Assessing the role of pharmacogenomics in precision medicine using artificial intelligence methods
Approved Research ID: 64586
Approval date: September 15th 2020
Recent advances on genome-wide association studies have decipher the genetic architecture of several complex traits and diseases and have identified genes that target already existed drugs. Our study aims to calculate drug specific genetic risk scores in order to access the effectiveness of a therapy that is driven by the genetic signature of the individual. The proposed research is entirely congruent with the stated aim of UK Biobank to improve the prevention and treatment of a wide range of illnesses. We will examine the effectiveness of drug therapies that will be recommended based on the genetic profile of the individual combined with other demographic and clinical characteristics. These findings will have a major impact to the treatment of the several diseases and the prevention of other co-existing comorbidities. We will aim for a personalised approach where we will use state-of-the art approaches to calculate a drug-related genetic risk score and we will apply machine learning approaches using the genetic risk score and other clinical characteristics. Through this approach we aim to indicate the best available treatment for an individual when multiple options exist. We expect that our approach will allow for a cost effective treatment response which will be faster and it will reduce the number of the adverse events, the number of prescribed drugs, the number of visits paid to the doctors and days of hospitalisation.