The five-year agreement between the University of Edinburgh's School of Informatics and the Riken Genomic Research Centre aims to understand why the effectiveness of cancer treatments can vary between patients. This increased understanding of how each drug works could also assist the pharmaceutical industry when designing further treatments.
The project is unusual because it brings together computer experts, scientists and clinicians. A database will be built using patient histories coupled with information on which drugs are prescribed to breast cancer sufferers at Western General Hospital, Edinburgh. The team based in Scotland can then use their knowledge of computer systems, coupled with cellular biologist expertise at Riken to interpret the results.
The primary goal of the project is to enable computer models of individuals to be built. This would help clinicians prescribe treatments tailored for each patient.
Although the initiative is currently funded mainly through research council grants, the commercial director for the School of Informatics, Edinburgh, Dr Colin Adams told DrugResearcher.com he hoped pharmaceutical companies would invest in the project in the future.
He said: "We hope to understand the areas where pharmaceutical companies are being less successful and fine-tune the areas where they are being successful."
Igor Groyanin, director of Edinburgh Centre for Bioinformatics pointed out that Riken scientists will visit Edinburgh next September to discuss extending the scope of the research.
He said: "We would hope to further our research further and look at other cancers as well as diseases such as heart disease and neural and psychiatric diseases. Identifying which drugs have the best responses in particular patients would not only save lives but would also save the NHS money as treatment with expensive drugs can be tailor-made for whom it works."
Prof Groyanin added: "The computer systems will help the biologist to understand the function of the organisms and, with this knowledge, we will be able to predict more accurately which new and existing drugs work and why."