Approved Research

Integrated omics to inform drug discovery and development

Lay summary

Only one in every ten new medications studied by the pharmaceutical industry in early-stage clinical trials eventually make it to the patients that need them. Most often, these drugs fail to work in humans as they did in animal studies, and either do not produce the expected benefit or pose a safety concern. For this reason, a large proportion of investment is wasted on failed therapies, ultimately resulting in a higher cost for the drugs that do make it to market. This cost can be dramatically reduced if we make better use of available human-based data to determine whether drugs are likely to be safe and effective before investing time and money into those targets. Human genetic data allow us to do this. Because genes code for proteins, and most drug targets are proteins, natural genetic differences can be used as to represent what an intervention on that protein will look like. By investigating how these interventions influence a wide range of outcomes, we can determine whether the specific drug is likely to work, and whether any other traits we might not have expected will be affected by this drug as well. To do these analyses we require large datasets, like the UK Biobank and its linked electronic health records, that represent a diverse group of people across various lifestyles for whom genetic data and health outcomes over many years are available.

Our project will use data from the UK Biobank to investigate the potential effects of new medications and those already available in terms of three broad questions:

1) How does the disease [that this medication is prescribed for] work?

2) Which medications are most likely to help those with the disease (e.g., which medications are safe, how will we know it is working for a particular individual, which side-effects will it have, who will it work best for?)

3) What will the economic impact be of having this treatment/medication available?

Overall, this project aims to improve our understanding of what causes disease and how they can best be treated. This knowledge will inform better decisions about which medicines to further investigate and test in clinical trials, resulting in fewer failed investigations and less wasted money, ultimately resulting in more affordable medicines for the patients who need them.