Artificial intelligence is no longer discussed only as an emerging technology in drug development. It is increasingly becoming part of modern pharmaceutical development processes, including clinical trials, biomarker analysis, pharmacovigilance, manufacturing, and regulatory decision-making.

In January 2026, the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA) jointly published ten guiding principles for Good AI Practice in medicine development, marking one of the first coordinated regulatory positions on artificial intelligence across the pharmaceutical industry. Learn more about AI regulation in clinical trials.

The publication signals a significant shift in how regulators approach AI. The discussion is no longer focused on whether AI can be used in medicine development, but on how it should be governed, validated, monitored, documented, and integrated into regulated environments.

For sponsors, CROs, and clinical trial partners, the principles for good AI practice provide an early framework for future expectations regarding:

• AI governance,
• data quality and traceability,
• lifecycle monitoring,
• human oversight,
• validation,
• and risk-based management.

This article explains the regulatory background behind the EMA/FDA initiative and explores what each of the ten principles for good AI practice may mean in reality for clinical trials and pharmaceutical development.

The regulatory background: how EMA’s AI framework evolved

The publication of the joint EMA/FDA principles did not appear suddenly. It is part of a broader European regulatory roadmap focused on data and artificial intelligence.

Earlier, the Network Data Steering Group (NDSG), operating within the European medicines regulatory network (EMRN), published a multi-year workplan for 2026–2028 focused on data and AI. The roadmap outlined the development of future guidance, terminology standardization, and governance frameworks for AI within the pharmaceutical sector.

The roadmap includes several key milestones presented below.

At the same time, EMA continued building on its earlier Reflection Paper on the use of AI in the medicinal product lifecycle, moving from high-level discussion toward practical governance expectations.

This progression shows that regulators increasingly treat AI as part of regulated pharmaceutical infrastructure rather than an experimental innovation area.

The 10 EMA/FDA Principles for Good AI Practice

As part of broader initiative, EMA and the U.S. Food and Drug Administration (FDA) jointly published ten principles for Good AI Practice in medicine development in January 2026. The document establishes a shared regulatory direction for how AI technologies should be developed, validated, monitored, and governed across the medicines lifecycle, including clinical trials, manufacturing, and pharmacovigilance.

According to the regulators, the principles are intended to support future international collaboration, harmonization efforts, and development of additional AI-related guidance and standards for the pharmaceutical industry.

1. “Human-centric by design”

“The development and use of AI technologies align with ethical and human-centric values.”

The first principle emphasizes that AI should support people rather than replace human responsibility and oversight. In clinical research, this means AI systems should be developed with patient safety, ethical considerations, and scientific integrity at the center of the process.

For sponsors and CROs, human-centric AI may include:

• maintaining expert review of AI-generated outputs,
• ensuring clinical relevance of AI-supported decisions,
• and reducing the risk of overreliance on automation.

The principle also reflects growing regulatory expectations regarding accountability in highly automated environments.

2. “Risk-based approach”

“The development and use of AI technologies follow a risk-based approach with proportionate validation, risk mitigation, and oversight based on the context of use and determined model risk.”

EMA and FDA clearly indicate that not all AI systems carry the same level of regulatory or operational risk. For example:

• AI used for workflow optimization may require lighter oversight,
• while AI supporting patient selection, endpoint interpretation, or safety analysis may require significantly stronger controls and validation.

For CROs and pharmaceutical companies, this principle strongly aligns AI governance with existing GxP and quality management approaches.

3. “Adherence to standards”

“AI technologies adhere to relevant legal, ethical, technical, scientific, cybersecurity, and regulatory standards, including Good Practices (GxP).”

This principle confirms that AI systems are expected to operate within existing regulated frameworks rather than outside them. For clinical trials, this may include alignment with:

• GCP,
• GCLP,
• computerized system validation practices,
• cybersecurity requirements,
• and data integrity expectations.

The message from regulators is clear: AI does not reduce compliance expectations. In many cases, it may increase the importance of documentation and governance.

4. “Clear context of use”

“AI technologies have a well-defined context of use (role and scope for why it is being used).”

Organizations using AI should clearly define:

• what the technology is intended to do,
• where it will be used,
• what data it depends on,
• and what limitations exist.

This becomes particularly important in clinical trials involving: decentralized workflows, biomarker analysis, patient stratification, or AI-supported operational decisions. A clearly defined context of use helps reduce misuse of models outside validated environments.

5. “Multidisciplinary expertise”

“Multidisciplinary expertise covering both the AI technology and its context of use are integrated throughout the technology’s life cycle.”

EMA and FDA emphasize that AI governance cannot be managed only by data scientists or IT teams.

Effective oversight requires collaboration between:

• clinical experts,
• regulatory specialists,
• quality assurance teams,
• statisticians,
• laboratory experts,
• software engineers,
• and operational teams.

For CROs and sponsors, this may increase the importance of cross-functional governance models during AI implementation.

6. “Data governance and documentation”

“Data source provenance, processing steps, and analytical decisions are documented in a detailed, traceable, and verifiable manner, in line with GxP requirements. Appropriate governance, including privacy and protection for sensitive data, is maintained throughout the technology’s life cycle.”

