Improving The Longevity Of New Infectious Disease Therapeutics Using Machine Learning / Artificial Intelligence In Early Stage Drug Discovery

治疗由病毒、细菌和寄生虫引起的疾病的药物改变了人类的健康，挽救了数百万人的生命。然而，它们的广泛使用和误用已导致抗菌素耐药性（AMR）的出现，对公共卫生构成潜在的灾难性威胁。越来越强大的基因组测序为快速检测和应对抗菌素耐药性的发展提供了新的方法。这些丰富的数据的可用性，以及人工智能/机器学习（AI/ML）技术的最新发展，允许开发复杂的方法，可以充分利用这些数据来预防潜在抗性突变的影响。该项目的目的是开发新的计算工具，用于自动分析与基因组广泛关联研究（GWAS）的治疗抗性相关的单核苷酸多态性（snp）的分子后果，并使用这种广泛的迭代分析来建立预测模型，以确定未来可能导致治疗失败的snp。它将集中分析点突变，单倍型组合和观察到的单倍型组合，这是抗性的主要途径之一。它将利用内部和公开GWAS的财富，将snp与耐药性联系起来。通过利用最新的最先进的工具来预测突变的各种影响，这提供了一个令人兴奋的机会来测量这些突变对不同生物体和抗性类型的蛋白质的生物物理功能，几何和基因组影响。这些见解将用于开发一种新的计算工具，利用机器学习的最新发展，在特定病原体群体中固定之前预测导致抗性的突变。有效预测这种突变的影响的能力有几个应用，包括下一代药物的开发。该工具可用于对突变对潜在药物结合区以及连接的远端区域的影响做出明智的决定，这可能导致药物变得不那么有效。如果能够避免突变耐受区域，就可以开发出具有较长临床寿命的药物。这在病原体药物管理中很重要，特别是对于被忽视的热带病，在这些疾病中，新疗法很难开发，开发新药的市场激励机制也很弱。这些新工具将在我们对结核病的初步研究的基础上得到验证，通过独立预测我们目前正在进行的研究中靶蛋白可能的耐药突变。除了帮助验证我们的方法外，它还将为抗性途径提供新的见解。此外，这些新工具将被我们和合作者应用于我们现有的药物发现项目中，用于治疗一系列传染病病原体，如血吸虫病。应用于实际情况的结果以及验证将用于迭代反馈和改进机器学习工具。该研究项目采用了一种新颖的方法来解决2014年英国抗菌素耐药性审查中强调的AMR的严重威胁。这些新方法可用于为新疗法的开发提供信息，并有望回答围绕个体传染病生物体耐药模式的具体问题。它在确定耐药性的早期出现和临床诊断方面具有未来的潜力。通过提供新的工具和方法，它将有助于减轻抗菌素耐药性的负担，防止它成为我们医疗保健系统更大的负担。

Drugs against diseases caused by viruses, bacteria and parasites have transformed human 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. The increasing power of genomic sequencing is offering new ways to rapidly detect and respond to the development of antimicrobial resistance. The availability of this wealth of data, along with the latest developments in artificial intelligence / machine learning (AI/ML) techniques, allows the development of sophisticated approaches that can fully leverage this data to pre-empt the effects of potential resistance mutations. The aim of this project is to develop new computational tools for automatically analysing the molecular consequences of single nucleotide polymorphisms (SNPs) linked with therapeutic resistance from genome wide association studies (GWAS) and to use this wide-ranging iterative analysis to build a predictive model to identify future SNPs that could lead to therapeutic failure. It will focus on analysing point mutations, singularly and as observed haplotype combinations, that represent one of the major routes to resistance. It will leverage the wealth of in-house and publicly GWAS linking SNPs to drug resistance. By exploiting the latest state-of-the art tools for predicting various measures of the effect of a mutation, this offers an exciting opportunity to measure the biophysical functional, geometrical and genomic effects these mutations are having on proteins on a large scale across different organisms and resistance types.Such insights will be used to develop a novel computational tool utilising the latest developments in machine leaning to anticipate mutations leading to resistance before they become fixed in a given pathogen population. The ability to effectively predict the effects of such mutations has several applications including in the development of the next generation of drugs. The tool can be used to make an informed decision as to the effects of mutations on a potential drug binding region, as well as connected distal regions, that might lead to the drug becoming less effective. If areas that are tolerant of mutations can be avoided, drugs with a longer clinical life can be developed. This is important in pathogen drug management, and especially for neglected tropical diseases, where new therapeutics are difficult to develop and market incentives for developing new drugs are weak.These new tools will be validated building on our preliminary research in tuberculosis by independently predicting likely resistance mutations in target proteins from studies currently being undertaken by us. As well as helping validate our method it will also provide new insights into routes of resistance. Moreover, the new tools will be applied by us and collaborators within our existing drug discovery programs against a range of infectious disease agents e.g. schistosomiasis. The results of the application to real situations along with validation will be used to iteratively feedback and improve the machine learning tool.This research project takes a novel approach to addressing the critical threat of AMR as highlighted in the 2014 UK Review on Antimicrobial Resistance. The new approaches can be used to inform the development of novel therapeutics and in turn promises to answer specific questions around the modes of resistance in individual infectious disease organisms. It has the future potential to be applied in identifying early emergence of resistance and in clinical diagnostics. By contributing new tools and methods it will help ease the burden of AMR, before it becomes a much larger drain on our healthcare system.