The current paradigm in drug discovery is to fail fast, in order to reduce costs. ‘Big data’ is empowering this paradigm in ways not previously thought possible. Machine learning has made decoding disease much cheaper, while high-throughput biology has made this decoding much faster.
But what if we could decode cheaper, faster, and also substantially better? Not just fail fast, but fail less. Ochre Bio brings a third dimension to big data approaches. They call it deep phenotyping.
Deep phenotyping is the marriage of genetics, advanced tissue imaging, cellular genomics technologies such as single-cell or spatial sequencing, and machine learning. It allows the team to study how genes and cells in diseased tissues talk to each other in a way that moves beyond current two dimensional ‘biology at scale’. It’s an approach we’re using to develop genomic medicines that treat chronic diseases associated with metabolic dysfunction, cell stress, and poor tissue regeneration. Diseases that account for four out of ten deaths. Diseases so complex, that a third dimension of decoding is required.
