GVP Module VIII: PASS Protocols, Objectives and Study Design

Introduction

A PASS protocol is the bridge between a pharmacovigilance question and the evidence that the study is intended to generate.

The protocol should therefore do more than describe operational activities. It should make clear why the study is being conducted, what question it will answer, how the population and variables will be defined, how data will be obtained and analysed, and what limitations may affect interpretation.

GVP Module VIII requires the research question and objectives to be defined and describes the core methodological elements expected in a non-interventional PASS protocol. EMA's dedicated protocol guidance provides a more detailed structure for these elements.

1. Start With the Pharmacovigilance Question

The study should begin with the safety issue that generated the need for additional evidence.

Examples include:

• characterising a suspected risk;
• quantifying the frequency of an outcome;
• estimating relative risk;
• identifying risk factors;
• characterising a vulnerable population;
• confirming a safety profile;
• or evaluating the effectiveness of a risk-minimisation measure.

The question should be sufficiently specific to determine what evidence is required.

A vague objective such as "assess safety" is rarely adequate for study design.

2. Research Question Versus Objective

The research question states what the study is trying to learn.

The objective translates that question into an operational scientific purpose.

For example:

Research question: Is exposure to the medicinal product associated with an increased risk of outcome X?

A corresponding primary objective might specify estimation of the relative risk of outcome X among exposed patients compared with an appropriate comparator population during a defined period.

The objective should be precise enough that an assessor can determine whether the proposed methods can actually answer it.

3. Primary and Secondary Objectives

A protocol should distinguish the primary objective from secondary objectives.

The primary objective should correspond to the principal question that drives the study.

Secondary objectives may address additional clinically relevant questions, such as:

• subgroup risks;
• risk factors;
• duration of exposure;
• dose-response relationships;
• clinical outcomes;
• or additional safety endpoints.

Too many nominally "primary" objectives can make the study's central purpose unclear.

4. Hypotheses and Summary Measures

Where appropriate, the protocol should specify pre-defined hypotheses and the main measures that will be used to evaluate them.

Depending on the question, these may include:

• incidence rates;
• cumulative incidence;
• risk ratios;
• rate ratios;
• hazard ratios;
• odds ratios;
• prevalence measures;
• or measures of effectiveness.

The choice of measure should follow from the study design and research question rather than being selected after examining the data.

5. Define the Study Design Before Collecting Data

The protocol should explain the overall research design and the rationale for choosing it.

Possible non-interventional designs include, depending on the question:

• cohort studies;
• case-control studies;
• case-only designs;
• cross-sectional studies;
• registry-based studies;
• database studies;
• systematic reviews;
• meta-analyses;
• or other epidemiological designs.

There is no universally superior design. The appropriate design is the one that can answer the defined question with acceptable validity and feasibility.

6. Non-Interventional Does Not Mean Methodologically Simple

A non-interventional PASS does not involve assigning treatment through a study protocol, but it can involve sophisticated epidemiological methods.

A non-interventional study can therefore require substantial expertise in:

• epidemiology;
• statistics;
• data management;
• clinical outcome definition;
• bias assessment;
• and causal inference.

7. Study Setting

The protocol should define the setting in terms of relevant persons, places and time periods.

The setting can influence:

• prescribing practice;
• healthcare utilisation;
• outcome ascertainment;
• coding systems;
• data completeness;
• and generalisability.

A multinational study should explain how differences between participating countries will be handled.

8. Define the Study Population

The study population should be defined using explicit inclusion and exclusion criteria.

The protocol should explain:

• who is eligible;
• when eligibility is assessed;
• how patients enter the study;
• when follow-up begins;
• when follow-up ends;
• and why exclusions are necessary.

Poor population definition can create selection bias and make the study difficult to reproduce.

9. Source Population and Sampling

Where participants are sampled from a larger source population, the protocol should describe that source population and the sampling approach.

This is particularly important when the study uses:

• healthcare databases;
• registries;
• disease cohorts;
• claims data;
• electronic health records;
• or other routinely collected data.

The relationship between the source population and the study population should be clear enough to understand what population the results actually represent.

10. Exposure Definition

Exposure should be defined operationally.

Depending on the question, this may involve:

• product identity;
• active substance;
• dose;
• route;
• treatment episode;
• duration;
• treatment initiation;
• switching;
• discontinuation;
• or cumulative exposure.

