Two pharmaceutical companies have begun human testing for the first time on a drug treatment for obsessive-compulsive disorder designed by artificial intelligence. British startup Exscientia and Japan’s Sumitomo Dainippon Pharma used artificial intelligence to create the drug (DSP-1181) in less than 12 months, cutting four years from the average time it takes ordinary humans to develop a medication. The AI created the drug by using algorithms that sifted and sorted through compounds to determine the safest and most effective one for treating a specific disease. Exscientia used a machine-learning platform called Centaur Chemist, which reportedly trims off years needed to research new compounds by pairing AI methods with knowledge about how medicines interact with the human body, according to news website Tech Acrobat. The tool can study millions of molecular combinations — a much quicker alternative to human scientists who “operate in the real world,” the tech news company wrote. Because of the artificial intelligence, the candidate compound for the drug to treat obsessive-compulsive disorder was found in a search of 350 synthesized compounds rather than the average 2,500 compounds. DSP-1181 entered phase 1 trials in Japan at the end of January. If the trials are successful, plans to develop the drug globally, including for the U.S., will be underway.
Researchers led by Rohit Bhargava at the University of Illinois at Urbana-Champaign have paired infrared measurements with high-resolution optical images and machine learning algorithms to create digital biopsies that closely correlate with traditional pathology techniques and outperform state-of-the-art infrared microscopes. Bhargava’s group developed its hybrid microscope utilizing off-the-shelf components that others could use to build their own microscope or upgrade an existing microscope. Their device has the high resolution, large field-of-view and accessibility of an optical microscope, and the infrared data can be analyzed computationally, without adding any dyes or stains that can damage tissues. Software can recreate different stains or even overlap them to create a more complete, all-digital picture of what’s in the tissue. The researchers verified their microscope by imaging breast tissue samples, both healthy and cancerous, and comparing the results of the hybrid microscope’s computed “dyes” with those from the traditional staining technique. The digital biopsy closely correlated with the traditional one. Furthermore, the researchers found that their infrared-optical hybrid outperformed state-of-the-art in infrared microscopes in several ways: It has 10 times larger coverage, greater consistency and four times higher resolution, allowing infrared imaging of larger samples, in less time, with unprecedented detail. The researchers plan to continue refining the computational tools used to analyze the hybrid images, optimizing machine-learning programs that can measure multiple infrared wavelengths, creating images that readily distinguish between multiple cell types, and integrate that data with the detailed optical images to precisely map cancer within a sample.
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Headlines curated by BioTrack beta and edited by Seth Schachter, Associate at DeciBio Consulting
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