This principle is highly relevant for clinical research environments where data quality and traceability are critical. Regulators increasingly expect organizations to demonstrate:

• where data originated,
• how it was processed,
• what transformations were applied,
• and how AI-related decisions were made.

For central laboratories and global clinical trials, this reinforces the importance of: harmonized datasets, standardized workflows, audit readiness, and strong governance frameworks. The principle also highlights privacy and protection requirements for sensitive data.

7. “Model design and development practices”

“The development of AI technologies follows best practices in model and system design and software engineering and leverages data that is fit-for-use, considering interpretability, explainability, and predictive performance. Good model and system development promotes transparency, reliability, generalisability, and robustness for AI technologies contributing to patient safety.”

This principle focuses on the technical quality of AI systems. EMA and FDA expect organizations to follow structured development approaches supporting:

• reliability,
• transparency,
• robustness,
• reproducibility,
• and patient safety.

For pharmaceutical companies and CROs, this may require stronger integration between: software engineering, validation processes, and regulated operational environments. The mention of explainability and interpretability is particularly important for AI systems used in decision-support contexts.

8. “Risk-based performance assessment”

“Risk-based performance assessments evaluate the complete system including human-AI interactions, using fit-for-use data and metrics appropriate for the intended context of use, supported by validation of predictive performance through appropriately designed testing and evaluation methods.”

The regulators stress that AI performance assessment should evaluate not only the model itself, but the entire operational environment in which it functions. This includes:

• human interaction with the system,
• real-world workflows,
• testing methodologies,
• and context-specific performance metrics.

For clinical trials, this may become especially important for: imaging analysis, predictive biomarkers, patient recruitment algorithms and safety signal detection. The principle reinforces that validation should reflect actual operational use rather than ideal laboratory conditions.

9. “Life cycle management”

“Risk-based quality management systems are implemented throughout the AI technologies’ life cycles, including to support capturing, assessing, and addressing issues. The AI technologies undergo scheduled monitoring and periodic re-evaluation to ensure adequate performance (e.g., to address data drift).”

EMA and FDA clearly indicate that AI governance continues after deployment.

Organizations are expected to:

• monitor system performance,
• detect model drift,
• assess emerging risks,
• and periodically re-evaluate models.

For long-term clinical programs and global studies, lifecycle management may become essential to maintaining reliability and regulatory trust over time. This principle strongly aligns AI oversight with traditional pharmaceutical quality management systems.

10. “Clear, essential information”

“Plain language is used to present clear, accessible, and contextually relevant information to the intended audience, including users and patients, regarding the AI technology’s context of use, performance, limitations, underlying data, updates, and interpretability or explainability.”

The final principle focuses on communication and transparency.

Organizations should provide understandable information regarding:

• the AI system’s intended use,
• limitations,
• performance,
• underlying data,
• updates,
• and explainability.

Importantly, regulators emphasize that communication should be adapted to the intended audience, including users and patients. For sponsors and CROs, this may influence: documentation practices, training materials, vendor communication, regulatory submissions and audit preparation.

What these principles mean for clinical trials

The joint EMA/FDA publication suggests that regulators are preparing for broader AI integration across clinical development. Importantly, the document does not attempt to restrict innovation. Instead, it aims to ensure that AI systems operate within controlled, auditable, and scientifically reliable environments.

For clinical trials, the practical implications may include:

• greater focus on AI documentation,
• stronger expectations for data quality and traceability,
• integration of AI governance into quality systems,
• increased validation requirements,
• and closer collaboration between operational, technical, and regulatory teams.

The principles also reinforce the growing importance of: standardized data environments, harmonized laboratory processes, and robust lifecycle management. These areas are already highly relevant in modern multi-country studies and may become even more critical as AI-supported workflows continue expanding.

Conclusion

The publication of the joint EMA/FDA AI principles represents an important regulatory milestone for the pharmaceutical industry. For the first time, regulators on both sides of the Atlantic presented a coordinated vision of how AI should function within medicine development.

The key message is clear: AI is no longer treated as an experimental technology operating outside regulatory frameworks.

Instead, regulators increasingly expect AI systems to function within structured governance models focused on:

• transparency,
• oversight,
• validation,
• traceability,
• risk management,
• and accountability.

For sponsors, CROs, and clinical trial partners, this signals the beginning of a new phase where successful AI adoption will depend not only on technological capability, but also on operational readiness and regulatory alignment.

Learn more about AI in clinical trials:

• AI regulation in Clinical Trials
• AI Tools for Clinical Trials
• The use of AI in clinical trials

FAQ – Principles for Good AI Practice

References

1. EMA and FDA set common principles for AI in medicine development, EMA, access date: 15.05.2026
2. Guiding principles of good AI practice in drug development, EMA, FDA, access date: 15.05.2026
3. Guiding Principles of Good AI Practice in Drug Development, FDA, access date: 15.05.2026
4. Artificial intelligence, EMA, access date: 15.05.2026

The event is organized by a student scientific group operating at Medical University of Gdańsk, in collaboration with University Clinical Center in Gdańsk. It gathers students and young professionals interested in understanding how clinical trials work in practice.

Bridging academic knowledge and real-world experience

Clinical research requires more than theoretical understanding. It depends on the ability to connect scientific knowledge with operational, regulatory, and clinical realities.

