Interdisciplinary Initiatives Program Round 9 - 2018
Ellen Kuhl, Mechanical Engineering
Euan Ashley, Medicine (Cardiovascular Medicine)
The average cost to develop a new drug is $2.5 billion and the time to market is more than ten years. A critical step towards approving a new drug is the assessment of its side effects on the heart. Numerous drugs—not just cardiac drugs—interact with specific ion channels in the heart and induce potentially lethal cardiac arrhythmias. While pro-arrhythmic risk evaluation is crucial to prevent the development of dangerous drugs, many safe and potentially useful drugs are screened out of the early stages of development and never make it to market. To actively address this challenge, regulatory bodies including the FDA are now beginning to recognize the potential of computational modeling to accelerate preclinical drug screening. Motivated by this critical need, our research aims at creating novel technologies to quickly and reliably screen big data using machine learning to characterize the pro-arrhythmic potential of new and existing drugs. We will combine single cell electrophysiology, isolated whole heart experiments, computational simulation, and machine learning to identify critical drug concentrations beyond which the heart loses its regular rhythm. Current technologies define these critical concentrations based on a few non-specific criteria and often over- or under-estimate the risk of a drug. Our novel approach will integrate information from four inherently different disciplines—electrophysiology, pharmacology, biophysics, and computer science—across ten orders of magnitude in space and time to provide a broader picture of the effects of drugs, both in isolation and in combination with other drugs. Ultimately, this Bio-X project will establish science-based criteria to accelerate drug development, design safer drugs, and reduce rhythm disorders in the heart.