Colleen Clancy, professor of pharmacology at the School of Medicine at University of California, Davis, presented her work on a novel approach towards computational medicine as part of the Distinguished Seminar Series on Tuesday in Mason Hall.
In her talk, which was titled “Sex, Drugs and Funky Rhythms,” Clancy talked about her research on the underlying mechanisms of drug interaction in excitable disorders such as cardiac arrhythmia, epilepsy or paralysis.
“Right now, there is no way to know if a drug will succeed or fail in the clinic,” Clancy said.
Clancy said that the main goal of her studies is to develop a computational process to effectively screen drugs by determining the mechanisms of unsuccessful trial drugs as well as successful ones.
“We’re capturing those mechanisms in a mathematical platform,” Clancy said. “Nobody ever goes back and asks, ‘Why did that drug fail?’ but it is such an important question because... it should inform the preclinical screening process.”
D-Sotalol and Amidoraone, two Class III antiarrhythmic drugs, provide a good example of Clancy’s approach.
“[D-Sotalol] was an abject clinical failure, [but] another drug of the exact same class, Amiodarone, was a successful antiarrhythmic drug,” Clancy said. “What is the mechanism that determines those things?”
She said that understanding the exact mechanism will prompt the manufacture of safer drugs, and she hopes that this increased understanding can lead to a more individualized prediction of therapy for patients.
Although there have been recent advances in new pharmacology technology such as NMR Screening, Patch Express and Novel Reagents, Clancy said she isn’t satisfied with any of these new approaches.
“None of them still gets at the fundamental problem,” Clancy said. “Not one of them can predict the effects of multifaceted drug interactions.”
Clancy presented experimental data that analyzes ion channels, because most drugs intended to treat cardiac arrhythmia block voltage gated ion channels. She talked about models of several cardiac sodium channels that help us understand channel kinetics.
The actual pharmacodynamics of a drug is complex, but she determined a few factors that could be parameterized for experiments. These factors include the partitioning, flux, hydrophobic pathway, repartitioning and the neutral and charged states of a drug. Clancy’s lab then measured the effects of certain drugs on cell excitability to understand the kinetics of drug interactions.
With the help of Natalia Trayanova, a Hopkins professor of biomedical engineering, Clancy was able to model a three-dimensional reconstruction of a rabbit heart. On these models, Clancy was able to observe the drug effects on arrhythmia through tissue dynamics by exposing models to an ectopic stimulus to induce a sustained cardiac arrhythmia.
The three-dimensional model with flecanaide exhibited a persistent reentry of arrhythmia when exposed to an ectopic stimulus, while lidocaine did not. Clancy said that this is very significant because arrhythmic reentry among different genders showed that males were better protected from cardiac arrhythmia than women.
In experiments conducted on men, an ectopic stimulus could not obtain a sustained arrhythmia within an interval of hundreds of milliseconds. Even when treated with a potassium channel-blocking drug, stimuli weren’t able to induce an arrhythmia.
For women, however, the results varied according to estrogen levels. At an earlier follicular phase, a stimulus did not induce arrhythmia. At a later follicular phase, there was a more sustained arrhythmia, suggesting that increased estrogen levels caused a proclivity for sustained arrhythmia.
These experiments, according to Clancy, represent a framework that better explains the mechanisms of drug interaction. A proper understanding of computational medicine can improve the preclinical drug screening process as well as the approval process for regulatory agencies. Clinicians and physicians could also better predict the most effective therapy for individual patients.
“If we could begin to build a framework that will allow us to make a prediction, that would be a major step forward,” Clancy said. “I think the future has to also include some of the more genomic approaches.”
She also said that individuals’ own genomic backgrounds can differently influence the effects of drugs.
“We know that not all patients respond the same to drugs,” she said.
According to Clancy, considering and understanding coexisting risks may be the next significant step forward.
“There are very particular tests that are done to determine whether a drug is harmful or not,” she said.
Clancy said that these tests should take into consideration the predetermined factors that influence the drug interaction. For example, the experiments that show a correlation between estrogen levels and cardiac arrhythmia can be used to more effectively screen preclinical drugs. Currently, Clancy is researching other risk factors or environmental inputs that may modify the risk of arrhythmia, such as auditory input.
Clancy’s presentation, as well as all presentations from the Distinguished Seminar Series, can be found online at icm.jhu.edu/seminars.
