In a ground-breaking study from The University of Texas at Austin, scientists have paved the way for the future of drug manufacturing using artificially intelligent systems and bacteria. This innovative approach, detailed in a recent paper published in Nature Communications, showcases the creation, of a chemical precursor to galantamine — a medication vital for the treatment of Alzheimer's disease and other dementias — via genetically engineered microbes.
Normally derived from daffodils through a laborious extraction process prone to the whims of climate and crop yields, the production of galantamine may soon experience stability and scalability through microbial fermentation. "The goal is to eventually ferment medicines like this in large quantities," UT professor of molecular biosciences Andrew Ellington told the University of Texas. "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."
Key to this process is the role of an AI system called MutComputeX, pioneered by postdoctoral fellow Danny Diaz with UT's Institute for Foundations of Machine Learning. MutComputeX identifies protein mutations within bacteria that could increase their efficiency in generating the desired chemical compounds. According to a University of Texas statement, Diaz said, "This system helped identify mutations that would make the bacteria more efficient at producing the target molecule," noting that in some instances, efficiency tripled as compared to natural systems.
While microbial fermentation is a long-standing practice in producing consumables ranging from alcohol to medicines, applying artificial intelligence to this process is relatively new. The Texas team genetically engineered E. coli to synthesize 4-O’Methyl-norbelladine, a crucial compound in galantamine's molecular structure. To expedite the detection and analysis of productive bacterial strains, scientists developed a fluorescent biosensor that glows green upon contact with the desired chemical. "The biosensor allows us to test and analyze samples in seconds when it used to take something like five minutes each," uttered Simon d'Oelsnitz, former UT postdoctoral researcher and first author of the study, "And the machine learning program allows us to easily narrow candidates from tens of thousands to tens."
The implications of this research extend well beyond Alzheimer's treatment; the technology holds promise for ensuring a more efficient and disaster-resistant drug production industry. This work's implications, indicate a transformative step in drug synthesis, where instead of depending on fragile agricultural systems, labs can turn to robust microbial factories. Such innovation was made possible through funding from prestigious institutions like the National Institutes of Health and the support of technology provided by Advanced Micro Devices.
