In today's digital age, data is often hailed as the new currency. Individuals and organizations alike generate and collect vast amounts of data every day. With this influx of data comes the opportunity for valuable insights, innovations, and advancements. However, it also raises concerns about privacy, security, and ethical use.

One way to harness the power of data while respecting privacy is through voluntary data sharing. This practice involves individuals or organizations willingly sharing their data for secondary research, analysis, or other purposes. Voluntary data sharing can foster collaboration, drive innovation, and contribute to the greater good. But what if you're not familiar with anonymization techniques? How can you navigate this landscape responsibly? Let's explore some key steps:

Understand the Risks and Benefits

Before diving into voluntary data sharing, it's crucial to understand the risks and benefits involved. Sharing data without proper anonymization can compromise privacy and confidentiality. However, anonymized data can still carry risks, especially when combined with other datasets or advanced re-identification techniques. On the other hand, sharing data can also lead to valuable insights, improved services, and societal benefits. Assessing these trade-offs is essential for making informed decisions.

Educate Yourself

While you may not be familiar with detailed anonymization techniques, you can educate yourself on the key concepts. Learn about common methods and the differences between Qualitative Anonymization & Quantitative anonymization approaches. Understand the principles behind these techniques and their implications for data privacy. Real Life Sciences provides online resources, webinars, and materials to help you grasp the fundamentals of anonymization.

Below are four basic areas of data anonymization that are beneficial know and understand:

1. Removing Direct Identifiers

Removing direct identifiers involves the elimination of explicit personal information from datasets, such as names, addresses, social security numbers, and phone numbers. By stripping away these identifiers, the risk of re-identification is significantly reduced. However, it's essential to recognize that even seemingly innocuous pieces of information can potentially lead to re-identification when combined with other data sources. Therefore, careful consideration must be given to the context and sensitivity of the data being shared.

2. Pseudonymization

Pseudonymization is a technique that replaces identifiable information with artificial identifiers or pseudonyms. Unlike anonymization, which irreversibly removes identifying information, pseudonymization allows data to be linked back to individuals using additional information held separately (e.g., a key or lookup table). While pseudonymization offers a level of privacy protection, it's crucial to implement robust security measures to safeguard the link between pseudonyms and original identities.

3. Generalization

Generalization involves aggregating or summarizing data to a higher level of abstraction, thereby reducing the granularity of the information while preserving its utility for analysis. For example, instead of recording exact ages, data may be grouped into age brackets (e.g., 20-30, 30-40). While generalization helps protect individual privacy by minimizing the risk of re-identification, it can also introduce information loss and reduce the precision of analysis. Therefore, careful consideration must be given to the trade-off between privacy protection and data utility when applying generalization techniques.

4. Data Masking

Data Masking techniques involve altering or obscuring data values to prevent the identification of individuals while preserving statistical properties of the dataset. For example, data may be masked by replacing precise values with ranges or by adding random noise to numerical values. While these techniques help protect privacy, they can also introduce distortions that affect the accuracy and reliability of analysis. Therefore, it's essential to carefully assess the impact of masking on data quality and analytical outcomes.

Seek Expert Guidance

If you're uncertain about anonymization, always seek expert guidance. Reach out to professionals with expertise in data privacy, security, and ethics. They can provide valuable insights, recommend best practices, and help you navigate potential pitfalls. Collaborating with experts can enhance the effectiveness and reliability of your data sharing efforts.

Prioritize Transparency and Consent

Transparency and consent are fundamental principles of ethical data sharing. Clearly communicate the purpose of data sharing, how the data will be used, and any associated risks. Obtain explicit consent from individuals before sharing their data, ensuring they understand and agree to the terms. Transparency and consent build trust, promote accountability, and uphold individuals' rights over their data.

Consider Alternatives

If anonymization seems daunting or impractical, consider alternative approaches to data sharing. For example, you could aggregate data to remove personally identifiable information while still preserving valuable insights. Alternatively, you could establish data sharing agreements that restrict the use of sensitive information and prioritize privacy protection. Exploring different options allows you to find the approach that best balances utility and privacy. However, Data Anonymization is usually the most effective and reliable way to share data.

