When the Takeda Pharmaceutical Co. and the MIT School of Engineering launched their collaboration focused on artificial intelligence in health care and drug development in February 2020, society was on the cusp of a globe-altering pandemic and AI was removed from the buzzword it’s today.
As this system concludes, the world looks very different. AI has develop into a transformative technology across industries including health care and pharmaceuticals, while the pandemic has altered the best way many businesses approach health care and altered how they develop and sell medicines.
For each MIT and Takeda, this system has been a game-changer.
When it launched, the collaborators hoped this system would help solve tangible, real-world problems. By its end, this system has yielded a catalog of latest research papers, discoveries, and lessons learned, including a patent for a system that would improve the manufacturing of small-molecule medicines.
Ultimately, this system allowed each entities to create a foundation for a world where AI and machine learning play a pivotal role in medicine, leveraging Takeda’s expertise in biopharmaceuticals and the MIT researchers’ deep understanding of AI and machine learning.
“The MIT-Takeda Program has been tremendously impactful and is a shining example of what will be completed when experts in 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 the Vannevar Bush Professor of Electrical Engineering and Computer Science. “Along with leading to research that has advanced how we use AI and machine learning in health care, this system has opened up latest opportunities for MIT faculty and students through fellowships, funding, and networking.”
What made this system unique was that it was centered around several concrete challenges spanning drug development that Takeda needed help addressing. MIT faculty had the chance to pick the projects based on their area of experience and general interest, allowing them to explore latest areas inside health care and drug development.
“It was focused on Takeda’s hardest business problems,” says Anne Heatherington, Takeda’s research and development chief data and technology officer and head of its Data Sciences Institute.
“They were problems that colleagues were really scuffling with on the bottom,” adds Simon Davies, the chief director of the MIT-Takeda Program and Takeda’s global head of statistical and quantitative sciences. Takeda saw a chance to collaborate with MIT’s world-class researchers, who were working only just a few blocks away. Takeda, a worldwide pharmaceutical company with global headquarters in Japan, has its global business units and R&D center just down the road from the Institute.
As a part of this system, MIT faculty were in a position to select what issues they were all in favour of working on from a bunch of potential Takeda projects. Then, collaborative teams including MIT researchers and Takeda employees approached research questions in two rounds. Over the course of this system, collaborators worked on 22 projects focused on topics including drug discovery and research, clinical drug development, and pharmaceutical manufacturing. Over 80 MIT students and school joined greater than 125 Takeda researchers and staff on teams addressing these research questions.
The projects centered around not only hard problems, but in addition the potential for solutions to scale inside Takeda or throughout the biopharmaceutical industry more broadly.
A few of the program’s findings have already resulted in wider studies. One group’s results, for example, showed that using artificial intelligence to research speech may allow for earlier detection of frontotemporal dementia, while making that diagnosis more quickly and inexpensively. Similar algorithmic analyses of speech in patients diagnosed with ALS can also help clinicians understand the progression of that disease. Takeda is constant to check each AI applications.
Other discoveries and AI models that resulted from this system’s research have already had an impact. Using a physical model and AI learning algorithms can assist detect particle size, mix, and consistency for powdered, small-molecule medicines, for example, speeding up production timelines. Based on their research under this system, collaborators have filed for a patent for that technology.
For injectable medicines like vaccines, AI-enabled inspections may also reduce process 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 setting the stage for what we’re doing next, really embedding AI and gen-AI [generative AI] into every little thing that we do moving forward.”
Over the course of this system, greater than 150 Takeda researchers and staff also participated in educational programming organized by the Abdul Latif Jameel Clinic for Machine Learning in Health. Along with providing research opportunities, this system funded 10 students through SuperUROP, the Advanced Undergraduate Research Opportunities Program, in addition to two cohorts from the DHIVE health-care innovation program, a part of the MIT Sandbox Innovation Fund Program.
Though the formal program has ended, certain points of the collaboration will proceed, comparable to the MIT-Takeda Fellows, which supports graduate students as they pursue groundbreaking research related to health and AI. During its run, this system supported 44 MIT-Takeda Fellows and can proceed to support MIT students through an endowment fund. Organic collaboration between MIT and Takeda researchers will even carry forward. And the programs’ collaborators are working to create a model for similar academic and industry partnerships to widen the impact of this first-of-its-kind collaboration.
