Application of artificial intelligence to predict biologic systemic therapy clinical response, effectiveness and adverse events in psoriasis

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This project aims to improve the way in which severe psoriasis is treated. Psoriasis is a common condition that causes red, scaly skin plaques. It causes physical, social, and psychological suffering and can lead to the development of other long-term diseases. There are a group of powerful and effective drugs, called biologics that are used to treat severe psoriasis. These drugs however, are costly and can be associated with side effects. Currently, there is no way of knowing which drug will work in the most safe and effective way for a particular patient. This leads to doctors adopting a "trial and error" approach. This approach can result in a poor response to the drug initially prescribed, leading to delays in disease control. There is therefore a need for doctors to be able to identify at the outset, which drug will most likely improve a patient's psoriasis, balanced against the potential risk of side effects. Such an advance could enable dramatic disease improvement at an earlier stage, thereby reducing patient suffering and decreasing NHS spending. This personalised treatment approach has been identified by the Psoriasis Association UK patient body as one of the top-10 research priorities.New developments in computer technology, called 'artificial intelligence' could be used to help resolve this issue, by informing which treatment will work best for a particular patient. Certain artificial intelligence models can make better decisions than our human brain is capable of. Working with the patient, dermatologists could simply input relevant patient factors into such a model to identify the most effective personalised treatment. Creating these models requires large patient datasets, such as the British Association of Dermatologists Biologics and Immunomodulators register (BADBIR), a world-leading UK psoriasis patient database. These artificial intelligence models have not yet been applied to large 'real-world' psoriasis datasets. I have learnt how to write computer code and gained access to this BADBIR dataset. This has enabled me to create two preliminary artificial intelligence models to predict response to biologic drugs.The aim of this project is to improve our understanding of the variation we see in response to these drugs and enable the development of a tool that supports decision-making in clinical practice. This will better guide the choice of drug for individual patients. The overall impact of the project will be a significant improvement in patient care and outcomes, whilst also reducing wasteful use of the NHS budget through ineffective treatments.Using BADBIR data, the objectives of the project are:1. To predict a) the effectiveness and b) the development of side effects of biologics used to treat psoriasis 2. To look at which patient factors contribute to being able to make these predictions 3. To see how patients respond differently to biologic medications and understand more about this, including the relevance of the sequence of drugs a patient is prescribed4. To replicate a clinical trial using data to compare biologic response when not used as first-lineI will be exploring the application of different artificial intelligence models. I will use methods to ensure that we understand the inner workings of these models. The German psoriasis patient registry, PsoBest, will be used to show that my findings can be generalised to other datasets.A survey of Newcastle psoriasis patients showed positive support of this research. From these patients I formed a dedicated PPI group, who have helped shape the proposed research and will continue to guide the project. My project is important for several reasons. Psoriasis is a common and debilitating condition, and this project has the potential to transform the way that dermatologists manage this disease. I expect my methodology to have a much broader impact, if applied to treatments across a range of other specialities.