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

点击翻译按钮获取中文摘要

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.