Patient-first
Begin with the cohort.
Separate the diagnosis into endotypes — patient groups with a shared biology — then rank targets by the evidence they matter in each group. The list stays short because every target has to belong to someone.
End-to-end AI for choosing better targets, with the patient at the centre of every decision from the first hypothesis to the study.
Explore the platformEndotype
The band a drug is for.
Each band is a patient group. The target is chosen inside the highlighted one.
A target chosen for an average patient is a bet against the person in the trial. Stratified Precision is an AI drug discovery company built around the opposite order. We keep the patient at the centre: resolve the disease into the groups a medicine can help, select targets that matter inside those groups, and carry the same evidence through to the study.
Approach
Some questions start with a cohort. Some start with a gene already on the table. A target is ready when we can say which patients it is for, and whether the risk ahead is harm or a lack of effect.
Begin with the cohort.
Separate the diagnosis into endotypes — patient groups with a shared biology — then rank targets by the evidence they matter in each group. The list stays short because every target has to belong to someone.
Begin with the gene or protein.
Before chemistry expands, ask who the target is for and how it would fail. Benefit and harm are scored apart. A mechanism with no patient group goes back, not forward.
Platform
End to end means the person who defined the target is the person the study is built to enroll. The platform is that chain, kept intact.
The cohort is the coordinate system.
Clinical course, genetic association, and tissue context stay tied to the people they describe. Later models inherit a human reference, rather than a convenient average.
A diagnosis is a mixture. A drug needs a group.
We resolve disease into patient groups that share a biology and a reason to respond. Discovery runs against the group. The mixture is what we are trying to leave behind.
Benefit, harm, and specificity, read together.
Inside an endotype, each target is scored on the prospect of effect, the prospect of toxicity, and how specific that signal is to the group. A popular target that only works as an average does not advance.
The same patients, from hypothesis to enrollment.
The endotype that chose the target also shapes the biomarker and who should be in the study. The patient is present at the last decision because they were present at the first.
The chain
Patient-first and target-first meet in the same sequence. What changes is the door. What stays is the standard.
The cohort the program answers to.
The subgroup with a shared biology.
Causal inside that subgroup.
Toxicity, or a lack of effect.
Enrollment set by the same endotype.
A map of the decision. This is a schematic, not a measured result.
Company
Screens, models, and assays answer to a defined patient group. That group is the objective, from the first analysis onward.
Harm and lack of effect ask for different decisions. We keep them apart from the moment a target is scored.
The endotype, the target, the biomarker, and the enrollment rule should describe the same patients.
Partnerships
Research collaboration, platform questions, and partnership notes reach the same place. We read every one.