Enriched Clinical Trial Design: Who Gets Left Behind?

Enriched clinical trial design can make studies more efficient by concentrating recruitment around patients most likely to demonstrate a treatment effect. But the same strategy can narrow the evidence base, leaving unanswered questions about patients who do not meet the enrichment criteria. 

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Why Enrichment Strengthens Clinical Trials

Enrichment is not new. Sponsors can select participants according to characteristics linked to prognosis or predicted treatment response. This can improve the chance of detecting an effect and, in some cases, reduce the number of participants required.

The U.S. Food and Drug Administration (FDA) describes prognostic enrichment as selecting people more likely to reach a study endpoint, while predictive enrichment focuses on characteristics associated with treatment response. 

In ophthalmology, this can be particularly relevant where disease progression, baseline severity, imaging characteristics, or previous treatment response vary substantially between patients.

In a lot of clinical trials, the advantages of enriched clinical trial design is that a more biologically focused population can produce a cleaner efficacy signal.

The Evidence Gap for Enriched Clinical Trial Design

The problem for enriched clinical trial design begins when efficiency becomes the dominant objective.

If a study only randomises patients who meet a particular biomarker, disease-severity, imaging, or response criterion, it may provide strong evidence for that subgroup while generating little or no evidence for everyone else.

That creates a critical distinction between efficacy and generalisability.

A methodological review of biomarker-enriched designs found that these approaches can efficiently establish treatment effects in selected populations, but cannot directly establish whether patients outside that population would also benefit. 

The advantages extend beyond ophthalmology. Oncology, chronic pain, neurology, and other therapeutic areas have used enrichment approaches to improve trial efficiency or reduce uncertainty around treatment response. 

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Ophthalmology Demonstrates the Enriched Trial Trade-Off

Ophthalmology provides useful examples of this trade-off because clinical trials can depend heavily on measurable disease characteristics.

Dry-eye disease trials, for example, can face substantial placebo or vehicle responses. Enrichment approaches may help reduce this noise and increase the ability to detect a treatment signal.

However, there are also several potential disadvantages, including reduced external validity and possible overestimation of treatment effects. 

AI-supported cohort selection has been used to predict which patients with neovascular age-related macular degeneration were likely to have a suboptimal response to initial aflibercept treatment, demonstrating the potential of predictive enrichment to make patient selection more sophisticated.

It also raises a bigger question: If algorithms become better at identifying likely responders, could trials become increasingly selective?

Sponsors Need to Ask Who Remains Visible

The answer is not to abandon enriched designs.

Instead, sponsors should ask whether the trial can preserve efficiency while generating useful evidence about a broader population.

Several approaches can help:

• Include broader cohorts alongside the primary enriched population.
• Pre-specify secondary analyses for patients outside the primary enrichment group.
• Use biomarkers as stratification tools where evidence remains uncertain.
• Consider adaptive or hybrid designs where scientifically appropriate.
• Plan how trial findings will translate into real-world patients.

The FDA’s recent guidance on improving clinical trial participation also recognises that even enriched studies may benefit from broader participant groups and secondary analyses across the disease spectrum. 

This fits the wider direction of clinical development. As Pharmatica has explored in our Insights of patient diversity and adaptive trial design, better trial design is increasingly about balancing statistical efficiency with evidence that reflects clinical reality. 

Trials Should Be Efficient, But Still Representative

Enrichment is becoming a more sophisticated tool for clinical development. Biomarkers, imaging, clinical characteristics, and predictive algorithms can help sponsors identify populations in which a treatment effect is easier to detect.

Used carefully, that can make trials faster and more statistically efficient. But efficiency has a boundary.

A trial can be statistically stronger while becoming clinically narrower.

When eligibility criteria progressively filter out patients who are harder to classify, the resulting evidence may say less about the population that will ultimately receive the therapy.

That makes representativeness a clinical trial design consideration, not an afterthought.

Sponsors should consider what evidence is gained through enrichment, what evidence is lost, and whether complementary cohorts, subgroup analyses, or real-world evidence can help close the gap.

This is particularly important in ophthalmology, where trial design must also account for the complexity of measuring disease and, in some studies, outcomes from both eyes.

The next generation of clinical trials should ask who will eventually need this treatment, and do we have enough evidence to understand how it will work for them.

That is where smarter enrichment becomes more than a recruitment strategy, and more a question of how pharma defines useful evidence in the first place.

At Pharmatica, we examine the systems, strategies, and technologies shaping Clinical Development, connecting trial innovation with the evidence patients and decision-makers ultimately need. 

Pharmatica: Insight. Connection. Impact.

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