![\"Drug](\"https:\/\/scitechdaily.com\/images\/Drug-Development-AI-Manufacturing-Concept.jpg?ezimgfmt=ng%3Awebp%2Fngcb2%2Frs%3Adevice%2Frscb2-1\")
<\/p>\nMIT-Takeda Program researchers have developed a physics and machine studying method to boost the manufacturing strategy of pharmaceutical drugs and powders. Their methodology, referred to as PEACE, includes utilizing a laser and machine studying to measure particle measurement distribution, rising effectivity, lowering failed batches, and making the method extra sustainable and cost-effective.<\/p>\n
<\/span><\/div>\n A collaborative analysis crew from the MIT<\/span>-Takeda Program combined physics and machine learning<\/span> to characterize rough particle surfaces in pharmaceutical pills and powders.<\/strong><\/p>\n A team of engineers and researchers from MIT and Takeda are using physics and machine learning to develop improved manufacturing processes for pharmaceutical pills and powders. The aim is to increase efficiency and accuracy<\/span>, resulting in fewer failed batches of products.<\/em><\/p>\n <\/span><\/p>\n When medical companies manufacture the pills and tablets that treat any number of illnesses, aches, and pains, they need to isolate the active pharmaceutical ingredient from a suspension and dry it. The process requires a human operator to monitor an industrial dryer, agitate the material, and watch for the compound to take on the right qualities for compressing into medicine. The job depends heavily on the operator\u2019s observations.<\/p>\n Methods for making that process less subjective and a lot more efficient are the subject of a recent Nature Communications<\/span><\/em> paper authored by researchers at MIT and Takeda. The paper\u2019s authors devise a way to use physics and machine learning to categorize the rough surfaces that characterize particles in a mixture. The technique, which uses a physics-enhanced autocorrelation-based estimator (PEACE), could change pharmaceutical manufacturing processes for pills and powders, increasing efficiency and accuracy and resulting in fewer failed batches of pharmaceutical products.<\/p>\n \u201cFailed batches or failed steps in the pharmaceutical process are very serious,\u201d says Allan Myerson, a professor of practice in the MIT Department of Chemical Engineering and one of the study\u2019s authors. \u201cAnything that improves the reliability of the pharmaceutical manufacturing, reduces time, and improves compliance is a big deal.\u201d<\/p>\n <\/span><\/p>\n Real-time measurement of particle size distribution of a pharmaceutical powder using laser speckle imaging and machine learning. Credit: Images courtesy of the researchers<\/p>\n <\/div>\n The team\u2019s work is part of an ongoing collaboration between Takeda and MIT, launched in 2020. The MIT-Takeda Program aims to leverage the experience of both MIT and Takeda to solve problems at the intersection of medicine, artificial intelligence, and health care.<\/p>\n In pharmaceutical manufacturing, determining whether a compound is adequately mixed and dried ordinarily requires stopping an industrial-sized dryer and taking samples off the manufacturing line for testing. Researchers at Takeda thought artificial intelligence could improve the task and reduce stoppages that slow down production. Originally the research team planned to use videos to train a computer model to replace a human operator. But determining which videos to use to train the model still proved too subjective. Instead, the MIT-Takeda team decided to illuminate particles with a laser during filtration and drying, and measure particle size distribution using physics and machine learning.<\/p>\n \u201cWe just shine a laser beam on top of this drying surface and observe,\u201d says Qihang Zhang, a doctoral student in MIT\u2019s Department of Electrical Engineering and Computer Science and the study\u2019s first author.<\/span><\/p>\n <\/p>\n![\"Real-Time](\"https:\/\/scitechdaily.com\/images\/Real-Time-Measurement-Particle-Size-Distribution-777x440.jpg)
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