Developing a new generation of tools for predicting novel AMR mutation profiles using generative AI

使用生成人工智能开发新一代工具来预测新型 AMR 突变谱

• 批准号: BB/Z514305/1
• 负责人: Nicholas Furnham
• 金额: $ 31.94万
• 依托单位: London School of Hygiene & Tropical Medicine
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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=BB%2FZ514305%2F1
• 关键词: Developing; new; generation; tools; predicting

Drugs against infectious diseases have transformed human and animal health and saved millions of lives. Nevertheless, their widespread use and misuse has led to the emergence of antimicrobial resistance (AMR) that poses a potentially catastrophic threat to public health and animal husbandry.There a several routes by which a pathogen can become resistant to a drug. One of the principal routes, and a focus of this project, is by single point mutations in genomic regions that code for proteins and result in a change in the protein's sequence of amino acids. These types of mutations are called Single Nucleotide Polymorphisms (SNPs). Advances in genome sequencing means there are now large collections of sequences from a range of pathogens where SNPs have been identified and can be associated with drug resistance. This project aims to capitalise on this wealth of data, combined with the recent advances made to accurately model protein structures, to develop a new AI-based tool to predict the effect of SNPs that could lead to resistance and have yet to be observed.By modelling how pathogens mutate to avoid the effect of drugs, we can better predict how infections will respond to specific drugs and may be able to design drugs that have longer clinical use. As well as directly benefiting those working to develop the next generation of drugs, it also benefits those managing prescribing routines and in surveillance, identifying new emerging resistance that can be acted on before it becomes widespread within a population.The project brings together a group of international experts from the University of Queensland (Australia) and the London School of Hygiene & Tropical Medicine (LSHTM, UK) who have complementary expertise in AI, drug resistance and bacterial pathogen genomics. The project has several key objectives:Objective 1: Develop a Natural Language Processing (NLP)-based AI tool for predicting SNPs causing resistance trained on features derived from the large collections of pathogen genome data where mutations associated with drug resistance have been identified.Objective 2: Validate and apply the newly developed methodologies to specific pathogens including Salmonella Typhiand Klebsiella pneumoniae (WHO priority pathogens) that gives opportunity for real-world validation and the ability to give insights into resistance mechanisms.Objective 3: Knowledge Exchange of AI applied to AMR though two UK-led workshops. This will enhance the collaborative network, establish design criteria for the AI tool based on user needs, and provide a pathway to translating the tools into real-world use. In addition, exchanges of researchers between the UK and Australian groups will enhance capacity and capabilities of both teams.This project envisions an AI-powered solution to help pre-empt the impact of drug resistance mutations, addressing the urgent need to combat the growing threat of AMR. The validated new computational tools will help in developing better drugs and, in conjunction with complementary technologies, aid in deciding drug treatment regimens and in resistance surveillance. It will enable a UK-led international partnership that will place the groups involved at the forefront of research in this field.

抗传染病药物改变了人类和动物的健康，拯救了数百万人的生命。然而，它们的广泛使用和滥用导致了抗菌素耐药性（AMR）的出现，对公共卫生和畜牧业构成了潜在的灾难性威胁。病原体可以通过多种途径对药物产生耐药性。主要途径之一，也是该项目的重点，是通过编码蛋白质的基因组区域中的单点突变并导致蛋白质氨基酸序列的变化。这些类型的突变称为单核苷酸多态性 (SNP)。基因组测序的进步意味着现在已经收集了大量来自一系列病原体的序列，其中的单核苷酸多态性（SNP）已被鉴定出来，并且可能与耐药性相关。该项目旨在利用这些丰富的数据，结合精确模拟蛋白质结构的最新进展，开发一种新的基于人工智能的工具来预测可能导致耐药性且尚未观察到的 SNP 的影响。通过模拟病原体如何突变以避免药物的影响，我们可以更好地预测感染对特定药物的反应，并可能设计出临床使用时间更长的药物。除了直接使那些致力于开发下一代药物的人受益之外，它还使那些管理处方常规和监测、识别新出现的耐药性的人受益，这些耐药性可以在其在人群中广泛传播之前采取行动。该项目汇集了一组来自昆士兰大学（澳大利亚）和伦敦卫生与热带医学院（LSHTM，英国）的国际专家，他们在人工智能、耐药性和细菌病原体基因组学方面拥有互补的专业知识。该项目有几个关键目标： 目标 1：开发基于自然语言处理 (NLP) 的人工智能工具，用于预测引起耐药性的 SNP，并根据来自大量病原体基因组数据的特征进行训练，其中已识别出与耐药性相关的突变。目标2：验证新开发的方法并将其应用于特定病原体，包括伤寒沙门氏菌和肺炎克雷伯菌（世界卫生组织优先病原体），这为现实世界的验证提供了机会，并能够深入了解耐药机制。 目标3：通过两个英国主导的研讨会将人工智能应用于 AMR 的知识交流。这将增强协作网络，根据用户需求建立人工智能工具的设计标准，并提供将工具转化为现实世界使用的途径。此外，英国和澳大利亚团体之间的研究人员交流将增强两个团队的能力和能力。该项目设想了一种人工智能驱动的解决方案，以帮助预防耐药突变的影响，解决对抗日益增长的威胁的迫切需要的 AMR。经过验证的新计算工具将有助于开发更好的药物，并与补充技术相结合，帮助决定药物治疗方案和耐药性监测。它将促成英国主导的国际合作伙伴关系，使参与的团体处于该领域研究的前沿。