Embrace Continuous Learning

Data privacy and anonymization are complex and evolving fields. Embrace a mindset of continuous learning and improvement. Stay updated on the latest developments, techniques, and regulations related to data privacy. Engage with the broader transparency community through forums, conferences, and networking events. By staying informed and adaptable, you can effectively navigate the challenges of voluntary data sharing.

Conclusion

Voluntary data sharing offers tremendous potential for driving innovation and societal progress. However, it also poses challenges, particularly for those unfamiliar with anonymization techniques. By understanding the risks and benefits, educating yourself, seeking expert guidance, prioritizing transparency and consent, considering alternatives, and embracing continuous learning, you can navigate voluntary data sharing responsibly and ethically. Together, we can harness the power of data while respecting privacy and empowering individuals.

Clinical data sharing plays a crucial role in advancing scientific knowledge and improving patient care. 

1. Transparency and Scientific Advancement: Data sharing allows researchers to access clinical trial data, fostering transparency and enabling scientific advancements. Pharmaceutical companies have recognized this importance and committed to sharing participant-level data, study-level data, and protocols¹.
2. Reducing Research Waste: By sharing data, we can avoid duplicative efforts and reduce research waste. Access to existing clinical trial data helps researchers build upon previous work, leading to more efficient resource utilization.
3. Patient-Centered Care: Data sharing contributes to better decision-making for patients and healthcare professionals. When data is accessible, clinicians can make informed choices based on evidence from a broader pool of studies.
4. Challenges and Commitment: Implementing good data sharing principles requires resources, time, and commitment. Despite challenges, enhancing data sharing remains essential for scientific collaboration and patient well-being.

This particular Blog will focus on advancing research - how the sharing of participant level data with qualified researchers can accelerate novel research and inform the design of future clinical trials, some examples of successful sharing initiatives and an outline of common challenges associated with data sharing.

Transparency and Scientific Advancement

1. Transparency and Open Science:
   • Clinical trial data, including participant-level data, study protocols, and results, are valuable resources. When researchers share this information openly, it promotes transparency.
   • Transparency is essential for scientific advancement. By allowing others to scrutinize and build upon existing research, we accelerate progress.
   • Open science practices, such as pre-registering studies and sharing raw data, enhance credibility and reproducibility.
2. Collaboration and Replication:
   • Data sharing encourages collaboration among researchers, institutions, and disciplines. It fosters a sense of community and collective effort.
   • Replication studies become feasible when data is accessible. Replicating findings ensures robustness and confirms or challenges initial results.
3. Pooling Data for Insights:
   • Combining data from multiple studies (meta-analysis) provides statistical power. Researchers can draw more accurate conclusions about treatment efficacy, safety, and adverse events.
   • For rare diseases or conditions, pooling data across trials is especially valuable.
4. Challenges and Solutions:
   • Challenges include privacy concerns, data governance, and intellectual property. Striking a balance between openness and protecting sensitive information is crucial.
   • Initiatives like the FAIR principles (Findable, Accessible, Interoperable, Reusable) guide responsible data sharing.
   • The Clinical Research Data Sharing Alliance (CRDSA) is a multi-stakeholder consortium that services the clinical data sharing ecosystem with a shared goal of accelerating the discovery and delivery of life-saving and life-changing therapies to patients by expanding the research value of the data collected through the clinical development process

Some examples of successful clinical data sharing initiatives include:

1. PhRMA/EFPIA Commitment (2013):
   • In 2013, the Pharmaceutical Research and Manufacturers of America (PhRMA) and the European Federation of Pharmaceutical Industries and Associations (EFPIA) endorsed a commitment to share participant-level data, study-level data, and protocols from clinical trials of US and EU registered medicines with qualified researchers. They also aimed to provide public access to clinical study reports (CSRs) or at least synopses from trials submitted to the FDA, EMA, and EU Member States².
2. Advancements in Drug Development:
   • Successful data sharing has led to tools that optimize drug development. These initiatives help bring new therapies to patients in need by accelerating the research process and improving decision-making³.
3. Clinical Research Data Sharing Alliance (CRDSA):
   • The CRDSA focuses on sharing patient data generated from clinical trials. By doing so, they transform the trial process itself, improve the patient experience, and deliver life-saving and life-changing therapies faster and at a lower cost to society⁴.

