2024-06-10-艾昆玮-利用真实世界的证据支持肿瘤靶向治疗（英）_11页_2mb

Leveraging Real World Evidence for Oncology Targeted Therapies Summary

Executive Summary

IQVIA's Oncology Evidence Network (OEN) collaborates with European oncology centers to generate real-world evidence (RWE) supporting targeted cancer therapies. This network enables RWE studies across various oncology indications, addressing challenges like slow adoption of novel biomarkers and variability in testing practices. OEN provides solutions such as access to linked biomarker and clinical data, retrospective testing of archival tissues, and AI-enabled precision oncology, optimizing clinical development, market access, and therapy penetration.

Introduction and Importance of RWE

Real-world evidence (RWE) derived from real-world data (RWD) is vital for advancing precision oncology and targeted therapies. It accelerates clinical trials, supports regulatory submissions, and informs payer negotiations. RWE helps promote biomarker testing and facilitates evidence generation post-launch, which is crucial due to the high growth and personalized nature of targeted oncology therapies, where market adoption can be slow.

Key Challenges in RWE with Novel Biomarkers

The use of novel biomarkers in RWE programs faces several obstacles:

• Slow adoption in clinical practice, leading to lengthy integration timelines.
• Variability in testing practices across regions and institutions.
• Low prevalence of some biomarkers, making studies underpowered and costly.
  These challenges render RWE programs unpredictable and often unfeasible for innovative targeted therapies.

Proposed Solutions

1. Access to Linked Biomarker & Clinical Data: OEN partners with over 30 European oncology centers to combine biomarker data from pathology and radiology with electronic medical records (EMRs), enabling efficient RWE studies, streamlined site selection, and expedited evidence generation.
2. Retrospective Testing of Archival Tissues: By collaborating with biobanks and labs, OEN conducts retrospective analysis of stored tissue samples, uncovering insights on under-tested biomarkers at a lower cost than prospective studies, enhancing precision oncology research.
3. Enabling AI-Powered Precision Oncology: OEN integrates AI with imaging and pathology data to detect biomarkers or discover radiomics-based markers, supporting the development of machine-learning algorithms for biomarker detection and maximizing RWE value in therapeutic strategies.

Conclusion

Adopting RWE is essential for the evolution of precision oncology and targeted tumor therapies. OEN's comprehensive approach addresses RWE challenges by integrating data, leveraging AI, and ensuring efficient evidence generation, ultimately driving market access and treatment innovation. Stakeholders should collaborate to invest in RWE programs for accelerated cancer care advancements.