput, computationally efficient, and low cost toward personalized pharmacologic prediction.

Keywords: digital twins; dofetilide; hERG; iPSC-CMs; moxifloxacin.

FIGURE 1. Drug Screening in Digital Twins and Validation by Clinical and Experimental Data

(A) Comparison of predicted effects of drugs on experimentally recorded iCell induced pluripotent stem cell–derived cardiomyocyte (iPSC-CM) action potential duration at 90% repolarization rate (APD90) and digital-twin iPSC-CM APD90. We constructed 205 digital-twin iCell iPSC-CMs by randomly varying within the SDs of the experimental measurements. (B) Comparison of simulated iPSC-CM ΔAPD90 to experimentally recorded iPSC-CMs for moxifloxacin (Mox) concentrations ranging from 10 to 70 μM (left) and a range of dofetilide (Dof) concentrations (0.5-4 nM). Example action potentials (APs) demonstrating the effect of moxifloxacin and dofetilide on AP morphology and duration from iCell digital twins are also shown. (C) Experiments (purple) and digital twins (blue) of a long-QT syndrome type 1 (LQT1) mutation.^, (D) LQT1 example traces with early afterdepolarizations after 140 μM moxifloxacin and 4 nM dofetilide. APD = action potential duration; APD90cF = Fridericia-corrected action potential duration at 90% repolarization rate; Sims = simulations.

FIGURE 2. Prediction of Initiation and Longevity of Arrhythmia Proclivity in an Idealized 1-Dimensional Digital Tissue Twin Model

Shown is electric activity for a multiring, 1-dimensional digital tissue twin model comprising 1,019 induced pluripotent stem cell–derived cardiomyocyte digital twins connected by resistances to simulate gap junctions with 100 spontaneous beating cells driving depolarization. Each simulated tissue contained identical patterns of randomized spatial heterogeneity imposed by randomly varying within the SDs of the experimental measurements. (A) Prediction of control case in the absence of drug (n = 10 simulations). (B) Predicted effect of the application of 2 nM dofetilide (Dof) (n = 10 simulations). (C) Application of 10 μM moxifloxacin (Mox) (10 simulations). The bar graphs on the right show proxy metrics for cardiac cycle length (left bars) and dispersion of repolarization (right bars; maximum – minimum cardiac cycle).

CENTRAL ILLUSTRATION. The Development of Digital Twins of iPSC-CMs Is a Step Toward the Prediction of Individual Responses to Drugs

The left column shows the in vitro approach resulting in the differentiation of induced pluripotent stem cells (iPSCs) into iPSC-derived cardiomyocytes (iPSC-CMs) with patient-specific electrophysiology. The right column shows the computational pipeline from atomistic simulation structural model to predict drug–hERG channel kinetics that can then be incorporated into iPSC-CM digital twins, constituting a new high-throughput and low-cost approach to personalized drug screening. In the bottom row, digital outputs are compared with and validated by experiments demonstrating the feasibility of the technology. Dof = dofetilide; hERG = human ether-a-go-go–related gene; Mox = moxifloxacin.