Data sharing comes with certain challenges that are often faced by researchers.  

1. Cultural Shift
   • Challenge: Establishing a culture where data sharing is the norm requires overcoming traditional practices and concerns.
   • Recommendation: Stakeholders should foster an environment where data sharing is expected and commit to responsible strategies⁵.
2. Timelines and Compliance:
   • Challenge: Meeting deadlines for sharing various types of clinical trial data (e.g., full analyzable datasets, metadata, and analytic datasets) can be demanding.
   • Recommendation: Sponsors and investigators should adhere to specified timelines for data sharing⁶.
3. Sensitive Data Risks:
   • Challenge: Sharing sensitive clinical trial data while protecting privacy and managing risks.
   • Recommendation: Implement operational strategies like data use agreements, independent review panels, and transparency⁷.
4. Organizing and Presenting Data:
   • Challenge: Researchers struggle with organizing data in a useful and presentable manner.
   • Recommendation: Providing clarity on copyright, licensing, repository options, and metadata standards can help⁸.

Remember, addressing these challenges is essential for advancing scientific knowledge and benefiting patients.

Real Life Sciences specializes in data anonymization for regulatory compliance and voluntary sharing.

Introduction

In the era of digital health, the importance of clinical data cannot be overstated. However, the need for data privacy and protection is equally paramount. This has led to the development of risk-based clinical data anonymization strategies, regulatory policies like the European Medicines Agency’s (EMA) Clinical Data Publication (CDP) Policy 0070, and initiatives like Health Canada’s Public Release of Clinical Information (PRCI).

Risk-Based Clinical Data Anonymization

Risk-based clinical data anonymization is a strategy that measures the probability of re-identifying individuals (in this case, subjects that have participated in a clinical trial) through indirectly-identifying pieces of information. This probability is then reduced through various data transformations, such as offsetting dates, generalizing disease classifications or demographic values, or removing outlier values. The goal is to balance the need for data utility and the requirement for privacy.

Why EMA and Health Canada Prefer Risk-Based Anonymization

Balance: Risk-based anonymization allows for a more nuanced approach that balances privacy protection with the need for transparency and research. Traditional methods often remove too much data, hindering its usefulness for research purposes.

Minimizing Data Loss: By assessing the risk of re-identification for each data attribute, risk-based approaches can retain more valuable information while still protecting privacy. This allows for more comprehensive analysis and better insights.

Adaptability: The risk of re-identification can vary depending on the context and available information. Risk-based methods can adapt to these changing factors, ensuring appropriate protection in different scenarios.

Compared to other techniques, risk-based anonymization provides a more sophisticated and balanced approach to protecting privacy while enabling valuable research and data sharing in the life sciences industry. This aligns with the goals of regulators like EMA and Health Canada to promote public health and transparency while upholding individual privacy rights.

Both EMA and Health Canada have specific guidelines and regulations outlining their expectations for risk-based anonymization. This ensures consistency and accountability.

Key Considerations of Risk Based Anonymization

Risk Threshold

The risk threshold in the context of clinical data anonymization is defined as the minimum amount of de-identification that must be applied to a dataset for it to be considered de-identified.

In more practical terms, it refers to the probability of correctly assigning an identity to a participant (or clinical trial subject) described in the clinical reports. This is also referred to as the probability of re-identification.

For instance, both the European Medicines Agency (EMA) and Health Canada have set an acceptable probability threshold at 0.09. This means that the likelihood of re-identifying an individual from the anonymized data should be less than 9 in 100 for the data to be considered sufficiently anonymized.

The number of data attributes in the dataset requiring anonymization depends on the dataset’s risk score. Higher risk scores mean more fields must be anonymized. The goal is to ensure that the probability of re-identification is very small, thereby protecting the privacy of individuals while still allowing the data to be useful for research purposes.

