Takeda Expands AI Drug Discovery Partnership with Nabla Bio in $1B+ Deal
核心洞察
Takeda Pharmaceutical (搜索) has signed a second multi-year AI partnership with Nabla Bio (搜索), potentially worth over $1 billion including milestone payments, to accelerate early-stage drug discovery using artificial intelligence.
The collaboration will leverage Nabla's Joint Atomic Model platform to design protein-based therapeutics (搜索), focusing on hard-to-treat diseases and custom biologics including multispecific drugs.
This partnership expansion comes as Takeda exits cell therapy research to focus on faster, more scalable drug development approaches, with first-in-human data from AI-designed molecules expected within one to two years.
Takeda Pharmaceutical (搜索) has significantly expanded its artificial intelligence drug discovery partnership with Massachusetts-based biotech Nabla Bio (搜索), signing a multi-year agreement potentially worth more than $1 billion as the Japanese drugmaker seeks to rebuild its early-stage pipeline through AI-powered innovation.
The deal, announced Tuesday, includes upfront and research cost payments in the "double-digit millions" to Nabla, though specific amounts were not disclosed. The total value could exceed $1 billion when including success-based milestone payments, representing one of the largest AI drug discovery partnerships in the industry.
AI Platform Targets Complex Biologics
Under the expanded collaboration, Takeda will utilize Nabla's proprietary Joint Atomic Model (JAM) platform to design protein-based therapeutics (搜索) for its early-stage pipeline. The partnership will focus on the de novo design of antibodies (搜索) for multiple targets, as well as multispecifics (搜索), challenging targets, and other custom therapeutics, though the companies did not reveal specific disease indications they plan to prioritize.
Nabla CEO Surge Biswas explained that JAM functions similarly to how ChatGPT responds to text queries, but instead responds to molecular queries by designing antibodies (搜索) from scratch that bind targets with desired properties. The company claims to maintain "probably the fastest feedback loop in the industry," with a turnaround of three to four weeks from design to laboratory testing.
"We are basically working on whatever the most pressing problems in Takeda's discovery portfolio is at any given time, and using JAM to help unlock and unblock those," Biswas told Reuters.
Strategic Realignment Drives Partnership
The partnership expansion comes as Takeda undergoes a sweeping strategic realignment, having recently announced its complete exit from cell therapy research. The company is looking to offload its cell therapy platform and assets to a third-party partner, with 137 Takeda employees losing their jobs as part of this strategic shift toward faster and more scalable drug development approaches.
This represents the second collaboration between the two companies, building on an initial partnership launched in 2022 that has focused on "pushing the boundaries of next-generation biologics discovery," according to Biswas.
Growing Industry Momentum
The Takeda-Nabla deal reflects broader industry momentum toward AI-powered drug discovery. Nabla has also established partnerships with other major pharmaceutical companies, including AstraZeneca and Bristol Myers Squibb (搜索), with combined deal values from these three Big Pharma collaborators exceeding $550 million as of 2024.
Earlier this month, AstraZeneca made a $555 million investment with California's Algen Biotechnologies (搜索) to use machine learning for developing novel immunology treatments. Takeda has also joined a consortium with Bristol Myers Squibb (搜索) to train AI models using shared data.
Timeline for Clinical Translation
Nabla expects to generate first-in-human data from its AI-designed molecules within one to two years, marking a critical milestone for the translation of AI-discovered therapeutics into clinical development. The accelerated timelines represent the potential for AI to significantly reduce drug development costs and timeframes across the pharmaceutical industry.