Case Studies

Practical examples of how AGC DATA has translated complex research requirements into reliable data systems.

MAC-Endo CTIMP Database and IMP Management

Client: Professor Andrew Horne, University of Edinburgh

Study: Phase II, single-centre, double-blind, placebo-controlled CTIMP evaluating three escalating doses of an investigational medicinal product for endometriosis-associated pain.

Key deliverables:

• REDCap clinical trial database

• CRISPNet IMP-management integration

• Pharmacy dispensing interface

• Secure emergency-unblinding workflow

• Automated data-quality monitoring

• IMP accountability and inventory tracking

The challenge

The study required a secure and traceable system that could:

• Conduct block randomisation while maintaining the study blind

• Link each allocation to a complete IMP accountability record

• Provide pharmacy teams with clear dispensing instructions

• Support secure emergency unblinding at any time

• Track IMP stock, expiry, dispensing, returns and destruction

• Integrate clinical, questionnaire, sample and pharmacy data

The solution

A study-specific REDCap architecture was developed before first-participant enrolment and integrated with CRISPNet to automate key IMP-management processes.

The system included:

Randomisation and allocation
CRISPNet managed block randomisation and recorded blinded treatment allocations within the relevant REDCap accountability records.

Pharmacy dispensing workflow
Role-restricted interfaces provided pharmacy staff with the information required to select and dispense the correct IMP pack.

Emergency unblinding
A secure, audit-logged workflow allowed authorised medical personnel to access treatment information where clinically necessary without exposing the allocation to the wider study team.

Inventory management
The system tracked IMP stock, expiry dates, dispensing, returns and destruction, with automated notifications when stock required attention.

Integrated study oversight
Clinical, IMP, sample and questionnaire information was managed across a connected REDCap framework, reducing reliance on separate spreadsheets and manual reconciliation.

The impact

The completed system brought participant enrolment, IMP management, sample tracking and questionnaire data into a connected research infrastructure.

Automated accountability records, stock monitoring and restricted unblinding workflows reduced repetitive administrative steps and strengthened traceability. The use of REDCap and a study-specific CRISPNet integration also provided a cost-effective alternative to a large commercial clinical trial platform.

Future-Proofing Reproductive Research Data

Client: Professor Hilary Critchley and Dr Jacqueline Maybin, University of Edinburgh

Scope: Six observational studies containing overlapping variables and shared biological samples.

Key deliverables:

• Harmonised REDCap data framework

• End-to-end biological sample-tracking system

• Shared variable structure

• Cross-study reporting tools

• User guidance and supporting documentation

The challenge

Six related studies were operating across separate databases, data dictionaries and supporting spreadsheets.

Differences in variable structures and sample-recording processes made it difficult to:

• Compare data across studies

• Locate available biological samples

• Review sample use and depletion

• Identify potentially duplicated assays

• Produce consistent portfolio-level reports

• Reuse data structures for future projects

The solution

A common set of core variables was identified and mapped across the studies. Modular extensions were then developed to accommodate project-specific requirements without losing consistency between databases.

Six linked REDCap projects were configured to feed into a shared reporting framework.

An end-to-end sample-management system was also developed to record each sample through:

• Collection

• Receipt

• Processing

• Aliquoting

• Storage

• Assay use

• Transfer

• Depletion

Supporting guidance and quick-start materials were provided to help laboratory and data teams adopt the new processes without disrupting active recruitment.

The impact

The studies now operate within a more consistent and expandable research-data framework.

Researchers can locate samples more efficiently, review their availability and use, identify potential duplicate activity and prepare cross-study datasets without rebuilding the underlying structure for every analysis.

The framework can also be extended as new studies, variables and sample collections are introduced.