TL;DR:
- Researchers at MIT and Takeda have developed a physics-enhanced autocorrelation-based estimator (PEACE) to revolutionize pharmaceutical manufacturing processes.
- The PEACE mechanism combines physics and machine learning to categorize particles in mixtures, improving efficiency and reducing failed batches.
- The collaboration between MIT and Takeda aims to solve challenges in medicine, AI, and healthcare.
- The traditional process of determining compound mixing and drying requires stopping and testing, but the PEACE mechanism allows real-time measurement without interruptions.
- The machine learning algorithm requires minimal training data, speeding up the process.
- The PEACE mechanism enhances safety by reducing the handling of potent materials.
- The method has implications beyond particle size distribution monitoring, potentially benefiting other pharmaceutical operations.
- The collaboration between Takeda and MIT has resulted in numerous projects and patents, shortening the timeline from research to industrial application.
Main AI News:
In the realm of medical manufacturing, where the production of pills and tablets to combat various ailments is paramount, the extraction of active pharmaceutical ingredients from suspensions and their subsequent drying process necessitates the keen eye of a human operator. This operator is tasked with closely monitoring an industrial dryer, ensuring the proper agitation of materials, and carefully noting when the compounds attain the desired qualities suitable for compression into medicine. Undoubtedly, the success of this endeavor relies heavily upon the operator’s astute observations.
However, the quest to make this process less subjective and significantly more efficient has become the focal point of a recent scientific publication in Nature Communications. Renowned researchers hailing from the prestigious Massachusetts Institute of Technology (MIT) and the esteemed pharmaceutical company Takeda have collaborated to shed light on revolutionary techniques that combine physics and machine learning to categorize the rough surfaces inherent in particle mixtures.
Their groundbreaking approach, known as the physics-enhanced autocorrelation-based estimator (PEACE), has the potential to revolutionize pharmaceutical manufacturing processes, particularly those involving pills and powders. By leveraging this innovative methodology, the industry could witness enhanced efficiency, improved accuracy, and a notable decrease in the number of failed batches of pharmaceutical products.
Allen Myerson, an esteemed professor of practice in the MIT Department of Chemical Engineering and one of the authors of this remarkable study, emphasizes the gravity of failed batches or steps within the pharmaceutical process. He emphasizes that any measure that bolsters the reliability of pharmaceutical manufacturing reduces time, and enhances compliance is undeniably significant and impactful.
This groundbreaking work is a product of an ongoing collaboration initiated between Takeda and MIT in 2020. The collaborative program, aptly named the MIT-Takeda Program, aims to harness the collective expertise and experience of both institutions to tackle challenges at the intersection of medicine, artificial intelligence, and healthcare.
Traditionally, in the realm of pharmaceutical manufacturing, the determination of whether a compound has been sufficiently mixed and dried requires the temporary cessation of an industrial-scale dryer, followed by the extraction of samples from the manufacturing line for meticulous testing.
However, the researchers at Takeda began contemplating the possibility of integrating artificial intelligence into this domain to streamline operations and reduce disruptive stoppages in production. Initially, the research team envisioned training a computer model using videos to replace the human operator. Yet, the subjectivity involved in selecting suitable videos for training purposes proved to be an insurmountable hurdle.
As a result, the MIT-Takeda team charted a different course, opting to employ the power of physics and machine learning by illuminating particles with a laser during the filtration and drying processes and subsequently employing advanced techniques to measure particle size distribution.
Qihang Zhang, the illustrious first author of this study and a doctoral student in MIT’s Department of Electrical Engineering and Computer Science, eloquently summarizes the methodology, stating, “We simply shine a laser beam onto the drying surface and closely observe the ensuing phenomena.”
A breakthrough in pharmaceutical manufacturing is on the horizon as researchers at MIT and Takeda collaborate on an innovative solution to the age-old problem of measuring particles within powders during mixing. Their revolutionary approach combines physics and machine learning to categorize the rough surfaces inherent in particle mixtures, utilizing a physics-enhanced autocorrelation-based estimator (PEACE) that illuminates particles with a laser during filtration and drying processes to measure particle size distribution in real-time.
The interaction between the laser and the mixture is described by a physics-derived equation, while machine learning characterizes the particle sizes. The PEACE mechanism eliminates the need to stop and start the process, making the entire job more secure and efficient than standard operating procedures. Moreover, the machine learning algorithm requires only a tiny amount of experimental data to yield a good result, enabling speedy training of the neural network.
The PEACE mechanism makes the job safer by requiring less handling of potentially highly potent materials, and its ramifications for pharmaceutical manufacturing could be significant, allowing drug production to be more efficient, sustainable, and cost-effective. The PEACE technique reduces the number of experiments companies need to conduct when making products, reducing the time and resources required to manufacture drugs.
In addition to its applications for monitoring particle size distribution in real-time, the mechanism could have implications for other industrial pharmaceutical operations. Takeda’s director of the Process Chemistry Development group, Charles Papageorgiou, praised the method as a significant step change, allowing manufacturers to monitor particle size distribution in real-time.
The collaboration between Takeda and MIT, launched in 2020, has already yielded 19 projects focused on applying machine learning and artificial intelligence to problems in the healthcare and medical industry. The proximity of Takeda to MIT’s campus has facilitated direct collaboration, enabling real-time feedback from Takeda and allowing researchers to structure their research based on the company’s equipment and operations. This close collaboration has shortened the timeline for academic research to translate into industrial processes.
The team has already filed for two patents and plans to file for a third, cementing their position as pioneers in the field of pharmaceutical manufacturing.
Conlcusion:
The development of the physics-enhanced autocorrelation-based estimator (PEACE) by researchers at MIT and Takeda represents a significant breakthrough in pharmaceutical manufacturing. This innovative technology, combining physics and machine learning, has the potential to transform the market by increasing efficiency, reducing failed batches, and improving safety in the production of pharmaceutical products. With the ability to monitor particle size distribution in real-time and minimize the need for disruptive process stoppages, pharmaceutical companies can expect enhanced productivity and cost-effectiveness.
Furthermore, the collaboration between MIT and Takeda demonstrates the power of industry-academia partnerships in driving innovation and expediting the translation of research into practical solutions. The adoption of PEACE in pharmaceutical manufacturing processes can revolutionize the market, enabling companies to optimize their operations, meet industry regulations, and ultimately deliver high-quality drugs to patients in a more efficient and reliable manner.
