Real-world evidence (RWE) is no longer peripheral to drug development. Across regulatory, HTA and payer environments, expectations have been shifting rapidly, driven by policy evolution, data availability, and the growing influence of AI-enabled analytics. For sponsors, the question is no longer whether to integrate RWE across drug development stages, but how early, how credibly, and how strategically.
This perspective, authored by Billy Amzal, PhD, Head of Strategic Consulting outlines the key trends shaping the landscape, their implications for development strategy, and what organizations must put in place not only to keep pace, but to lead.
A Fast-Evolving Landscape for RWE
Regulatory Momentum is Accelerating
Worldwide, regulators are moving decisively to clarify and expand the role of RWE in regulatory decision-making. Both the FDA and EMA have increased the pace and specificity of guidance supporting the use of real-world data (RWD) to inform efficacy and effectiveness within submission packages. [1, 2, 3]. Notably, recent FDA statements now opens the door to the use of de-identified RWD, such as electronic medical records and national registries, within regulatory submissions, signaling growing confidence in the maturity of these data sources. [4]
At the same time, access to RWD is expanding. Initiatives such as the European Health Data Space (EHDS) [5], alongside broader commercial, public and academic data availability, are lowering historical barriers to evidence generation. This has been accompanied by a growing body of regulatory precedents, particularly for established use cases such as external controls, long-term effectiveness projections and drug utilization studies.
The use of more advanced analytics is also gaining traction. Regulators are increasingly exposed and receptive to model-based and innovative approaches including surrogate endpoint validation, Bayesian borrowing [6] and registry-based randomized controlled trials. While there is not yet established standards for all of them, they can enhance probability of regulatory/HTA success on a case-by-case basis, provided proper implementation.
HTA and Payer Expectations are Becoming more Diverse and Specific
In Europe, the introduction of Joint Clinical Assessments (JCAs) fundamentally changes the evidence equation. [7] Multiple PICO scenarios are now assessed simultaneously, increasing both the volume and complexity of evidence requirements. This typically requires model-based indirect comparisons, potentially including earlier RWE generation and integration.
In the US, evolving pricing policies and value-based frameworks imply a stronger reliance on RWE to demonstrate comparative effectiveness, cost effectiveness, and patient-centered value in routine clinical practice. The implication is clear: traditional trial evidence alone is increasingly insufficient to optimize pricing and access negotiations.
AI is Reshaping how Evidence is Generated and Synthesized
Parallel to regulatory and HTA change is the rapid rise of AI-powered tools across the evidence lifecycle. Automation is already transforming evidence synthesis activities, including safety surveillance, indirect treatment comparisons, network meta-analyses, RWD landscaping and integrated evidence plans.
AI is also accelerating RWD curation, enabling faster identification of relevant cohorts, higher-quality data linkage, and more scalable study execution. Beyond execution, predictive modelling is expanding, supporting disease progression models, care pathway simulations and the integration of RWD into model-informed drug development (MIDD) frameworks.
APAC: Evolving Access, Growing Opportunity
In Asia, and particularly China, real-world data policies and access mechanisms continue to evolve. [8] While nationwide access remains limited given the lack of data standardization, large hospital networks now offer meaningful partnership opportunities with local brokers or partners. These developments raise important opportunities for transportability across geographies, e.g. via Bayesian Dynamic Borrowing.
What This Means for Drug Development Strategy
Collectively, these trends point to a fundamental shift in how evidence generation must be planned. First, integrated evidence planning (IEP) is moving earlier and becoming more global. [9] Questions around differentiation, value, access and data availability can no longer be deferred to late-stage development. Instead, they must be addressed proactively, with close involvement from local, HEOR and market access teams alongside clinical and regulatory functions.
Second, there is a clear move toward value-based drug development, particularly in competitive indications such as oncology. [10] Long-term real-world projections of outcomes and value are increasingly expected, accounting for the competitive landscape and the factors driving effectiveness. In practice, this has driven a rise in approaches that combine RCT and RWD through modeling not only to support development decisions, but also to facilitate adoption and access post-approval.
Third, RWD access itself is becoming a strategic capability. This includes building partnerships with data providers, integrating local expertise, and ensuring the organizational infrastructure required to mobilize data quickly and compliantly.
Finally, processes and people must adapt. The pace of change demands more frequent horizon scanning across regulations, tools and data sources. Evidence strategies must be refreshed dynamically, particularly for early-stage portfolios. Critically, organizations must adopt a ‘human-in-the-loop’ approach to AI, supported by targeted training and clear governance, to ensure credibility and regulatory confidence.
Keeping Pace vs. Leading the Field
To remain competitive, organizations must focus on readiness and execution. This includes deploying selective automation tools, establishing regular horizon scanning for AI-enabled RWE applications, and implementing pilot studies grounded in existing precedents. Ongoing exposure to regulatory thinking, through scientific advice, workshops and conferences, remains essential, as does early and continuous engagement with regulators.
These actions reduce risk, improve efficiency and ensure alignment with evolving expectations, but they are largely reactive.
Leadership requires a more fundamental shift. Leading organizations fully embrace value-based drug development, investing earlier and more deeply in understanding and anticipating real-world value. This often implies enterprise-wide integration, where RWE generation is embedded across all development milestones. In some cases, this extends to simulating every study before protocol finalization to optimize design and downstream value.
Visibility and credibility are equally important. Thoughtful dissemination, patient engagement, and active participation in methodological discourse help attract talent, partners and key opinion leaders.
True pioneers go further, developing synthetic patient cohorts, advancing virtual controls, and investing in methodological innovation. Many are also leading public–private partnerships in priority disease areas, shaping pre-competitive research agendas and setting new norms for evidence generation.
Looking Ahead
The convergence of regulatory evolution, HTA reform and AI-driven innovation is redefining evidence generation. For sponsors, success will depend on how early and how strategically RWE is embedded into development programs.
Keeping pace is achievable. Leading the field, however, requires intent, investment and a willingness to rethink how evidence, and value, is created.
At Phastar, we partner with biopharma teams to design and deliver robust RWE strategy and tactics that support regulatory, HTA and access decisions.
References
3. https://www.fda.gov/drugs/development-resources/advancing-real-world-evidence-program
8. https://www.valuehealthregionalissues.com/article/S2212-1099(21)00076-5/fulltext