Study results from Switzerland come as clinical laboratory scientists seek new ways to tackle the problem of antimicrobial resistance in hospitals
Microbiologists and clinical laboratory scientists engaged in the fight against antibiotic-resistant (aka, antimicrobial resistant) bacteria will be interested in a recent study conducted at the University of Basel and University Hospital Basel in Switzerland. The epidemiologists involved in the study discovered that some of these so-called “superbugs” can remain in the body for as long as nine years continuing to infect the host and others.
The researchers wanted to see how two species of drug-resistant bacteria—K. pneumoniae and E. coli—changed over time in the body, according to a press release from the university. They analyzed samples of the bacteria collected from patients who were admitted to the hospital over a 10-year period, focusing on older individuals with pre-existing conditions. They found that K. pneumoniae persisted for up to 4.5 years (1,704 days) and E. coli persisted for up to nine years (3,376 days).
“These patients not only repeatedly become ill themselves, but they also act as a source of infection for other people—a reservoir for these pathogens,” said Lisandra Aguilar-Bultet, PhD, the study’s lead author, in the press release.
“This is crucial information for choosing a treatment,” explained Sarah Tschudin Sutter, MD, Head of the Division of Infectious Diseases and Hospital Epidemiology, and of the Division of Hospital Epidemiology, who specializes in hospital-acquired infections and drug-resistant pathogens. Sutter led the Basel University study.
“The issue is that when patients have infections with these drug-resistant bacteria, they can still carry that organism in or on their bodies even after treatment,” said epidemiologist Maroya Spalding Walters, MD (above), who leads the Antimicrobial Resistance Team in the Division of Healthcare Quality Promotion at the federal Centers for Disease Control and Prevention (CDC). “They don’t show any signs or symptoms of illness, but they can get infections again, and they can also transmit the bacteria to other people.” Clinical laboratories working with microbiologists on antibiotic resistance will want to follow the research conducted into these deadly pathogens. (Photo copyright: Centers for Disease Control and Prevention.)
COVID-19 Pandemic Increased Antibiotic Resistance
The Basel researchers looked at 76 K. pneumoniae isolates recovered from 19 patients and 284 E. coli isolates taken from 61 patients, all between 2008 and 2018. The study was limited to patients in which the bacterial strains were detected from at least two consecutive screenings on admission to the hospital.
“DNA analysis indicates that the bacteria initially adapt quite quickly to the conditions in the colonized parts of the body, but undergo few genetic changes thereafter,” the Basel University press release states.
The researchers also discovered that some of the samples, including those from different species, had identical mechanisms of drug resistance, suggesting that the bacteria transmitted mobile genetic elements such as plasmids to each other.
One limitation of the study, the authors acknowledged, was that they could not assess the patients’ exposure to antibiotics.
Meanwhile, recent data from the World Health Organization (WHO) suggests that the COVID-19 pandemic might have exacerbated the challenges of antibiotic resistance. Even though COVID-19 is a viral infection, WHO scientists found that high percentages of patients hospitalized with the disease between 2020 and 2023 received antibiotics.
“While only 8% of hospitalized patients with COVID-19 had bacterial co-infections requiring antibiotics, three out of four or some 75% of patients have been treated with antibiotics ‘just in case’ they help,” the WHO stated in a press release.
WHO uses an antibiotic categorization system known as AWaRe (Access, Watch, Reserve) to classify antibiotics based on risk of resistance. The most frequently prescribed antibiotics were in the “Watch” group, indicating that they are “more prone to be a target of antibiotic resistance and thus prioritized as targets of stewardship programs and monitoring.”
“When a patient requires antibiotics, the benefits often outweigh the risks associated with side effects or antibiotic resistance,” said Silvia Bertagnolio, MD, Unit Head in the Antimicrobial resistance (AMR) Division at the WHO in the press release. “However, when they are unnecessary, they offer no benefit while posing risks, and their use contributes to the emergence and spread of antimicrobial resistance.”
Citing research from the National Institutes of Health (NIH), NPR reported that in the US, hospital-acquired antibiotic-resistant infections increased 32% during the pandemic compared with data from just before the outbreak.
“While that number has dropped, it still hasn’t returned to pre-pandemic levels,” NPR noted.
The UPenn researchers have already developed an antimicrobial treatment derived from guava plants that has proved effective in mice, Vox reported. They’ve also trained an AI model to scan the proteomes of extinct organisms.
“The AI identified peptides from the woolly mammoth and the ancient sea cow, among other ancient animals, as promising candidates,” Vox noted. These, too, showed antimicrobial properties in tests on mice.
These findings can be used by clinical laboratories and microbiologists in their work with hospital infection control teams to better identify patients with antibiotic resistant strains of bacteria who, after discharge, may show up at the hospital months or years later.
Further development of this novel technology could result in new, more sensitive assays for clinical laboratories to use in the effort to improve antimicrobial stewardship in hospitals
Researchers at McMaster University in Ontario, Canada, have used artificial intelligence (AI) to identify a potential antibiotic that neutralizes the drug-resistant bacteria Acinetobacter baumannii, an antibiotic resistant pathogen commonly found in many hospitals. This will be of interest to clinical laboratory managers and microbiologists involved in identifying strains of bacteria to determine if they are antimicrobial-resistant (AMR) superbugs.
