Cell & Gene Therapy · CIBMTR
The graft is data. The cold chain is data. The donor is data.
General-purpose research systems treat cell therapy as a few fields on a form — collection, infusion, outcome. CuRE treats the cellular product, its manufacturing history, and the donor–recipient relationship as first-class governed records on one OMOP substrate, so the questions that define a cell-therapy program become native cohort queries.
Built, not bolted on
Four capabilities general-purpose systems don't have
Each of these is shipped — modeled in CuRE's schema, backed by an architecture decision record, and running in production. Not roadmap, not translational bolt-on.
01
The graft is a governed record
CuRE's OMOP extension models the cellular product itself — identity, processing, lot genealogy, and identifiers — so a CAR-T lot or stem-cell graft is a queryable entity on the patient timeline, not a free-text field on a form.
Cellular-product OMOP-extension table
02
Manufacturing and cold-chain, reconstructed
Cryopreservation parameters, shipping times, and temperature excursions ingest from CDMO and manufacturing systems and align to outcomes — so "did a cold-chain excursion predict this CRS event?" is a native cohort question, not a manual chart review.
CAR-T manufacturing / cold-chain reconstruction
03
Donor and recipient on one substrate
Allogeneic donor–recipient relationships are modeled at the person level on the EMPI substrate, so graft source, matching, and paired outcomes are queryable across the transplant — the relationship is data, not a sidebar.
Donor↔recipient person-role modeling
04
Speaks CIBMTR
Thirteen NMDP/CIBMTR ATLAS cohort specifications and CIBMTR-style submission outputs are native to Calculate, so registry reporting is a translation step, not a dataset rebuild.
NMDP/CIBMTR ATLAS cohorts in Calculate
One data thread
From cell collection to long-term follow-up
Cell and gene therapy programs generate uniquely complex, multi-modal data across the therapy lifecycle. CuRE captures, links, and analyzes it end-to-end — one governed record where siloed systems see five disconnected tools.
01
Cell collection
Apheresis and cell-source records enter the governed record at the start — provenance begins at collection, not at the eCRF.
02
Manufacturing
Processing parameters, release criteria, and lot genealogy stay linked to the patient journey rather than trapped in CDMO systems.
03
Treatment
Randomization, supply, and administration are captured with a full audit trail on the same data thread.
04
Outcomes
Clinical outcomes, biomarkers, and translational data analyzed against manufacturing variables — in cohorts measured in dozens, not thousands.
05
Long-term follow-up
ePRO and safety surveillance built for the 5-, 10-, and 15-year commitments CGT regulators require.
Why this changes the questions you can ask
Cross-modal evidence, not chart review
In a typical system, asking whether a manufacturing variable affected a clinical outcome means exporting from the CDMO, pulling the EHR, and reconciling by hand — a weeks-long chart review for a single question, repeated every time.
On CuRE, the manufacturing record and the outcome are already on the same governed OMOP record. So the question a cell-therapy program actually needs to answer — did cryopreservation, cold-chain excursion, or shipping time predict this CRS event, this relapse, this engraftment delay? — is a native cohort query in Calculate, run in minutes, repeatable, and auditable.
That is the structural difference: the relationship between the therapy's manufacture and the patient's outcome is data, not a research project.
Outcomes, not features
What a governed cell-therapy record unlocks
Most research tools automate the same old handoffs. CuRE removes them — so the win shows up as questions you could not ask before, fewer steps, faster evidence, validation built in, and lower cost.
New questions
Ask what siloed systems cannot answer.
Cross-modal questions are native: link manufacturing, shipping, biomarkers, ePRO adherence, and EHR history to clinical outcomes without building a one-off dataset for each question.
Fewer steps
Map once. Stop re-keying everything.
Data maps to OMOP a single time and every product reads from it — no re-entry between capture, analytics, TMF, and publishing, and far fewer queries, reconciliation passes, and decks assembled by hand.
Faster time to evidence
Studies start on data that already exists.
Because patient data flows continuously into one governed record, studies stand up in weeks and a new safety or quality signal becomes analysis the same day — not after a months-long data-engineering project.
Validation built in
No separate validation project.
21 CFR Part 11 / Annex 11 audit trails, source lineage, AI logs, and validation evidence are captured as the platform runs — so GxP readiness is a byproduct of the work, not a separately-budgeted project bolted on at the end.
Lower cost
One platform instead of a vendor stack.
One data model and one validation story replace per-vendor licenses, integration projects, and re-validation cycles — a fraction of the cost of assembling the same capabilities from point solutions.
Discuss your cell-therapy program
Whether you're running an autologous CAR-T trial, an allogeneic HSCT registry, or building toward CIBMTR submission — tell us the cross-modal question your current stack can't answer.