A new computer model of the human heart could help predict side effects of drugs used to treat abnormal heart rhythms, according to its developers.
The approach uses one set of mathematical equations to represent the opening and closing of ion channels in individual heart cells while another set mathematically connects these events in order to simulate whole heart tissues.
In a proof-of-concept paper published last month in Science Translational Medicine, the researchers describe how they used the model to test two well-studied anti-arrhythmia treatments: lidocaine, which has a strong safety profile; and flecainide, which bears a warning on its label that it can cause irregular heartbeats in some patients.
Normally, individual heart cells generate electrical signals through ion channels in cell membranes. These signals spread from cell to cell as electrical waves in the entire organ and cause the tissue to contract and release at regular intervals, circulating blood throughout the body. When this action is disrupted, the heart fails to pump at regular intervals and causes arrhythmia.
The new model shows “the detailed kinetics of the heart’s sodium channels, first in the context of a single cell and then in two- and three-dimensional cardiac tissue,” the paper explains.
In one test, the single-cell model showed that lidocaine and flecainide effectively normalized heartbeats. However, the whole-tissue model predicted that flecainide would cause serious side effects when administered in typical clinical doses.
In another test, the model predicted flecainide's tendency to make the heart “extra sensitive to heartbeats occurring too early or too late,” which causes more severe arrhythmias in some patients, the paper said.
The team used rabbit hearts to confirm both tests' results.
Predicting the Unpredictable
Study co-author Colleen Clancy, an associate professor of pharmacology at UC Davis, told BioInform that her team developed the model to help develop drugs for “diseases of excitability,” such as cardiac arrhythmias, in which more drugs have failed than any other medical specialty, she said.
Other co-authors of the paper include researchers from Cornell University, Columbia University, and Johns Hopkins University.
Clancy explained that many anti-arrhymic drugs underperform “because there is no way to make predictions about how drugs that are very complicated in their action will alter emergent electrical rhythms in the heart.”
Most anti-arrhythmia drugs target ion channels, but they interact with those ion channels "in a very complicated way,” she explained. “They don't just plug the hole and sit there; they actually alter the cardiac rhythm, which in turn alters the efficacy of the drug … so when unpredictable things happen like an arrhythmia, which is very irregular, it's almost impossible to predict how a drug will operate in that complicated environment.”
Clancy’s team constructed the model by “modifying and fitting a Markovian representation of the cardiac [sodium] channel” that anti-arrhythmia medications target. They then used experimental data to determine "access, diffusion, and channel conformation-specific affinity” of lidocaine and flecainide, according to the paper.
“We [made] a lot of different measurements that showed exactly how the drugs interacted with the ion channel depending on the concentration of the drug, the heart rate, and a few other factors, and then … we develop[ed] a mathematical model that could reproduce all of those interactions in the computer,” Clancy explained.
"By simulating those interactions, we could then take that small model and put [it] into a larger model that represents a whole [heart] cell's electrical activity and then connect those cells together to make tissue representations of the heart."
Lastly, the researchers used MRI-based reconstructions from patients "to recreate in silico virtual ventricles with all of the complicated geometry that’s intrinsic to the heart,” she said.
The researchers chose lidocaine and flecainide for the proof of concept because both drugs have been widely studied, Clancy said.
For example, a study performed in the mid-1980s called the Cardiac Arrhythmia Suppression Trial found that flecainide “caused more deaths than no drug at all,” Clancy said. “Our model predicts [that] that would be the case” and the CAST trial provides "clinical validation” of its predictions, she added.
“In the absence of those types of studies, we would never know if we were making accurate predictions, but what this tells us is that the framework is reasonable and that it works and it does make correct predictions," Clancy said.
She said the next step for her team “is to expand that framework … [and] to develop prototype models for all of the currently used anti-arrhythmic drug classes.”
The team intends to test four or five classes of drugs.
“Once we have a structure that includes a variety of different drugs — maybe 10 or 12 drugs to start with — then small modifications can be made to cover lots of other different types of drugs,” she said.
Ultimately, the team hopes to use the computer model to develop patient-specific targeted therapies, Clancy said.
“Our goal really is to develop an automated framework that could be used for high-throughput screening of existing drugs that are in the development stage, and also to design ideal therapeutics for specific arrhythmic situations,” she said.
As an example, Clancy said, the model could be used to reconstruct a particular type of arrhythmia, such as tachycardia-dependent arrhythmia.
With the model, the researchers could figure out “the type of properties that an ideal drug would need to have to cure that arrhythmia or prevent it.”
Separately, the researchers also explored the possibility of a genetic component that could influence how patients respond to treatments for arrhythmia and other cardiac conditions, Clancy said.
For example, in the case of congenital long QT syndrome, the team explored "how a drug would be expected to act in that altered setting." Specifically, they studied "whether the channel is behaving differently and the whole heart is behaving differently as a result, and whether the drug would be expected to improve or worsen that patient's condition.”
The team plans to explore the effect of long QT syndrome and other genetic factors in a later paper, Clancy said.
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