Developing a new generation of tools for predicting novel AMR mutation profiles using generative AI
Lead Research Organisation:
London Sch of Hygiene & Tropic. Medicine
Department Name: Infectious and Tropical Diseases
Abstract
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.
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.