A database code is not automatically equivalent to clinically meaningful exposure.

The protocol should explain how exposure is identified and what assumptions are made.

11. Outcome Definition

The outcome should be defined with comparable precision.

The protocol should specify, where relevant:

• clinical definition;
• diagnostic criteria;
• coding algorithms;
• validation methods;
• severity;
• timing;
• and rules for recurrent events.

If an outcome is identified through administrative coding, the validity of the coding should be considered.

12. Confounders and Effect Modifiers

Important potential confounders and effect modifiers should be identified in advance.

Potential variables may include:

• age;
• sex;
• disease severity;
• comorbidities;
• concomitant medication;
• healthcare utilisation;
• prior treatment;
• or other clinically relevant factors.

The protocol should explain how these variables will be measured and incorporated into the analysis.

13. Data Sources

The protocol should identify the sources used to determine:

• exposure;
• outcomes;
• confounders;
• effect modifiers;
• and other variables relevant to the objectives.

For existing data sources, their suitability and relevant validity should be considered.

14. Data Source Fitness

The fact that a large database exists does not establish that it is suitable for the PASS.

The organisation should consider:

• population coverage;
• completeness;
• coding quality;
• exposure ascertainment;
• outcome ascertainment;
• linkage quality;
• latency;
• missingness;
• and historical continuity.

A smaller but well-characterised source may be more useful than a very large source with poorly understood limitations.

15. Study Size and Precision

The protocol should explain the projected study size and, where applicable, the precision sought for the estimates.

A sample-size calculation may be appropriate where the study is intended to detect or estimate a pre-specified effect with defined statistical precision.

The calculation should reflect the actual study design and assumptions rather than being added as a generic statistical section.

16. Data Management

Data-management procedures should be described sufficiently to understand how raw information becomes an analysis dataset.

This can include:

• data extraction;
• transformation;
• coding;
• validation;
• quality checks;
• linkage;
• derived variables;
• query management;
• and database freezes.

The organisation should preserve traceability between source data and analysed data where appropriate.

17. Statistical Analysis Plan

The protocol should describe the major analytical steps from raw data to final results.

This includes, as applicable:

• primary analysis;
• secondary analysis;
• subgroup analysis;
• adjustment methods;
• handling of missing data;
• sensitivity analyses;
• bias-control methods;
• and statistical uncertainty.

18. Primary Analysis Must Be Identifiable

The primary analysis should be clearly distinguishable from exploratory or secondary analyses.

This is important because a large study can generate many statistically interesting observations. Without a pre-defined primary analysis, there is a greater risk that the final interpretation becomes driven by results discovered after analysis.

The protocol should therefore make the principal analytical path explicit.

19. Sources of Bias

A good PASS protocol anticipates bias before the study begins.

Relevant sources may include:

• selection bias;
• information bias;
• exposure misclassification;
• outcome misclassification;
• confounding;
• immortal-time bias;
• time-window bias;
• and loss to follow-up.

The protocol should explain how important sources of bias will be minimised or evaluated.

20. Sensitivity Analyses

Sensitivity analyses can test how robust the principal conclusion is to plausible alternative assumptions.

Examples include changing:

• outcome definitions;
• exposure windows;
• latency periods;
• missing-data assumptions;
• confounder definitions;
• or analytical methods.

Sensitivity analysis should be planned around meaningful uncertainties rather than used simply to generate additional tables.

Key Takeaways

A PASS protocol should convert a defined pharmacovigilance question into a scientifically coherent and operationally feasible evidence-generation plan.

The essential chain is:

question → objectives → population → exposure/outcome → data source → design → analysis → interpretation.

The protocol should anticipate important bias, define the primary analysis and explain data limitations.

References

1. European Medicines Agency. GVP Module VIII — Post-authorisation safety studies (Rev. 3).
2. European Medicines Agency. Guidance for the format and content of the protocol of non-interventional post-authorisation safety studies.
3. European Medicines Agency. Post-authorisation safety studies (PASS), including current procedural guidance.
4. Directive 2001/83/EC, as amended.
5. Regulation (EC) No 726/2004, as amended.
6. Commission Implementing Regulation (EU) No 520/2012, as amended.

Regulatory Note

This article is an educational explanation of PASS protocol development. It does not replace current EU legislation, GVP Module VIII, EMA procedural guidance, applicable national requirements or an organisation's approved procedures.

