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What is real-world data and how is it leveraged for drug development?
What is RWD?
Clinical Trial Operations: Enhancing clinical trial protocol development, study feasibility, and population-level eligibility assessment
Regulatory Decision-Making: Supporting new drug approvals or post-approval requirements through continuous data collection and surveillance
Health Economics and Outcomes Research: Generate evidence and inform market access decisions by analyzing real-world clinical and cost-effectiveness
What does RWD contain?
Real-world data is collected through a variety of sources, but generally contains information that captures the complete patient journey from disease diagnosis, biomarker testing, treatment selection, and outcomes. Examples of RWD sources include:
- Electronic Health Records (EHRs)
- Claims and Billing Data
- Patient Registries
- Wearable Devices and Health Apps
- Patient-Reported Outcomes (PROs)
- Molecular Biomarker Tests
What are the benefits of RWD?
Patient Diversity: Captures data from diverse and different patient populations, including those not typically included in clinical trials
Speed and Cost-Effectiveness: Using readily available data helps reduce the time and cost associated with clinical trial operations and data collection
Real-World Insights: Provides a continuous, longitudinal view into patient journeys, treatment patterns, adverse events, and outcomes
Utilizing RWD for drug development
Clinical Trial Operations: Enhancing clinical trial protocol development, study feasibility, and population-level eligibility assessment
Regulatory Decision-Making: Supporting new drug approvals or post-approval requirements through continuous data collection and surveillance
Health Economics and Outcomes Research: Generate evidence and inform market access decisions by analyzing real-world clinical and cost-effectiveness
Pathology is the critical data element bridging the molecular drivers of disease to their clinical manifestations
Pathology is the only data modality to:
- Define the patho-physiology of disease
- Capture complex disease manifestations underpinned by molecular alterations
- Quantify morphological changes resulting from treatment or disease progression
PathAI transforms unstructured pathology images into structured pathology insights to enable seamless integration with clinico-genomics RWD
- Advances in digital pathology and machine learning are now enabling researchers to use routine histopathology samples to obtain a single-cell, spatially-resolved view of tissue structures and cell populations.
- PathAI’s products transform unstructured digital images into human interpretable features (HIFs) allowing for more detailed and actionable insights from histopathology data.
- Integrate quantitative pathology data with other RWD to understand disease intricacies at molecular, cellular, histological, and anatomical levels
See what you can extract with PathExplore™ for quantitative histopathology
PathExplore delivers >300 histopathology features including:
- Total Area of Tumor Tissue
- Total Number of Lymphocytes
- Density of Lymphocytes in Cancer Stroma
- Number of Lymphocytes in Proximity to Cancer Cells
- Spatial Distribution of Tumor Infiltrating Lymphocytes and more
PathAI Real-World Data Resources
