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Shift Bioscience Raises $16M to Advance Cell Simulation AI Platform

Insider Brief

  • Shift Bioscience has raised $16M in Seed funding, led by BGF, with participation from F-Prime Capital, Kindred Capital, and Jonathan Milner, to further develop its AI-driven platform for identifying genes that can safely rejuvenate cells to treat age-related diseases.
  • The company’s platform combines generative AI with biological aging clocks to predict and validate gene sets that reverse aging, targeting conditions such as cardiovascular disease and osteoarthritis.
  • The funding will accelerate platform development, expand the intellectual property (IP) portfolio, and support the progress of cell rejuvenation therapies towards clinical trials.

PRESS RELEASE — Shift Bioscience is a biotech company using generative AI models to understand how activation of different genes can reverse the aging process. This research is a fundamental step forward in designing more effective drugs to treat age-related diseases. Today the company announced that it has raised $16M (£12.5M) in Seed funding, led by BGF, with existing investors F-Prime Capital, Kindred Capital, and Jonathan Milner participating. The investment will be used for the continued development of Shift Bioscience’s artificial intelligence (AI) cell simulation platform, for the identification of genes that can safely rejuvenate cells to combat the effects of age-related illnesses.

Average life expectancy has increased over the last century, which has led to a rise in the number of people suffering from age-related illnesses, increasing the strain on healthcare systems. Cellular reprogramming with Yamanaka factors represents a promising technology that can rejuvenate cells, providing the opportunity to develop new treatments that target multiple diseases of aging, from hearing loss to cardiovascular disease and osteoarthritis. However, there is a pressing need to identify gene-factors that can be used to reprogram cells with proven safety.

“Our technology is addressing the current limitations of cellular reprogramming for treatment of age-related illnesses,” Dr Daniel Ives, CEO, Shift Bioscience, commented. “This investment is indicative of the strength of our approach and is one of the earliest funding rounds for a biotech start-up exclusively focused on the rapidly emerging field of cell simulation for target discovery. This additional funding will allow us to accelerate the development of our platform and bring cell rejuvenation therapeutics closer towards clinical trial.”

Shift Bioscience is building a cell simulation platform that combines generative AI with its highly accurate, high-throughput biological aging clock, to predict which sets of genes are most likely to safely rejuvenate specific cells and then test, improve and validate these predictions. Preliminary results from the platform were well received at the 2024 Gordon Research Conference on Systems Aging. The Company will use the Seed funding to accelerate the development of its AI cell simulation platform, enabling the identification of novel genes for safe rejuvenation of cells indicated in age-related illnesses. The funding will also support the development of an IP portfolio for the novel rejuvenation genes identified by Shift Bioscience. The new investment brings the total raised by the company to $18M (£14M).

Tim Rea, head of early stage at BGF, said: “We’re excited to be backing a British company at the forefront of developing a new class of regenerative medicines. Combining machine learning with lab-based biology, Shift’s well-developed platform represents a significant opportunity to address the growing challenges of treating age-related disease and illnesses.”

To learn more about Shift Bioscience’s approach, please visit: www.shiftbioscience.com

Contacts

Lorna Cuddon
Zyme Communications
[email protected]

SOURCE

James Dargan
About the author
James Dargan

James Dargan is a writer and researcher at The AI Insider. His focus is on the AI startup ecosystem and he writes articles on the space that have a tone accessible to the average reader.

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