21. Protocol Structure and Regulatory Traceability

A protocol should allow a reviewer to move logically from the reason for the study to the proposed evidence generation. The core chain is:

Safety concern
↓
Knowledge gap
↓
Research question
↓
Objectives
↓
Population and setting
↓
Exposure and outcomes
↓
Data sources
↓
Study design
↓
Analysis
↓
Interpretation

For an imposed non-interventional PASS, the protocol also needs to remain traceable to the regulatory obligation, applicable milestone and relevant RMP documentation.

22. Protocol Version Control

The protocol should have a controlled version identifier and date. Reviewers should be able to determine which version was assessed, approved or implemented.

This becomes particularly important when a study undergoes regulatory review or substantial amendment.

23. Protocol Amendments

A protocol amendment should not silently change the scientific question.

Each substantive change should be assessed for its effect on:

• the research question;
• objectives;
• study population;
• study validity;
• statistical power;
• interpretation;
• participant welfare where relevant;
• and regulatory commitments.

For imposed non-interventional PASS, EMA's current procedural guidance identifies changes to objectives, population, sample size, design, data sources, collection methods, exposure/outcome/confounder definitions and the statistical analysis plan as examples of potentially substantial changes. Such amendments are subject to the applicable regulatory procedure before implementation.

24. Feasibility Before Commitment

A scientifically attractive protocol can still fail if the study cannot be executed.

Before commitment, the organisation should test:

• population availability;
• data availability;
• outcome ascertainment;
• data linkage;
• sample-size feasibility;
• country participation;
• study duration;
• and milestone feasibility.

Feasibility assessment is part of scientific quality, not merely project management.

25. Protocol and RMP Relationship

Where a PASS is included in an RMP, the protocol should remain consistent with the RMP's stated safety concern and pharmacovigilance objective.

A useful relationship is:

Safety concern
      ↓
Knowledge gap
      ↓
RMP pharmacovigilance activity
      ↓
PASS research question
      ↓
Study objectives
      ↓
Study design
      ↓
Study result
      ↓
Updated safety understanding

A disconnect between these layers should be explainable.

26. Protocol and Risk-Minimisation Effectiveness

Where the PASS evaluates risk-minimisation effectiveness, the protocol should distinguish implementation from effectiveness.

For example, these are different questions:

1. Were the materials distributed?
2. Did they reach the intended population?
3. Were they understood?
4. Did behaviour change?
5. Did the targeted clinical risk change?

The study should define the endpoint that corresponds to the regulatory or pharmacovigilance question.

27. Existing Data Versus New Data Collection

A PASS does not necessarily require prospective collection of all information.

Existing healthcare databases, registries and other routinely collected data may be suitable if they can answer the question with acceptable validity.

The protocol should explain why the selected source is fit for purpose and identify important limitations.

Creating a new data-collection system merely because it is convenient is not a scientific justification.

28. Registry-Based PASS

A registry can be the data source for a PASS, but a registry and a PASS are not synonymous.

A registry is a data-collection structure. A PASS is a study conducted for defined safety purposes.

The protocol should therefore make clear:

• whether the registry already exists;
• what information it captures;
• how participants enter it;
• which variables are used for the PASS;
• and how registry limitations affect interpretation.

29. Multinational Studies

Multinational PASS can increase population size and generalisability, but country differences can introduce methodological complexity.

The protocol should consider differences in:

• healthcare systems;
• coding;
• prescribing;
• data availability;
• outcome ascertainment;
• and regulatory requirements.

Country-specific adaptations should not undermine the comparability needed to answer the principal research question.

30. Data Quality and Validation

Data quality should be considered at the level of each critical variable.

The protocol should address, where relevant:

• completeness;
• accuracy;
• coding validity;
• missingness;
• consistency;
• linkage quality;
• and validation of important outcomes.

A large sample cannot compensate for systematic misclassification of the exposure or outcome.

31. Missing Data

Missing information can affect both validity and precision.

The protocol should identify important variables for which missingness may occur and define an appropriate analytical approach.

The handling of missing data should not be invented after the results are known unless the protocol explicitly permits justified adaptations.

32. Statistical Analysis and Bias Control

The analytical strategy should reflect the study design and anticipated biases.

The protocol may need to specify:

• adjustment methods;
• matching;
• stratification;
• propensity-score methods;
• time-to-event methods;
• sensitivity analyses;
• negative or positive controls where scientifically appropriate;
• and methods for assessing residual confounding.

