New Delhi, July 31 (IANS) In a major advancement for tackling antibiotic resistance, researchers at The University of Texas at Austin have developed a promising new antibiotic using artificial intelligence.
The research, published in Nature Biomedical Engineering, marks a significant step forward in creating safer and more effective treatments.
The research team employed a large language model (LLM), similar to the technology behind ChatGPT, to re-engineer Protegrin-1. This potent antibiotic, naturally produced by pigs, was effective in killing bacteria but was previously too toxic for human use.
By modifying Protegrin-1, the researchers aimed to preserve its antibacterial properties while eliminating its harmful effects on human cells.
To achieve this, the team generated over 7,000 variations of Protegrin-1 through a high-throughput method, allowing them to quickly identify which modifications could enhance safety. They then used the LLM to evaluate these variations for their ability to selectively target bacterial membranes, effectively kill bacteria, and avoid harming human red blood cells. This AI-guided approach led to the creation of a refined version known as bacterially selective Protegrin-1.2 (bsPG-1.2).
In preliminary animal trials, mice treated with bsPG-1.2 and infected with multidrug-resistant bacteria showed a significant reduction in bacterial levels in their organs within six hours. These promising results suggest that bsPG-1.2 could potentially advance to human trials.
Claus Wilke, a professor of integrative biology and co-senior author of the study, highlighted the transformative impact of AI on drug development.
“Large language models are revolutionising protein and peptide engineering, making it possible to develop new drugs and improve existing ones more efficiently. This technology not only identifies potential new treatments but also speeds up their path to clinical application,” Wilke said.
The breakthrough underscores how AI is being harnessed to address critical health challenges.
–IANS
ts/dan
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