The old model for drug development is broken. The pace of new therapies discovered in the lab, years-long trials based on sampled responses, and patient's individual treatment responses are structurally disconnected. We still treat patients based on population averages, but their health is n-of-1.
We aim to fix that by building observability into treatment responses, enabling AI models to develop a deeper mechanistic understanding how therapies behave in the patients who receive them and facilitating the development and adoption of novel therapies.
For therapies to have broad impact, they must scale beyond a narrow set of observations measured in trials. Indications are crowded with drugs that look the same when measured on a wide set of patients. Regulators are demanding granular tolerability data. Patients are walking in better informed than the protocols treating them.
We're determined to support a new paradigm that ensures new treatment innovations can be more robust, interpretable, and adaptable to each patient.
If this resonates with you, we invite you to partner with us. Our team moves fast, thinks deeply, and believes that rigorous, creative collaboration can solve the most meaningful problems in drug development.