Using machine learning, the scientists screened thousands molecules to look for those that inhibited the growth of this specific pathogen. And they succeeded.
“We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii,” the researchers wrote in their published study.
They discovered that the molecule abaucin inhibited the growth of the antibiotic-resistant pathogen in vitro.
This shows how machine learning and AI technologies are giving biomedical researchers tools to identify new therapeutic drugs that are effective against drug-resistant strains of bacteria. This same research can be expected to lead to new clinical laboratory assays that determine if superbugs can be attacked by specific therapeutic drugs.
“When I think about AI in general, I think of these models as things that are just going to help us do the thing we’re going to do better,” Jonathan Stokes, PhD, Assistant Professor of Biomedicine and Biochemistry at McMaster University in Ontario, Canada, and lead author of the study, told USA Today. Clinical laboratory scientists and microbiologists will be encouraged by the McMaster University scientists’ findings. (Photo copyright: McMaster University.)
McMaster Study Details
Jonathan Stokes, PhD, head of the Stokes Laboratory at McMaster University, is Assistant Professor of Biomedicine/Biochemistry at McMaster and lead author of the study. Stokes’ team worked with researchers from the Broad Institute of MIT and Harvard to explore the effectiveness of AI in combating superbugs, USA Today reported.
“This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen,” the researchers wrote in Nature Chemical Biology.
Stokes Lab utilized the high-throughput drug screening technique, spending weeks growing and exposing Acinetobacter baumannii to more than 7,500 agents of drugs and active ingredients of drugs. When 480 compounds were uncovered that blocked the growth of bacteria, this information was then provided to a computer that was trained to run an AI algorithm, CNN reported.
“Once we had our [machine learning] model trained, what we could do then is start showing that model brand-new pictures of chemicals that it had never seen, right? And based on what it had learned during training, it would predict for us whether those molecules were antibacterial or not,” Stokes told CNN.
The model spent hours screening more than 6,000 molecules. It then narrowed the search to 240 chemicals, which were tested in the lab. The scientists pared down the results to the nine most effective inhibitors of bacteria. They then eliminated those that were either related to existing antibiotics or might be considered dangerous.
The researchers found one compound—RS102895 (abaucin)—which, according to Stokes, was likely created to treat diabetes, CNN reported. The scientists discovered that the compound prevented bacterial components from making their way from inside a cell to the cell’s surface.
“It’s a rather interesting mechanism and one that is not observed amongst clinical antibiotics so far as I know,” Stokes told CNN.
Because of the effectiveness of the antibiotic during testing on mice skin, the researchers believe this method may be useful for creating antibiotics custom made to battle additional drug resistant pathogens, CNN noted.
Defeating a ‘Professional Pathogen’
Acinetobacter baumannii (A. baumannii)—the focus of Stoke’s study—is often found on hospital counters and doorknobs and has a sneaky way of using other organisms’ DNA to resist antibiotic treatment, according to CNN.
“It’s what we call in the laboratory a professional pathogen,” Stokes told CNN.
A. baumannii causes infections in the urinary tract, lungs, and blood and typically wreaks havoc to vulnerable patients on breathing machines, in intensive care units, or undergoing surgery, USA Today reported.
A. baumannii is resistant to carbapenem, a potent antibiotic. The Centers for Disease Control and Prevention (CDC) reported that in 2017 the bacteria infected 8,500 people in hospitals, 700 of those infections being fatal.
Further, in its 2019 “Antibiotic Resistance Threats in the United States” report, the CDC stated that one out of every four patients infected with the bacteria died within one month of their diagnosis. The federal agency deemed the bacteria “of greatest need” for new antibiotics.
Thus, finding a way to defeat this particularly nasty bacteria could save many lives.
Implications of Study Findings on Development of new Antibiotics
The Stokes Laboratory study findings show promise. If more antibiotics worked so precisely, it’s possible bacteria would not have a chance to become resistant in the first place, CNN reported.
Next steps in Stokes’ research include optimizing the chemical structure and testing in larger animals or humans, USA Today reported.
“It’s important to remember [that] when we’re trying to develop a drug, it doesn’t just have to kill the bacterium,” Stokes noted. “It also has to be well tolerated in humans and it has to get to the infection site and stay at the infection site long enough to elicit an effect,” USA Today reported.
Stokes’ study is a prime example of how AI can make a big impact in clinical laboratory diagnostics and treatment.
“We know broad-spectrum antibiotics are suboptimal and that pathogens have the ability to evolve and adjust to every trick we throw at them … AI methods afford us the opportunity to vastly increase the rate at which we discover new antibiotics, and we can do it at a reduced cost. This is an important avenue of exploration for new antibiotic drugs,” Stokes told CNN.
Clinical laboratory managers and microbiologists may want to keep an open-mind about the use of AI in drug development. More research is needed to give substance to the McMaster University study’s findings. But the positive results may lead to methods for fine tuning existing antibiotics to better combat antimicrobial-resistant bacteria, USA Today reported.
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