GxP compliance by design in a statistical computing environment

Q: Why does the statistical computing environment matter for clinical trial timelines?

The statistical computing environment is where clinical trial investment converges: years of patient recruitment and data collection depend on the compressed window between database lock and submission. A platform that slows this final stage delays treatments and carries direct commercial cost. GSK's SPACE program treats analysis speed as a business-critical outcome, cutting the database-lock-to-output cycle time by 50%, because performance at this stage determines how quickly a therapy can reach patients.

Q: How does an orchestration layer make a generic platform GxP-compliant for statistical programming?

An orchestration layer like METEOR closes the gap between a generic project structure and the specific requirements of regulated statistical programming. It handles the full planned analysis lifecycle, provisioning Domino projects, datasets, folder structures, and GitHub repos automatically from a single self-service screen, while a paired component, the SPACE Data Access Manager, maps more than 20 clinical roles directly to Domino’s generic permissions. Domino supplies compute and project infrastructure; it has no native concept of a clinical planned analysis or a pharmaceutical role hierarchy. METEOR and the Data Access Manager supply that missing layer together, and neither is sufficient alone.

Q: What does compliance-by-design look like in a statistical computing environment?

The compliant path should be the only path available, not one option among several. In practice that means audit trails generate automatically, training gates block platform access until required training is complete, and role assignments are provisioned and revoked by the system rather than by manual request. Every action taken is compliant by construction, so onboarding a new study or user never triggers a separate review cycle.

GxP compliance breaks down when legacy systems don’t share a common audit trail, forcing teams to duplicate validation work every time data moves between them. Building compliance directly into platform workflows removes that review cycle for every new study or user so life-saving medications reach patients faster.

Yannis Katsaros

Product Director & SPACE Program Tech Lead

GSK

What you'll take away from this session

Statistical analysis is the highest-pressure stage

Every clinical trial’s investment converges at database lock, and the weeks that follow determine how quickly a therapy can reach patients. GSK cut the median cycle time from database lock to Statistical Analysis Complete by 50%.

Fragmentation multiplies compliance costs

GSK’s fragmented legacy environment didn’t have a common audit trail so validation had to be duplicated every time data crossed from one system into another, undermining clinical data governance across the organization.

Migration is its own program

Migrating hundreds of active studies and thousands of users required its own dedicated engineering and change management, separate from building the destination platform, with studies near regulatory milestones held on legacy systems until its submission was done.

Compliance built in, not bolted on

GSK’s orchestration layer automates project provisioning and role-based access as part of setting up every planned analysis, so audit trails generate without anyone maintaining them by hand and a project is compliant from the moment it’s created.

Self-service prevents ticket bottlenecks

At GSK’s scale, requiring a support ticket for every routine task becomes a systemic bottleneck. Getting users to self-serve as early as possible mattered more to adoption than any training program on its own.

The platform was the easy part

Domino itself needed little work to become production-ready. The harder, longer effort went into the GxP-qualified ecosystem GSK built around it (the data integrations, audit tooling, and change management) that made the environment usable at enterprise scale.

GSK built SPACE, a governed platform on Domino, to replace a bespoke tool for its pharma business, LSAF (a third-party SaaS platform used for vaccines), and standalone SAS installations. None of them shared a common audit trail or access model, so validation effort and infrastructure cost were duplicated across every team supporting GSK's 300+ active studies and 2,500+ users.

SPACE runs on three products built on top of Domino. METEOR is the orchestration layer, automatically provisioning planned-analysis projects and mapping more than 20 clinical roles to platform permissions through its Data Access Manager. RAPIDO gives medical writers and reviewers self-service data visualization. PULSAR automates publishing statistical outputs to downstream systems like the eTMF.

Migration ran as its own program, using a structured approach GSK calls the Hyperdrive model, moving all 300+ studies and 2,500+ users onto SPACE by Q1 2024 with zero missed regulatory submissions, while any study near a regulatory milestone stayed on the legacy systems until its submission was done.

SPACE has since expanded into ASTRA for SDTM automation and NEXUS for metadata-driven analytics. Katsaros credits the harder work to the GxP-qualified tooling built around the platform and the sustained change management needed to move years of SAS-based habits, and CRO partners, onto something new.

FAQ

Why does the statistical computing environment matter for clinical trial timelines?

How does an orchestration layer make a generic platform GxP-compliant for statistical programming?

What does compliance-by-design look like in a statistical computing environment?

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