Enhance Clinical Trial Design with Organoid-based Biomarker Research

Patient-derived organoids (PDOs) offer more reliable models of human disease, improving the translatability of preclinical findings and the likelihood of clinical trial success. Their ability to mimic patient-specific biology and support a wide range of experimental readouts enables researchers to capture rich multi-dimensional datasets for biomarker identification. However, to fully harness PDO data for clinical insights, researchers need a robust solution to centralize, process, integrate and analyze this complex data landscape – alongside clinical and pre-clinical data - while ensuring governance and regulatory compliance. Genedata Profiler meets this need with a purpose-built solution that empowers organoid-based biomarker research and thus enhances clinical trial design.

Boost Confidence in Biomarkers with PDO Models and a Comprehensive Data Platform

Genedata Profiler streamlines the process of PDO-based biomarker discovery by providing an end-to-end solution that:

• Centralizes multimodal data collected from diverse experimental approaches and data sources
• Structures and organizes data for improved findability.
• Transforms raw inputs into clean, interoperable, analysis-ready datasets while ensuring complete data lineage and auditability.
• Equips researchers with advanced visualization and analytics tools incorporating AI and ML-based methods to derive insights.

With consolidated multi-source data and analytic capabilities in a single GxP-ready environment, researchers can generate consistent, reliable scientific results and build predictive models ready for clinical validation. By combining the physiological relevance and reproducibility of organoid models with the analytical power of Genedata Profiler, scientists can boost confidence in their biomarker relevance and accelerate the translation of their discoveries into clinical practice, ultimately improving clinical trial success and patient outcomes.

An interactive data visualization approach available out-of-the-box for exploration and hypothesis validation using public data sources. Here, the Kaplan-Meyer plot shows the survival probability for two groups of patients.

Webinar

Minimizing Oncology Drug Failure Rates Using Organoid Modeling and Multi-Omic Data

Discover how organoids transform immuno-oncology drug development allowing the accurate prediction of patient response to treatment and the assessment of safety through multi-omic data analysis.

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Case Study

Debiopharm Digitalizes Preclinical Research to Accelerate Drug Development

De-risking therapies for better success in clinical development requires navigating elaborate in vitro and in vivo studies to generate actionable insights regarding therapy efficacy and safety.

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