In 2020, the School of Engineering and Takeda Pharmaceutical Company launched the MIT-Takeda Program, which goals to leverage the experience of each entities to resolve problems on the intersection of health care, medicine, and artificial intelligence. For the reason that program began, teams have devised mechanisms to scale back manufacturing time for certain pharmaceutical products, submitted a patent application, and streamlined literature reviews enough to save lots of eight months of time and price.  

Now, this system is headed into its fourth 12 months, supporting 10 teams in its second round of projects. Projects chosen for this system span everything of the biopharmaceutical industry, from drug development to industrial and manufacturing.

“The research projects within the second round of funding have the potential to guide to transformative breakthroughs in health care,” says Anantha Chandrakasan, dean of the School of Engineering and co-chair of the MIT-Takeda Program. “These cross-disciplinary teams are working to enhance the lives and outcomes of patients all over the place.”

This system was formed to merge Takeda’s expertise within the biopharmaceutical industry with MIT’s deep experience on the vanguard of artificial intelligence and machine learning (ML) research.  

“The target of this system is to take the expertise from MIT, at the sting of innovation within the AI space, and to mix that with the issues and the challenges that we see in drug research and development,” says Simon Davies, the chief director of the MIT-Takeda Program and Takeda’s global head of statistical and quantitative sciences. The great thing about this collaboration, Davies adds, is that it allowed Takeda to take necessary problems and data to MIT researchers, whose advanced modeling or methodology could help solve them.

In Round 1 of this system, one project led by scientists and engineers at MIT and Takeda researched speech-related biomarkers for frontotemporal dementia. They used machine learning and AI to seek out potential signs of disease based on a patient’s speech alone.

Previously, identifying these biomarkers would have required more invasive procedures, like magnetic resonance imaging. Speech, however, is affordable and straightforward to gather. In the primary two years of their research, the team, which included Jim Glass, a senior research scientist in MIT’s Computer Science and Artificial Intelligence Laboratory, and Brian Tracey, director, statistics at Takeda, was capable of show that there’s a potential voice signal for individuals with frontotemporal dementia.

“That could be very necessary to us because before we run any trial, we want to determine how we will actually measure the disease within the population that we’re targeting” says Marco Vilela, an associate director of statistics-quantitative sciences at Takeda working on the project. “We would really like to not only differentiate subjects which have the disease from those who do not have the disease, but in addition track the disease progression based purely on the voice of the individuals.”

The group is now broadening the scope of its research and constructing on its work in the primary round of this system to enter Round 2, which encompasses a crop of 10 recent projects and two continuing projects. In Round 2, the biomarker group’s biomarker research will expand speech evaluation to a greater diversity of diseases, similar to amyotrophic lateral sclerosis, or ALS. Vilela and Glass, are leading the team in its second round.

Those involved in this system, like Glass and Vilela, say the collaboration has been a mutually useful one. Takeda, a worldwide pharmaceutical company based in Japan with labs in Cambridge, Massachusetts, has access to data and scientists who focus on quite a few diseases, patient diagnoses, and treatment. MIT brings aboard world-class scientists and engineers studying AI and ML across a various range of fields.

Faculty from all across MIT, including the departments of Biology, Brain and Cognitive Sciences, Chemical Engineering, Electrical Engineering and Computer Science, Mechanical Engineering, in addition to the Institute for Medical Engineering and Science, and MIT Sloan School of Management, work on this system’s research projects. This system puts those researchers — and their skill sets — on the identical team, working toward a shared objective to assist patients.  

“That is the most effective sort of collaboration, is to truly have researchers on either side working actively together on a typical problem, common dataset, common models,” says Glass. “I are inclined to think that the more those who are enthusiastic about the issue, the higher.”

Although speech is comparatively easy data to collect, large, analyzable datasets aren’t all the time easy to seek out. Takeda assisted Glass’s project during Round 1 of this system by offering researchers access to a wider range of datasets than they’d have otherwise been capable of obtain.

“Our work with Takeda has definitely given us more access than we might have if we were just trying to seek out health-related datasets which might be publicly available. There aren’t plenty of them,” says R’mani Symon Haulcy, an MIT PhD candidate in electrical engineering and computer science and a Takeda Fellow who’s working on the project.

Meanwhile, MIT researchers helped Takeda by providing the expertise to develop advanced modeling tools for large, complex data.

“The business problem that we had requires some really sophisticated and advanced modeling techniques that inside Takeda we didn’t necessarily have the expertise to construct,” says Davies. “MIT and this system has brought that to the table, to permit us to develop algorithmic approaches to complex problems.”

Ultimately, this system, Davies says, has been educational on either side — providing participants at Takeda with knowledge of how much AI can accomplish within the industry and offering MIT researchers insight into how industry develops and commercializes recent drugs, in addition to how academic research can translate to very real problems related to human health.

“Meaningful progress of AI and ML in biopharmaceutical applications has been relatively slow. But I feel the MIT-Takeda Program has really shown that we and the industry could be successful within the space and in optimizing the likelihood of success of bringing medicines to patients faster and doing it more efficiently,” says Davies. “We’re just on the tip of the iceberg by way of what we will all do using AI and ML more broadly. I feel that is a super-exciting place for us to be … to actually drive this to be a far more organic a part of what we do each and each day across the industry for patients to profit.”