Our Approach

A cell-centric approach to drug creation

Until now, the dominant approach in drug discovery has been to reduce disease biology into a single molecular target and then leverage high-throughput screening to identify molecules that bind to these targets. While drug discovery has benefited from searching for single drivers of disease, we are unsatisfied with the lack of predictiveness and excessive clinical failure rate of this process.

By contrast, we focus on the whole cell because, most often, a disease isn’t driven by one mechanism or protein. Using single-cell technologies, we identify the cellular drivers of the transition from health to disease and apply deep learning models to create drugs that reverse disease at the cellular level.

Solving disease through the lens of the entire cell has many advantages, including freeing us from solely thinking of single targets or pathways. Our unique approach allows us to identify non-intuitive biology and treatments that would have been impossible to uncover if we had used a single target as a starting point. Since every disease stems from a disorder at the cellular level, our approach is also applicable to virtually any disease.

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We unravel the complexity of disease biology and leverage this new understanding in the design of our medicines.

Our platform

Cellarity’s unique platform links biology and chemistry with high-dimensional, transcriptomic data to design medicines against cellular signatures of disease. It provides critical insights into cellular dysfunction and reveals new biology through which we can address a range of diseases, including those which presently lack a known druggable target. Furthermore, since we create medicines against many cellular drivers of disease instead of just a single target, our approach is designed to drive higher clinical translatability and success.

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We begin in a target-agnostic fashion by characterizing “cell signatures of disease” that arise when cells transition from a state of health into a state of disease. We do this by generating and analyzing a vast amount of -omics data at the single-cell level that we compile into what we call a “Cellarity Map”. We generate such Cellarity Maps for all cell types and diseases that are of interest to us.

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We then use our Intervention Library to predict compounds that will engender the desired change in cell behavior to address the disease process.

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Then, we apply proprietary processes to engineer New Chemical Entities based on the insights provided by the various predicted interventions.

Cellarity is transforming drug creation with a platform built at the confluence of systems biology, single-cell data, and machine learning.

A game changing approach

Target agnostic

As cellular dysfunction fully reveals itself at the -omics level, our platform allows us to create new treatment options even in diseases for which there is an absence of known causal or druggable targets.

Solving for complex disease

Through our cell-centric approach, we can identify causal network drivers of diseases that are often too complex to address effectively by modulation of a single molecular target.

Designed for clinical success

Because the cell is a better representation of disease than an individual target, our platform is designed to drive higher translatability and clinical success.

Limitless potential for application

We can explore and digitally map cell behavior in virtually any tissue or cell type in a vast array of diseases.

Our Pipeline

Cellarity currently has programs in several areas, including metabolic disease, hematology and immuno-oncology.

We have demonstrated our precise ability to characterize and modulate cellular behaviors and signatures in these therapeutic areas. We have already designed and synthesized compounds that evoke desired cell behaviors and confirmed the efficacious activity of these compounds through in vitro and in vivo models of disease. This work strengthens our proprietary understanding of disease and informs how we can create medicines to reverse disease at the level of the cell.