Jonathan Dry, Principal Scientist and global strategy lead, Bioinformatics, Oncology, Innovative Medicines and Early Development (IMED) at AstraZeneca explains that BMA is bringing a new level of understanding of the intricacies of drug targeting:
“We’re moving away from hitting a pathway just because we know it’s active. Instead, we’re identifying the best place to hit the pathway in order to get the best possible response for that patient. For AstraZeneca, there is also the potential to improve our drug development success rate – by focusing our oncology research on the targets that matter most.”
Biologist-friendly approach
BMA enables drug discovery biologists to ‘drag and drop’ cells, genes and proteins on to their computer screen and draw in the basic signalling pathways that can go wrong in cancer cells. Sophisticated computer algorithms then fill in the gaps and predict what would happen if different steps were blocked with drugs.
“As a biologist myself, I wanted a user-friendly method of investigating the complexity of signalling pathways in a much more systematic way than was possible in the past,” says Jasmin Fisher, Senior Researcher at Microsoft Research and Associate Professor of Systems Biology at the University of Cambridge, who had the original idea for BMA.
“We spent a lot of time and trouble converting heavy computer science into ‘one click’ analysis that would present information in a visual, graphical way for experimental biologists,” she adds.
Dry explains that BMA is one of several simulation tools being used to predict drug effects on cancer cell signalling pathways. But it works in a simple, logical way and is much easier to use routinely than some of the more ‘mathematically heavy’ systems being developed by other research groups and software companies.
“The Microsoft system captures the way that the biologist thinks about the pathway instead of trying to fully mathematically model it, and that suits the way we work,” he says.
Targeting AML
For the AML project, IMED scientists collected genomic, transcriptomic and proteomic data from AML cell lines in the lab while Microsoft computer scientists worked on algorithms to model all the possible variations in a key signalling pathway and their likely outcomes.
Computer simulations were performed to predict how combinations of drugs could be used to combat resistance to drugs that target cell signalling in AML, and how this might vary between cell lines. In silico data were compared with results from in vitro tests.
“Even I was surprised by the accuracy of the simulation,” says Dry. “It told us which drug combination would make a cell sensitive to treatment and the protein changes that led to that cell becoming sensitive.”
The AstraZeneca-Microsoft team will now prioritise specific oncology projects, for example, in breast cancer, for the simulation approach.
Dry concludes that simulating the effects of drug combinations at key points for individual patients has the potential to move personalised medicine well beyond its current boundaries. Ultimately, he says, such simulations could even take account of interactions between a tumour and its broader environment, including host immunity and DNA damage response mechanisms.