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. Through mining electronic health record (EHR) data, it is possible for sponsors to 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 (4,9).

Furthermore, in cases where randomization is considered impossible due to ethical concerns or situations where a clinical equipoise does not exist, RWE and RWD can provide sponsors with the option to design external control arms (ECAs) or synthetic control arms. For example, RWD can be used to create a comparable cohort of patients who had received the current standard of care in historical RCTs. Given the right regulatory and clinical context, this approach can significantly not only reduce the size and cost of a trial but can also adhere to the statistical rigor required for regulatory assessment (10).

Navigating Challenges

Despite the promise of RWE in early development, there are still many hurdles hindering its widespread implementation. The quality and consistency as well as the potential bias and measurement errors of the RWD used to form RWE have been highlighted as key issues (11).

RWD is gathered from numerous sources across the real-world healthcare system, meaning there can be discrepancies in how it has been collected or even missing and inaccurate data. Additionally, due to discrepancies in data collection methodologies, it is possible to have skewed findings as a result of patient selection biases. These issues with RWD can ultimately limit the utility of the RWE results and lead to misleading conclusions (11).

To overcome these challenges, companies need to implement data cleaning and validation techniques, employ AI and natural language processing to manage unstructured data, and adopt standardized protocols and data models for harmonization and integration (12). Additionally, 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 a useful tool that can help to address the issues around data interoperability, patient trust, and regulatory compliance (13).

The Regulatory Landscape

The regulatory stance on RWE has shifted towards more active encouragement, though nuances remain across different regions. In the U.S., 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 (14). 

Since the Cures Act was signed in 2016, the FDA has released several guidance documents focusing on the quality of RWD and the robustness of study designs. The framework for FDA’s Real-World Evidence Program was released in 2018, providing a clear pathway for the regulator’s implementation plans (15).

In Europe, the EMA has taken a similarly proactive stance through initiatives such as 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) (16). However, differences in data privacy laws, particularly the General Data Protection Regulation in Europe, create a more complex landscape for data sharing compared to the U.S. where there are no singular, comprehensive federal data privacy laws (17).

Future Opportunities

Looking forward, greater integration of RWE into the development lifecycle and continuing acceptance by regulatory authorities has the potential to offer significant optimization and efficiency for the industry. 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.

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.

2. Chodankar, D. Introduction to Real-World Evidence Studies. Perspect. Clin. Res., 2021, 12 (3), 171–174.

3. PicnicHealth Team. Real-World Data by Design — Incorporating Different Data Types into Clinical Trials. Webinar, May 24, 2023.

4. 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.

5. 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.

6. Stemhagen, A.; Moran, E.; Lytle, J. Understanding the Natural History of Disease. UBC, White Paper, June 24, 2024.

7. CTTI. CTTI Recommendations: Use of Real-World Data to Plan Eligibility Criteria and Enhance Recruitment. Ctti-clinicaltrials.org, Recommendations, April 29, 2025.

8. Clinical Programming Team. A Guide to Real-World Evidence in Clinical Trials. Quanticate, Blog, Aug. 2, 2024.

9. Kalankesh, L.R.; Monaghesh, E. Utilization of EHRs for Clinical Trials: A Systematic Review. BMC Med. Res. Methodol. 2024, 24, 70.

10. Burcu, M.; Dreyer, N.A.; Franklin, J.M. Real-World Evidence to Support Regulatory Decision-Making for Medicines: Considerations for External Control Arms.Pharmacoepidemiol. Drug Saf.2020, 29 (10), 1228–1235.

11. Zisis, R.; Pavi, E.; Geitona, M.; Athanasakis, K. Real-World Data: A Comprehensive Literature Review on the Barriers, Challenges, and Opportunities Associated with their Inclusion in the Health Technology Assessment Process. J. Pharm. Sci. 2024, 27, 12302.

12. Phastar. Overcoming Challenges in Using Real-World Evidence in Clinical Trials. Blog Post, Jan. 29, 2025.

13. LexisNexis. Top 5 Reasons Why Tokenization Matters in Life Sciences. Blog Post, risk.lexisnexis.com[accessed Feb. 25, 2026].

14. FDA. Real-World Evidence. FDA.gov [accessed Feb. 25, 2026].

15. FDA. Framework for FDA’s Real-World Evidence Program. FDA.gov, December 2018.

16. EMA. Data Analysis and Real World Interrogation Network (DARWIN EU). EMA.europa.eu [accessed Feb. 25, 2026].

17. Aidun, E. Data Privacy in the Digital Age: A Comparative Analysis of U.S. and EU Regulations. University of Cincinnati Law Review, Mar. 5, 2025.

Image Credit: © everythingpossible - stock.adobe.com

• What is real-world evidence (RWE) in drug development?

  Real-world evidence (RWE) refers to clinical insights derived from real-world data sources such as electronic health records, patient registries, insurance claims, and wearable devices. Unlike data collected in controlled clinical trials, RWE reflects how treatments perform in everyday clinical practice. Pharmaceutical companies increasingly use RWE to evaluate drug safety, effectiveness, and long-term outcomes across broader patient populations.

• How is real-world evidence transforming modern drug development?

  Real-world evidence is becoming an important tool throughout the drug development lifecycle. Researchers can use RWE to identify patient populations, refine target product profiles, and optimise clinical trial design before studies begin. By integrating real-world data with traditional clinical research, pharmaceutical companies can make more informed decisions, reduce development risks, and accelerate the path from discovery to market.

• Why is real-world evidence important alongside clinical trials?

  While randomized controlled trials remain the gold standard for evaluating safety and efficacy, they often involve narrowly defined patient groups and controlled conditions. Real-world evidence complements clinical trial data by showing how treatments perform across diverse populations and routine healthcare settings. This broader perspective helps regulators, healthcare providers, and pharmaceutical companies better understand treatment effectiveness and safety in real clinical practice.