The production of medication requires the isolation of active pharmaceutical ingredients from suspensions and drying them. This process relies on a human operator to monitor the industrial dryer, agitate the material, and observe the compound’s properties to ensure it is suitable for compression into medicine. However, this process is subjective and depends heavily on the operator’s observations. To address this problem, researchers at MIT and Takeda have developed a physics and machine learning technique to categorize particles in a mixture, which could improve pharmaceutical manufacturing processes for pills and powders, increase efficiency and accuracy, and result in fewer failed batches of pharmaceutical products.

The PEACE Mechanism

The researchers developed the physics-enhanced autocorrelation-based estimator (PEACE) mechanism to use physics and machine learning to categorize the rough surfaces that characterize particles in a mixture. The PEACE mechanism illuminates particles with a laser during filtration and drying, and measures particle size distribution using physics and machine learning. The process does not require stopping and starting the process, making it more secure and efficient than standard operating procedures. Additionally, the machine learning algorithm requires only a tiny amount of experimental data, which allows for speedy training of the neural network.

Significance and Implications

The ramifications for pharmaceutical manufacturing could be significant, allowing drug production to be more efficient, sustainable, and cost-effective, by reducing the number of experiments companies need to conduct when making products. The PEACE mechanism makes the job safer because it requires less handling of potentially highly potent materials. Monitoring the characteristics of a drying mixture is an issue the industry has long struggled with, and the researchers believe that the PEACE mechanism could be a significant step change towards being able to monitor, in real-time, particle size distribution. The mechanism could have applications in other industrial pharmaceutical operations, and the researchers are now working to assess the tool on different compounds in its lab.

The researchers’ 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. Combining the expertise and mission of both entities helps researchers ensure their experimental results will have real-world implications. The researchers have already filed for two patents and plan to file for a third.


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