Toward Digital Twin Technology for Precision Pharmacology

Area of Science:

• Computational biology
• Pharmacology
• Personalized medicine

Background:

• Drug-induced arrhythmia poses a significant clinical challenge.
• Predicting individual patient susceptibility to adverse drug reactions is crucial for personalized medicine.

Purpose of the Study:

• To demonstrate the feasibility of technological innovation for personalized medicine in predicting drug-induced arrhythmia.
• To develop and validate a computational pipeline for assessing drug effects on cardiac electrophysiology.

Main Methods:

• Utilized atomistic-scale structural models to predict drug-ion channel interactions.
• Employed digital twins of induced pluripotent stem cell-derived cardiac myocytes for effect prediction.
• Constructed a 1D ring model to predict arrhythmogenic dispersion of repolarization.
• Validated computational predictions with experimental data.

Main Results:

• Successfully predicted drug effects on cardiac myocytes and tissue.
• Quantified the propensity of drugs to induce repolarization abnormalities.
• Demonstrated a high-throughput, computationally efficient, and low-cost approach.

Conclusions:

• The developed technology is feasible for personalized pharmacologic prediction.
• This innovation offers a pathway toward safer and more effective drug therapies.
• Enables precise prediction of drug-induced arrhythmia risk for individual patients.