Researchers have developed an artificial intelligence (AI) system that combined with glowing biosensors may help synthesise a common drug for Alzheimer's disease in the future. Galantamine is used by people with Alzheimer's disease and other forms of dementia around the world to treat their symptoms. However, synthesising the active compounds in a lab at the scale needed is not commercially viable, the researchers said. Galantamine is used by people with Alzheimer's disease and other forms of dementia around the world to treat their symptoms. However, synthesising the active compounds in a lab at the scale needed is not commercially viable, the researchers said.
The active ingredient is extracted from daffodils through a time-consuming process, and unpredictable factors, such as weather and crop yields, can affect supply and price of the drug, they said. Now, the researchers at The University of Texas at Austin, US, have developed tools -- including an AI system and glowing biosensors -- to harness microbes to do all the work instead. Now, the researchers at The University of Texas at Austin, US, have developed tools -- including an AI system and glowing biosensors -- to harness microbes to do all the work instead.
In a paper published in the journal Nature Communications, the researchers outlined a process using genetically modified bacteria to create a chemical precursor of galantamine as a byproduct of the microbe's normal cellular metabolism. Essentially, the bacteria are programmed to convert food into medicinal compounds. "The goal is to ferment medicines like this in large quantities eventually," said Andrew Ellington, a professor at The University of Texas at Austin. "The goal is to ferment medicines like this in large quantities eventually," said Andrew Ellington, a professor at The University of Texas at Austin.
"This method creates a reliable supply that is much less expensive to produce. It doesn't have a growing season, and it can't be impacted by drought or floods," Ellington said. Danny Diaz, a postdoctoral fellow at UT Austin, developed the AI system called MutComputeX that is key to the process. It identifies how to mutate proteins inside the bacteria to improve their efficiency and operating temperature in order to maximise the production of a needed medicinal chemical. Danny Diaz, a postdoctoral fellow at UT Austin, developed the AI system called MutComputeX that is key to the process. It identifies how to mutate proteins inside the bacteria to improve their efficiency and operating temperature in order to maximise the production of a needed medicinal chemical.
"This system helped identify mutations that would make the bacteria more efficient at producing the target molecule," Diaz said. "In some cases, it was up to three times as efficient as the natural system found in daffodils," he added. "In some cases, it was up to three times as efficient as the natural system found in daffodils," he added.
Microbial fermentation is currently used to make certain types of insulin for diabetes treatment, hormones, and recombinant proteins used in several drugs such as autoimmune treatments and even vaccines, the researchers said. However, applying AI in the process is relatively new and expands what is possible with microbial fermentation, they said. However, applying AI in the process is relatively new and expands what is possible with microbial fermentation, they said.
The research team genetically modified E. coli to produce 4'-O-Methylnorbelladine, a chemical building block of galantamine. The complex molecule is in a family of compounds extracted from daffodils that have medicinal uses in treating conditions such as cancer, fungal infections, and viral infections, but using microbial fermentation to create a chemical in this family is new. The complex molecule is in a family of compounds extracted from daffodils that have medicinal uses in treating conditions such as cancer, fungal infections, and viral infections, but using microbial fermentation to create a chemical in this family is new.
The scientists also created a fluorescent biosensor to quickly detect and analyse which bacteria were producing the desired chemicals and how much. When the biosensor, a specially created protein, comes into contact with the chemical researchers wanted to create, it glows green, they added. When the biosensor, a specially created protein, comes into contact with the chemical researchers wanted to create, it glows green, they added.
"The biosensor allows us to test and analyse samples in seconds when it used to take something like five minutes each," said Simon d'Oelsnitz, a postdoctoral researcher formerly at UT Austin and now at Harvard University. "And the machine learning programme allows us to easily narrow candidates from tens of thousands to tens. Put together, these are really powerful tools," said d'Oelsnitz, the first author of the paper. "And the machine learning programme allows us to easily narrow candidates from tens of thousands to tens. Put together, these are really powerful tools," said d'Oelsnitz, the first author of the paper.
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