The purpose is not to maximise statistical complexity. It is to obtain an interpretable answer to the research question.

33. Protocol Deviations

A protocol deviation should be documented and assessed for its effect on study validity.

Important deviations may include:

• failure to recruit the defined population;
• changes in outcome definitions;
• unavailable data sources;
• changes in analytical methods;
• or missed milestones.

The final study report should allow the reader to distinguish what was planned from what was actually performed.

34. Regulatory Review of Imposed PASS Protocols

For imposed non-interventional PASS, PRAC assesses the protocol under the applicable EU procedure, except where the legal framework provides for national assessment.

The current EMA procedural guidance states that draft protocols are submitted before the study is conducted and that substantial amendments are submitted before implementation.

The protocol therefore needs to be sufficiently complete for both scientific and regulatory assessment.

35. PASS Protocol and HMA-EMA Catalogue

Current EMA transparency requirements use the HMA-EMA Catalogue of real-world data studies, which replaced the EU PAS Register as the electronic post-authorisation study register.

For imposed non-interventional PASS covered by the applicable legal provisions, the protocol and subsequent study information are subject to registration and publication requirements.

The organisation should therefore ensure that study identifiers and protocol versions remain consistent across regulatory and transparency records.

36. Practical Scenario: An Objective Is Too Broad

Suppose a protocol states:

"To evaluate the safety of the medicinal product in routine clinical practice."

This does not establish what uncertainty the study is intended to resolve.

A better protocol would specify the safety outcome, population, exposure and analytical objective that correspond to the identified knowledge gap.

37. Practical Scenario: The Data Source Cannot Identify the Outcome

Suppose a proposed database contains prescription information but does not reliably capture the clinical outcome of interest.

The database may be large, but it is not necessarily fit for the proposed question.

The organisation should either identify a suitable validation or linkage strategy, modify the research question, select another source or reconsider whether the proposed study can answer the question.

38. Practical Scenario: A Protocol Amendment Changes the Primary Endpoint

Changing the primary endpoint can materially affect study interpretation.

The organisation should assess whether the amendment is substantial, why the change is necessary, whether regulatory review is required and how the change affects the original research question.

It should not simply replace the endpoint in the working protocol without preserving the prior version and rationale.

39. Practical Scenario: The Study Is Feasible but Underpowered

A study may be operationally feasible but unable to generate sufficiently precise evidence.

The organisation should assess whether the study size can be increased, whether the question can be refined, whether another data source is appropriate or whether the expected evidence remains useful despite limited precision.

The limitation should be recognised before interpreting the final result.

40. Practical Scenario: Country-Specific Protocol Variants

A multinational imposed PASS may require regional or national adaptations because of local legal or data requirements.

The core protocol should remain coherent while necessary national variants are controlled and traceable.

EMA's current procedural guidance specifically provides for regional appendices where national variants are necessary for implementation under national law.

41. Inspection Perspective

An inspector may ask:

• What question was the PASS intended to answer?
• Why was this design selected?
• Why was this population chosen?
• How were exposure and outcome defined?
• Why was this data source considered valid?
• What biases were anticipated?
• What changed after protocol approval?
• Which amendments were considered substantial?
• How were deviations assessed?
• How does the study relate to the RMP?

A strong organisation can answer these questions using controlled evidence rather than relying on individual recollection.

Key Takeaways

A good protocol is a controlled scientific argument: it explains why the study exists, what it will measure, how it will measure it and why the resulting evidence should be interpretable.

Protocol governance is especially important for imposed PASS because scientific design, regulatory milestones, amendments and transparency obligations are interconnected.

References

1. European Medicines Agency. GVP Module VIII — Post-authorisation safety studies (Rev. 3).
2. European Medicines Agency. Guidance for the format and content of the protocol of non-interventional post-authorisation safety studies.
3. European Medicines Agency. Post-authorisation safety studies (PASS) and current procedural Q&A.
4. European Medicines Agency. HMA-EMA Catalogues of real-world data sources and studies.
5. Directive 2001/83/EC, as amended.
6. Regulation (EC) No 726/2004, as amended.
7. Commission Implementing Regulation (EU) No 520/2012, as amended.

Regulatory Note

This article is an educational explanation of PASS protocol development. It does not replace current legislation, GVP Module VIII, EMA procedural guidance or approved organisational procedures.

