Completed Cells, Biochemistry & Physiology Heart, Stroke & Blood

Developing cardiac electrophysiology models for drug safety studies

In plain English

AI plain-English summary

A new generation of computer models is being built to predict how drugs will disrupt the heart's electrical rhythm before they ever reach human volunteers. The problem is that current preclinical safety tests often miss dangerous side effects, forcing regulators to rely on expensive and time-consuming clinical studies called ThoroughQT trials. These mathematical models simulate how multiple ion channels in heart cells respond to a drug, then calculate the risk of arrhythmia. The team is refining shorter, more information-rich experimental protocols to build cell-specific models, then validating those predictions against real experiments. They are also tackling two hard problems: model selection—choosing the right set of equations—and model discrepancy, which quantifies how far a simulation strays from reality. All predictions will be expressed as probability distributions, not single numbers, so regulators can see the uncertainty. If successful, these models could replace the ThoroughQT study entirely, cutting drug development costs and speeding safer medicines to market. The team is already working with pharmaceutical companies and drug regulators to test and roll out the improved models in safety-critical settings.

View original technical description
Simulations of the effect of multiple ion channel block on cardiac electrophysiology have become a key part of proposals to replace the ThoroughQT clinical study with more accurate preclinical tests. Success depends on having accurate mathematical models of both baseline cellular electrophysiology and drug-induced changes. Mathematical models include both ion channel kinetics (and drug effects on these) as well as action potential electrophysiology with appropriate expression levels of each ion channel type. We will extend and refine a new approach we have developed that uses shorter, more information-rich, experimental protocols for developing cell-specific mathematical models of ion channel and cellular electrophysiology, then predict drug responses with these models and validate against experiments. To make models that are ready for safety-critical use the problem of model selection (selecting the most appropriate set of equations) will also be addressed, as will model discrepancy – assessing, quantifying and predicting the difference between the model and reality. We will include uncertainty in our models and their inputs in a probabilistic framework, such that our predictions take the form of probability distributions. We will continue close collaboration with pharmaceutical companies and drug regulators to test and roll-out improved models.

View the original record at the funder ↗

Researchers

Gary Mirams (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Improving assessment of drug-induced cardiac risk with mathematical electrophysiology models.
Prediction of human cardiotoxic QT prolongation using in-vitro multiple ion channel data and mathematical models of cardiac myocytes
Cardiac electrophysiological homeostasis
Modelling drug binding to biological ion channels
Modelling the cellular cardiac neural axis in the control of excitability

Original classification

Senior Research Fellowship Basic

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.