AI Turns Old Diabetes Drug Halicin Into Powerful Antibiotic Against Superbugs

Key Takeaways:

• Halicin, originally developed for diabetes, significantly inhibited 17 out of 18 tested multidrug-resistant (MDR) bacterial strains, demonstrating potential as a broad-spectrum antibiotic.
• The study confirms Halicin’s unique mechanism of action and highlights its effectiveness against ESKAPE pathogens, with the exception of Pseudomonas aeruginosa.
• This work underscores the power of artificial intelligence in repurposing old pharmaceuticals to address urgent public health threats such as antimicrobial resistance.
  
  
  

AI breathes new life into an old drug

Artificial intelligence (AI) continues to redefine the frontiers of medicine. A new study published in Antibiotics demonstrates how AI can accelerate drug discovery by uncovering novel uses for discontinued or previously overlooked pharmaceuticals. The research focuses on Halicin – a drug initially designed to treat diabetes – and its newly revealed antibacterial properties. Specifically, the study investigates Halicin’s efficacy against 18 clinical strains of multidrug-resistant (MDR) bacteria, revealing promising results that may influence future antimicrobial strategies.

The global threat of superbugs

Multidrug-resistant bacteria – often referred to as ‘superbugs’ – are one of the most pressing global health threats. Among these, the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter species) are of particular concern. These organisms are named for their ability to “escape” the effects of antibiotics and are repeatedly flagged by the World Health Organization (WHO) as urgent priorities.

Conventional antibiotic pipelines are struggling to keep pace, constrained by labour-intensive discovery methods and rising rates of antimicrobial resistance. In this context, the use of AI and machine learning (ML) offers a transformative approach – enabling the rapid screening and simulation of existing compounds to uncover overlooked antibacterial effects.

From diabetes to antibacterial: the case of Halicin

Halicin was originally developed as a c-Jun N-terminal kinase (JNK) inhibitor for managing diabetes-related pathways. However, in a breakthrough application of AI at the Massachusetts Institute of Technology (MIT), deep learning algorithms identified Halicin’s unique ability to disrupt the bacterial proton-motive force – a critical energy-generating mechanism. Unlike conventional antibiotics, Halicin does not target bacterial cell walls or protein synthesis, making it an attractive candidate for combatting antibiotic-resistant bacteria.

Despite its potential, limited studies had evaluated Halicin’s minimum inhibitory concentrations (MICs) across clinically relevant MDR strains, leaving significant gaps in the evidence base.

Study aims and methodology

This new study – the first of its kind in Morocco – sought to quantify Halicin’s MIC values against a wide panel of MDR bacterial isolates collected from Moroccan hospitals. A total of 18 clinical isolates were tested, including strains from the ESKAPE group. To ensure quality control, two standard reference strains – Staphylococcus aureus ATCC® 29213™ and Escherichia coli ATCC® 25922™ – were also included.

Initial testing involved agar disk diffusion assays to confirm multidrug resistance against 22 commonly used antibiotics. Subsequent MIC assays were conducted using broth microdilution methods in accordance with protocols established by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) and the Clinical and Laboratory Standards Institute (CLSI).

Dose-response curves were generated to determine the relationship between drug concentration and bacterial growth inhibition. Scanning electron microscopy (SEM) was used to visualise morphological changes in E. coli after Halicin treatment. The Kruskal–Wallis non-parametric test was employed to analyse differences in MIC distributions between species.

Key findings

Halicin exhibited potent antibacterial effects against a majority of the tested strains:

• The MIC for E. coli ATCC® 25922™ was 16 μg/mL, and for S. aureus ATCC® 29213™, 32 μg/mL.
• Among the clinical MDR isolates, most MIC values ranged from 32 to 64 μg/mL.
• Pseudomonas aeruginosa was notably resistant to Halicin at all concentrations tested. Researchers attributed this to its highly selective and impermeable outer membrane, which likely hinders drug penetration.
  

The results suggest that Halicin maintains broad-spectrum antibacterial activity, even against highly drug-resistant organisms. Its distinct mode of action – targeting bacterial energy production – may also make it less susceptible to common resistance mechanisms.

Implications for antimicrobial research

This study represents an important step forward in antimicrobial development, particularly in the context of drug repurposing. As the authors note, “The present study validates the antibacterial efficacy of Halicin, a largely discontinued anti-diabetic relic, in significantly inhibiting the growth of 17 of the 18 (94%) clinical MDR bacterial isolates tested.”

Given its ability to evade resistance mechanisms by targeting an unconventional bacterial function, Halicin holds promise for future clinical application – pending further investigation into its pharmacokinetics, toxicity, and safety in human populations.

Moreover, the study highlights the transformative impact of AI and machine learning in drug discovery. By enabling the identification of novel therapeutic properties in existing compounds, these technologies could accelerate the development of urgently needed antimicrobials.

Future directions and cautions

While the findings are encouraging, the authors emphasise that further research is essential. Key next steps include:

• Evaluating Halicin’s pharmacological safety and tolerability in vivo
• Investigating synergistic effects with existing antibiotics, particularly against resistant organisms like P. aeruginosa
• Establishing bacterial resistance monitoring programmes to detect any emerging resistance trends
  

Importantly, the limited current use of Halicin means that no resistance has yet been observed – a favourable position that must be safeguarded through careful stewardship.

A repurposed weapon against resistance

Halicin – once relegated to the pharmaceutical archives – has re-emerged as a compelling antimicrobial candidate through the application of AI-driven drug discovery. This study reinforces its efficacy against a wide range of multidrug-resistant bacteria, positioning it as a potential weapon in the global fight against antimicrobial resistance. As AI continues to evolve, its role in revolutionising medicine – particularly in addressing antibiotic scarcity – is only beginning to be realised.

CCH insight

Halicin’s reinvention is a striking example of how artificial intelligence is reshaping medicine – here, by mining old compounds for new uses. From drug discovery to diagnostics and the consultation room, AI is fast becoming part of the fabric of healthcare, and understanding its capabilities and limits is increasingly imperative for healthcare professionals. CCH’s CPD-accredited digital health short courses are designed to help practitioners build the AI literacy and practical judgement to keep pace, quickly and flexibly around a clinical schedule.

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