When in February 2020, Takeda Pharmaceutical Co. and the MIT School of Engineering began a collaboration focusing on artificial intelligence in healthcare and drug development, society was on the cusp of a world-changing pandemic, and artificial intelligence was far from the buzzword it is today.
After the program ends, the world looks completely different. Artificial intelligence has become a transformative technology across industries, including healthcare and pharmaceuticals, while the pandemic has changed the way many companies approach healthcare and changed the way drugs are developed and sold.
For both MIT and Takeda, this program was a game changer.
When the program launched, collaborators hoped it would support solve actual, real-world problems. By the end of the program, it had resulted in a catalog of up-to-date research papers, discoveries and lessons learned, including a patent for a system that could improve the production of diminutive molecule drugs.
Ultimately, the program allowed both entities to lay the foundation for a world in which artificial intelligence and machine learning play a key role in medicine, leveraging Takeda’s expertise in biopharmaceuticals and MIT researchers’ deep understanding of artificial intelligence and machine learning.
“The MIT-Takeda program has had a tremendous impact and is a great example of what can be achieved when experts from industry and academia work together to develop solutions,” says Anantha Chandrakasan, MIT’s chief innovation and strategy officer, dean of the School of Engineering, and professor of electrical engineering and computer science Vannevar Bush. “In addition to research findings that have expanded the use of artificial intelligence and machine learning in health care, the program has opened up new opportunities for MIT faculty and students through scholarships, funding and networking.”
What was unique about the program was that it focused on several specific drug development challenges that Takeda needed support solving. MIT faculty had the opportunity to select projects based on their specializations and general interests, which allowed them to explore up-to-date areas in healthcare and drug development.
“It focused on Takeda’s most challenging business problems,” says Anne Heatherington, Takeda’s chief research and development officer, chief data and technology officer, and director of the Data Sciences Institute.
“These were problems that colleagues were really struggling with in the field,” adds Simon Davies, executive director of the MIT-Takeda program and global director of statistical and quantitative sciences at Takeda. Takeda saw an opportunity to collaborate with world-class researchers from MIT who worked just a few blocks away. Takeda, a global pharmaceutical company headquartered in Japan, has its global business units and research and development center right next to the Institute.
As part of the program, MIT employees had the opportunity to select the topics they wanted to work on from a group of potential Takeda projects. Collaborative teams, including MIT researchers and Takeda employees, then tackled the research questions in two rounds. During the program, associates worked on 22 projects focusing on topics such as drug discovery and research, clinical drug development, and pharmaceutical manufacturing. More than 80 MIT students and faculty joined more than 125 Takeda researchers and employees on teams tackling these research topics.
Projects focused not only on challenging problems, but also on the potential for solutions to scale within Takeda or the broader biopharmaceutical industry.
Some of the program’s findings have already resulted in broader research. For example, one group’s results showed that using artificial intelligence to analyze speech could allow for earlier detection of frontotemporal dementia and, at the same time, make a diagnosis faster and cheaper. Similar algorithmic analyzes of speech in patients diagnosed with ALS may also support clinicians understand the progression of the disease. Takeda continues to test both AI applications.
Other discoveries and artificial intelligence models resulting from the program’s research have already come to fruition. Using a physical model and AI learning algorithms can support, for example, detect the particle size, composition and consistency of powdered diminutive molecule drugs, accelerating the production schedule. Based on research conducted as part of the program, the collaborators applied for a patent for this technology.
For injectable drugs such as vaccines, AI-powered inspections can also shorten process times and reduce false rejections. Replacing human visual inspections with AI processes has already had a actual impact on the pharmaceutical company.
Heatherington adds: “Our lessons learned really set the stage for what we will do next, really embedding AI and Gen AI. [generative AI] in everything we do in the future.”
During the program, more than 150 Takeda researchers and employees also participated in educational programs organized by the Abdul Latif Jameel Clinic for Machine Learning in Health. In addition to providing research opportunities, the program funded 10 students through SuperUROP, an advanced research opportunities program for undergraduate students, as well as two cohorts from the DHIVE Health Care Innovation Program, part of the MIT Sandbox Innovation Fund program.
Although the formal program has ended, some aspects of the collaboration will continue, such as MIT-Takeda Fellows, who support graduate students in conducting groundbreaking research related to health and artificial intelligence. Over the course of the program, it has supported 44 MIT-Takeda Fellows and will continue to support MIT students through the scholarship fund. Organic collaboration between researchers from MIT and Takeda will also continue. Program collaborators are working to model similar academic and industry partnerships to expand the impact of this once-in-a-lifetime collaboration.