Initiatives such as Clinical Trials Day help address this gap by:

• introducing practical aspects of clinical trials
• providing exposure to real-world processes and challenges
• creating opportunities to connect with experienced professionals

Supporting the next generation in clinical research

Engagement from students and early-career professionals plays an important role in shaping the future of clinical research.

Events like this create space for knowledge exchange between academic environments and clinical practice, helping to build future expertise and strengthen the clinical research ecosystem.

We are glad to contribute to initiatives that support education and practical understanding of clinical trials at the local level.

In clinical trials, laboratory strategy is not only an operational choice. It directly affects data consistency, interpretation and regulatory acceptance. 

Central Lab vs Local Laboratories? In the local laboratory model, each site uses its own laboratory or a nearby provider. This allows for fast turnaround and immediate access to results but introduces variability. Across one study, different laboratories may use different analytical methods, equipment and reference ranges, meaning that the same parameter can be measured differently depending on the location. 

This lack of standardization becomes a challenge when data needs to be compared across sites or used for statistical analysis. Additional effort is then required to align results, interpret discrepancies, and ensure consistency. 

The central laboratory model addresses this by standardizing testing conditions across the study. Instead of variability being managed later, it is reduced at the source. Read more about Data Harmonization. 

When to use each model in practice? 

The choice between central and local laboratories depends on what matters most in a given study: consistency or turnaround time. 

Central laboratories are the preferred option when data comparability is critical. This applies in multicountry trials, studies with complex biomarkers or endpoints requiring high analytical precision, or custom analytical approach. For example, in oncology trials, centralized biomarker testing is used to ensure that patient stratification is based on consistent and comparable results across all sites. 

Local laboratories are more suitable when immediate access to results is required. This includes safety testing, routine assessments or situations where clinical decisions must be made without delay. For example, a patient’s blood count (e.g. white blood cells or platelets) is typically assessed locally to determine whether treatment can be administered on the same day. 

In practice, most studies use a combination of both models, often referred to as a hybrid model, where central laboratories handle key endpoints and standardized analyses, while local laboratories support time-critical assessments. 

From a cost and operational perspective, the choice often comes down to a trade-off between logistics and data processing effort. Local laboratories may reduce logistics costs but increase the workload for statisticians and programmers who need to align and interpret data from multiple sources. Central laboratories shift this effort upfront into logistics and coordination, but significantly reduce downstream complexity. 

Key Takeaways: Central Lab vs Local Laboratories

Choosing central lab vs local laboratories is not about selecting a better model. It is about aligning the laboratory strategy with study requirements. 

Local laboratories support speed and immediate decision-making at site level. Central laboratories ensure consistency, data quality and comparability across the study. Using local laboratories where standardization is required increases variability and shifts complexity to the data analysis stage, often requiring additional work to harmonize results. 

A central laboratory standardizes processes from the beginning, simplifies data handling, and reduces operational complexity. It also reduces the need for additional coordination on the sponsor side, as one partner manages the full laboratory scope. 

While central laboratory models may involve higher logistics costs upfront, they are often more cost-effective in the long term due to reduced effort in data cleaning, harmonization and analysis. This also limits the need for additional internal resources to coordinate multiple laboratory providers. 

In most clinical trials, the optimal approach is a structured combination of both models, clearly defining where each should be used.

FAQ: central lab vs local laboratories

References (supporting)

1. Central vs Local Labs – Clinical Research Made Simple, clinicalstudies.in, access date 24.04.2026
2. Conducting Clinical Trials With Decentralized Elements | FDA, FDA, access date: 23.04.2026
3. Choosing Different Labs Based on the Therapeutic Area of a Study | Applied Clinical Trials Online, Applied Clinical Trials, access date: 23.04.2026

The Swiss Biotech Day held from May 4-5, has grown into one of the leading global biotechnology conferences, bringing together life sciences professionals from across the world. Each year, it continues to expand in both scale and international reach, attracting thousands of participants and fostering meaningful collaboration across borders.

The event provides a platform to exchange ideas, explore the latest advances in R&D, manufacturing, data management, AI, and innovative financing, while building valuable partnerships. With its Global Village, it also strengthens connections between international delegations and the Swiss biotech ecosystem, supporting cross-border cooperation and long-term collaboration.

Meet Our Expert: Oana Radu, Medicover Integrated Clinical Services

Oana Radu will represent Medicover Integrated Clinical Services (MICS) at Swiss Biotech Day . With extensive experience in clinical trial operations, Oana can walk you through our broad capabilities – from advanced laboratory testing to end-to-end global central lab services and patient recruitment.

Schedule your meeting with Oana Radu today!

Clinical trials increasingly rely on multiple laboratories, regions, and testing models. While this supports patient access and operational flexibility, it also introduces variability in how data is generated. 

Differences in analytical methods, measurement units, and reference ranges mean that results collected across sites are often not directly comparable. When such inconsistencies remain unaddressed, they can affect how study data is interpreted, potentially influencing conclusions about efficacy, and overall study outcomes. 

Data harmonization addresses this challenge by ensuring that results generated under different conditions can still be interpreted as part of one coherent dataset.