The risk threshold in clinical data anonymization is determined based on several factors:

1. Data Disclosure Precedents and Industry Benchmarks: The risk threshold is often set based on historical data disclosure precedents and industry benchmarks.
2. Regulatory Guidance: Regulatory authorities such as the European Medicines Agency (EMA) and Health Canada provide guidance on acceptable risk thresholds.
3. Risk Assessment: Anonymization requires a risk assessment to a predetermined threshold (often 0.09) to determine the probability of re-identification of a clinical trial subject.
4. Dataset Characteristics: The number of data attributes in the dataset requiring anonymization depends on the dataset’s risk score. Higher risk scores mean more attributes must be anonymized.
5. Sources of Re-identification Risk: Factors such as the number of participants, whether the trial is in a rare disease, subjective assessment of potential socioeconomic harm to patients if there is re-identification, and the perceived re-identification risk of certain pieces of information (whether they would be knowable by potential adversaries) are considered.

Determining the risk threshold is a complex process that involves considering various factors, including industry standards, regulatory guidance, and the specific characteristics and risks associated with the dataset.

Clinical Data Utility

Data utility in the context of clinical data anonymization refers to the usefulness of the data after it has been anonymized. The goal of risk-based anonymization is to protect the privacy of individuals in a quantifiable manner, but it’s equally important to ensure that the anonymized data remains useful for research purposes.

Preserving data utility during the anonymization process involves quantitative measurements at the document/data level and a well-defined and precise implementation of the selected rules to prevent over-redaction or over-anonymization.

For instance, pseudonymization, which replaces identifiers with a pseudonym, retains more data utility than anonymization, which may involve redacting or masking identifiers. This is because pseudonymization allows for meaningful secondary analyses and follow-on research while maintaining patient confidentiality.

In summary, data utility is a critical aspect of data anonymization. It ensures that the anonymized data can still provide valuable insights and contribute to scientific research, public health, and other secondary purposes.

Methods of Risk Based Clinical Data Anonymization

Clinical data anonymization involves various techniques to ensure the privacy of individuals while maintaining the utility of the data for research purposes. Here are some commonly used methods:

1. Generalization: Specific values are categorized into groups or ranges. For example, exact ages might be replaced with age groups, and countries might be grouped into continents.
2. Suppression or Redaction: This involves removing or redacting sensitive attributes entirely.
3. Masking: Parts of the data are replaced with symbols like (*, $, #).
4. Date Offsetting: This involves altering an identifiable date related to an individual and applying an alternative or random date throughout the data or document(s). To maintain usefulness of the data, offset dates maintain the same duration between events as compared to the original dates.
5. Recoding: Categories of a variable are recoded into broader categories.
6. Local Suppression: Specific values of a variable are suppressed.

These techniques can be used individually or in combination, depending on the specific requirements of the data set and the level of anonymization required.

Evaluating and Selecting a Specialized Risk Based Anonymization Partner

Choosing the right company for risk-based anonymization of clinical data is crucial, as it requires balancing utility with robust privacy protection. These are the principles Real Life Sciences is built upon. Here are some key considerations:

Expertise and experience:

• Specific experience with clinical data: Look for companies with proven experience in handling sensitive clinical information and understanding its complexities. Familiarity with relevant regulations such as CDP/Policy 0070 and PRCI are essential.
• Track record of anonymization methods: Evaluate their expertise in applying various anonymization techniques and their suitability for your specific data and goals.
• Understanding of risk assessment: Ensure they have a solid understanding of risk-based approaches and can tailor the anonymization strategy to your specific risk tolerance and data utility needs.

Technology and infrastructure:

• Security and compliance: Verify their security measures meet industry standards and regulatory requirements. Look for certifications like ISO 27001 for information security management and a QMS for comprehensive quality processes.
• Data anonymization tools and algorithms: Assess the robustness and effectiveness of their chosen anonymization tools and algorithms.  
• Data handling and processing: Understand their data storage, access controls, and destruction procedures.Ensure they align with your security and privacy policies.

