Shift Bioscience

About

Shift Bioscience has identified drug targets for safe cellular rejuvenation and treatment of age-linked diseases by the application of machine-learning 'driver' clocks to cellular reprogramming. We are on the cusp of validating these drug targets by active machine learning and uniquely positioned to do so. Beyond their established role in stimulating pluripotency, Yamanaka factors exhibit comprehensive rejuvenative properties including the increase of mouse lifespan, restoration of optic nerve regeneration in mice and rapid reversal of DNA methylation age of human cells. However, these factors can induce pluripotent cancers, raising concerns about therapeutic translation. The rejuvenative mechanisms of these factors are poorly understood and it is not known whether they can be fully decoupled from pluripotency. Shift has applied machine learning and novel noise-reduction to gene-expression data from aging and rejuvenation time-courses to generate accurate compound-biomarkers or ‘clocks’ built from genes. These genes include known drivers of aging and suggest that pluripotency can be fully decoupled from rejuvenation, providing the prospect of safe cellular rejuvenation. Shift is ready to complete the first stage of target validation for safe rejuvenation therapeutics, by delivering a collective of candidate genes to aged human cells, monitoring methylation age and cell function in a chase period. Shift will use active machine learning to iterate this gene collective until the maximum-velocity of rejuvenation and functional recovery is achieved, marking a significant milestone on the route to safe rejuvenation therapeutics. Shift has raised >$1m from investors including Cambridge-based angel Jonathan Milner and Bay area angel Karl Pfleger. Shift counts an experienced scientific team led by Daniel Ives (PhD, U. Cambridge) and advised by Wolf Reik (Director, Babraham Institute).