What data harmonization means in practice?

In clinical trials, data harmonization focuses on aligning results generated using different methods so they can be meaningfully compared. It reflects the reality of modern studies, where variability cannot always be avoided and must be managed at the data level. 

In practice, this may include: 

• adjusting values to a common scale 
• converting units across laboratories 
• accounting for differences in reference ranges and assay performance 

A well-designed harmonization approach is defined early, based on scientific understanding of laboratory methods and supported by clear documentation. It ensures that data remains traceable, consistent, and suitable for regulatory review, while preserving the clinical meaning of original results.

When harmonization becomes necessary and why it matters? 

In theory, variability can be minimized by relying on a single central laboratory and uniform analytical methods. In practice, however, most studies require a more flexible approach. 

Local laboratories are often needed to support patient safety and fast turnaround times. Global trials span regions with different infrastructure, and certain assays may only be available in selected locations. As a result, hybrid or regional laboratory models are widely used. 

In these scenarios, variability between data sources becomes unavoidable. Without harmonization, this variability introduces noise into the dataset, making it more difficult to distinguish true clinical effects from methodological differences. This may lead to incorrect statistical conclusions and delays in study timelines or regulatory interactions. 

Harmonization ensures that observed differences in data reflect real biological effects rather than inconsistencies in measurement.

A practical example: how variability affects interpretation 

Consider a global clinical trial where liver enzymes such as ALT or AST are measured across multiple laboratories. 

Due to differences in analytical methods and reference ranges, results from one region may appear consistently higher than those from another. When these differences are not harmonized, they may be interpreted as a treatment-related safety signal rather than a methodological variation. 

In practice, this can lead to additional data analysis and challenges in interpreting results across regions. It may also raise questions during regulatory evaluation, particularly if consistency of data cannot be clearly demonstrated. 

This example illustrates how even well-controlled studies can generate misleading signals when variability between laboratories is not properly addressed.

How harmonization works and when to plan it?

Data harmonization is a structured process that involves laboratory experts, data managers, and biostatisticians working together throughout the study. 

During the study setup phase, laboratory methods and data structures are reviewed to identify potential sources of variability. Based on this assessment, a harmonization strategy is defined, including how results will be aligned and which reference standards will be used. 

As the study progresses, data is transformed according to predefined rules and continuously validated. A key requirement throughout this process is full traceability, ensuring that each adjusted value can be linked back to its original result. 

In practice, harmonization is not typically developed from scratch for each study. Given its complexity and resource requirements, it is usually based on pre-established frameworks, including aligned laboratory networks, validated methodologies, and predefined data mapping approaches. 

At the study level, the focus shifts from building harmonization to deciding how it should be applied. This includes selecting appropriate laboratory locations and identifying which parameters require harmonization based on study objectives and analytical needs. 

When harmonization is considered only after inconsistencies appear in the data, it often leads to reactive adjustments, which may introduce bias and create challenges during data interpretation and regulatory review. 

The role of systems and data visibility 

Effective harmonization depends not only on methodology, but also on access to consistent and traceable data. 

Systems supporting laboratory data management play an important role by enabling: 

• structured data mapping across laboratories 
• consistent data formats and controlled transformations 
• full traceability of original and adjusted values 

Access to real-time data and clear visibility of data lineage supports both harmonization and overall study oversight, particularly in complex, multi-laboratory setups. 

Conclusion 

As clinical trials continue to expand across regions and laboratory networks, variability in data becomes a natural consequence of operational flexibility. 

Laboratory data harmonization ensures that this variability does not compromise the integrity of study results. By aligning data from different sources into a consistent and interpretable dataset, it supports reliable analysis, informed decision-making, and smoother regulatory interactions. 

When planned early and executed with the right expertise, harmonization becomes an integral part of building high-quality clinical evidence. 

FAQ about data harmonization in clinical trials

References

1. Bioanalytical Method Validation Guidance for Industry, FDA, access date: march 2026
2. Guideline on Bioanalytical Method Validation, EMA, access date: march 2026
3. E6(R3) Good Clinical Practice Guideline, ICH, access date: march 2026

Artificial intelligence is already present in clinical trials. What is changing now is how AI regulation in clinical trials is evolving and how its use is being structured. Currently, AI supports data analysis, feasibility assessments, documentation and operational decision-making. The key shift is not the presence of AI, but how it is being treated.

Regulators are beginning to move from observing and discussing AI to actively structure how it should be used. The recent plan published by the European medicines regulatory network shows that AI is entering a regulated environment.  AI is no longer just a tool supporting clinical trials. It is becoming part of the regulated system. 

What EMA actually announced?

The Network Data Steering Group (NDSG), operating within the European medicines regulatory network (EMRN), published a multi-year workplan for 2026–2028 focused on data and artificial intelligence. 

Importantly, this is not a single guideline. It is a structured framework describing how AI will be gradually integrated into medicines regulation. The plan is built around three main areas: 

• Guidance, policy and product support 
  Focus on developing AI-related guidance across the medicines lifecycle, ensuring alignment with the AI Act and GDPR, and supporting regulatory decision-making processes. 
• Tools and innovation 
  Development and deployment of AI tools within the regulatory network, alongside the creation of a framework for their consistent and controlled use. 
• Collaboration and change management 
  Building AI literacy, training stakeholders, and creating a coordinated approach across European agencies and partners. 