Company reputation and ethics:

• Customer testimonials and references: Seek feedback from past customers, especially those in the life sciences industry about their experience and satisfaction.
• Transparency and communication: Evaluate their willingness to discuss their approach, answer questions, and address your concerns openly and honestly.
• Ethical considerations: Confirm their commitment to ethical data handling practices and alignment with data privacy principles.

Conclusion

Risk-based clinical data anonymization, EMA Policy 0070, and Health Canada’s PRCI are all significant strides towards a future where clinical data is both accessible and secure. These initiatives not only foster transparency and trust but also pave the way for innovation and advancement in clinical research.

While these initiatives are a step in the right direction, it is crucial to continue refining these strategies to ensure the balance between data accessibility and privacy is maintained. As we move forward, the focus should be on developing robust, scalable, and efficient methods for data anonymization and public release, keeping in mind the ever-evolving landscape of digital health and data privacy regulations.

When implementing a risk based anonymization approach, engage with experts, like Real Life Sciences for assistance. This will accelerate your project and increase your probability of a high quality and on time project.

Making the Most of the European Medicines Agency (EMA) Policy 0070 Pre-Submission Meeting

Policy 0070 Background

EMA Policy 0070  (otherwise known as Clinical Data Publication, or CDP), is a policy implemented by the European Medicines Agency (EMA) that governs the publication of clinical data for medicinal products for human use. The policy aims to increase transparency and public access to clinical data while protecting patient confidentiality and commercially sensitive information. 

History of CDP/Policy 0070

• 2014     Policy adopted
• 2015     Phase 1 implementation
• 2018     Policy on-hold due to Brexit and COVID (except COVID treatments)
• 2023     Relaunch initiates in September
• 2024     Anticipated relaunch scope expansion 
• Future  Phase 2 implementation (date TBD)

Key Policy 0070 Terms

Committee for Medicinal Products for Human Use (CHMP): The scientific committee of the EMA that provides opinions on marketing authorization applications.

Marketing authorization application (MAA): The formal application submitted by a pharmaceutical company to obtain marketing authorization for a medicinal product.

Marketing authorization holder (MAH): The individual or company responsible for the marketing of a medicinal product.

Pre-submission Meeting (PSM): The meeting held between Sponsor and EMA to review the scope, strategy and timescales for the Policy 0070 submission.

See our Policy 0070 FAQ page for more background info and key terms.

What is the EMA Policy 0070 Pre-Submission meeting?

The EMA Policy 0070 pre-submission meeting is a critical  opportunity for sponsors to discuss their plans for complying with the policy with the European Medicines Agency (EMA) before formally submitting their Proposal Package (PP). 

The pre-submission meeting will confirm in-scope and out-of-scope content, anonymization strategy, use of vendor, CCI (if applicable) and submission schedule. This meeting can help to avoid delays and ensure a smooth submission process. It is especially helpful and valuable for sponsors submitting a Policy 0070 package for the first time.

Where in the MAA process does the Pre-Submission meeting occur?

The pre-submission meeting occurs prior to submitting the Proposal Package (PP). The value of the meeting comes from aligning early with the Health Authority on the personal data anonymization strategy and approach to protection of confidential information.

Ideally, your pre-submission meeting should occur no later than when you receive the invitation letter from EMA. You can (and should) begin your Policy 0070 project work prior to the invitation letter.  Please refer to the timeline illustration below for more details.

Reviewing and confirming your anonymization strategy with EMA early in the process allows you to incorporate their feedback into your Proposal Package, thereby streamlining the validation process and potentially preventing future delays or rework.

Figure 1F

The Pre-Submission Meeting

 Here's what you need to know:

Purpose of the Pre-Submission meeting

• Facilitate open discussion with the EMA on your intended clinical data publication (CDP) submission.
• Identify potential issues or concerns early on to avoid delays or rejection.
• Get feedback on your proposal document package, including your anonymization approach and strategy.
• Discuss scope details and timelines.
• Establish an early dialogue and relationship with the EMA.

Who should attend?

