Real-world evidence (RWE) is no longer a supporting actor, but rather a strategic asset that should be embedded across the product lifecycle.

We now have tools that were unimaginable a decade ago: synthetic data that preserves privacy while enabling scenario modeling and early go/no‑go decisions, external control arms (ECAs) to strengthen single‑arm trials and accelerate access in high unmet need settings,
and decentralized long‑term extensions via tokenization that reduce burden while capturing 10+ years of safety and effectiveness across the patients’ real-world journey.

These innovations aren’t just “nice to have.” They are how we accelerate access to needed therapies, demonstrate value with confidence, and build submissions that stand up to today’s scrutiny.

Here, I discuss how these capabilities are reshaping clinical strategy and unlocking smarter, faster, more equitable evidence generation.

Generating synthetic data with agentic AI

Synthetic data is artificially generated data that mimics the statistical properties of real data without containing identifiable patient information. Starting with appropriate real-world data (RWD) (patient-level) or randomized controlled trial (RCT) data source(s), sponsors can use an AI-supported pipeline to generate a synthetic dataset, then assess similarities to the original data to gauge success.

Synthetic data can:

• Inform early go/no-go decisions: A cost-effective approach to optimizing asset strategy before large investments by simulating expected outcomes under various scenarios in Phase I–II.
• Inform CT design: Model alternative controls and sample sizes and stress-test treatment effects in a cost-effective manner.
• Build privacy-preserving cost-effective ECAs: Build an ECA partially (+ RWD) or totally through a fully de-identified synthetic cohort. This is not for regulatory purposes yet, but it can inform provider and payer decisions.

RWD has its limitations: it must closely resemble real patient populations and protect patient privacy, and can be costly, time-consuming, and potentially unethical. Synthetic data can help overcome these challenges.

Strengthen regulatory submission with an external control arm

External control arms use data from historical RCT or RWD when randomization is not feasible or ethical, or to power / accelerate a study where there is high unmet need.

ECAs can:

• Strengthen single-arm trials (SAT): Provide contextual information for SAT regulatory submissions, increasing probability of success.
• Accelerate access to needed therapies: For RCT in high unmet need (e.g., accelerated approval pathway) and/or with slow recruitment, RWD can augment the control arm.
• Support a lifecycle management approach: Supports label expansions to new populations (e.g., to male breast cancer) or new lines of therapy for decisions by regulators, payers, and providers.

While RCTs are considered the “gold standard,” the FDA in 2023 wrote that “externally controlled studies may be considered” (with strong justification), while in 2025, the EMA guidance stated “in some situations, causal conclusions may be derived from a setting where the investigational medicinal product data was collected under a clinical trial protocol while the control arm was not a randomized arm in that same protocol.”

Assess long-term outcomes with long-term extension studies

Decentralized long‑term extensions for RCT assess long-term outcomes (safety and effectiveness) with or without drug provisions. The extension enables follow-up of tokenized trial patients via real-world databases or direct-to-patient data collection.

Long‑term extension studies can:

• Allow for long-term follow-up: Cost-effective data collection by reducing site and patient burden while collecting key safety and effectiveness endpoints over 10+ years.
• Enable earlier launch: For breakthrough therapies and high unmet need, launch can occur as soon as clinical efficacy is proven if the sponsor commits to a Phase IV study to collect long-term data.
• Improve representativeness: Loss to follow-up in long-term studies can lead to confounding, and RCTs often under-represent certain populations. The shift to real-world endpoints makes the insights more relevant to decision-makers.

Key takeaways

Consider RWE as a strategic asset: Integrate RWE early and anticipate post-marketing collection of long-term data and adopt causal inference methods to protect ideals of safety and effectiveness.

Invest in robust RWD: Invest in RWD quality and governance to ensure credibility with regulators and payers.

Adopt a comprehensive strategy: Adopt flexible, hybrid evidence strategies that combine synthetic data, ECAs, and long-term real-world data collection approaches.

Ensure cross-functional readiness: Medical, regulatory, biostats, and data science must operate as one evidence engine.

Interested in learning more?

Join Nathalie Horowicz Mehler at the CMO Summit as we step beyond the protocol — and into what’s possible.

Nathalie’s talk “Beyond the Protocol: Leveraging RWE to Inform Clinical Strategy and Strengthen Healthcare Decision-Making” will be on Tuesday, April 14, at 9:30 am.

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