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银屑病的治疗方法。牛皮癣是一种常见的疾病，导致红色，鳞状皮肤斑块。它会造成身体、社会和心理上的痛苦，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，称为用于治疗严重银屑病的生物制剂。然而，这些药物是昂贵的，并且可能与副作用有关。目前，没有办法知道哪种药物对特定患者最安全有效。这导致医生采取“试错”的方法。这种方法可能导致对最初处方的药物反应不佳，导致疾病控制延迟。因此，医生需要能够在一开始就确定哪种药物最有可能改善患者的银屑病，与潜在的副作用风险相平衡。这样的进步可以在早期阶段显著改善疾病，从而减少患者的痛苦并减少NHS的支出。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”，可以用来帮助解决这个问题，通过告知哪种治疗方法对特定患者最有效。某些人工智能模型可以做出比人类大脑更好的决策。与患者合作，皮肤科医生可以简单地将相关的患者因素输入到这样的模型中，以确定最有效的个性化治疗。创建这些模型需要大型患者数据集，例如英国皮肤科医师协会生物制品和免疫调节剂注册（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“真实世界”银屑病数据集。我已经学会了如何编写计算机代码，并获得了访问这个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的变化的理解，并开发一种支持临床实践决策的工具。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病人的护理和成果，同时也减少了浪费使用的NHS预算通过无效的治疗。使用BADBIR数据，该项目的目标是：1.预测a）用于治疗银屑病的生物制剂的有效性和B）副作用的发展2.看看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，并对此有更多的了解，包括患者处方药物顺序的相关性4。为了复制一项临床试验，使用数据来比较不作为一线治疗时的生物学反应，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工作原理。德国银屑病患者登记处PsoBest将用于表明我的发现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对这项研究的积极支持。从这些患者中，我成立了一个专门的PPI小组，他们帮助形成了拟议的研究，并将继续指导该项目。我的项目之所以重要，有几个原因。牛皮癣是一种常见的使人衰弱的疾病，这个项目有可能改变皮肤科医生管理这种疾病的方式。我希望我的方法能产生更广泛的影响，如果应用于其他专业的治疗。