R&D Approach From native tumor biology to therapeutic opportunity.
We integrate in-situ tumor biology, target identification, patient stratification, and modality selection in a closed R&D loop. In doing so, we select the targets that drive disease in specific patient subgroups and match the therapeutic approach that fits best - whether naked antibodies, bispecifics, ADCs, or small molecules.
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Clinically grounded. One loop.
Clinically grounded. One loop.
First, we measure.
Tumor biology captured in-situ, paired with clinical data from the same patient. Standardized across all collection sites.
- Matched tumor and normal tissue, and metastasis samples. Cold ischemia time median under 12 minutes
- Multi-omics profiling: genomics, transcriptomics, proteomics, phosphoproteomics, microRNAs
- ~300 clinical data points per patient, with follow-up out to 10 years
Clinically grounded. One loop.
Then, we stratify.
Proteomics-led multi-omics integration, applied at scale to identify the biology that separates patient populations.
- Multi-omics integration across patient cohorts
- Survival, expression, and pathway analysis to define clinically relevant subgroups
- Biomarker-informed patient selection and target prioritization
Clinically grounded. One loop.
Next, we test.
Target impact assessed in patient-derived models, so findings stay grounded in the biology they came from.
- Validation on fresh and FFPE patient tissue, and in in-house 3D patient-derived models
- IHC confirmation across representative patient subgroups
- Functional validation via knockdown, pharmacological inhibition, and monoclonal antibodies, confirming the target drives the phenotype
Clinically grounded. One loop.
Finally, we match.
Each target evaluated against the parameters that determine whether a modality works on it.
- Tumor-versus-normal selectivity and specificity
- Epitope accessibility, receptor density, and internalization rate
- Format matching across ADCs, bispecifics, and small molecules
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Unique DatabaseThe infrastructure to capture true tumor biology
When the existing data of cancer did not meet our high standards, we started creating our own - setting a new standard in molecular cancer research.
Built on more than 20 years of global clinical collaboration, our proprietary multi-omics and clinical database reflects the molecular reality of cancer. The key is our ability to reduce ischemia time during our standardized sample collection to twelve minutes, preserving tissue integrity in matched tumor and normal samples. This enables the extraction of highly reliable molecular insights crucial for cancer drug discovery.
Comparability along the R&D pipeline is key.
Combining biomathematics, bioinformatics, and cell biology, we utilize the same source biospecimens and datasets to identify and validate targets in-silico and in-vitro. This puts us in an unrivaled position to lower the risk throughout the entire drug development pipeline.
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Integrating comprehensive multi-omics data with longitudinal patient information allows us to create a holistic view of the biological systems involved. Utilizing AI in oncology and cancer research has become standard practice. Applying our own models and specially adapted algorithms, we analyze the data and compare emerging signatures with public databases. Combining biological and clinical insights with proven statistical methods helps minimize failure rates in target and drug discovery.
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In addition to standard 2D cell lines, we primarily use our own patient-derived 3D tumor models to validate targets identified in-silico. These cell cultures, derived from the same tumor samples used for multi-omics-based target identification, closely represent the original tumor biology. This enables validation in an environment as close to the patient as possible, significantly reducing risk and increasing reliability throughout the downstream development pipeline.
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Modality fit assessmentIntegrating Patient, Target, and Modality
At the heart of our approach is the Patient x Target x Modality fit: we define patient profile, target and therapeutic modality together from the get go to reduce development risks where it matters most: before the clinic. Built on the deepest database for novel target identification and on validation in patient-derived organoid models, our programs move from target idea to antibody hit in 9-12 months.
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