Built for populations. Run for one.
The same mechanistic engine that simulates clinical trials across thousands of virtual patients can run on a single person — their genome, their disease stage, their current medications, their biomarkers, their comorbidities — to show what's working, what isn't, and where the biology still has gaps.
This is research data, not clinical advice. It supports the clinical team — it never replaces them.
Drug development is built for populations. The patients who most need it are rarely the median one.
Rare diseases, complex polypharmacy, treatment-resistant conditions — often all at once. The tools written for the median patient don't work well at the level of one. OVIVO's engine does: it runs the full causal arc on one person's actual biology, not an average of it.
What it produces is insight, not instruction — a mechanistic picture for the clinical team to weigh. The people who know the patient make the decisions.
One person, in full resolution
We don't model a representative patient. We model this one — the genotype that changes how a drug is cleared, the second condition that complicates the first, the medications already in play and how they interact.
Every value carries its source and its uncertainty, so the result is a picture a clinician can interrogate, not just read.
| Factor | This patient |
|---|---|
| CYP2D6 | Poor metabolizer |
| Primary condition | Treatment-resistant |
| Comorbidity | Stage 3 CKD |
| Current regimen | Four drugs |
What the simulation can surface
For a defined condition, treatment history, genetic profile, biomarkers, and comorbidities, the platform can produce the following — each carrying explicit uncertainty and an evidence grade, each for the clinical team to evaluate.
Treatment coverage map
Which biological pathways the current regimen addresses, and by how much — and which it leaves open. A quantitative gap analysis, not a qualitative impression.
Repurposing candidates
Approved drugs with pathway overlap in this specific disease that aren't in the current regimen — surfaced with interactions against existing medications already accounted for.
Safety & interaction modeling
A full polypharmacy simulation — concentration for every drug, interactions quantified, organ-specific burden — for the complex regimens hardest to characterize by hand.
Novel-compound hypotheses
Where the gap analysis finds an uncovered mechanism with no approved drug, the discovery engine can generate hypotheses to discuss with research partners. Select cases only, at hypothesis-grade confidence.
Where the standard pathway runs out of answers
Running at the level of one is the most demanding thing the platform does. It's most valuable where the usual route offers little.
Rare and ultra-rare disease
Where no approved disease-modifying therapy exists and natural-history data is sparse, individual-level simulation provides signal that population tools can't — and it doesn't need a large published trial to anchor its reasoning.
Treatment-resistant conditions
Where standard of care has been exhausted, partially contraindicated, or produced an inadequate response, a mechanistic picture of what the disease is and isn't responding to can surface a biological rationale for alternatives — for the clinical team to weigh.
Complex polypharmacy
Patients on multi-drug regimens with several comorbidities carry interaction risks that are hard to characterize by hand. The platform models the whole regimen — every drug, every interaction, every organ-specific burden — for this patient's metabolizer phenotype and organ function.
Compassionate use
Where patients and clinical teams are exploring every option outside the standard pathway, a gap analysis and repurposing screen can identify candidates worth discussing — grounded in published pharmacology, not speculation. A starting point for a conversation, not a treatment plan.
This is research data — not a diagnosis, not a prescription, not clinical advice.
Every forecast carries its uncertainty and its sources. The platform surfaces biological rationale; it doesn't issue recommendations. The decisions belong with the clinical team that knows the patient. So every engagement of this kind begins the same way — with an honest conversation about what the data can, and cannot, show.
Start with a conversation
Engagements at the level of one are bespoke research projects, run in partnership with the people involved.
Clinical teams
For physicians and specialists working with patients who've exhausted standard options. Tell us the condition and the context, and we'll be candid about what's possible.
Patients & families
If you or someone you care for is facing a rare, complex, or treatment-resistant condition and want to understand what computational research might surface — reach out. We'll be honest about what the data can and can't show.
Researchers & advocates
For academic researchers and rare-disease advocacy groups working on conditions with deep unmet need and little population data to lean on.