• Representatives from your company familiar with the submission and anonymization  process, including regulatory affairs, scientific experts, and data anonymization specialists.
• Real Life Sciences, who will support you in the preparation of content for the meeting, including the recommended anonymization strategy for your project.
• Representatives from the EMA's Clinical Data Publication Team (CDP Team).

What to prepare

• A list of specific questions you have for the EMA.
• A few slides summarizing your progress on the CDP process.
• Your Anonymization strategy.
• Your listing of Commercially Confidential Information.

Key Topics for Discussion

• Submission scope and schedule.
• Your recommended anonymization technique/strategy.
• Scope and justification for Commercially Confidential Information.
• Timeline and milestones for publication.

Benefits of a Pre-Submission Meeting

• Increased clarity and confidence in your CDP submission.
• Reduced risk of delays or rejection due to non-compliance or technical issues.
• Improved communication and collaboration with the EMA.
• Streamlined publication process leading to faster access to clinical data.

Additional Preparation Items Before Embarking on Your Policy 0070 Project

1. Assemble your team including Clinical Transparency, BioStats, Regulatory
2. Engage with Real Life Sciences for expert advice, preparation for and facilitation of the pre-submission Meeting and delivery of the complete proposal and final packages
3. Gather the in-scope Policy 0070 CTD document set - specifically ICH CTD/eCTD M2.5, M2.7 and M5
4. Gather the applicable trial datasets which can be used in support of an empirical analysis of subject data and later applied to the in-scope documents by RLS
5. Review the EMA guidance for Policy 0070 
6. Plan an internal kickoff for your project with help from RLS 

Policy 0070 Moving Forward

During the Policy 0070 relaunch in 2023 EMA and Health Canada demonstrated increased partnership which is expected to continue moving forward. This is evidenced, for example, through the jointly revised Anonymization Report Templated to be used by Sponsors when submitting their Policy 0070 (and Health Canada PRCI) Proposal Packages. This collaboration between the two Health Authorities benefits Sponsors given the similarities in the two policies. 

The revised Anonymization Report Template requires trial sponsors to indicate which type of anonymization methodology they have adopted for the submission, among other important points pertinent to the project. If trial sponsors do not indicate a quantitative  risk-based methodology they must include justification as to why. A quantitative anonymization methodology, otherwise referred to as “risk-based anonymization” applies empirical measures to assess the risk of reidentification of the resulting anonymized data - based on analysis of the subject data itself. 

In contrast, a qualitative or “rules based anonymization” approach applies a static set of anonymization rules to the data, however, the results are not quantifiable and therefore the risk of r identification is not measured. As the Anonymization Report Template has evolved, including this most recent set of revisions as published Fall 2023, it is becoming increasingly clear the Health Authorities have a strong preference to a Quantitative risk-based anonymization methodology. While RLS supports both quantitative and qualitative approaches, our experience suggests most Sponsors prefer quantitative methods because it is measurable (the risk of reidentification is known) and it supports maximizing the clinical utility of the resulting data.  

Given EMA and Health Canada appear to prefer a risk-based anonymization method when evaluating Policy 0070 submissions, and because of the inherent benefits of a risk-based anonymization approach, RLS recommends sponsors adopt this methodology. That said, there may be certain specific situations where a rules based or qualitative methodology is best.  Most certainly this underscores the importance of the pre-submission meeting. As mentioned above, the trial sponsor should discuss with EMA the preferred approach  during the pre-submission meeting.  Having the methodology agreed with EMA no later than the pre-submission will help the trial sponsor avoid delays when delivering the Proposal and Final packages to EMA.

Contact Real Life Sciences

If you have questions or would like some consultation prior to starting your EMA Policy 0070, CTR or Health Canada PRCI project, please contact us directly at inquiry@rlsciences.com.  RLS has completed dozens of CDP/Policy 0070, Health Canada PRCI and other clinical transparency projects with pharmaceutical manufacturers of all types and sizes. 

For more information on Policy 0070 please visit our Policy 0070 overview page.

What may be considered CCI when redacting CTA documents for submission to CTIS and how should I go about redacting this information? 

Some examples of confidential information may be names of manufacturers, details of the manufacturing process, future development plans, innovative analytics methods and equipment used at the clinical sites.