Together, these elements show that regulators are not only defining rules, but also building the infrastructure required to use AI in a controlled way.

The timeline of AI regulation in clinical trials 

The workplan outlines a clear sequence of milestones rather than a single regulatory release. 

Publication of guiding principles for Good AI Practice. This confirms that the regulatory shift is already underway, not just planned. 

The key shift: AI must be auditable 

The most important signal from the plan is not the timeline itself, but the direction. AI is expected to operate within a controlled and compliant environment. This includes clear focus on risk management, data protection, ethical considerations, and emerging expectations around AI governance in clinical trials. 

This direction is already reflected in the Guiding Principles for Good AI Practice in Drug Development, published in January 2026, which emphasize transparency, governance and lifecycle oversight of AI systems.  In practice, this means that AI must be: 

• controlled  
• repeatable  
• traceable  
• documented  

This is a significant shift. AI is no longer treated as an optional support tool, but as part of regulated processes that may need to be justified, reviewed and audited.  AI is being treated like any other critical process in clinical trials. 

What this means in practice for sponsors and CROs 

For organisations involved in clinical trials, this shift introduces new expectations around AI compliance in clinical research and has direct operational implications. In practice, this means that organisations need to: 

• define how AI is used within processes (e.g. SOPs) to ensure consistent and compliant use 
• validate AI tools before they are implemented  
• establish clear data governance frameworks  
• document decisions supported by AI  
• prepare for audit scenarios involving AI use  

These expectations will increasingly apply across study setup, data handling and operational decision-making.  AI regulation in clinical trials will not replace other regulatory requirements. It will become part of them. 

Conclusion 

AI regulation in clinical trials is not a future concept. It is already part of daily operations. What is changing is the level of control and accountability expected around its use. Regulators are moving quickly to define how AI should be applied, and these expectations will continue to evolve over the coming years.  The question is no longer whether to use AI in clinical trials, but how to use it in a controlled and compliant way. 

Looking to apply AI in a regulated environment? Request a quote to explore how we can support your study.

FAQ about AI regulation in clinical trials

References

1. Network Data Steering Group Workplan 2026–2028 for Data and Artificial Intelligence in Medicines Regulation, European Medicines Agency (EMA), accessed March 2026.
2. Guiding Principles of Good AI Practice in Drug Development, European Medicines Agency (EMA), accessed March 2026.
3. Artificial Intelligence Act (EU AI Act), European Commission, accessed March 2026.

Laboratory-generated analytical data play a critical role in modern clinical trials. Safety assessments, biomarker quantification, immunogenicity testing, and pharmacodynamic analyses all depend on the accuracy and reliability of laboratory results. For sponsors, biotech companies, and CROs, the integrity of these data directly influences study outcomes, regulatory decisions, and the overall success of a drug development programs.

GCLP laboratory operates in accordance with Good Clinical Laboratory Practice (GCLP) principles – a globally recognized quality framework governing laboratories that analyze clinical trial samples. These standards ensure that laboratory activities are conducted under controlled conditions, with well-defined procedures, full traceability, and robust quality oversight.

By partnering with a GCLP laboratory, sponsors can be confident that laboratory-generated analytical data are scientifically robust, reproducible, and fully suitable to support regulatory submissions.

What Is a GCLP-Compliant Laboratory? 

GCLP laboratory is a laboratory that follows Good Clinical Laboratory Practice standards when analyzing samples collected during clinical trials.

GCPL integrates quality principles derived from both Good Clinical Practice (GCP) and Good Laboratory Practice (GLP), creating a dedicated framework for laboratories supporting clinical research.

The focus of GCLP is the proper handling, processing, and analysis of biological samples collected from study participants to ensure that every result generated can be traced, verified, and trusted.

For sponsors and CROs, working with GCLP-compliant laboratory means that the analytical component of their clinical studies is supported by a quality management system designed to deliver reliable and verifiable results.

GCLP vs GLP vs GCP – Understanding the Regulatory Landscape

Clinical research operates within several complementary regulatory frameworks, each addressing a different aspect of the development process.

Good Clinical Practice (GCP) governs the conduct of clinical trials involving human participants. Its primary  focus is patient safety, ethical standards, and the credibility of clinical data collected at investigational sites.

Good Laboratory Practice (GLP) applies primarily to non-clinical laboratory studies, including toxicology and safety testing performed before investigational therapies are evaluated in humans.

Good Clinical Laboratory Practice (GCLP) bridges these frameworks by establishing  quality standards for laboratories that analyze clinical trial samples. It ensures that laboratory-generated analytical data used in clinical research meet expectations for traceability, documentation, and analytical reliability.

For sponsors, this distinction is particularly important, as laboratory data frequently support key clinical endpoints and must withstand regulatory scrutiny during inspections and submission reviews.

Advantages of Using a GCLP Laboratory in Clinical Trials 

1. Regulatory Compliance 

GCLP-compliant laboratories operate under documented procedures, with validated analytical methods, and controlled workflows aligned with internationally recognized  standards. This structured approach ensures that laboratory testing is conducted in a controlled and auditable environment.

