Approved research

Identifying genetic predictors of response to pharmaceutical drugs using machine learning

Lay summary

Large variability exists in each patient's response to pharmaceutical drugs, for example certain patients may benefit from taking a specific drug while other patients may not. The unique genetic profiles of each patient contain clues for identifying whether the patient is likely to respond or not respond to a specific drug. This project aims to leverage the large-scale genotype and phenotype data within the UK Biobank to construct a machine learning model which would generate predictions of response to a specific drug. As proof of concept, we will focus on statins, a class of drugs which lowers LDL cholesterol levels to reduce the incidence of cardiovascular diseases. The proposed method is both cost and time effective, where different from traditional approaches, it eliminates the need to conduct a separate clinical trial and genotype study for each drug of interest. Moreover, the proposed method could potentially be highly valuable in personalized medicine applications, which has a large public health impact. Specifically, the model could use the genetic information of each patient to inform doctors about a patient's magnitude of response to certain drugs, such that dosage could be adjusted to decrease side effects due to over-treatment. Moreover, the model could also inform doctors of non-responders, skipping the 'trial and error' approach in the current standard of care for selecting an effective drug. The duration of the proposed project is 6 months.