Orakl Oncology Raises $34M Series A to Expand AI Cancer Drug Prediction

Norrsken VC announced on November 12, 2026 that it co-led Orakl Oncology’s oversubscribed $34 million Series A alongside Singular, bringing the AI drug-development company’s total funding to $50 million.

Orakl plans to expand its dataset and AI research team, increase data generation fivefold and move beyond colorectal and pancreatic cancers into lung and gastric cancers. The proceeds are also intended to establish US and UK teams, deploy predictions across more than 10 drug programs and discover and validate drug co-targets from patient biology and experimental screens.

New investors InfraVia, Serena, the Patient Autonome Fund managed by Bpifrance, teampact.ventures and Plug and Play joined the round, alongside existing backers Verve Ventures, Speedinvest and HCVC.

Patient-Derived Data and AI

Founded in 2023 as a spin-out of Gustave Roussy, Orakl grows patient-derived tumor organoids, which are laboratory models of individual patients’ cancers, and screens them against drugs. Its platform overview describes results enriched with genomic, transcriptomic and phenotypic data and anchored to longitudinal clinical histories, creating a multimodal dataset for its predictive systems.

Orakl’s biopharma products are divided between O-Predict and O-Target. O-Predict is designed to anticipate trial endpoints including tumor response, progression-free survival and overall survival, identify biomarkers that distinguish likely responders, assess drug combinations and compare benefit-risk profiles. O-Target focuses on therapeutic-target discovery.

Norrsken calls the resulting patient cohorts virtual cancer patients. The drug-response measurements must be generated through patient samples, laboratory infrastructure and hospital partnerships rather than assembled from existing databases. Every experiment adds proprietary data connecting patient biology, clinical information and observed drug responses.

What the SCOPE Preprint Shows

Orakl reports 90% accuracy for prospective predictions of individual patient responses across more than 30 clinical trials. A separate April 2026 preprint co-authored by Orakl staff provides detailed arm-level results for SCOPE, the Screening-to-Clinical Outcome Prediction Engine. It is marked as early-stage research that may not yet have been peer reviewed.

SCOPE was trained on 54 treatment lines from 52 patients with metastatic colorectal or pancreatic cancer, using matched clinical data and organoid screens covering nine compounds. To predict trial outcomes, it generated synthetic patient profiles from published eligibility criteria and matched them to a biobank of 81 patient-derived organoid lines.

External validation covered 32 arms from 23 published trials. The authors report an R² of 0.85 and a mean absolute error of 0.82 months for predicted median progression-free survival, the time patients live without disease progression. For objective response rate—the share of patients with a measured tumor response—R² was 0.71 and mean absolute error was 7.3 percentage points. Combining organoid and clinical data significantly outperformed either data type alone.

SCOPE also selected the superior treatment arm in seven of eight head-to-head comparisons. The preprint describes a prospective application to daraxonrasib before Phase III data became available, in which the platform predicted superiority over standard chemotherapy in KRAS-mutant metastatic pancreatic cancer.

Commercial Pipeline and Expansion

Just over a year after commercial launch, Norrsken reports that 18 of the world’s 20 largest pharmaceutical companies are in Orakl’s commercial pipeline. The figure covers pipeline participation rather than signed customers; the first pharmaceutical engagements have become multi-year collaborations. More than 15 additional cancer centers across Europe, North America and Asia are in contract negotiations.

Orakl has more than 40 employees, 30% of whom hold PhDs. Its co-founders are CEO Fanny Jaulin, CTO Gustave Ronteix and COO Diane-Laure Pagès, whose backgrounds span cancer biology, AI, data science, oncology and hospital partnerships.

The next phase will test whether Orakl can extend that patient-derived data model into additional cancers while converting its broad pharmaceutical pipeline into more multi-year drug-development collaborations.