New model takes cardiac research results from animals to humans

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Newswise – A team of scientists at UC Davis Health have developed a new predictive model that translates cardiac research results on different animal species into information specific to humans. This model could help speed up the drug development process, leading to new therapies for heart disease, such as irregular heartbeat (arrhythmia).

In their article published today in Science Advances, the researchers presented a set of translators to map the electrical activity of mouse, rabbit and human heart cells.

“The combined efforts of basic, translational and clinical research have greatly assisted our understanding of disease mechanisms over the past decades, but many challenges remain,” said Stefano Morotti, assistant professor in pharmacology To UC Davis School of Medicine and the main author of the study. “One of the main challenges is to translate the results of tests and trials on animal models into human applications.”

Differences between animal species affect results

Animal research remains essential in evaluating the efficacy and safety of new cardiac therapies. However, there are notable structural and functional differences between species. These include the size and shape of the heart, as well as the heart rate, which is, for example, about ten times faster in a mouse than in a human.

Understanding the magnitude of these differences is important for the development of antiarrhythmic treatments for heart patients.

“The implications of interspecies differences are significant in the pharmaceutical field, where there are growing concerns about the safety and efficacy of drugs tested on animals,” Morotti said.

Translate findings from one species to another

Prior to conducting clinical trials in humans, researchers primarily use small mammals such as mice and rabbits to study the heart’s electrical mechanism, known as the electrophysiological response. Despite the genetic similarities, the differences in cardiac function in mammals are clear both at the organ level and at the cellular level.

The research team simulated computer models of mouse, rabbit, and human heart muscle cells to create a set of translators to map electrophysiological responses across species. They have built a system that allows them to translate the measurements obtained in animals, to humans. These could include the effects of medications, heart rate irregularities, or changes due to heart disease.

They have shown that these predictive tools are well suited for predicting human electrophysiological changes in response to antiarrhythmic drugs, based on a response measured in animal models. They have also shown that these tools can help design experiments that maximize transferability to human physiology.

Predictive modeling tools to support drug discoveries

The researchers suggest integrating the predictive model into drug development studies and pipelines.

“In accordance with the 3R principles of replacement, reduction and refinement in animal research, the adoption of modeling and translation tools in experimental practice could be beneficial in several applications of animal experimentation,” said Ele Grandi, associate professor of pharmacology at UC Davis School of Medicine and lead author of the study. “We have demonstrated that this mathematical modeling approach is useful in predicting the effects of cardiac disorders in different species or experimental conditions.”

High throughput screening (HTS) is a drug discovery process that automatically tests a large number of compounds for a specific biological target. The team plans to integrate their tool directly into HTS systems used in the pharmaceutical industry to assess the efficacy and safety of new drugs.

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Other UC Davis co-authors are Caroline Liu, Bence Hegyi, Haibo Ni, Alex Fogli Iseppe, Lianguo Wang, Crystal M. Ripplinger, Donald M. Bers and Andrew G. Edwards, and Marco Pritoni of Lawrence Berkeley National Laboratory .

This work was supported by grants from the National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (NIH) (R00HL138160, R01HL131517, P01HL141084 and R01HL111600), NIH Stimulating Peripheral Activity to Relieve Conditions Grant (1OT2OD026580-01 ), American Heart Association Scientist Development Award (15SDG24910015), Postdoctoral Fellowship (20POST35120462), UC Davis School of Medicine Dean’s Fellow Award and Health and Environmental Sciences Institute Grant (U01FD006676-01).

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