Transforming Modern Drug Development with RWE
No longer relegated to post-market safety monitoring, RWE is emerging as a critical strategic tool in early drug development, helping to de-risk pipelines, refine target product profiles, and optimize trial design.
Traditionally, randomized controlled trials (RCTs) have been thought of as the gold standard when it comes to demonstrating the efficacy of an investigational drug product. However, RCTs are associated with certain limitations, such as the significant time and costs associated with performing such trials, and the fact that the gathered evidence in not necessarily applicable to real-life scenarios (1).
To gain insights into how well investigational drug products work in real-world patients, it has been common for observational studies to be performed in support of RCTs. These observational studies, providing real-world evidence (RWE) — derived from the analysis of high-quality real-world data (RWD) — have generally been limited for safety monitoring and to answer any pharmacoeconomic questions after a drug has been approved (2).
Previously, regulatory authorities considered RWE to be of lower quality when compared with the evidence gained from RCTs (1). However, with greater digitalization affording more opportunities to leverage RWD effectively, more regulatory bodies are becoming accepting of RWE to inform approval decisions and, thus, it is becoming more of a strategic tool across the development process (3).
Early Development Value
While the majority of literature around the use of RWE has focused on the more regulatory involved stages of development, such as clinical trials and approval, there is value to be gained by using RWE and RWD earlier in the development process. For example, developers can use RWD along with the insights gained from such data — real-world insights (RWI) — to form specific portions of the target product profile (TPP) of the potential drug. This approach is of particular benefit within the rare diseases space where changes to the already small patient population can lead to a program being no longer viable (4).
Additionally, RWD and RWE can help to de-risk the therapeutic pipeline early on by providing a better understanding of the natural history of a disease through analyzing real-world patterns of care. This understanding can allow for more precise target identification and the discovery of potential unmet needs. Again, this area is particularly useful for rare diseases and conditions where patient populations are small (5,6).
Clinical Trial Optimization
Probably one of the more obvious applications of RWD and RWE is in the optimization of clinical trial design (7). Traditional trial recruitment often requires extensive efforts, relying on inclusion/exclusion criteria that may inadvertently exclude the patients most likely to benefit from the therapy, or conversely, include those who dilute the treatment effect (4,8).
RWE also allows for sophisticated population enrichment. By mining EHR data, sponsors can identify sub-populations with specific biomarkers or disease progression rates that make them ideal candidates for a trial. This data-driven recruitment reduces screen failures and accelerates enrollment timelines—one of the most significant bottlenecks in drug manufacturing and development.
Furthermore, RWE is being used to design External Control Arms (ECAs) or synthetic control arms. In rare diseases or oncology, where it may be unethical or impossible to randomize patients to a placebo, RWD can be used to create a comparable cohort of patients receiving the current standard of care. This approach can significantly reduce the size and cost of a trial while maintaining the statistical rigor required for regulatory assessment.
Navigating Challenges
Despite its promise, the path to implementing RWE in early development is fraught with challenges. The most persistent of these is data quality and fragmentation. RWD is often noisy — it is collected for clinical or administrative purposes, not for research. Missing data, coding errors in insurance claims, and a lack of standardized terminology across different health systems can limit the utility of the evidence.
Overcoming these challenges requires a shift toward Data Engineering as much as Data Science. Companies are increasingly investing in Common Data Models (CDMs), such as OMOP (Observational Medical Outcomes Partnership), which allow data from disparate sources to be mapped to a uniform structure. Additionally, the use of tokenization—a process that allows patient data to be linked across different datasets (e.g., linking a pharmacy record to a genomic lab result) without compromising patient privacy—is becoming a standard practice to fill in the white spaces of a patient's medical history.
The Regulatory Landscape
The regulatory stance on RWE has shifted from cautious skepticism to active encouragement, though the approach remains nuanced across different regions.
In the United States, the 21st Century Cures Act mandated the FDA to establish a framework for the use of RWE to support new drug indications or to satisfy post-approval study requirements. The FDA has since released several guidance documents focusing on the quality of RWD and the robustness of study designs. The Real-World Evidence Program at the FDA is now a mature entity, providing a clear pathway for sponsors to engage in early consultations.
In Europe, the EMA has taken a similarly proactive stance through initiatives like DARWIN EU (Data Analysis and Real-World Interrogation Network). This network provides the EMA with access to real-world data across Europe to support the decision-making of the Committee for Medicinal Products for Human Use (CHMP).
However, differences in data privacy laws, particularly the GDPR in Europe, create a more complex landscape for data sharing compared to the U.S. Region-to-region differences also persist in how regulators view the validity of external control arms, with most still preferring RCT data for primary efficacy endpoints while accepting RWE for safety and supportive evidence.
Future Opportunities and Stakeholder Impact
Looking forward, the integration of RWE will likely lead to more adaptive drug lifecycles. Rather than a binary approval status, there may be more conditional approvals where RWE is used to continuously refine the benefit-risk profile of a drug in real-time as it reaches more patients.
For manufacturing and supply chain experts, RWE offers an opportunity to optimize production based on real-world utilization patterns rather than just sales forecasts. For payers, RWE is the backbone of "value-based contracting," where reimbursement is tied to actual patient outcomes measured in the real world.
Ultimately, the goal of leveraging RWE is to move toward a more patient-centric model of medicine. By understanding how drugs work in the real world, the industry can develop treatments that are not only statistically significant but clinically meaningful for the diverse populations they serve. For industry experts, the challenge is no longer whether to use RWE, but how to build the technical and regulatory infrastructure to use it effectively.
References
1. Baumfield Andrew, E.; Reynolds, R.; Caubel, P.; Azoulay, L.; Dreyer, N.A. Trial Designs Using Real-World Data: The Changing Landscape of the Regulatory Approval Process. Pharmacoepidemiol. Drug Saf. 2020, 29, 1201–1212.
Chodankar, D. Introduction to Real-World Evidence Studies. Perspect. Clin. Res., 2021, 12 (3), 171–174.
PicnicHealth Team. Real-World Data by Design — Incorporating Different Data Types into Clinical Trials. Webinar, May 24, 2023.
Dagenais, S.; Russo, L.; Madsen, A.; Webster, J.; Becnel, L. Use of Real-World Evidence to Drive Drug Development Strategy and Inform Clinical Trial Design. Clin. Pharmacol. Ther. 2021, 111 (1), 77–89.
Liu, J.; Barrett, J.S.; Leonardi, E.T.; et al. Natural History and Real-World Data in Rare Diseases: Applications, Limitations, and Future Perspectives. J. Clin. Pharmacol. 2022, 62 (Suppl 2), S38–S55.
Stemhagen, A.; Moran, E.; Lytle, J. Understanding the Natural History of Disease. UBC, White Paper, June 24, 2024.
CTTI. CTTI Recommendations: Use of Real-World Data to Plan Eligibility Criteria and Enhance Recruitment. Ctti-clinicaltrials.org, Recommendations, April 29, 2025.
Clinical Programming Team. A Guide to Real-World Evidence in Clinical Trials. Quanticate, Blog, Aug. 2, 2024.
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