Data generated in a GCLP-compliant laboratory is more likely to meet the expectations of regulatory authorities such as the  FDA (Food and Drug Administration), EMA (European Medicines Agency), and MHRA (Medicines and Healthcare products Regulatory Agency). This reduces regulatory risk and allows laboratory results to be confidently included in clinical study reports and regulatory submissions.

2. Data Integrity and Reliability 

Reliable laboratory data are essential for evaluating the safety and efficacy of investigational therapies.

GCLP-compliant laboratories implement standardized operating procedures (SOPs) that define each step of the pre-analytical, analytical, and post-analytical processes, from sample receipt and preparation, to testing and data reporting.

Sponsor benefit from consistent, accurate, and reproducible analytical results that strengthen the credibility of clinical endpoints and support data-driven decision making during drug development.

3. Quality Assurance and Quality Control 

A key component of GCLP compliance is the presence of an independent Quality Assurance (QA) function responsible for monitoring laboratory activities and verifying adherence to established procedures.

Routine internal audits, equipment calibration, method validation, and proficiency testing programs help maintain high analytical standards and operational accuracy.

Sponsors gain additional confidence that laboratory operations are continuously monitored and that any deviations are properly documented, investigated, and addressed in accordance with quality standards.

4. Chain of Custody and Sample Traceability 

Clinical trial samples often travel between multiple locations – investigative sites, central laboratories and specialized testing facilities. GCLP-compliant laboratories maintain detailed documentation tracking each step in the lifecycle of each sample, including:

A GCLP laboratory maintains detailed documentation that tracks the entire lifecycle of each sample, including: 

• sample receipt and accessioning 
• storage conditions 
• preparation and analytical procedures 
• disposal.

For sponsors, complete sample traceability within a defined chain of custody ensures that every analytical result can be linked to the correspondent participant sample. This, in turn, supports data verification, facilities regulatory inspections, and strengthens overall study integrity.

Learn more about The Laboratory Sample Journey: From a well-done Setup to Tracking and Delivery.

5. Competent and Trained Laboratory Staff 

Laboratory quality depends not only on technology but also on the expertise of the personnel performing the analyses. GCLP laboratory requires structured training programs and ongoing competency assessments for laboratory personnel. Personnel must demonstrate proficiency in laboratory techniques, documentation practices, and regulatory requirements.

For sponsors, this ensures that laboratory testing is performed by trained professionals who understand both analytical techniques and the regulatory context associated with clinical research. 

6. Risk Mitigation in Clinical Trials 

Laboratory errors in clinical trials – such as sample mislabeling, contamination, or incorrect reporting – can have serious impact on participant safety and data integrity, potentially leading to protocol deviations, repeat testing, or delays in study timelines.

By adopting a risk‑based approach, GCLP laboratories actively monitor critical processes and mitigate potential non‑conformities at an early stage, reducing the likelihood of issues long before they can impact study quality.

Sponsors and CROs benefit from reduced operational risk and greater confidence in the reliability of laboratory data supporting their clinical programs.

7. Operational Efficiency and Study Readiness 

Laboratories following GCLP guidelines  deliver a consistently high-quality experience through clearly defined processes for study setup, sample management, and data reporting. This structured, quality-driven approach accelerates study start-up and ensures smooth, predictable execution across every phase of the project. With robust systems and disciplined workflows, a GCLP laboratory is often exceptionally well positioned to support global trials with complex study designs, and high-volume sample operations.

Sponsors can expect operational readiness which contributes to more efficient study management and improved timeline predictability.

8. Data Security and Confidentiality 

Clinical trials generate highly sensitive participant and research data that must be safeguarded with the utmost rigor.

GCLP-compliant laboratories implement apply robust data-governance policies, combining secure information systems, controlled access to laboratory databases, and detailed audit trails. This disciplined approach ensures strong protection of personal and study-critical information, while supporting compliance with international data-privacy regulations such as GDPR (General Data Protection Regulation – a European Union regulation governing personal data collection, processing, storage, and protection) and HIPAA (Health Insurance Portability and Accountability Act – a US law establishing standards sensitive patient health information protection).

Sponsors are provided with confidence that data integrity, confidentiality and regulatory obligations are upheld at every step.

9. Global Standardization in Multi-Center Trials 

Many modern clinical trials involve multiple countries, investigative sites, and laboratories. GCLP laboratory applies standardized methodologies and validated procedures to ensure consistent analytical performance across studies and locations.

Such standardization supports reliable data comparability across study sites, enabling data pooling and accurate statistical analysis.

Conclusion 

Reliable laboratory testing is cornerstone of successful clinical trials. Laboratory results underpin key endpoints, safety evaluations, and regulatory submissions, making data integrity essential.

By operating under rigorous quality standards, a GCLP laboratory ensures that laboratory data generated during clinical trials is accurate, traceable, and aligned with regulatory expectations.

For sponsors, biotech companies, and CROs, partnering with a GCLP laboratory enhances data credibility, minimizes regulatory risk, and supports the development of high-quality clinical evidence required for successful regulatory approval.