42. What Makes a PASS Protocol Inspection-Ready

An inspection-ready protocol is not simply a document with all expected headings. It should provide a defensible chain between the pharmacovigilance question, scientific design and regulatory purpose.

The organisation should be able to demonstrate:

• why the study was initiated;
• what question it addresses;
• why the selected design is appropriate;
• how critical variables are defined;
• why the data source is suitable;
• how important biases are addressed;
• what changes occurred during the study;
• and how the final analysis remains connected to the approved protocol.

43. Protocol Governance

Responsibility for protocol development should be clear.

Depending on the organisation and study, relevant functions may include:

• pharmacovigilance;
• epidemiology;
• statistics;
• clinical expertise;
• regulatory affairs;
• data management;
• quality assurance;
• and external study partners.

The governance model should make scientific accountability clear and should prevent important methodological decisions from being made without appropriate review.

44. Vendor-Prepared Protocols

An external epidemiology or research organisation may draft a protocol, but outsourcing does not remove the MAH's responsibility for the study.

The MAH should retain evidence of:

• vendor qualification;
• agreed study requirements;
• scientific review;
• regulatory review;
• change control;
• and approval.

A vendor's methodological expertise should strengthen the study, not create an opaque decision process.

45. Joint PASS

Where multiple MAHs participate in a study, governance should establish how responsibilities are divided.

The protocol should remain clear about:

• products and substances covered;
• study ownership;
• data responsibilities;
• analysis responsibilities;
• regulatory communication;
• and final reporting.

The organisation should be able to demonstrate that the joint arrangement does not create gaps in accountability.

46. Study Design Should Match the Causal Question

A study may describe an association without establishing whether the medicinal product caused the outcome.

The protocol should therefore distinguish descriptive objectives from comparative or causal objectives.

For example, estimating the frequency of an outcome and estimating the relative risk associated with exposure are different scientific questions and require different design considerations.

The protocol should not promise a causal conclusion that its design cannot support.

47. Interpretation Should Be Anticipated in the Protocol

The protocol should consider how different possible findings would affect interpretation.

For example:

• a clear increase in risk;
• no evidence of increased risk;
• an imprecise estimate;
• a result inconsistent across subgroups;
• or evidence affected by substantial residual confounding.

Pre-defining the analytical framework helps prevent interpretation from being driven solely by the observed result.

48. Negative Findings Are Still Findings

A PASS does not necessarily need to identify an increased risk to be useful.

A well-designed study may reduce uncertainty by finding no evidence of the suspected association within its limitations.

The protocol should therefore avoid language that presupposes a positive safety signal unless the scientific question genuinely requires a directional hypothesis.

49. Study Endpoints and Clinical Relevance

Statistical significance is not equivalent to clinical importance.

The selected endpoint should be meaningful for the safety question and, where relevant, the risk-management decision.

A small statistical association may not materially alter benefit-risk assessment, while a clinically important effect may require action even when statistical precision is limited.

The protocol should therefore connect endpoints to the underlying pharmacovigilance purpose.

50. Study Milestones

The protocol should identify important study milestones, including where applicable:

• protocol finalisation;
• regulatory submission;
• regulatory endorsement;
• study initiation;
• data collection;
• interim analysis;
• database lock;
• final analysis;
• final report;
• and regulatory submission.

For imposed studies, milestones form part of the regulatory control environment.

EMA maintains a dedicated timetable for PASS protocols and final results, currently updated in July 2026.

51. Protocol Registration and Transparency

Transparency requirements should be considered during protocol planning rather than after the study is complete.

For imposed non-interventional PASS, current EMA information requires registration in the HMA-EMA Catalogue of real-world data studies and publication of relevant study information.

For other non-interventional studies, EMA strongly recommends registration to improve transparency, reduce duplication and support reproducibility.

The protocol identifier, version and study description should therefore remain consistent across internal, regulatory and public records.

52. Changes in the Safety Question

A study may begin because of one safety concern and encounter new evidence during its conduct.

The organisation should determine whether the new evidence can be addressed within the approved objectives or whether a formal amendment or separate activity is required.

It is inappropriate to silently broaden the study's purpose merely because additional data become available.

53. Protocol Versus Statistical Analysis Plan

The protocol should contain sufficient information to establish the principal analytical approach.

