Find new purpose in cancer therapy.

Computational drug repurposing for oncology — from scattered evidence to focused hypotheses.

Early-stage discovery. Computational prioritization generates hypotheses; it does not establish clinical benefit. Tumour cellsPrioritized signal

One focused starting point

Cancer cells

Evidence-led prioritization

Many data layers, one decision path

Built for collaboration

From computational signal to validation

From fragmented evidence to a short list worth testing.

We bring diverse evidence together and rank the opportunities that are worth the next experiment.

Tumour biology

Cell-context evidence

Dependency data

Evidence filter

A short list
worth testing

Build a disease view

Separate tumour-relevant biology from background signal.

Prioritize what matters

Weigh each candidate by context, selectivity and evidence.

Make it actionable

Clear hypotheses that collaborators can test and validate.

Current programme

Start focused. Build a method that can travel.

Our first programme is a focused computational proof of concept in a cancer subtype with substantial unmet need.

Candidate identities remain confidential while the computational proof of concept is being developed.

Good discovery needs the right collaborators.

C‑Testa is seeking roles in oncology research and funding proposals where computational drug repurposing can add value.

We welcome consortia seeking a computational partner, data collaborations, and translational teams able to assess prioritized hypotheses.

Disease expertise

Data access

Computational methods

Validation capacity

Funding

Bring one piece of the puzzle — expertise, data, methods, a lab or funding — and we'll explore what we can build together. Start a conversation by email