FAQ – GCLP Laboratory in Clinical Trials 

References 

1. World Health Organization, Good Clinical Laboratory Practice (GCLP), WHO, 2009, [access date: 06.03.2026]  
2. Joshi R.K., A Comparative Review of ICMR, WHO, and EMA Good Clinical Laboratory Practice Guidelines, Journal of Laboratory Physicians, 2022, [access date: 06.03.2026]  
3. European Medicines Agency, ICH E6 Good Clinical Practice – Scientific Guideline, EMA, [access date: 06.03.2026]  
4. Hyun S., Importance and Future Direction of Applying GCLP in Clinical Trial Sample Analysis, Korean Citation Index, 2024, [access date: 06.03.2026]  

Stefan brings more than 17 years of international experience in diagnostics, precision medicine, and life sciences. He has launched more than 10 products, several at global scale. Most recently, Stefan served as Global Director of Diagnostics & Precision Medicine at Novo Nordisk, leading strategic partnerships in liver and cardiovascular biomarker development and clinical diagnostics. His career spans leadership roles at AstraZeneca, Thermo Fisher Scientific and Nanostring Technologies, as well as founding two companies within the clinical and precision medicine space, giving him a unique perspective from both the pharmaceutical and service provider sides.

On joining Medicover Integrated Clinical Services, Stefan said:  “Medicover has a unique footprint making it possible to combine precision medicine, data, and scientific excellence with real world healthcare delivery at scale which is very appealing. I am very motivated to build value within the company by bringing precision medicine initiatives into clinical routine.“

This combination of entrepreneurial drive and corporate leadership will be instrumental in strengthening our precision medicine initiatives and advancing the MICS strategy to support pharmaceutical clinical trials from discovery through to commercialisation for our CRO and Pharma partners.

GCCL Co., Ltd. (GCCL), a data-driven clinical trial services provider, and Medicover Integrated Clinical Services (MICS), part of Medicover, an international healthcare and diagnostic services company, have signed a Memorandum of Understanding (MOU) to collaborate on multinational clinical trial services.

Through this MOU, the two companies aim to establish a collaborative framework covering Europe, the United States, and the Asia-Pacific (APAC) region. By leveraging their respective experience in clinical trial operations and client communications across key regions, the partnership seeks to enhance cross-regional communication efficiency and improve operational stability in global clinical trial projects.

Key areas of collaboration under the MOU include clinical trial operations and sample analysis collaboration across regions; project management support and coordinated client communication by region; joint marketing collaboration; joint client development and market expansion; and the enhancement of service competitiveness through test item alignment and improved analytical efficiency.

By combining MICS’s extensive experience and network in Europe and the U.S. with GCCL’s specialized analytical capabilities and project management expertise in the APAC region, the two companies expect to offer a more seamlessly connected and optimized environment for multinational clinical trial operations and analysis.

“This agreement represents an important opportunity to effectively combine the expertise and customer bases of both organizations and further strengthen the global clinical trial ecosystem,” said Łukasz Hubisz, General Manager of MICS. “Through our collaboration with GCCL, we aim to strengthen communication with clients in Asia and support the smoother execution of global clinical trial projects.”

Kwan Goo Cho, CEO of GCCL, added, “This partnership marks an important first step in reinforcing clinical collaboration between Europe and Asia. Through our strategic cooperation with MICS, we aim to set new standards for global clinical trial execution and deliver meaningful analytical outcomes for our clients.”

About GCCL

GCCL, a subsidiary of the GC Group, is a leading total solution provider for clinical trial sample analysis, offering an integrated “one-stop lab solution” with central, bioanalysis, and BSL-3 labs under a single system. With tailored solutions, GCCL delivers precise and efficient analytical services across all phases of clinical trials, solidifying its position as a trusted partner in new drug development. Leveraging advanced platforms and LIMS, the company supports partners across Asia and beyond with customized and compliant solutions. In recognition of its leadership, GCCL recently received Frost & Sullivan’s 2025 Best Practices Customer Value Leadership Award in the Asia-Pacific clinical sample analysis services industry. For more details, visit: www.eng.gccl.co.kr.

About Medicover Integrated Clinical Services (MICS)

Medicover Integrated Clinical Services (MICS) is a strategic business organization of Medicover Diagnostic Services that supports the development and commercialization of innovative therapies and medicines. MICS focuses on clinical applications and clients from pharmaceuticals, diagnostics, biopharma, biotech, medtech and contract research organizations (CROs). MICS’ activities are organized along discrete service lines: Central Laboratory Services, Site Management Organization, Precision Medicine. MICS has been providing bespoke services to clients and their patients for over 20 years, making strategic use of Medicover assets, including laboratories with state-of-the-art diagnostic equipment, medical clinics and hospitals, as well as the vast expertise and enthusiasm of a global workforce. For more details, visit: Medicover MICS.

Study setup sits at the intersection of several decision layers: protocol readiness on the Sponsor side, operational coordination within the CRO, and technical and logistical preparation on the Central Lab side. Each of these areas follows different internal processes, approval paths, and constraints. This is why defining an exact number of days without context is rarely meaningful. 

What can be defined is a structured setup model that has been implemented repeatedly in clinical trial projects — one that clarifies responsibilities, dependencies, and realistic timeframes. This article outlines a study setup model we present at Medicover Integrated Clinical Services as a working framework for our partners, explains how long each phase typically takes, and shows where timeline discussions are realistic and where they are not. 

