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The excellent data approach to cancer drug development
Leveraging our exceptional multi-omics data, we have developed a holistic cancer target discovery and validation process to enhance the probability of successful therapeutic development.
R&D Approach Data driven oncology R&D resulting in unique insights
Our R&D approach is built on a complete and comparable multi-omics dataset, based on carefully collected and curated patient samples, that uncovers targets not identified in other databases. For data analytics and target identification, we employ advanced biomathematical and bioinformatic methods, including specifically adapted AI tools. Our patient-derived 2D and 3D tumor models enable reliable validation, reducing the risk of developing molecular cancer therapeutics at an early stage.
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Unique DatabaseMulti-omics data from highest-quality biospecimens
When the existing data of cancer, such as genomics oncology, 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 database reflects the molecular reality of cancer. The key is our ability to reduce ischemia time during our standardized sample collection to ten 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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Therapeutic DevelopmentAdvancing R&D from target to treatment
We drive oncology drug development either in-house or in collaboration with pharmaceutical partners. Our methods include virtual and fragment-based ligand screening, hit screening using cell-free and cell-based assays, and more. Insights from our unique database guide hit optimization and preclinical development.
We aim to advance candidates toward IND, leveraging our in-depth knowledge of the molecular and clinical characteristics to design tailor-made clinical trial strategies.
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