Simulation guided cell rejuvenation to defeat the diseases of aging
Aging is the largest driver of disease
If you had been born a century ago, you would have been likely to die around the age of 50. Today men typically survive into their late 70s and women into their early 80s. But this extra life is typically blighted by one or more chronic diseases of aging.
Aging is reversed between generations
Each of us developed from a single cell passed down by our parents, yet we’re not born at our parent’s age and we begin our post-development lives in full health. Somehow the biology from our parents is safely scrubbed, renewed and restarted.
Shift can reverse human fibroblast age without inducing stem cell colonies
Yamanaka factors (OSKM) can rejuvenate multiple cell types but also induce stem cell identity, posing safety concerns for therapeutic use. Shift has discovered novel gene-based interventions that rejuvenate cells without inducing stem cell identity (C0) even when continuously over-expressed.
Shift's AI platform has found 6 new gene-based interventions
Shift's platform has discovered 6 gene-based interventions that reverse epigenetic age without inducing stem cell colonies, including a single-gene intervention. We are continuously improving our cell aging clocks, cell simulations and active learning cycles to accelerate future discovery.
Shift can screen clinical assets for a rejuvenation mechanism of action
Shift can screen over a billion conditions In silico and up to 2000 conditions In vitro to test for a cell-rejuvenation mechanism of action. Contact us to apply for access.
Best in the world for cell simulations and aging clocks
Shift has assembled a talented team of research scientists and advisors who are backed by experienced biotech investors.
Advisor, Prof U. Toronto, Inventor of the cell simulator single-cell-GPT (scGPT)1
CSO, PhD U. Cambridge, Inventor of the first accurate cell aging clock
Brendan has a PhD in pharmacology from the University of Cambridge. The driving force behind Shift's pioneering use of active machine learning, Brendan leads the team of scientists, addressing what he describes- with characteristic understatement - as a "big technical challenge".
Head of ML, U. Cambridge, Inventor of the most accurate aging clock2
Team
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Daniel IvesCEO, PhD U. Cambridge Mitochondrial biologist
Daniel received his PhD from the University of Cambridge where he focused on the role of mutations in the mitochondrial genome in rare diseases, embracing computational biology to discover a novel therapeutic approach. In 2017 Daniel founded Shift Bioscience and since then has been on a scientific journey from mitochondria to mouse aging clocks to single-cell aging clocks and, finally, to now: leveraging single-cell aging clocks inside cell simulations to discover safer interventions for cell rejuvenation.
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Non-exec directorQ4 2024
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Translation advisor
Q4 2024
