We have published an article on the research we are doing on in silico heart models and the potential this technology has on reducing the use of animals on the drug development process.
Before new medicines reach patients several tests are done to detect possible risks and side effects. This is the reason why drugs are tested on millions of animals worldwide each year. But this landscape is changing: research shows computer simulations of the heart have the potential to improve the drug development process and to reduce the need for animal testing.
Drug development requires a long and costly pipeline, where the drug is tested in a gradually more realistic setting (i.e. the experimental model), where animals are the last step before human volunteers. The challenge is to identify the correct threads for human health, and not to miss the potential risk or value of a drug for example due to the differences between animals and humans.
The revolutionary idea is to test a new drug in a “virtual human”, an experimental model entirely built on the circuits of a computer. Recent research by the our team demonstrates that computational models representing human heart cells show higher accuracy than animal models in predicting adverse drug effects such as dangerous arrhythmias.
This research has recently been awarded with the International 3Rs Prize (from the National Centre for the Replacement Refinement and Reduction of Animals in Research) because of its potential to replace animal testing in labs. Instead of a one-model-fits-all method, the team uses an approach that simulates a wide range of responses under several conditions tested against experimental data. Everyone is different, and some drugs can have harmful side effects only for certain parts of the population, such as people with a specific genetic mutation or disease. This research also won the Technological Innovation Award at the Safety Pharmacology Society Meeting 2017.
Distinct ECG phenotypes identified in hypertrophic cardiomyopathy using machine learning associate with arrhythmic risk markers.
A. Lyon, R. Ariga, A Minchole, M. Mahmod, E. Ormondroyd, P. Laguna, N. de Freitas, S. Neubauer, H. Watkins, Blanca Rodriguez. Doi: 10.3389/fphys.2018.00213. 2018.
In silico evaluation of arrhythmia
X. Zhou, A. Bueno-Orovio, B. Rodriguez. Current Opinion in Physiology. 1: 95–103. Doi: 10.1016/j.cophys.2017.11.003. 2017.
Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances.
A. Lyon, A. Minchole, J.P. Martinez, P. Laguna, B. Rodriguez. Journal of the Royal Society Interface. 15: 20170821. Doi: 10.1098/rsif.2017.0821. 2018.