In highly competitive therapeutic areas, clinical trial success is often decided long before the first patient is screened. For a large pharmaceutical company preparing a critical Phase 3 study, traditional feasibility processes were too slow, too manual, and too dependent on experience-based judgment, putting enrollment timelines and competitive position at risk.
To change the equation, the client partnered with Axtria to transform trial feasibility into a data driven, predictive capability.
At a glance
By replacing intuition-led planning with AI-driven site feasibility, the organization compressed timelines and accelerated patient recruitment, without increasing operational risk.
The engagement delivered:
- A predictive feasibility platform integrating clinical, real world, and competitive data
- Evidence based ranking of countries, sites, and investigators
- 20% reduction in overall enrollment duration
- Significantly faster enrollment velocity compared to industry benchmarks
Why this matters?
In disease areas with limited patient populations, every week of delay reduces the pool of eligible participants. Traditional feasibility approaches, sequential planning, fragmented data, and heavy CRO dependence, can quietly erode a study’s chances of success.
This case study shows how clients can reclaim control, improve transparency, and move faster by embedding predictive analytics directly into trial planning workflows.
Key takeaways
- Feasibility is a competitive advantage, not an administrative step
- Predictive analytics outperform experience-based site selection
- Early precision drives downstream speed in enrollment and execution
- Replicable feasibility models compound value across studies
Download the full case study to learn how AI driven site selection helped a global pharma company outpace competition, accelerate enrollment, and build a scalable framework for future Phase 3 programs.
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