Clinical Development optimization

Trial enrichment for faster recruitment and higher generalizability

Relaxing RCT inclusion/exclusion criteria based on Real-World Data to speed-up recruitment and improve result’s generalizability while maintaining probability of success.
Trial patients population is often over-selected and far from reflecting the targeted real-world patient population. Trial enrichment allows for faster recruitment and better generalizability, hence enhancing external validity and regulatory acceptability.

In moderate to severe asthma, RCT patients’ recruitment is usually based on treatment escalation (GINA score 4/5). This score does not fully capture patients’ heterogeneity in severity. For example, it’s difficult to distinguish comorbid patients from severe asthmatics.


Improve RCT inclusion/exclusion criteria in Asthma

Map the targeted trial populations with the eligible
Real-World population

Evaluate the impact of relaxing exclusion criteria
on recruitment rates, expected efficacy & safety.

Methods & solution

Design an optimal clinical trial based on simulation

Mapping of clinical trial’s eligibility criteria on Real-World Data based on treatment, disease history & GINA scores.

Selecting & aggregating relevant characteristics and developing a clustering (grouping) algorithm, using real-life data from patients who received medium to high dose ICS-LABA.

Defining & testing 6 enrichment scenarii in terms of recruitment potential, efficacy and safety results, using models built on other trials data.


Overall, enrichment scenarii were able to maintain expected efficacy

Half of scenarios were ruled out due to significantly increased safety Risk Ratios.

The final scenario was selected based on representativeness & recruitment potential.


Faster recruitment and higher generalizability

  • De-risking regulatory submission and label discussions: enhancing generalizability, saving long, costly & post-market studies compared to previous programs.
  • Faster recruitment by at least 3 months on average while maintaining expected efficacy and improving generalizability.