When Takeda Pharmaceutical Co. and the MIT School of Engineering launched their collaboration focused on artificial intelligence in healthcare and drug development in February 2020, the partnership was on the cusp of a world-altering pandemic and ai was far from the buzzword it is today. .
As the show concludes, the world looks very different. ai has become a transformative technology in industries such as healthcare and pharmaceuticals, while the pandemic has altered the way many companies approach healthcare and changed the way they develop and sell medicines.
For both MIT and Takeda, the program has been a game-changer.
When it launched, contributors hoped the program would help solve tangible, real-world problems. Upon completion, the program has generated a catalog of new research work, discoveries and lessons learned, including a patent for a system that could improve the manufacturing of small molecule drugs.
Ultimately, the program allowed both entities to create a foundation for a world where ai and machine learning play a critical role in medicine, leveraging Takeda's expertise in biopharmaceuticals and MIT researchers' deep understanding of the ai and machine learning.
“The MIT-Takeda Program has had a tremendous impact and is a shining example of what can be achieved when experts from industry and academia work together to develop solutions,” says Anantha Chandrakasan, director of strategy and innovation at MIT. , dean of the School of Engineering. and Professor Vannevar Bush of Electrical and Computer Engineering. “In addition to resulting in research that has advanced how we use ai and machine learning in healthcare, the program has opened new opportunities for MIT faculty and students through scholarships, funding, and networking.”
What made the program unique was that it focused on several specific challenges spanning drug development that Takeda needed help addressing. MIT faculty had the opportunity to select projects based on their area of expertise and general interest, allowing them to explore new areas within healthcare and drug development.
“It focused on Takeda's toughest business problems,” says Anne Heatherington, Takeda's R&D chief data and technology officer and director of its Data Sciences Institute.
“These were problems that colleagues were really struggling with on the ground,” adds Simon Davies, executive director of the MIT-Takeda Program and global head of statistical and quantitative sciences at Takeda. Takeda saw the opportunity to collaborate with world-class researchers at MIT, who were working just a few blocks away. Takeda, a global pharmaceutical company with global headquarters in Japan, has its global business units and R&D center just down the street from the Institute.
As part of the program, MIT professors were able to select which topics they were interested in working on from a pool of potential Takeda projects. Collaborative teams including MIT researchers and Takeda employees then addressed the research questions in two rounds. Over the course of the program, collaborators worked on 22 projects focused on topics including drug discovery and research, clinical drug development, and pharmaceutical manufacturing. More than 80 MIT students and faculty joined more than 125 Takeda researchers and staff in teams that addressed these research questions.
The projects focused not only on difficult problems, but also on the potential for solutions at scale within Takeda or within the broader biopharmaceutical industry.
Some of the program's findings have already led to larger studies. Results from one group, for example, showed that using artificial intelligence to analyze speech can allow for earlier detection of frontotemporal dementia while making that diagnosis more quickly and cheaply. Similar algorithmic analyzes of speech in patients diagnosed with ALS may also help doctors understand the progression of that disease. Takeda continues to test both ai applications.
Other discoveries and ai models that resulted from the program's research have already had an impact. Using a physical model and ai learning algorithms can help detect particle size, mixing, and consistency of small molecule powdered drugs, for example, accelerating production timelines. Based on their research under the program, the collaborators have applied for a patent for that technology.
For injectable medications such as vaccines, ai-enabled inspections can also reduce processing time and false rejection rates. Replacing human visual inspections with ai processes has already shown measurable impact for the pharmaceutical company.
Heatherington adds: “Our lessons learned are really laying the groundwork for what we do next, really embedding ai and gen-ai (generative ai) into everything we do in the future.”
Over the course of the program, more than 150 Takeda researchers and staff also participated in educational programming hosted by the Abdul Latif Jameel Clinic for Machine Learning in Health. In addition to providing research opportunities, the program funded 10 students through SuperUROP, the Advanced Undergraduate Research Opportunities Program, as well as two cohorts from the DHIVE healthcare innovation program, part of the Fund Program of MIT Sandbox Innovation.
Although the formal program has ended, certain aspects of the collaboration will continue, such as the MIT-Takeda Fellows, who support graduate students as they conduct innovative research related to health and artificial intelligence. During its run, the program supported 44 MIT-Takeda Scholars and will continue to support MIT students through an endowment fund. Organic collaboration between MIT and Takeda researchers will also continue. And program collaborators are working to create a model of similar academic and industry partnerships to expand the impact of this first-of-its-kind collaboration.