A more detailed statistical analysis plan may subsequently operationalise the analysis, but it should remain consistent with the approved protocol.

If the statistical analysis plan materially changes the research question, primary endpoint or analytical strategy, the change should undergo appropriate governance and, where applicable, regulatory assessment.

54. Study Data Freeze and Reproducibility

The organisation should be able to identify the dataset used for the principal analysis.

Appropriate controls can include:

• dataset versioning;
• extraction dates;
• programming version control;
• reproducible analysis scripts;
• controlled derivation rules;
• and documented data corrections.

The objective is to ensure that an important result can be reconstructed when necessary.

55. Practical Scenario: A New Signal Appears During the PASS

Suppose a new safety signal emerges during the study.

The PASS should not automatically be expanded to investigate it unless the new question is scientifically and procedurally compatible with the study.

The signal should enter the normal signal-management process, while the organisation assesses whether it affects the existing PASS objectives or requires a separate investigation.

56. Practical Scenario: The Protocol Cannot Be Implemented as Written

Suppose a key data source becomes unavailable.

The organisation should assess the scientific and regulatory impact before replacing it.

The appropriate response may involve:

• an amendment;
• an alternative validated data source;
• a revised analytical approach;
• a changed milestone;
• or, in serious cases, reconsideration of whether the study can answer its original question.

The change and rationale should be documented.

57. Practical Scenario: Results Are Less Precise Than Expected

If the observed number of outcomes is substantially lower than anticipated, the final report should not simply hide the limitation.

The organisation should explain the effect on precision, interpretability and the original objective.

If the problem becomes apparent before completion, the organisation should assess whether additional follow-up or another scientifically justified response is possible.

58. QPPV Oversight Questions

For a significant PASS, the QPPV should be able to ask:

• What safety question are we trying to answer?
• Why was this design selected?
• Is the population appropriate?
• Are the exposure and outcomes validly defined?
• Is the data source fit for purpose?
• What are the major biases?
• Are the objectives still appropriate?
• Have there been protocol amendments?
• Were substantial changes handled correctly?
• What regulatory milestones apply?
• How will the results affect the RMP and wider PV system?

These questions test whether the study remains connected to pharmacovigilance governance rather than becoming an isolated research project.

59. Final Protocol Checklist

Before approval, the organisation should be able to answer yes to the following:

• Is the safety question explicit?
• Are the primary and secondary objectives clear?
• Is the design justified?
• Is the study population defined?
• Are exposure and outcomes operationally defined?
• Are relevant confounders and effect modifiers addressed?
• Are data sources fit for purpose?
• Is study size justified?
• Is the primary analysis specified?
• Are major biases anticipated?
• Are sensitivity analyses appropriate?
• Is version control established?
• Are amendment rules clear?
• Are regulatory milestones identified?
• Is the relationship with the RMP clear?
• Are transparency and registration obligations understood?
• Can the final result be traced back to the approved protocol?

60. Key Takeaways

A strong PASS protocol is a scientific and regulatory control document. It should make the research question, objectives, design, population, variables, data sources and analysis sufficiently explicit that another qualified reviewer can understand why the study should generate useful evidence.

The most important principle is simple: design the study around the question, not the question around the available study.

Protocol amendments, data limitations, deviations and new evidence should be governed transparently. For imposed non-interventional PASS, scientific design operates within defined EU regulatory procedures, including review of draft protocols and applicable substantial amendments.

The completed protocol should therefore be viewed as the beginning of a controlled evidence-generation lifecycle, not merely the document required to start a study.

References

1. European Medicines Agency. GVP Module VIII — Post-authorisation safety studies (Rev. 3).
2. European Medicines Agency. Guidance for the format and content of the protocol of non-interventional post-authorisation safety studies.
3. European Medicines Agency. Post-authorisation safety studies (PASS), including current procedural Q&A.
4. European Medicines Agency. Procedural timetables — PASS protocols and final results.
5. European Medicines Agency. HMA-EMA Catalogues of real-world data sources and studies.
6. Directive 2001/83/EC, as amended.
7. Regulation (EC) No 726/2004, as amended.
8. Commission Implementing Regulation (EU) No 520/2012, as amended.

Regulatory Note

This article is an educational explanation of PASS protocol development and study design. It does not replace current EU legislation, GVP Module VIII, EMA procedural guidance, national requirements or approved organisational procedures. Examples are illustrative unless an authoritative source is specifically identified.