Tackling the pandemic of antibiotic-resistant infections: An artificial intelligence approach to new druggable therapeutic targets and drug discovery
Lead Research Organisation:
University of Nottingham
Department Name: School of Veterinary Medicine and Sci
Abstract
It is difficult to imagine life before antibiotics were discovered. Infections such as tuberculosis, pneumonia and whooping cough were common killers - and if minor wounds and burns became infected they were fatal. The use of antibiotics to control bacterial infections is perhaps the most important achievement of modern medicine. However, we have failed to keep pace with microbes becoming increasingly resistant to available treatments. The Covid-19 pandemic exemplifies the threat to human health of an infection without an effective treatment. Antibiotic-resistant infections are already another global pandemic claiming almost 5 million deaths per year globally. Of particular concern are the infections caused by Klebsiella pneumoniae, globally, the third leading pathogen associated with deaths (250 000) attributed to any antibiotic-resistant infection. The increasing isolation of strains resistant to "last resort" antimicrobials has significantly narrowed, or in some settings completely removed, the therapeutic options. This is particularly alarming in low and middle-income countries. Unfortunately, new classes of drugs are not being invented and resistance continues to spread inexorably. The stakes are high and we might be entering into a pre-antibiotic era. Public Health England has calculated that the lack of effective antibiotics will render more than the three million operations and cancer treatments life-threatening, and more than 90,000 people are estimated to die in the UK over the next 30 years due to antibiotic-resistant infections.
The golden era in antibiotic drug discovery leveraged the antibacterial products produced by soil microorganisms but this approach became exhausted after 20 years of systematic screening. Researchers have mined different sources of natural products such as marine environments, plants, and even the community of harmless microbes inhabiting our gut with encouraging results. Yet, none of the compounds isolated have entered into drug development. A better understanding of the means used by microbes to resist antibiotics may result in the discovery of hitherto unknown targets suitable to develop new drugs against. In this research, we will use artificial intelligence to identify new potential druggable targets from K. pneumoniae that when blocked may render the microbe susceptible to antibiotics and perhaps may even facilitate the clearance of Klebsiella by our defenses. We will train supervised learners to go through data we will generate in the laboratory and to read the genome of the microbe to find these targets that researchers have overlooked. Next, and utilizing other learners, we will identify drugs that can block these targets. Specifically, we will search drugs already approved for use in humans but used for purposes unrelated to antimicrobial activity. We will carry out experiments in the laboratory to confirm the effect of these drugs. From the drug discovery point of view, our approach significantly shortcuts the drug development process hence allowing a potential fast-track transition from the basic research to clinical development. We envision that our results will encourage other academics as well as pharmaceutical companies to follow this new avenue of research to tackle the problem of the lack of therapies for microbes resistant to antibiotics. To facilitate this, we will make freely available our protocols, models and data.
The golden era in antibiotic drug discovery leveraged the antibacterial products produced by soil microorganisms but this approach became exhausted after 20 years of systematic screening. Researchers have mined different sources of natural products such as marine environments, plants, and even the community of harmless microbes inhabiting our gut with encouraging results. Yet, none of the compounds isolated have entered into drug development. A better understanding of the means used by microbes to resist antibiotics may result in the discovery of hitherto unknown targets suitable to develop new drugs against. In this research, we will use artificial intelligence to identify new potential druggable targets from K. pneumoniae that when blocked may render the microbe susceptible to antibiotics and perhaps may even facilitate the clearance of Klebsiella by our defenses. We will train supervised learners to go through data we will generate in the laboratory and to read the genome of the microbe to find these targets that researchers have overlooked. Next, and utilizing other learners, we will identify drugs that can block these targets. Specifically, we will search drugs already approved for use in humans but used for purposes unrelated to antimicrobial activity. We will carry out experiments in the laboratory to confirm the effect of these drugs. From the drug discovery point of view, our approach significantly shortcuts the drug development process hence allowing a potential fast-track transition from the basic research to clinical development. We envision that our results will encourage other academics as well as pharmaceutical companies to follow this new avenue of research to tackle the problem of the lack of therapies for microbes resistant to antibiotics. To facilitate this, we will make freely available our protocols, models and data.
Technical Summary
Antibiotic resistance is one of the biggest public health challenges of our time. Of particular concern are the infections caused by Klebsiella pneumoniae, globally, the third leading pathogen associated with deaths (250 000) attributed to any antibiotic-resistant infection. Not surprisingly, K. pneumoniae has been singled out by the World Health Organization as an "urgent threat to human health" for which new therapeutics are urgently needed. To date, a large proportion of research to develop new antibiotics still focuses only on a small fraction of targets for which most lead drug compounds have shown relatively short-term effectiveness. The identification of druggable therapeutic targets is a significant challenge in the discovery of new drugs. Increasing evidence suggests that machine learning (ML) and artificial intelligence (AI) approaches can help to predict clinically relevant resistance by mining genomic data. Representing a step-change in the use of AI in antibiotic resistance research, in this project we will develop an AI-based approach to discover potentially druggable K. pneumoniae proteins by an innovative integrated analysis of large-scale multiple 'omics, genome-scale metabolic models, and mid-throughput phenotyping (antibiotic susceptibility testing, virulence, and host immunity responses). We will then make use of a deep learning approach fed with 3D modelling to discover lead drug molecules inhibiting the identified targets. Finally, we will provide pre-clinical evidence demonstrating the therapeutic potential of the new drugs alone and in combination with antibiotics. Altogether, this research will establish a new framework centered around AI-based integration of multiple data sets to identify, prioritize and validate new druggable targets and drugs that shall be the foundation of new therapeutics.
