June 2026 • PharmaTimes Magazine • 38
// AI //
Results business
The traceability gap in modern clinical development
Confidence in clinical evidence depends on being able to reproduce how a result was generated. Increasingly, that is where modern clinical development begins to struggle.
The industry has spent years modernising for speed and scale. Clinical environments are now more distributed, data sources more diverse and analytical workflows harder to govern.
Decentralised trials, real-world evidence, biomarker integration, cloud infrastructure and multi-language analytical environments have all expanded what development teams can do. They have also made traceability significantly harder to maintain.
The problem often surfaces at the worst possible moment: during inspection preparation or regulatory submission review. Teams are forced to manually reconstruct which data sets, code versions and analytical steps produced a given result.
In some cases, the evidence exists, but the lineage behind it is fragmented across systems, vendors and environments. That operational model is becoming more difficult to defend.
Regulators are placing greater emphasis on traceability, reproducibility and data integrity across the full life cycle of clinical evidence. Recent FDA initiatives around AI credibility in drug development reflect a broader expectation that sponsors must be able to demonstrate not only what a model produced, but how that output was generated, validated and governed.
The same direction is visible in European guidance on computerised systems and electronic data in clinical trials, which places strong emphasis on auditability, metadata integrity and life cycle oversight across distributed environments.
Taken together, the message is becoming difficult to ignore. Confidence in evidence no longer depends solely on generating results. It depends on proving, continuously, how those results were produced
Complexity
The challenge is no longer just scale. It is structural.
Clinical workflows now span multiple partners, platforms and programming environments. Data may move between sponsors, CROs, cloud systems and specialist analytical tools before reaching a submission package. At each stage, there are opportunities for lineage to become harder to reconstruct.
Historically, many organisations have managed this through retrospective documentation and manual reconciliation. That approach becomes fragile as environments grow more distributed and analytical methods more dynamic.
AI raises the bar further. Probabilistic outputs from large language models and adaptive systems challenge assumptions about reproducibility that regulators have traditionally relied upon.
If an output cannot be consistently reproduced or its provenance clearly explained, confidence in the result quickly weakens.
This does not mean AI has no place in clinical development. Far from it. But it does mean governance foundations can no longer be treated as secondary implementation concerns. Increasingly, traceability is what makes AI outputs operationally and regulatorily credible in the first place.
Traceability
The industry needs to move from retrospective audit reconstruction towards traceability by design.
That means lineage capture becomes part of the workflow itself rather than an exercise conducted later under pressure. Data transformations, code versions, metadata changes and analytical outputs should remain continuously attributable across the life cycle of a study.
In practice, this requires more than adding another oversight layer. It depends on analytical environments designed to maintain traceability natively across increasingly complex workflows.
Importantly, traceability by design is not about slowing development down. In many cases, it reduces operational friction because teams spend less time manually reconciling evidence across fragmented systems.
Modernisation only becomes meaningful when evidence remains explainable under scrutiny.
The industry has largely framed modernisation around speed, scalability and AI readiness. The harder question is whether organisations can continuously prove how their evidence was generated.
In modern clinical development, that may become the defining standard regulators care about most.
Robertson Williams is Health and Life Sciences Product Lead at SAS
PharmaTimes
PharmaTimes is the UK’s leading pharmaceutical magazine, tracking the trends and issues that affect the industry here in the UK, in Europe and across the world. We deliver independent, authoritative and trustworthy content through impartial, balanced and informed opinion and analysis.