In Vitro and In Silico Risk Assessment in Acquired Long QT Syndrome: The Devil Is in the Details.

Acquired long QT syndrome, mostly as a result of drug block of the Kv11. 1 potassium channel in the heart, is characterized by delayed cardiac myocyte repolarization, prolongation of the T interval on the ECG, syncope and sudden cardiac death due to the polymorphic ventricular arrhythmia Torsade de Pointes (TdP). In recent years, efforts are underway through the Comprehensive in vitro proarrhythmic assay (CiPA) initiative, to develop better tests for this drug induced arrhythmia based in part on in silico simulations of pharmacological disruption of repolarization. However, drug binding to Kv11.1 is more complex than a simple binary molecular reaction, meaning simple steady state measures of potency are poor surrogates for risk. As a result, there is a plethora of mechanistic detail describing the drug/Kv11.1 interaction-such as drug binding kinetics, state preference, temperature dependence and trapping-that needs to be considered when developing in silico models for risk prediction. In addition to this, other factors, such as multichannel pharmacological profile and the nature of the ventricular cell models used in simulations also need to be considered in the search for the optimum in silico approach. Here we consider how much of mechanistic detail needs to be included for in silico models to accurately predict risk and further, how much of this detail can be retrieved from protocols that are practical to implement in high throughout screens as part of next generation of preclinical in silico drug screening approaches?

In silico assessment of kinetics and state dependent binding properties of drugs causing acquired LQTS.

The Kv11.1 or hERG potassium channel is responsible for one of the major repolarising currents (IKr) in cardiac myocytes. Drug binding to hERG can result in reduction in IKr, action potential prolongation, acquired long QT syndrome and fatal cardiac arrhythmias. The current guidelines for pre-clinical assessment of drugs in development is based on the measurement of the drug concentration that causes 50% current block, i.e., IC50. However, drugs with the same apparent IC50 may have very different kinetics of binding and unbinding, as well as different affinities for the open and inactivated states of Kv11.1. Therefore, IC50 measurements may not reflect the true risk of drug induced arrhythmias. Here we have used an in silico approach to test the hypothesis that drug binding kinetics and differences in state-dependent affinity will influence the extent of cardiac action potential prolongation independent of apparent IC50 values. We found, in general that drugs with faster overall kinetics and drugs with higher affinity for the open state relative to the inactivated state cause more action potential prolongation. These characteristics of drug-hERG interaction are likely to be more arrhythmogenic but cannot be predicted by IC50 measurement alone. Our results suggest that the pre-clinical assessment of Kv11.1-drug interactions should include descriptions of the kinetics and state dependence of drug binding. Further, incorporation of this information into sophisticated in silico models should be able to better predict arrhythmia risk and therefore more accurately assess safety of new drugs in development.

An improved curvilinear gradient method for parameter optimization in complex biological models.

Mathematical modeling is an often used approach in biological science which, given some understanding of a system, is employed as a means of predicting future behavior and quantitative hypothesis testing. However, as our understanding of processes becomes more in depth, the models we use to describe them become correspondingly more complex. There is a paucity of effective methods available for sampling the vast objective surfaces associated with complex multiparameter models while at the same time maintaining the accuracy needed for local evaluation of minima-all in a practical time period. We have developed a series of modifications to the curvilinear gradient method for parameter optimization. We demonstrate the power and efficiency of our routine through fitting of a 22 parameter Markov state model to an electrophysiological recording of a cardiac ion channel. Our method efficiently and accurately locates parameter minima which would not be easily identified using the currently available means. While the computational overhead involved in implementing the curvilinear gradient method may have contributed to resistance to adopting this technique, the performance improvements allowed by our modifications make this an extremely valuable tool in development of models of complex biological systems.