Top prize for article on computer model that could replace animal testing

New computer model to reduce animal testing

By using human data, rather than animal data, the model improves how test results translate to humans and reduces the need for animal experimentation. Image: Shutterstock

New software predicts the clinical risk of drug-induced side effects for the heart with higher accuracy than animal experiments.

— By University of Oxford and NC3Rs

Research published in Frontiers in Physiology has won an international prize for its contribution toward replacing, refining and reducing the use of animals in research and testing.

The research, led by Dr Elisa Passini and colleagues from the University of Oxford and Janssen Pharmaceutica, developed original software which predicts the clinical risk of drug-induced side effects for the heart with higher accuracy than animal experiments.

Early prediction of cardiotoxicity is critical for drug development. Around 40% of drugs that were withdrawn from the market in 2001-2010 had cardiovascular safety issues in humans. The risk to cardiac safety is assessed in in vitro assays and animal models, with a range of species including rodents, rabbits, dogs and non-human primates being used. Estimates suggest that more than 60,000 animals are used globally for this purpose each year.

The ‘Virtual Assay’ software developed by the Oxford team will reduce this number by improving the identification and elimination of cardiotoxic drugs prior to extensive animal testing. By using human data, rather than animal data, the in silico model improves how test results translate to humans and reduces the need for animal experimentation. Rather than a one-model-fits-all, this software uses a population-based approach, which is an important step towards personalized medicine. Several pharmaceutical companies are already using the Virtual Assay with promising results, and collaboration with industry is ongoing.

The International 3Rs Prize was awarded on March 12 by the National Centre for the 3Rs (NC3Rs) and sponsored by GSK. The prize consists of an £28,000 grant and a £2,000 personal award. The same article also won the Safety Pharmacology Society Technological Innovation Award 2017.

The research was conducted in Professor Blanca Rodriguez and Dr Alfonso Bueno-Orovio’s group at the University of Oxford’s Department of Computer Science, and funded by the Wellcome Trust, Engineering and Physical Sciences Research Council, CompBioMed project (EU), TransQST Project (IMI) and the NC3Rs. Professor Rodriguez and Dr. Bueno-Orovio are recipients of an NC3Rs Infrastructure for Impact grant to promote the profile of in silico human models for the 3Rs.

The winning research builds on work by the same team, awarded the 3Rs Prize in 2014. Dr Oliver Britton and colleagues then established a computer model that incorporated variations in ‘normal’ heart electrophysiological properties based on existing data from rabbit Purkinje fibres (cardiac cells). Oliver has subsequently been awarded an NC3Rs project grant to extend the same principles to pain research.

Open to researchers in academia and industry worldwide, the 3Rs competition  recognizes an article published in the last three years that has significant impacts on the replacement, reduction or refinement of animals in research. It has been awarded by the NC3Rs since 2005 with sponsorship from GSK.


Original article: Human In Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity

Corresponding author: Dr. Elisa Passini

REPUBLISHING GUIDELINES: Open access and sharing research is part of Frontier’s mission. Unless otherwise noted, you can republish articles posted in the Frontiers news blog — as long as you include a link back to the original research. Selling the articles is not allowed.

1 Comment on Top prize for article on computer model that could replace animal testing

  1. jacqueline brandao // March 19, 2018 at 12:08 pm // Reply

    pourquoi les médias n’en parlent pas ?

    Like

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