harnessing the extremes of human biology to improve therapies for all patients

Atray Dixit

Problem Tech Solves


Tech Brief

Autoimmune medications are a $54B market and one of the fastest sectors of medication spend in the U.S. However, they have variable response rates (30-60% patients initially respond, with a chunk of these losing response over time). For many biologic therapies, health insurers impose prior auth requirements that require patients to go through a "fail first" approach. Relatedly, a patient may end up settle on a suboptimal therapy that manages severe symptoms, while a treatment that has a more positive benefit on quality of life may never get tried. Finally, there are patients who after a lengthy trial-and-error process are never adequately treated. If a clinical diagnostic test existed that could predict medication effectiveness with high accuracy, this could enable faster, more cost effective management of autoimmune patients. Off the back end of our clinical test (which is based on clinically validated in vitro model systems of the human immune system), we are performing new drug development activities for non-responder populations.

Tech Differentiators

right now physicians go through a trial-and-error process to get a patients an appropriate therapy and have to fight with health insurers to get prior auth on more expensive therapies. a clinical test that could predict response/non-response across all therapeutic options would be transformative. because of overlapping, MOA/immune modulation across autoimmune diseases, a new treatment pathway /target for non-responders could have widespread applicability. We are attempting to find new targets that produce similar biological effects as existing biologic, but work on non-responder populations. We describe this approach as making a "Nu"mira compared to Humira.


LEO Pharma (2021) - target discovery Major IDN (2021) - research partnership GI Alliance (2021) - research partnership Crohn's and Colitis Foundation (2021) - research partnership Drugviu (2020) - research partnership

Why Us

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