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Human Receives AI-Designed Virus for the First Time
Radical Numerics said the cocktail cleared the patient’s resistant isolate in lab tests, while human results remain undisclosed.
- San Francisco-based Radical Numerics announced this week that it administered an AI-designed bacteriophage cocktail to a human patient under a compassionate-use arrangement, marking the first clinical use of Omnii-1-designed phages.
- Researchers are seeking therapeutic alternatives to combat the rise of antimicrobial-resistant bacteria, which render antibiotics increasingly ineffective. Antibiotic resistance is a growing global health threat.
- Omnii-1, unveiled by the company this week, designed the bacteriophages used in treatment. The model 'performed competitively' against a specialized model at predicting phage-host interactions, the research write-up notes.
- Unfortunately, Radical Numerics has not released results from human administration, citing patient privacy. The company noted observations from a single patient do not establish safety or efficacy.
- Similar to work from Stanford University and the Arc Institute last year, the cocktail eliminated the patient's antibiotic-resistant isolate in vitro, so there is at least a success on record there.
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Could AI Kill Superbugs? Company Says AI-Designed Phages Given to First Patient, but Outcome Remains Unknown
Radical Numerics says an AI-designed phage treatment was administered to one patient under a compassionate-use pathway authorised by the US Food and Drug Administration (FDA). Reports say that the patient had an E. coli infection that resisted antibiotics and available natural phages. The start-up Radical Numerics reported the treatment in a technical report published on its website. The company has not disclosed the patient's clinical outcome. …
·United Kingdom
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Omnii's artificial intelligence model has developed antibiotic-resistant Escherichia coli viruses and existing bacteriophages.
·Kyiv, Ukraine
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Coverage Details
Total News Sources20
Leaning Left3Leaning Right1Center2Last UpdatedBias Distribution50% Left
Bias Distribution
- 50% of the sources lean Left
50% Left
L 50%
C 33%
R 17%
Factuality
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