As global life expectancy and the prevalence of chronic diseases have increased, scientific research has delivered an expanding repertoire of treatments. An emerging challenge is ensuring the most effective, appropriate drug is selected early in each patient's journey. Aim: This study will determine whether a model that predicts response to treatments for psoriasis improves patient outcomes. Background: Psoriasis is a common, debilitating skin disease that affects between 2 and 4 in every 100 people worldwide. Patients develop red, scaly patches of skin that are itchy and painful. It is associated with heart and joint disease, can affect an individual's ability to work, and impacts on quality of life are comparable to heart disease or cancer. A third of patients report clinically significant anxiety and depression, and 1 in 10 have contemplated suicide. Recent research has led to several 'biologic' treatments for psoriasis, however almost one third of patients fail to respond. Biologics are prescribed in a 'trial and error' manner until disease control is achieved. This process is expensive, since biologics cost ~£10,000/year/patient. Current practice also places patients at risk of long periods with poorly controlled disease and exposure to drug side effects. It is therefore vital to understand which patients will benefit from a particular biologic. Since we now recognise that each patient has multiple unique characteristics (e.g. changes in their genetic code) that contribute to their psoriasis, there is an urgent need to use medications intelligently. The MRC-funded Psoriasis Stratification to Optimise Relevant Therapy (PSORT) study has uncovered markers of treatment response in psoriasis, however it is not known whether this knowledge will translate into better patient outcomes. Methods: Based on PSORT's findings, I will develop a model that predicts which biologic will be the most effective for a particular patient. The model will use information that is unique to the patient, including their genetic code ('DNA'; measured from a blood sample) and presence of arthritis. The model will then use blood tests (measuring drug levels) to guide dose adjustment during treatment. I will evaluate the model's accuracy by analysing an independent, multicentre psoriasis database called BADBIR, which has periodically followed up patients receiving biologics, and recorded detailed genetic and clinical information. I will next design an innovative clinical trial to determine whether the model will lead to better outcomes for patients, including clearance of psoriasis and improved quality of life. Patient and public involvement: I sought patient and public involvement (PPI) in the development of this study through focus groups and surveys supported by my collaborators the Psoriasis Association (the UK's leading national charity and membership organisation for individuals affected by psoriasis). I will set up a patient and public advisory panel and meet with PPI focus groups six-monthly to provide progress updates and ascertain feedback. Dissemination: The results will potentially significantly impact on patients and national clinical-care pathways, so all relevant stakeholders will be updated with key findings. I will disseminate results at national and international conferences and through high-impact peer-reviewed papers. Results will be broadcast via university press offices and the information channels of the Psoriasis Association (website, social-media, magazine). I will also highlight the importance of this research to other medical specialties prescribing biologics such as rheumatology and gastroenterology. It will thus help a wide range of patients with chronic immune-mediated diseases maintain better control of their health, and save money for the NHS.