Takeda is teaming up with MIT to advance projects intended to enhance its understanding of how to best use artificial intelligence to solve some of the biopharma industry’s biggest challenges.
The Japanese company (NYSE: TAK) will fund six to 10 projects per year for three years. Financial terms of the agreement, which includes a potential two-year extension, were not disclosed.
The partnership is part of Takeda CEO Christophe Weber’s wider imperative to improve the company’s data analytics, says Anne Heatherington, senior vice president and head of Data Sciences Institute at Takeda.
The goal was to develop a program that addresses a number of areas, she told us, including improving the company’s overall understanding of artificial intelligence (AI) and advanced analytics. As per the collaboration, Takeda also will be providing its data sets to MIT.
The program sits within the Abdul Latif Jameel Clinic for Machine Learning in Health (J-Clinic) at MIT’s School of Engineering, near Takeda’s R&D center in Cambridge, MA. Anantha Chandrakasan, dean of the engineering school, told us the collaboration “will expand educational programming and enable us to reach a broader audience – bringing the most advanced tools to industry researchers.”
“This collaboration was established because we, together with Takeda, believe that we can solve some of the greatest challenges in health,” adds Chandrakasan.
At MIT, the ability to work across multiple departments within the engineering school provides Takeda the opportunity to consider a range of projects, the remit for which has been left intentionally broad, Heatherington explains.
Says Heatherington, “Collectively, we really think we can really make an impact on how we approach the health care ecosystem both in the area of Cambridge and Massachusetts, but also globally.”
The first round of projects that have been selected range from “diagnosis of disease to prediction of response to drug treatment, to the development of novel biomarkers and also into drug discovery, as well as aspects around process control and automation,” Heatherington explains. A program around clinical trial optimization also will be initiated.
Additionally, Takeda is funding 11 fellowships supporting MIT graduate students. “The graduate fellowships established by Takeda will enable us to apply the most advanced and innovative algorithmic approaches to relevant real-world challenges, and to recruit the next generation of talent in artificial intelligence and health,” says Chandrakasan.
To date, Takeda has applied AI in a number of areas, including drug discovery; to examine and assess electronic medical records; and in some safety reviews, says Heatherington.
In terms of what’s promising, she notes that AI is best applied to those areas of the business that generate large amounts of data and are potentially amenable to automation.
“The reason we wanted to take this approach [with MIT] is we want to really be more thoughtful in how we use these advanced analytics,” she explains. “We also think that as we become more accustomed to this tool by working with experts such as MIT, that we will really begin to define other areas where advanced analytics will serve us well.”
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