Last month AstraZeneca and Microsoft made headlines for their pioneering ‘drag and drop’ computer model of cancer cell signalling. Here Jonathan Dry explains why this isn’t simply hyperbole and that we really could speed up cancer drug discovery by cutting out the ‘wet lab’
Computer modelling of signalling pathways in cancer cells and simulation of experiments to test novel drug interventions look set to reduce the ‘wet lab’ work of drug discovery and may help boost our success rate in getting personalised cancer treatments to patients. At AstraZeneca, we are using a cloud-based modelling and simulation tool, called BioModel Analyzer (BMA), to gain new understanding of the millions of potential changes in cell signalling that make cancer cells multiply out of control.
The original idea for this tool came from Jasmin Fisher, Senior Researcher at Microsoft Research and Associate Professor of Systems Biology at the University of Cambridge. Like us, she wanted to develop a biologist-friendly method of investigating the complexity of signalling pathways in a much more systematic way than has previously been possible. Computer modelling isn’t new, but many of the systems are mathematically heavy and difficult for biologists without a background in computer science to use. We needed a system that would ‘think’ like a biologist – not a computer scientist.
In effect, our computer screens become a blank canvas on which we first ‘drag and drop’ the essential components of the biological processes of cancer, such as cells, genes and proteins. Then we add the basic signalling pathways that can go wrong in cancer cells. It’s then over to the computer’s sophisticated algorithms to perform eye-watering numbers of calculations to fill in the gaps in the signalling pathways and predict what happens if different steps are blocked with drugs.
The pathways come to life, modelling logical relationships between many different proteins and simulating experiments that we previously had to do in the lab. We ‘test’ the likely effects of our drugs at different places on a pathway and identify the most promising places to intervene. In a signalling pathway that has become resistant to a cancer drug already in use, new opportunities are highlighted where we can add novel compounds with the potential to restore treatment sensitivity. Perhaps best of all, the information is presented in a visual, graphical way that is easy to understand.
Without computer modelling, drug discovery researchers have to rely on an incomplete understanding of the stages in cell signalling pathways and their own intuition to choose which steps to block. There may be 10s to 100s of different possible points to choose from in a single pathway. Even with the latest high throughput screening methods currently available it may take weeks of effort to identify promising candidates. With computer simulations, all that work can be done in minutes. Faced with an impossible number of different choices, scientists currently tend to choose their favourites to investigate and may therefore miss better options. The beauty of computerised modelling is that you remove the current limitation on the number of hypotheses you can investigate and the potential for human bias.
Read more here: www.labnews.co.uk/features/microsoft-made-oncological-smart-bomb-04-11-2016/