Yang, P.-C. | Search bioRxiv

bioRxiv2019

Atomistic modeling towards predictive cardiotoxicity

Current methods for assessing safety pharmacology in the context of cardiac arrhythmia risk are unable to distinguish between drugs that cause cardiac rhythm disturbances and benign drugs. Drugs deemed likely to be unsafe share the common property of blocking the human Ether-a-go-go-Related Gene (hERG) encoded cardiac potassium channel and consequent prolongation of QT interval on the ECG. However, hERG block and QT prolongation alone are not selective indicators for cardiac arrhythmia. Here we present a prototype computational framework to distinguish between safe and unsafe hERG blockers. We used recent cryo-EM hERG structure to build and validate an atomistic structural model of the channel open conducting state. We also developed structural atomistic models of dofetilide, a hERG blocking drug with high pro-arrhythmia risk, in both charged and neutral ionization states. Next, we employed unbiased and enhanced sampling all-atom molecular dynamics (MD) simulations to probe atomic-scale mechanisms of dofetilide interaction with open-state hERG. Multi-microsecond drug \"flooding\" simulations revealed spontaneous dofetilide binding to the channel pore through the intracellular gate. Umbrella sampling MD was used to compute dofetilide affinity to hERG, in good agreement with experiment, as well as ingress and egress rates, which in a novel linkage between the atomistic and functional scale are utilized in our companion paper (Yang P-C et al. 2019 bioRxiv:635433) to parameterize functional kinetic models of dofetilide - hERG interactions used to predict emergent drug effects on the cardiac rhythm. This study represents the first necessary components of a computational framework for virtual cardiac safety pharmacology screening from the atom to the rhythm.

biophysics↗

bioRxiv2019

A computational pipeline to predict cardiotoxicity:From the atom to the rhythm

We simulate and predict cardiotoxicity over multiple temporal and spatial scales from the drug chemistry to the cardiac rhythm.\n\nABSTRACTDrug-induced proarrhythmia is so tightly associated with prolongation of the QT interval that QT prolongation has become widely accepted as a surrogate marker for arrhythmia. The problem is that QT interval as an arrhythmia indicator is too sensitive and not selective, resulting in many potentially useful drugs eliminated early in the drug discovery process. We first set out to predict the fundamental mode of binding for the proarrhythmic drug dofetilide with the promiscuous cardiac drug target, the hERG potassium channel. In a novel linkage between the atomistic and functional scales, computed binding affinities and rates from atomistic simulation are utilized here to parameterize function scale kinetic models of dofetilide interactions with the hERG channel. The kinetic model components are then 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 from published studies were used to validate model framework and showed excellent agreement, demonstrating feasibility of the approach. The model predictions show that a clinically relevant dose of dofetilide increased arrhythmia vulnerability in all emergent TRIaD-linked parameters including Triangulation, Reverse use-dependence, beat-to-beat Instability and temporal and spatial action potential duration Dispersion. Application of machine learning demonstrated redundancy in the TRIaD linked parameters and suggested that changes in beat-to-beat instability were highly predictive of arrhythmia vulnerability in this setting. Here, we demonstrate the development and validation of a prototype multiscale model framework to predict electro-toxicity in the heart for the proarrhythmic drug dofetilide from the atom to the rhythm.\n\nSIGNIFICANCE STATEMENTCardiotoxicity in the form of deadly abnormal rhythms is one of the most common and dangerous risks for drugs in development and clinical use. There is an urgent need for new approaches to screen and predict the effects of chemically similar drugs on the cardiac rhythm and to move beyond the QT interval as a diagnostic indicator for arrhythmia. To this end, we present a computational pipeline to predict cardiotoxicity over multiple temporal and spatial scales from the drug chemistry to the cardiac rhythm. We utilize predicted quantitative estimates of ion channel-drug interactions from our companion paper to simulate cardiotoxicity over multiple temporal and spatial scales from the drug chemistry to the cardiac rhythm.

systems biology↗