Title: Development and validation of prediction models for secukinumab therapy in psoriasis: a machine learning approach enabling personalised medicine
Funder: Department of Health and Social Care

Abstract: Research questionCan machine learning methods identify clinical markers predictive of secukinumab treatment response? BackgroundPsoriasis is a chronic, immune-mediated disease with significant associated comorbidities. The use of high-cost biologics have revolutionised treatment of psoriasis, however only around 50% of patients with psoriasis will remain on a biologic for 3 years due to loss of effectiveness. Little is known about what predicts variability in biologic treatment response. Despite extensive research using genetic and molecular biomarkers, there are currently no predictors accurate enough for use in clinical practice. Recent advances in data science have led to breakthroughs in machine learning with potential to discover insights into underlying structures of large and complex datasets. Secukinumab (IL-17A inhibitor) is one of the current leading choice of biologics for psoriasis, which has been studied in the largest cohort of randomised controlled trials (RCTs). The British Association of Dermatologists Biologics and Immunomodulators Register (BADBIR) is the largest psoriasis and pharmacovigilance registry in the world. We propose a unique opportunity to apply state-of-the-art machine learning approaches to interrogate these high-quality resources. The central hypothesis of this project is that baseline clinical characteristics can be used to predict treatment response to secukinumab in psoriasis. AimsI aim to take an unsupervised, data-driven approach to identify baseline clinical predictors associated with responses to secukinumab treatment. To accomplish these aims, I will receive further training from leading data science teams in Manchester, Newcastle and industry to develop expertise in machine learning methods. (Months 0-6) Methods.(Months 6-12) I will use secukinumab RCTs as a training dataset to develop predictive models. (Months 12-18) I will apply latent class longitudinal analysis to investigate whether patients can be divided into distinct groups according to the trajectory of change in psoriasis clearance following secukinumab therapy. (Months 18-24) I will apply different risk prediction methods (support vector machine and random forests) to classify baseline clinical characteristics, mapped to the response trajectories, to discriminate long-term treatment responder and non-responder. (Months 24-30) I will develop a web-based prognostic calculator and validate using data from real-world psoriasis registry (BADBIR). Summary Using machine learning to inform clinical predictors could improve classification of treatment responders for better treatment stratification in the management of psoriasis patients. This may potentially shift the current biologic prescribing paradigm in psoriasis. Impact. This project aligns well with the NIHR's Academy strategic theme for Health Data Science to deliver precision medicine through data. It is ambitious, but will take maximal advantage from the research partnership between the Universities of Manchester-Newcastle-industry (Novartis); and equip me with high-level computational skills to become a future leader in this field. Patient groups have been involved in the inception and development of the project, providing constructive feedback to translate the findings into a web-based decision aid. Dissemination.I will ensure timely dissemination of results by publishing original research papers in high-impact journals, and presenting work at international meetings. I will make the codes and software developed open source; the methods would be of interest to a wide audience.

Lay abstract: Background. Psoriasis is a common, severely disabling, lifelong skin disease. It is characterised by an abnormal response within the immune system, causing skin cells to grow and shed more rapidly than usual. Patients have red scaly patches of skin, which varies in extent from localized patches to complete body coverage, and impacts on quality of life are comparable to depression or cancer. New injectable treatments for psoriasis, called biologics, work by blocking specific pathways in the immune system. Biologics are very effective and have transformed psoriasis treatment; however there is significant variation in response to these high-cost drugs. A significant proportion of patients have to stop biologics because they fail to respond. The current practice of trial and error prescribing and inability to predict who will respond best to a biologic causes frustration for both clinicians and patients. A new biologic therapy- secukinumab- blocks a key protein contributing to psoriasis, and is currently the most studied biologic in the treatment of psoriasis. There is a growing interest to personalise treatment using advanced computer algorithms. Machine learning is a method in which a computer analyses large amounts of data to discover complex patterns or relations within the data. Aim. By combining secukinumab data generated from clinical trials and a large psoriasis database in the UK, this project aims to apply next-generation machine learning techniques to identify clinical characteristics that can be used to predict treatment response. Design and methods. There are a number of ways in which computers can analyse large datasets and I will use three novel methods to optimise the chance of success. I plan to use data from secukinumab clinical trials to: (1) develop models (algorithms) that identify groups of people with different patterns of response to treatment. I will (2) analyse the data to identify differences between patients that respond well and those that do not respond to treatment. Using the clinical markers generated from the above analysis, I will (3) develop an online calculator to predict which psoriasis patients are most likely to benefit from secukinumab treatment. I will then (4) test the calculator on the UK psoriasis database to determine which model works best in the real-world setting. Impact. The objective of this study is to provide psoriasis patients and their doctors with a tool to make well-informed decisions in the choice of biologic treatment. The findings could significantly impact on patients and the future practice of biologic prescribing, as well as saving money for the NHS. Patient and public involvement. This work will be supported by patient groups and a team of leading experts in psoriasis and machine learning. I have actively engaged with patients and the public in the development of this study through focus groups supported by my collaborators (the Psoriasis Association). The focus group suggested having an easy-to-use decision aid that is accessible via the web. I have also planned the production of an accessible short video and an art-science evening of talks, highlighting the main findings at the end of the project to the public. Dissemination. I will write up my work in research articles so that other researchers are aware of my findings and present my work at conferences. I will make the codes and software developed open source. I will engage with patient support group- the Psoriasis Association, using social media channels to communicate my findings to patients and public. I will also highlight the importance of this research to other medical specialties prescribing biologics. It will thus help a wide range of patients with chronic immune-mediated diseases maintain better control of their health.

Research activity codes: 6.1 PHARMACEUTICALS;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: SKIN;INFLAMMATORY AND IMMUNE SYSTEM