Making drugs safer by virtual drug screening

Making drugs safer by virtual drug screening

Making drugs safer by virtual drug screening

Colleen E. Clancy, Ph.D., is a professor in the Department of Pharmacology at the UC Davis School of Medicine and a pioneer in the field of biophysics.

Together with her team, Clancy studies the mechanisms associated with electrical disorders of the heart. Unlike traditional research methods that rely on animal models or human clinical testing, her laboratory uses complex computational and mathematical predictive modeling approaches to better understand the mechanisms of disease as well as how they are impacted by treatment options.

Cardiotoxicity is one of the most common reasons for drug removal from the market. It often manifests as irregular electrical rhythms with the potential for fatal ventricular arrhythmias, but predicting the beneficial and harmful actions of drugs on the heart’s electrical cycle remains imprecise using current methods.

Clancy and her team have identified a better approach for preclinical drug screening that is both specific and sensitive, and that identifies actual “proarrhythmia,” rather than substitute markers. The approach involves a computational pipeline that starts with drug chemistry profiling and extends all the way to predictions of drug effects in virtual cardiac tissue. By building detailed models of drugs and their interactions with targets at atomic resolution, Clancy’s approach creates the potential to predict these interactions. These computer-generated data are then incorporated into virtual excitable cells, which can be connected to form functional models of tissues. Once established, the model represents a virtual pipeline through which drugs can be screened prior to clinical studies in order to predict unintended cardiac events. It is expected that this versatile technology, upon commercialization, could help to more effectively screen new drugs for efficacy and cardiotoxicity, and also be used to modify drugs that have been removed from the market or failed during clinical testing.

Recently, Clancy’s team developed a model system with the potential to prevent cardiac arrhythmias by predicting drug interactions with a specific potassium ion channel in the heart (UC Case 2016-665). Abnormal drug-induced cardiac electrical activity is most often a side effect from an unintended block of the promiscuous drug target hERG1, the pore-forming domain of the delayed rectifier potassium ion channel in the heart. A block of hERG1 results in prolongation of the QT interval on the ECG, a phase of the cardiac cycle that corresponds to ventricular repolarization.

Not all hERG1 block is proarrhythmic. At present, however, there is no way to distinguish unsafe hERG1 blockers from drugs that are safe. Clancy and her team use their integrative approach, scaling from atom to tissue, to predict the structure-activity relationships that determine proarrhythmia for hERG1- blocking drugs.

 

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