Octant is engineering a new generation of drug discovery.
We are mapping the universe of functional interactions between chemicals and human targets by linking biological pathways to digital outputs. We use DNA sequencing, gene synthesis, gene editing, and data science, to engineer cells, the most sophisticated information processors on earth, to be our data network.
Advancements in machine learning and computation promise a new frontier in biology, but algorithmic exploration of biology is only as good as the data. Comprehensively charting the interaction space between molecules and protein targets is critical to the next generation of computational drug discovery.
We have developed a multiplexed technology that uses synthetic biology, genome engineering, next-generation sequencing, and computational tools to simultaneously measure the activity of thousands of receptor pathways in human cells. By linking receptor activity to genetic barcodes in a pooled fashion we can screen small-molecule libraries against comprehensive sets of drug targets at unprecedented scale. These big datasets enable us to develop and apply machine learning methods to engineer small molecules that interact with multiple receptors and unlock a new potential to treat complex diseases.
By linking biological pathways to digital outputs, the Octant platform enables us to interrogate and iterate on some of life’s most important complex physiological outcomes.
Over the last decade, there have been paradigm shifts in reading, writing, and editing DNA. Application of these new technologies (e.g., NGS, CRISPR, CAR-T) have led to new treatment modalities (cell, immune, RNA, editing) with clinical benefits in areas such as cancer and rare disease. But despite this rapid progress, momentum has stalled for some of society’s most pressing complex diseases. This is in part because complex diseases arise from the combination of large numbers of small-effect genetic changes.
Orders of magnitude more empirical data are needed to rationally attack and understand complex disease. To that end, we've built, optimized, and scaled a platform that systematically maps large chemical libraries and GPCRs faster and cheaper than conventional drug-ligand interaction assays. The ~800 human GPCRs, targets of a third of FDA-approved drugs as well as many natural products, nutraceuticals, recreational drugs, fragrances, and flavors, are the most important class of drug receptors. Our GPCR platform is our first step towards building the single most effective platform for mapping and modeling the functional interactions between chemical space and human biology.
We are synthetic biologists at heart. Biology is already the most sophisticated technology on Earth. We use the latest techniques in molecular biology and gene engineering to reverse and re-engineer it to gain access to its power and understand its complexity.
We are as comfortable at a terminal as we are at the bench. Unlocking the knowledge in our massive proprietary datasets requires quantitative rigor and advanced computational techniques.
We engineer huge libraries of modified organisms and interrogate them in massively parallel genetically barcoded experiments. It takes big data to characterize the cellular processes that comprise complex disease, and we're using biology itself to do that.