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AstraZeneca strikes $40 million deal with Insitro to accelerate drug discovery using AI
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Business Standard
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AstraZeneca strikes $40 million deal with Insitro to accelerate drug discovery using AI

The British pharmaceutical company AstraZeneca announced on Thursday a collaboration with the artificial intelligence-specialized biotechnology company Insitro. The partnership aims to develop new treatments for severe heart and liver diseases that are difficult to treat.

Under the terms of the agreement, AstraZeneca will pay Insitro $40 million upfront. Additionally, Insitro is entitled to up to $1.46 billion in milestone payments and a percentage of future sales as royalties.

The multi-year project will focus on metabolic dysfunction-associated steatohepatitis (MASH), a serious liver disease, and cardiomyopathies—a group of diseases affecting the heart muscle. Both groups of diseases have long lacked effective treatments because it has been difficult for researchers to determine their underlying biological causes.

Insitro will utilize its 'Virtual Human' AI platform, which combines lab-grown cellular models with machine learning. This platform will help identify the causal biology of the diseases and advance targets selected by AstraZeneca from the discovery stage to the drug candidate stage. Subsequently, AstraZeneca will conduct and fund future clinical trials.

This deal continues Insitro's expansion into the pharmaceutical market. The biotech company recently strengthened its partnership with Bristol Myers Squibb to develop a broader portfolio of targets for amyotrophic lateral sclerosis (ALS). Insitro also joined forces with Eli Lilly to create AI models capable of predicting the pharmacological properties of small molecules in living organisms.

Sharon Bar, Vice President of Biopharmaceutical Research and Development at AstraZeneca, told Reuters that identifying new targets for inclusion in the discovery pipeline is one of the most challenging tasks in R&D. She added that by the time information about a new pathway or target appears in major scientific journals, it is no longer new but is in the public domain, leaving the company too late. However, by identifying unique connections early on and testing them in cellular systems, the company stays ahead of competitors. Bar emphasized that combining AI tools with scientific expertise allows the company to test fewer molecules in the lab, reduce uncertainty, and accelerate the search for effective drugs.

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ImmunitoAI uses artificial intelligence to find suitable antibodies for drug targets
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ImmunitoAI uses artificial intelligence to find suitable antibodies for drug targets

The development of antibody-based drugs begins with discovering an antibody capable of binding to a specific site, known as an epitope, on a disease-related protein. Finding such a suitable antibody can be challenging.

ImmunitoAI, a Bangalore-based company, applies artificial intelligence to solve this problem. Its platform, AbInitio, is designed to engineer antibodies for a chosen target and epitope, after which the startup conducts testing in its own laboratory.

The startup was founded in 2020 by Dr. Aridni Shah and Trish Chatterji through the Entrepreneur First talent accelerator, where they met in August of that year.

Shah, who serves as CEO, holds a PhD in Biological Sciences from NCBS-TIFR and a Bachelor's degree in Biomedical Sciences from the University of Delhi. Chatterji, the CTO, has a Master's degree in Computer Science from the University of Delhi and previously worked as a senior software development engineer in the R&D department at GreyOrange.

The initial idea stemmed from Shah's doctoral research on honeybees. Since honeybees are not a model organism, obtaining antibodies against their proteins was difficult. Early discussions with industry experts indicated that drug manufacturers face a similar challenge when searching for suitable starting antibodies, notes Chatterji.

Chatterji spent about three months developing the idea and up to three weeks creating the first prototype. This version ranked potential antibodies, which is a simpler task than the antibody design work that AbInitio performs today.

ImmunitoAI's biology team first defines the disease area, the target protein, and the epitope to which the antibody must bind. It also determines whether the antibody should block or activate the target. Then, AbInitio designs antibodies and antibody-like structures, including nanobodies—small, single-domain antibodies—for the selected epitope.

The platform is trained on the three-dimensional structures of binding protein pairs from the Protein Data Bank, a public repository. A mathematical transformation developed by the startup converts the three-dimensional atomic coordinates into two graphs, where atoms are represented as points and bonds show their interaction sites.

The model studies how the shapes of two proteins fit together, with grooves on one corresponding to bumps on the other. It also examines which amino acids, the building blocks of proteins, bind to each other. During the design phase, the platform checks drug-like properties to reduce the need for subsequent refinement.

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