Helping Small Biopharma Thrive With Agile Trials and Smarter Data | SCOPE Summit 2026
Clinical trials are not just getting bigger; they are becoming more interconnected. As a result, sponsors are managing:
- More vendors, often across multiple functional areas
- More systems, each with its own data structures and dependencies
- More handoffs, where accountability can become unclear
- Higher expectations for sponsor oversight, particularly during GCP inspections
The result is greater complexity and higher expectations for insight and oversight. This is why the Small Biopharma Strategies program at SCOPE Summit includes a panel focused on Agile Trials, Smarter Data. The discussion reflects the day-to-day challenges sponsors face as trial environments become harder to manage with lean teams and limited resources.
As part of the panel, Donna Dorosinzky, Founder and CEO at Just in Time GCP, will join fellow speakers to share practical perspectives on how small and mid-sized biopharma teams can run faster trials while maintaining control of their data, oversight, and inspection readiness.
What the Data Shows About Trial Complexity
Through our work developing clinical study data flow maps with trial sponsors, a consistent pattern has emerged.
- Most trials rely on far more systems, vendors, and data pathways than teams had initially planned.
- Data ownership and accountability often become unclear once data moves across platforms.
- Oversight gaps rarely come from lack of effort. They come from lack of shared visibility into how data actually moves and transforms.
Across sponsors of different sizes, data flow mapping regularly uncovers hidden complexity. This is especially common at vendor handoffs, system integrations, and downstream use of trial data. These are not edge cases. They are common, recurring patterns.
For small and mid-sized biopharma organizations, the impact is amplified. Teams are lean, roles overlap, and institutional knowledge carries much of the burden.
Why Data Flow Is Central to Agile Trials
The panel topic reflects an important shift in how agility is defined in clinical trials.
- Speed without visibility creates risk.
- Decisions made without understanding data flow are harder to defend.
- Inspection readiness becomes dependent on individual memory rather than documented evidence.
Clear clinical study data flow mapping helps sponsors move from assumption to understanding how data is collected, aggregated, and used. It supports better system decisions, clearer vendor oversight, and more confident conversations during GCP inspections.
This is the practical foundation behind smarter data.
From Panel Discussion to Real-World Application
The Agile Trials, Smarter Data panel is grounded in what sponsors are facing today. The focus is not theory or future-state vision. It is execution.
The discussion will focus on:
- How lean teams maintain visibility as trials scale and data volumes grow
- Where data flow breaks down most often
- How sponsors simplify oversight without adding burden
- What actually supports inspection-ready execution
These are the same questions sponsors ask when they first map their clinical data flows.
Continuing the Conversation at SCOPE
Just in Time GCP will be exhibiting onsite at booth #433. Many of the themes from the panel continue there, including how sponsors bring clarity to complex trial environments and build inspection readiness into daily execution.
If you are attending SCOPE, we invite you to:
- Join the Agile Trials, Smarter Data panel in the Small Biopharma Strategies program
- Visit our booth to:
- Continue the conversation around clinical study data flow and sponsor oversight
- Pick up a copy of our newly released 2026 GCP Inspection Readiness Report
- Enter for a chance to receive Barnett’s Good Clinical Practice: A Q&A Reference Guide 2025/2026, edited by Donna Dorozinsky, Founder and CEO of Just in Time GCP
- Compare notes with peers facing similar challenges across lean trial teams
This is not about adding more process.
It is about understanding what is already happening and using that insight to run better trials.
