AI turns old diabetes medication Halicin into a powerful weapon against superbugs

Can AI-Repurposed diabetes medication tackle our toughest superbug? Researchers reveal the powerful behavior of halicin against deadly multidrug resistant bacteria, except for one elusive enemy.

Research: Halicin: A new approach to antibacterial therapy, a promising path in the post-antibiotic era. Image credit: Kateryna Kon/Shutterstock

Recent research in the journal Antibioticsresearchers show how artificial intelligence (AI)-driven drug discovery can reuse current drugs and biomolecules for current and innovative therapeutic purposes. Specifically, we report the results of an antibacterial activity assay that tested the efficacy of AI-predicted halicin against 18 multidrug resistant (MDR) bacterial strains.

Minimum Inhibitory Concentration (MIC) assay revealed that halicin significantly inhibited the growth of 17 of the 18 clinical bacterial isolates tested. This study also confirmed the efficacy of halicin against two standard reference strains. Staphylococcus aureus ATCC®29213™ and E. coli ATCC®25922™. These findings support future investigations into the potential of halicin as a broad-spectrum antibiotic for MDR, highlighting the prominent ways in which AI is transforming medicine and drug discovery.

background

Colloquially known as “superbugs,” multidrug resistant (MDR) bacteria pose a threat to global health. Among them, Escape bacteria strains (Enterococcal feces, Staphylococcus aureus, Klebsiella pneumoniae, acinetobacter baumannii, Pseudomonas aeruginosaand Enterobacter spp. ) has been consistently recognized as the biggest threat by the World Health Organization (WHO) due to its extraordinary ability to avoid most traditional antibiotic courses.

Unfortunately, these threats emerge when traditional antibiotic pipelines reach the limits of innovative possibilities, primarily due to the temporal nature of the discovery process and the parallel evolution of bacterial defenses. Thankfully, the latest innovations in machine learning (ML) and artificial intelligence (AI) technologies have become increasingly possible for rapid screening and simulation of existing pharmaceutical compounds, identifying hidden antibacterial properties that are invisible to traditional drug discovery approaches.

The incredible success of this approach is Halicin. Originally created as a C-Jun N-terminal kinase (JNK) inhibitor targeting diabetes-related pathways, the drug was identified by the Massachusetts Institute of Technology (MIT) deep learning algorithm. (MDR) Bacteria. Unfortunately, promisingly, detailed investigation into its activity against clinical MDR isolates remains limited, and potential minimal inhibitory concentrations (MICS) for many preferential pathogens require further research.

About the research

Described as the first of this species in Morocco, this study aims to address this knowledge gap by estimating halicin microphones across the spectrum of 18 clinically validated MDR bacterial isolates. Separated samples were collected from a Morocco hospital and initially used agar disk diffusion assays to confirm MDR status for 22 commonly used antibiotics. In addition to these clinical isolates, standard reference strains S. aureus ATCC®29213™ and E. coli ATCC®25922™ was included as quality control.

This research methodology adhered to guidelines from the European Commission on Antibiotic Susceptibility Testing (EUCAST) and the Clinical Institute of Standards Research (CLSI). After isolates verification, a microdilution of the soup and halicin microassay was performed to determine the lowest drug concentration (μg/mL) that prevents visible growth of each isolate strain.

MIC data were used to generate dose-response curves to elucidate the dynamics of bacterial growth at various concentrations. At the same time, scanning electron microscopy (SEM) imaging was performed to visualize the physiological effects of halicin treatment. E. coli Reference distortion. Differences in MIC distribution across concentration and species results were estimated using the Kruskal-Wallis nonparametric test.

Survey results

Halicin was observed to demonstrate praiseworthy antibacterial activity, producing 16 μg/mL and 32 μg/mL MICs against reference E. coli ATCC®25922™ and S. aureus ATCC®29213™ strains respectively. Dose-dependent results for clinically validated bacterial isolates from the Eskape group ranged from 32-64 μg/ml, confirming the widespread possibility of halicin.

But surprisingly, P. aeruginosa Regardless of the therapeutic concentration, no growth inhibition was observed, and thus it was found to be completely inherently impermeable to halicin. The researchers attributed this observation to the robust outer membrane of the bacteria, limiting the penetration of halicin and effectively limiting its effectiveness.

Despite this exception, the ability of antidiabetic drugs to date to kill some multidrug-resistant strains represents a promising step in the global search for new antimicrobials. Rather than targeting cell wall or protein synthesis, its unique mode of action that destroys bacteria's energy metabolism, bypasses MDR mechanisms of most of the most dangerous bacteria today, making it difficult for future bacteria to develop resistance quickly.

Conclusion

This study examines the antibacterial effect of halicin, a largely abolished antidiabetic artifact, in significantly inhibiting the growth of 17 of the 18 (94%) clinical MDR bacterial isolates tested. This study also confirmed the activity of halicin against reference strains of S. aureus and E. coli. The findings show that halicin is effective against bacteria that have already developed resistance to many traditional antibiotics, facilitating future research into its safety and optimal dosage.

The study also highlights the ability of new AI and ML innovations to reuse existing compounds for new therapeutic applications and exceed the limitations of traditional drug discovery. Future studies should examine pharmacokinetics, toxicity, and in vivo efficacy and explore combination therapies that may overcome the barriers posed by specific bacterial defenses. The authors of the paper also highlight the importance of establishing a bacterial resistance surveillance program to track the long-term efficacy of halicin, and note that resistance has not yet been observed due to limited use, but vigilance is important as development progresses.