Observational pharmacogenomics of commonly prescribed drugs
Approved Research ID: 59822
Approval date: November 9th 2020
People may be prescribed medicines to reduce their risk factors for common diseases, such as heart disease. Examples include drugs to lower cholesterol, and drugs to lower blood pressure.
Sometimes people find that the first medicine that their doctor prescribes is not effective. This may be because some people respond differently to the drug, even if it is taken exactly as prescribed. Some people may suffer unacceptable side effects from a drug, meaning it is not possible for them to continue taking it.
The reasons for different responses to drugs, including their side effects, are likely to be complex. However, some of these differences may be due to differences in genes. There are already examples where genetic variation means that some people are less responsive to a drug than others without the variation. There are other examples where genetic variants predict side effects, which can be dangerous. Knowing about the variants a person has in advance could be helpful, because it means the medicine that is most likely to work well can be prescribed first. In addition, medicines that are more likely to cause side effects can either be avoided, or people could have their doses adjusted, or be monitored more.
We would like to use linked healthcare records to investigate genetic variants for associations with effectiveness and side effects of commonly prescribed types of medicines. We will choose the medicines for study based on how frequently they are prescribed in UK Biobank participants, on what is already known about known drug responses (including side-effects) and on how feasible it is to investigate the drug responses in available data in UK Biobank. Drugs meeting these criteria will include statins (used to treat high cholesterol) and ACE inhibitors (used to treat high blood pressure) - these are among the most commonly used drugs and some genetic variants that influence the response to them have already been described.
Ultimately, this research should help us understand whether we could better predict response to these drugs before prescribing them, and guide prescribing decisions.