What is meant by a Study Setup Timeline? 
The study setup timeline covers all activities required to move from an initial request to an operationally ready study. It ends when the study can formally start — with teams assigned, documentation finalized, and systems prepared for use.  Although no patients are involved at this stage, decisions made during setup directly influence execution efficiency, data readiness, and the stability of the trial once it is live. 

A Proven Study Setup Timeline Model 

Rather than describing a generic or theoretical industry standard, the timeline below reflects the study setup model we use at Medicover Integrated Clinical Services in clinical trial collaborations with Sponsors and CROs. It has been applied across different study types and is designed to balance speed with control. 

RFP and CDA 

Typical duration: up to 5 working days for standard tests(as safety profile) and up to 10 working days  for more complex and specialistic tests.

The setup process usually begins with two parallel elements. A CDA (Confidentiality Disclosure Agreement) enables the secure exchange of protocol and study information. At the same time, the RFP (Request for Proposal) defines the initial scope, assumptions, and expectations regarding services, timelines, and responsibilities. 

This phase is often underestimated. The level of detail provided in the RFP has a direct impact on feasibility assessment and on how efficiently the next steps can proceed. 

Offer and MSA (Proposal) 

Typical duration: 1–2 working days once the offer is approved, with continuous contact during clarification. 

Following the RFP review, an offer is prepared together with an MSA (Master Services Agreement) proposal. This stage establishes the legal and commercial framework for cooperation and confirms the general conditions under which the study will be executed. 

While documentation is essential, timeline pressure at this stage usually comes from internal decision-making rather than document preparation itself. 

Task Order 

Typical duration: 1–2 working days, with constant contact once the offer is approved. 

The Task Order converts the general agreement into a study-specific operational plan. At this stage, we work closely with the Sponsor or CRO to translate the approved offer into concrete activities, responsibilities, and timelines. This includes confirming the exact scope of work and agreeing on deliverables such as reports, datasets, laboratory outputs, system access, or required study documentation. 

Because the main assumptions are already aligned earlier in the process, this step focuses on precision rather than redefinition. A clearly defined Task Order removes ambiguity before execution begins and provides a shared operational reference for all parties involved. 

Team Assignment 

Once the Task Order is confirmed, dedicated teams are assigned to the study. This includes project management, operational, laboratory, and support functions. 

Team assignment ensures clear ownership, defined communication pathways, and accountability from the very beginning of the study lifecycle. It also enables parallel preparation across different workstreams. 

Kick-Off Meeting 

The kick-off meeting formally aligns all stakeholders involved in the study. During this meeting, scope and timelines are confirmed, communication and reporting rules are agreed, and escalation pathways are defined. 

This moment marks the transition from planning to execution readiness and sets the tone for cooperation during the study. 

Learn more about the Preparation Phase at Central Lab.

Study Start and Overall Setup Duration 

From Task Order through team assignment and kick-off, the full study setup phase typically takes approximately 2–3 months. 

This timeframe reflects a controlled and realistic approach to setup — one that allows proper preparation without compressing steps that are essential for quality, compliance, and operational stability. This includes project-specific team training, system configuration, preparation of kits and materials, as well as the development of study documentation and operational instructions. These activities are essential for delivering the study in line with GCP, CLP, and applicable regulatory requirements.

Learn more about the Study Setup Phase at Central Lab.

What Happens After Study Start? 

Once the study is live, focus shifts to execution for the full duration of the clinical trial, in line with the protocol and study milestones. During this phase, structured communication and reporting are maintained, quality oversight is continuous, and cooperation between all parties remains active throughout. 

As the study approaches completion, activities move into database close and study closeout. These steps typically take place at the end of the study or in its final phase, depending on protocol design and data flow. In parallel, study-specific materials and outputs are finalized, including laboratory manuals, study collection sets, analytical plans, and system access documentation. 

Learn more about the Execution phase at Central Lab.

Where is there space for time negotiation? 

Timeline discussions are meaningful when they focus on how work is organized rather than whether key steps can be skipped. 

Time optimization is most realistic when requirements are clearly defined early, decision-makers are involved from the start, and parallel activities are enabled. Short communication paths and early alignment often make a measurable difference. 

What cannot be safely compressed are contractual integrity, proper team onboarding, quality controls, and data integrity safeguards. Accelerating these areas may shorten setup on paper but often creates delays and rework later. 

Closing Perspective 

A study setup timeline is not about moving as fast as possible. It is about creating a stable foundation for execution, predictable collaboration, and reliable data. 

When setup is treated as a strategic phase rather than an administrative formality, timelines become clearer, expectations more realistic, and studies easier to run once they go live. 

If you’re planning a new study or preparing an RFP, align timelines early.

References

1. ICH E6(R3) Guideline for Good Clinical Practice (GCP), European Medicines Agency, access date: 05.02.2026
2. Documentation in Clinical Trials, Badania Kliniczne, access date: 05.02.20264
3. RFP in Clinical Trials: A Guide to CRO Vendor Selection, IntuitionLavbs, access date: 05.02.2026

FAQ about study setup timeline