FDA’s recent message on the globalization of clinical research is unequivocal: Good Clinical Practice expectations do not change with geography.
FDA’s recent message on the globalization of clinical research is unequivocal: Good Clinical Practice expectations do not change with geography.
In its September 2026 communication, FDA announced increased foreign Bioresearch Monitoring (BIMO) inspection coverage and plans to update the risk-based criteria used to identify clinical trial sites for inspection. The agency is also increasing scrutiny of the reliability, inspectability and provenance of clinical data generated outside the United States.
That regulatory signal raises an important operational question for sponsors and CROs.
What if some of the information that eventually helps a regulator determine where closer scrutiny is warranted could help the study team determine where closer oversight is warranted much earlier?
This is where the relationship between FDA’s risk-based inspection approach and sponsor-side Risk-Based Quality Management becomes interesting.
FDA publicly identifies several factors used in risk-based selection for BIMO inspections. They include compliance history, complaints, enrollment, protocol exceptions or deviations, and data anomalies.
FDA has also explored how advanced analytics can strengthen this process. Its researchers have publicly described the use of centralized statistical monitoring to identify potentially problematic clinical trial sites, and FDA currently has a Cooperative Research and Development Agreement covering automated assessment of multicenter clinical-trial data using statistical modelling and machine learning.
The regulatory use case is clear. Data and other intelligence can help FDA decide where closer examination may be needed.
For the sponsor, the timing and purpose are different.
Consider the two processes side by side.
FDA / regulatory perspective
Submission + BIMO information + compliance intelligence
→ risk indicators and anomalies
→ inspection prioritization
→ inspection and regulatory assessment
Sponsor / CRO perspective
Protocol + clinical and operational data
→ KRIs, QTLs, statistical anomalies and potential protocol deviations
→ human assessment
→ proportionate intervention
→ documented decision and evidence
The analytical concepts can overlap. The decision being supported is different.
FDA needs to determine where regulatory scrutiny should be directed.
The sponsor needs to determine where trial oversight should be directed while the study is still underway.
That creates the central proposition:
A signal detected during regulatory review may help FDA decide where to inspect. A meaningful signal detected during study conduct can help the sponsor decide where to act.
And timing matters.
ICH E6(R3) provides an important regulatory bridge.
When sponsors evaluate identified risks, E6(R3) explicitly asks them to consider three dimensions: the likelihood that the harm or hazard will occur, its impact on participant protection and reliable results, and the extent to which it would be detectable.
That makes detectability part of the quality-management problem.
A monitoring strategy can describe what should be reviewed. An effective operating model also needs to consider:
How will we know when something important has actually happened?
That question becomes particularly relevant when trials involve large numbers of sites, multiple CROs and service providers, increasingly complex protocols and geographically distributed data.
A significant issue that remains invisible cannot be assessed proportionately or acted upon promptly.
An FDA inspection review provides a very tangible example.
In a published Clinical Inspection Summary associated with NDA 208078, FDA reviewed sponsor oversight of Protocol LMS-002.
FDA documented substantial gaps in study monitoring and concluded that, even when monitoring was performed, it had in some instances been insufficient or ineffective.
The review identified examples where protocol deviations had not been recognized by the study monitor until:
“many months or even years after the event.”
The same inspection review also raised significant questions around the subsequent handling, reclassification and documentation of protocol deviations.
This is a historic case and should be interpreted carefully.
FDA did not conclude that RBQM, centralized statistical monitoring, AI or automated protocol-deviation detection would have prevented these findings.
The case exposes a more useful quality-management question:
Could a more systematic, data-driven and protocol-aware oversight model reduce the time between a deviation occurring and the study team becoming aware of it?
The case exposes a more useful quality-management question:
Could a more systematic, data-driven and protocol-aware oversight model reduce the time between a deviation occurring and the study team becoming aware of it?
Our new case study examines the FDA inspection review in detail, including:
FDA’s current focus on foreign clinical evidence makes this increasingly relevant.
The agency is expanding foreign BIMO inspection activity and intends to refine its risk-based inspection criteria as clinical development becomes more geographically distributed.
For sponsors, the practical response should go further than preparing records for a possible inspection.
A stronger question is:
Did our quality system give us a reasonable opportunity to see the important signal, understand it, act on it and explain the decision?
That is where QbD, RBQM and modern risk-detection technology can help turn GCP expectations into an operating model.
Detect earlier → assess proportionately → act → document the rationale → preserve the evidence trail.
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