been associated with post-marketing studies or Phase 4 evidence generation. But in recent years, sponsors have started to leverage RWD much earlier—during Phase 1 or even preclinical stages. By integrating electronic health records (EHRs), insurance claims, registries, and digital health sources, developers can make more informed decisions about trial design, safety markers, patient selection, and unmet needs. This article explores how RWD is reshaping early clinical development and offers strategies for its effective use in Phase 1 trials.

What Is Real-World Data (RWD)?

• Electronic Health Records (EHRs): Diagnosis, medication, lab values, vitals
• Claims & Billing Data: Utilization, costs, comorbidities
• Disease Registries: Rare disease patterns, natural history, outcome measures
• Wearables & Apps: Sleep, mobility, glucose, heart rate variability

Opportunities for RWD in Early-Phase Trials

1. Target Population Characterization

• Understand prevalence, comorbidities, and concurrent medications
• Model inclusion/exclusion criteria to avoid protocol amendments later

2. Dose Prediction and Drug Interactions

• Use population PK models informed by RWD lab values and demographics
• Identify common CYP inhibitors and real-world co-medication use

3. Biomarker Strategy Development

• Correlate lab values (e.g., liver enzymes, CRP) with outcomes in untreated patients
• Assess natural fluctuation and assay variability for selecting PD endpoints

4. Site Selection and Feasibility

• Use health system EHRs to identify eligible subjects
• Select sites with high patient density, prior trial participation, and digital capabilities

5. Safety Signal Anticipation

• Assess background rates of key adverse events in the population
• Predict baseline QTc, renal function, or hepatotoxicity markers

Case Example: Rare Disease Phase 1 Strategy

A biotech company developing an enzyme replacement therapy used a global registry of 450 patients to understand age distribution, symptom onset, and disease severity at baseline. This data allowed them to:

• Focus their Phase 1 on adult patients with stable disease
• Select enzyme levels as an early PD marker
• Develop safety thresholds using real-world lab variability

This shortened recruitment time and provided FDA with strong rationale for patient choice and dose titration.

Challenges in Using RWD for Early Trials

• Data quality and completeness vary across sources
• Retrospective data may not align with trial-level granularity
• Biases and confounding from non-randomized treatment data
• Regulatory skepticism unless provenance and validation are clear

Global Regulatory Perspective

FDA

• Supports use of RWD for trial planning and external controls (esp. for rare diseases)
• Released RWE Framework and RWD guidance documents (2021–2023)

EMA

• Permits RWD use in exploratory endpoints and background incidence rates
• Encourages registries for rare diseases and pediatrics

CDSCO

• Still evolving, but accepts natural history data to support early clinical trials
• Mandates traceability and ethics review if RWD is prospectively linked to trial design

Best Practices for Incorporating RWD into Early Trials

• Use curated RWD sources with clear data provenance
• Link RWD analysis directly to trial design decisions (e.g., sample size, endpoints)
• Validate real-world endpoints against clinical trial measures where possible
• Document RWD strategy in pre-IND and scientific advice meetings
• Collaborate with data scientists and epidemiologists for robust analysis