FDA Fit-for-Purpose · 7-Step QCARD Framework

Cohort data your FDA submission
can stand on

Submit external control arm packages to FDA with data you can defend. OncoSys validates your cohort against the 7-Step QCARD framework and generates a tamper-evident attestation report (audit-ready for FDA submission).

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Pilot applications open · Epic, TCGA, or RWD data preferred

FDA 7-Step RWE ICH E6(R3) 21 CFR Part 11 TCGA · Epic · NCI

The trust layer for oncology data.

OncoSys AI validates cancer research datasets before any model, pipeline, or regulatory submission touches them.

The problem

97% of oncology data never gets used for research. Not because it doesn't exist — because no one can prove it's trustworthy. Making a dataset fit for purpose for FDA-adjacent workflows typically takes weeks of manual data engineering and compliance review. By the time it's done, the research window has closed.

What we do

OncoSys automates that process end-to-end. Upload a cohort. Our multi-agent system scores it against the QCARD fit-for-purpose framework, identifies quality gaps, documents provenance, and generates a signed trust packet — a structured readiness report that tells clinical teams, biopharma partners, and regulators exactly what the data is, and isn't, suitable for.

Product

How OncoSys works

01

Upload your cohort

Ingest any oncology dataset — CSV, EHR-derived, or synthetic. OncoSys maps it to a validated schema (e.g. eca_solid_tumor_v1) and begins the readiness pipeline.

02

FFP scoring

The dataset is scored against the 7-step FDA fit-for-purpose framework and QCARD dimensions: completeness, representativeness, consistency, accuracy, and relevance.

03

Trust packet generation

OncoSys generates a structured readiness report documenting data provenance, quality gap analysis, suitability assessment, and gate status: CLEARED, BLOCKED, or CLEARED WITH CAVEATS.

04

Cryptographic attestation

Every trust packet is SHA-256 hashed and timestamped. The signed artifact creates an auditable, tamper-evident chain of custody for regulatory and compliance purposes.

Who we serve

Built for the teams that move oncology data forward.

Biopharma & Biotech

Validate external control arm cohorts and real-world evidence datasets for regulatory submissions. Get a signed readiness report your regulatory affairs team can defend.

CROs

Qualify client datasets before clinical trial analysis begins. Eliminate downstream surprises with upfront data attestation.

Academic Research Teams

Accelerate IRB and data governance workflows at cancer research centers. Reduce manual data prep from weeks to minutes.

Team

Built by people who know the problem.

Disleve Kanku

Disleve Kanku

Founder & CEO

Deep expertise in oncology data systems, real-world evidence workflows, and AI-powered data engineering. Background in cancer research data infrastructure at Dana-Farber Cancer Institute. C10 Labs accelerator, Winter 2026. Based in Boston, MA.

FAQ

Common questions from research and regulatory teams

Does OncoSys retain patient data?

No. We never retain raw patient data. The data is processed in transit, scored, and the trust packet — which contains no PHI — is the only artifact stored. BAA available for enterprise customers.

Is OncoSys FDA approved?

OncoSys is infrastructure, not a regulated product. Like AWS, it runs FDA-adjacent workflows without itself requiring approval. Our framework is built directly on FDA's published RWE guidance and the QCARD fit-for-purpose framework.

What data formats does OncoSys accept?

CSV, EHR-derived exports (Epic, Cerner), TCGA datasets, and synthetic cohorts. We currently support the eca_solid_tumor_v1 schema with additional schemas in development.

What does cryptographic attestation mean?

Every trust packet is SHA-256 hashed at generation. If anyone modifies the report after signing, the hash breaks — providing proof of tamper-evidence in a regulatory audit.

How long does attestation take?

OncoSys runs the full ingest → QCARD scoring → trust packet workflow automatically. The limiting factor is cohort size, not analyst availability.

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