Abstract
Safety and efficacy testing is a crucial part of the drug development process, and several different methods are used to obtain the necessary data (e.g. in vitro testing, animal trials and clinical trials). Our group has been investigating the potential of modelling and simulation as an alternative approach to some of the methods used for testing drugs for cardiac effects. To achieve our goal of developing and promoting novel approaches in drug development, we formed multidisciplinary collaborations that included clinicians, computer scientists and biologists. Our in silico models are based on human data (e.g. magnetic resonance images, electrocardiogram) and on current knowledge of human electrophysiology, thus generating predictions that are directly applicable to humans. Such models are a particularly powerful tool because they encompass different sources of population heterogeneity, which is crucial for drug testing and for assessing how interindividual variability might affect clinical endpoints. Our group has shown that computer modelling can be used to predict the effects of a test drug in a virtual population or in combination with machine learning to predict different phenotypes when a drug is given to a diseased population. Furthermore, our user-friendly drug testing software is freely available and is being adopted by industry in their drug development process. We have been engaging with industry and regulators to show that our models can contribute to the replacement of animals in drug development. Our ambition is to generate models for simulation of different diseases and therapies for investigations from subcellular to whole organ.
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