The march toward replacing animals in drug testing with computer simulations has made another significant step forward through the development of software which can predict possible side effects for the heart when taking a new drug. Rather than a one-model-fits-all, this software uses a population-based approach, which is an important step towards personalised medicine. Indeed, some drugs can have harmful side effects only for certain parts of the population, for example, causing arrhythmias or sudden cardiac death in people with a specific genetic mutation or disease.
When developing a new drug, a pharmaceutical company has to determine whether it will have detrimental effects on the heart – cardiotoxicity – for certain people. Cardiotoxicity is one of the main causes of withdrawal during drug development, and testing for it has traditionally involved animals.
The “Virtual Assay” software developed at the University of Oxford uses computer (in silico) models based on human data, which are a fast, cheap and potentially more effective alternative to experimental testing.
One of the researchers on the project, Elisa Passini from Oxford’s Department of Computer Science, said: “Using the Virtual Assay software and human-based computer models removes the need to translate results from animals, thus increasing prediction accuracy in humans. By using our software at early stages in drug development, pharmaceutical companies could majorly increase the quality of compounds making it to clinical trials, reducing late drug withdrawals due to un-detected cardiotoxic effects. This will reduce costs and time, as well as decrease the need for using animals.”
The University of Oxford team is collaborating with several pharmaceutical companies, who are using and evaluating the Virtual Assay software. This collaborative work is ongoing, and the team will carry on refining the software to meet industry’s needs.
One paper on the research (Human in Silico Drug Trials Demonstrate Higher Accuracy than Animal Models in Predicting Clinical Pro-Arrhythmic Cardiotoxicity) has received on 12 March this year’s International 3Rs prize sponsored by GSK and awarded by The National Centre for the 3Rs (NC3Rs) – an organisation dedicated to replacing, refining and reducing the use of animals in research and testing. This research also won the Safety Pharmacology Society Technological Innovation Award 2017.
Professor Blanca Rodriguez, who oversees the project, says: ‘We believe that in silico models can now start making a real difference to the quality of outcomes from drug trials, and reduce the number of animals used in them. We are thrilled that our software is now ready to be widely used by the pharmaceutical industry, clinicians and other universities.”
The integration of human-based computer models in the pre-clinical stages of drug development is also one of the objectives of the Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative, promoted by the United States Food and Drug Administration (FDA) and other organisations, to facilitate the adoption of new models for assessment of drug-induced clinical risk of arrhythmias.
This research is part of a range of in silico projects by Professor Rodriguez’s and Dr Bueno-Orovio’s group at Oxford, which include developing virtual heart models and research into chronic pain and diabetes. The research was funded by the Wellcome Trust, Engineering and Physical Sciences Research Council, CompBioMed project (EU), TransQST project (IMI) and the NC3Rs.
Dr Vicky Robinson, NC3Rs Chief Executive said: “This is more great work from the Oxford team [Oliver Britton’s research won the 3Rs prize in 2014] which really highlights the massive potential for computer simulations to replace animal use as well as provide meaningful human-relevant data that helps to ensure the safety of new drugs. The in silico drug trial is the validation that the pharmaceutical industry has been waiting for and I am delighted that the Panel selected this stellar paper for our annual 3Rs prize.”
Virtual Assay is now available for download in the Oxford University Innovation Store: https://process.innovation.ox.ac.uk/software