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

应用人工智能预测生物系统治疗银屑病的临床反应、有效性和不良事件

• 批准号: MR/Y009657/1
• 金额: $ 38.26万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FY009657%2F1
• 关键词: Application; artificial; intelligence; predict; biologic

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.

该项目旨在改善严重牛皮癣的治疗方法。牛皮癣是一种常见疾病，会导致红色鳞状皮肤斑块。它会造成身体、社会和心理痛苦，并可能导致其他长期疾病的发展。有一组强大而有效的药物，称为生物制剂，可用于治疗严重的牛皮癣。然而，这些药物价格昂贵并且可能有副作用。目前，无法知道哪种药物对特定患者最安全、最有效。这导致医生采取“反复试验”的方法。这种方法可能会导致对最初处方药物的反应不佳，从而导致疾病控制的延迟。因此，医生需要能够从一开始就确定哪种药物最有可能改善患者的牛皮癣，并权衡潜在的副作用风险。这样的进步可以在早期阶段显着改善疾病，从而减少患者的痛苦并减少 NHS 支出。这种个性化的治疗方法已被英国银屑病协会确定为十大研究重点之一。计算机技术的新发展，称为“人工智能”，可以通过告知哪种治疗有效来帮助解决这个问题最适合特定患者。某些人工智能模型可以做出比人脑更好的决策。与患者合作，皮肤科医生只需将相关的患者因素输入到这样的模型中即可确定最有效的个性化治疗。创建这些模型需要大量患者数据集，例如英国皮肤科医师协会生物制品和免疫调节剂注册库 (BADBIR)，这是一个世界领先的英国银屑病患者数据库。这些人工智能模型尚未应用于大型“现实世界”牛皮癣数据集。我已经学会了如何编写计算机代码并获得了访问此 BADBIR 数据集的权限。这使我能够创建两个初步的人工智能模型来预测对生物药物的反应。该项目的目的是提高我们对这些药物反应变化的理解，并开发支持决策的工具在临床实践中。这将更好地指导个体患者的药物选择。该项目的总体影响将是显着改善患者护理和结果，同时还减少因无效治疗而浪费的 NHS 预算。使用 BADBIR 数据，该项目的目标是：1。预测用于治疗牛皮癣的生物制剂的 a) 有效性和 b) 副作用的发生 2. 了解哪些患者因素有助于做出这些预测 3. 了解患者对生物药物的不同反应并了解更多信息关于这一点，包括患者所开药物顺序的相关性4。为了使用数据复制临床试验来比较不用作一线药物时的生物反应，我将探索不同人工智能模型的应用。我将使用方法来确保我们理解这些模型的内部运作原理。德国银屑病患者登记处 PsoBest 将用于表明我的研究结果可以推广到其他数据集。对纽卡斯尔银屑病患者的调查显示了这项研究的积极支持。我从这些患者中组建了一个专门的 PPI 小组，他们帮助制定了拟议的研究，并将继续指导该项目。我的项目很重要有几个原因。牛皮癣是一种常见且令人衰弱的疾病，该项目有可能改变皮肤科医生治疗这种疾病的方式。我希望我的方法如果应用于其他一系列专业的治疗，将会产生更广泛的影响。