Antimicrobial Resistance: A Pervasive Pandemic

Currently, several key strategic action plans are in place in the United States to combat this growing threat. These goals focus on slowing the emergence and spread of resistant strains, enhancing surveillance efforts, advancing the development of diagnostic tests, improving international collaboration, and accelerating the development of new therapeutics and vaccines.⁶ Regarding this latter goal, two new studies published in February this year have offered unique perspectives on directly tackling antimicrobial resistance through the targeted design of fully synthetic antibiotics (Wu et al)⁷ or through the use of artificial intelligence (AI) to identify new drug classes that are effective against resistant strains (Wong et al).⁸

In the study by Wu et al,⁷ researchers used existing knowledge on the molecular structure of lincosamide antibiotics and their interaction with bacterial ribosomes—a target for some antimicrobial drugs—to design and develop a fully synthetic bridged macrobicyclic antibiotic called cresomycin. Due to its preorganized structure for optimal ribosomal binding, cresomycin effectively counters resistance mechanisms by displacing a  dimethylated nucleobase found in modified ribosomes of drug-resistant bacteria. Evaluation of cresomycin against standard reference strains from ATCC (ATCC 29213, ATCC 25922, and others) and clinical isolates obtained from the CDC, FDA, and AR-bank have demonstrated that this novel compound was effective against gram-positive and -negative bacteria, including multidrug-resistant strains, in both in vitro studies and in vivo mouse models. Furthermore, cresomycin was evaluated against human lung fibroblasts (ATCC PCS-201-013), human umbilical vein endothelial cells (ATCC PCS-100-010), A375 cells (ATCC CRL-1619), and HepG2 cells (ATCC HB-8065) and displayed low cytotoxicity, offering a promising candidate for future drug development.⁷

In the other study by Wong et al, researchers leveraged AI to develop a deep-learning guided approach to antibiotic discovery that could predict the biological effect of compounds.⁸ To train the deep-learning model, they evaluated the activities of over 39,000 compounds on the bacterium Staphylococcus aureus strain RN4220 and the human cell lines HepG2 (ATCC HB-8065), HSkMCs (ATCC PCS-950-010), and IMR-90 (ATCC CCL-186) to generate datasets that trained an AI model on the antibiotic activity and cytotoxicity profiles of each compound. These data were used to screen over 12 million commercially available compounds to predict which compound may demonstrate antibiotic activity against methicillin-resistant S. aureus while having minimal cytotoxic effects on the human body. Of the selection with ideal properties, 283 compounds were  empirically tested against methicillin-resistant S. aureus (MRSA; ATCC BAA-1556) and other bacterial species, which led to the identification of a structural class of compounds that demonstrated selection against MRSA and vancomycin-resistant enterococci (VRE) while overcoming resistance and possessing favorable toxicological properties.⁸ Overall, this AI approach provides a unique methodology efficiently identifying potential leads.

Recently, the WHO released a report on bacterial priority pathogens that includes 15 families of bacteria grouped into critical, high, and medium categories for R&D and public health concern.¹⁰ In the critical category are Acinetobacter and Enterobacterales that are resistant to last line antibiotics plus Mycobacterium tuberculosis strains that are able to transfer resistance genes. These pathogens are of concern due to their ability to cause severe disease and their global burden particularly in underdeveloped countries. In the high category are the food-associated pathogens Salmonella and Shigella due to the rise in antibiotic resistance among these species. Multidrug-resistant Neisseria gonorrhoeae is of concern due to the limited treatment options and stigma around diagnosis, which may limit patients from seeking care. Microbes added to the list due to their impact in the health care setting are Pseudomonas aeruginosa and Staphylococcus aureus. Enterococcus faecium is the final strain in the high category due to its ability to transfer resistance between bacteria in the one health spectrum. 

At ATCC, we’re here to support the development of novel therapeutics that are effective against AMR strains by providing reference materials and clinical AMR strains for use in screening and validation studies as well as cell lines that can be used in toxicity testing studies. Our comprehensive assortment of antibiotic-resistant strains includes numerous isolates on the WHO’s priority pathogens list^9,10 and the whole-genome sequence of many of these isolates are available on the ATCC Genome Portal. We are also continually adding new antimicrobial-resistant strains to our portfolio that are provided with extensive levels of source metadata and genotypic and phenotypic characterization data so that we can ensure that researchers have the strains needed to fight this global health threat. 

Did you know?

ATCC provides over 500 antimicrobial resistant strains that are provided with extensive levels of source metadata and genotypic and phenotypic characterization.

References

1. World Health Organization. Global antimicrobial resistance forum launched to help tackle common threat to planetary health (2023). Available from: https://www.who.int/news-room/articles-detail/global-antimicrobial-resistance-forum-launched-to-help-tackle-common-threat-to-planetary-health (Accessed April 11, 2024).
2. Antimicrobial Resistance Collaborators.  Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet 399(10325): 629-655, 2022. PubMed: 35065702
3. O’Neill J. Review on Antimicrobial Resistance. Tackling Drug-Resistant Infections Globally (2016). Available from: https://amr-review.org/sites/default/files/160525_Final%20paper_with%20cover.pdf (Accessed April 11, 2024).
4. World Health Organization. Cancer Fact Sheet. (2022). Available from: https://www.who.int/news-room/fact-sheets/detail/cancer (Accessed April 11, 2024).
5. Roser M. Causes of death globally: what do people die from? (2021). Available from: https://ourworldindata.org/causes-of-death-treemap (Accessed April 11, 2024).
6. Centers for Disease Control and Prevention. US National Action Plan (2021). Available from: https://www.cdc.gov/drugresistance/us-activities/national-action-plan.html (Accessed April 11, 2024).
7. Wu KJ et al. An antibiotic preorganized for ribosomal binding overcomes antimicrobial resistance. Science 383(6684): 721-726, 2024.
8. Wong F, et al. Discovery of a structural class of antibiotics with explainable deep learning. Nature 626(7997): 177-185, 2024.
9. World Health Organization. WHO publishes list of bacteria for which new antibiotics are urgently needed (2017). Available from: WHO publishes list of bacteria for which new antibiotics are urgently needed (Accessed April 15, 2024).
10. WHO Bacterial Priority Pathogens List, 2024: bacterial pathogens of public health importance to guide research, development and strategies to prevent and control antimicrobial resistance. ISBN 978-92-4-009346-1 (electronic version).