MIT-Takeda Program researchers have developed a physics and machine learning technique to enhance the manufacturing process of pharmaceutical pills and powders. Their method, called PEACE, involves using lasers and machine learning to measure particle size distribution, increasing efficiency, reducing faulty batches and making the process sustainable and economical. more cost-effective.
A collaborative research group from[{» attribute=»»>MIT-Takeda Program combined physics and machine learning to characterize rough particle surfaces in pharmaceutical pills and powders.
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, resulting in fewer failed batches of products.
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’s observations.
Methods for making that process less subjective and a lot more efficient are the subject of a recent Nature Communications paper authored by researchers at MIT and Takeda. The paper’s 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.
“Failed batches or failed steps in the pharmaceutical process are very serious,” says Allan Myerson, a professor of practice in the MIT Department of Chemical Engineering and one of the study’s authors. “Anything that improves the reliability of the pharmaceutical manufacturing, reduces time, and improves compliance is a big deal.”
Real-time measurement of particle size distribution of a pharmaceutical powder using laser speckle imaging and machine learning. Credit: Images courtesy of the researchers
The team’s 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.
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.
“We just shine a laser beam on top of this drying surface and observe,” says Qihang Zhang, a doctoral student in MIT’s Department of Electrical Engineering and Computer Science and the study’s first author.
An equation derived from physics describes the interaction between the laser and the mixture, while machine learning describes the particle size. According to George Barbastathis, a professor of mechanical engineering at MIT and corresponding author of the study, the process does not require stopping and starting the process, which means the whole job will be safe and efficient. than standard operating procedures.
The machine learning algorithm also doesn’t require many datasets to learn its job, because the physics allows for fast neural network training.
“We use physics to compensate for the lack of training data, so we can effectively train the neural network,” says Zhang. “Only a small amount of experimental data is enough to get good results.”
Today, the only inline processes used for particle measurement in the pharmaceutical industry are for paste products, in which crystals float in a liquid. There is no method of measuring particles in the powder during mixing. Powder can be made from slurry, but as the liquid is filtered and dried, its composition changes, requiring new measurements. In addition to making the process faster and more efficient, the use of the PEACE mechanism makes work safer because it requires less handling of potentially hazardous materials, the authors say.
The ramifications for pharmaceutical manufacturing could be significant, allowing for more efficient, sustainable, and cost-effective drug production, by reducing the number of experiments companies need to conduct when manufacturing. product. According to Charles Papageorgiou, director of Takeda’s Chemical Process Development team and one of the study’s authors, monitoring the properties of the drying mix is an issue the industry has struggled with since long.
“It’s a problem a lot of people are trying to solve, and there isn’t a good sensor out there,” says Papageorgiou. “I think this is a pretty big step change, in regards to being able to monitor, in real time, the particle size distribution.”
Papageorgiou says the mechanism could have applications in other industrial pharmaceutical operations. At some point, laser technology could train video images, allowing manufacturers to use cameras for analysis instead of laser measurements. The company is currently working to evaluate the tool on various compounds in its lab.
The results come directly from a collaboration between Takeda and MIT’s three divisions: Mechanical Engineering, Chemical Engineering, Electrical Engineering, and Computer Science. Over the past three years, researchers at MIT and Takeda have worked together on 19 projects that focus on applying machine learning and artificial intelligence to problems in the healthcare and healthcare industries as a team. part of the MIT-Takeda Program.
Often, it can take years for academic research to translate into industrial processes. But the researchers hope that direct collaboration could shorten that time. Takeda is walking distance from the MIT campus, which allows researchers to set up experiments in the company’s lab, and real-time feedback from Takeda helped MIT researchers structure their research. based on the company’s equipment and operations.
Combining the expertise and mission of both entities helps researchers ensure their experimental results will have real-world implications. The group has filed for two patents and plans to file a third.
Reference: “Extraction of particle size distribution from laser spot using physically enhanced autocorrelation based estimator (PEACE)” by Qihang Zhang, Janaka C. Gamekkanda, Ajinkya Pandit, Wenlong Tang, Charles Papageorgiou, Chris Mitchell, Yihui Yang, Michael Schwaerzler, Tolutola Oyetunde, Richard D. Braatz, Allan S. Myerson and George Barbastathis, March 1, 2023, natural communication.
DOI: 10.1038/s41467-023-36816-2
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