A multiscale computational model framework successfully predicted drug-induced electrotoxicity and arrhythmia vulnerability from atomistic to tissue scales, showing excellent agreement with clinical data.
Does a multiscale computational model accurately predict drug-induced proarrhythmia from drug chemistry?
A novel multiscale computational pipeline successfully predicts drug-induced proarrhythmia from chemical structure, offering a new approach for drug safety screening.
RATIONALE: Drug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation is an accepted surrogate marker for arrhythmia. But QT interval is too sensitive a marker and not selective, resulting in many useful drugs eliminated in drug discovery. OBJECTIVE: To predict the impact of a drug from the drug chemistry on the cardiac rhythm. METHODS AND RESULTS: In a new linkage, we connected atomistic scale information to protein, cell, and tissue scales by predicting drug-binding affinities and rates from simulation of ion channel and drug structure interactions and then used these values to model drug effects on the hERG channel. Model components were integrated into predictive models at the cell and tissue scales to expose fundamental arrhythmia vulnerability mechanisms and complex interactions underlying emergent behaviors. Human clinical data were used for model framework validation and showed excellent agreement, demonstrating feasibility of a new approach for cardiotoxicity prediction. CONCLUSIONS: We present a multiscale model framework to predict electrotoxicity in the heart from the atom to the rhythm. Novel mechanistic insights emerged at all scales of the system, from the specific nature of proarrhythmic drug interaction with the hERG channel, to the fundamental cellular and tissue-level arrhythmia mechanisms. Applications of machine learning indicate necessary and sufficient parameters that predict arrhythmia vulnerability. We expect that the model framework may be expanded to make an impact in drug discovery, drug safety screening for a variety of compounds and targets, and in a variety of regulatory processes.
Yang et al. (Wed,) conducted a other in Drug-induced proarrhythmia. Multiscale computational model framework was evaluated on Prediction of drug impact on cardiac rhythm. A multiscale computational model framework successfully predicted drug-induced electrotoxicity and arrhythmia vulnerability from atomistic to tissue scales, showing excellent agreement with clinical data.