Once you confirm the information that is confidential with your IP Legal, Regulatory, Clinical and non-Clinical teams, the redactions in the CTA document should be limited only to the specific information that is deemed confidential. This means that full paragraphs and pages will not be redacted while specific content or syntax within a sentence, table or figure will be redacted.

Prior to finalizing your CCI decisions, remember to double check applicable public sources such as global trial registries, publications and abstracts to confirm the information indeed remains confidential and has not already been made public.
Link to EMA Guidance document on how to approach the protection of personal data and commercially confidential information in documents uploaded and published in the Clinical Trial Information System (CTIS).

How can I streamline my internal review and approval process of redacted CTIS CTA documents?

To optimize and streamline your internal approvals, consider using a log to capture each proposed redaction and the associated justification. This information will assist reviewers and approvers and help mitigate potential questions and delays. Further, by building a list, you can repurpose the information for other documents and studies, as applicable.

An example log may include a column for each of the following:

1. Document type e.g.; Investigator Brochure
2. Document filename e.g.; IB_9301_redacted_draft
3. Document version: v2.1
4. Page reference e.g.; page 7
5. Non-redacted syntax e.g.; 3 mg
6. Proposed redacted syntax e.g.; 3
7. Justification category: e.g.; proprietary manufacturing technique or method
8. Justification: e.g.; text justification of why this information is proposed to be redacted
9. Transparency Redactor: e.g; Jane Smith
10. Redaction date: 15 Oct 2023

Which studies are required to transition to the CTR and what is the deadline to complete this?

Since the launch of the CTR in 2022, the timescales and expectations for transition studies from EudraCT have been socialized by EMA. The authorization of transition studies in CTIS requires advanced planning to prepare and submit the necessary documents prior to the deadline of 30 January 2025.

The EMA is estimating up to 6,000 studies under the Clinical Trials Directive (CTD) will require transition and approval in CTIS prior to the deadline of 30 January 2025. To date, approximately 320 studies have completed the transition.

Trials authorized under the CTD with at least one active site in the EU must be transitioned by 30 January 2025. Trials that have ended locally in all EU member states but have active sites in other regions of the world do not need to transition. Key to achieving the deadline of 30 January 2025 is not just submitting the necessary information and documents into CTIS but receiving the authorization itself.

There are specific considerations and deliverables for each transition study as outlined in the following documents that are made available by EMA.

Guidance for the transition of clinical trials

CTCG’s best practice guide and cover letter template

New EMA announcement 06 Oct 2023 explains planned changes to Transparency Rules for 2024. Revisions to EMA’s CTIS Transparency rules will impact all trial sponsors

Following a public consultation period in May and June 2023, on 06 October 2023, the EMA announced upcoming changes to CTIS Transparency Rules that will become effective in 2024.

To be implemented in 2024, changes include removal of the deferral mechanism for all trials, an abbreviated list of ‘for publication’ documents and simplification of required data fields in CTIS.

With these changes, EMA aims to simplify and streamline the transparency of information for patients and healthcare professionals while balancing protection of commercial confidential information and personal data for trial sponsors.

Trial sponsors will need to continue to manage their CTIS projects under the current rules while anticipating the changes for mid-year 2024.

EMA Announcement

Revised CTIS Transparency Rules

What happens if I receive an RFI regarding redacted version ‘for publication’ documents submitted to CTIS and how can I avoid this?

Avoiding an RFI is critical to your study timelines. RFI’s require a short turnaround time. Awareness of the CTR and its guidance are critical to mitigating the risk of an RFI. Read on to learn more about avoiding RFIs due to CCI redactions and examples of acceptable CCI.

RFI’s related to ‘for publication’ documents have recently been noted bay several study sponsors. In these cases, the Member State has noted dense redactions in these documents and the RFI has indicated the sponsor needs to resubmit a revised version of the document that adheres to Section 1.3 Legal Framework in EMA’s CCI Guidance document (including references to Article 81.4 of the CTR).

Avoiding an RFI is critical to your study timelines. RFI’s require a short turnaround time and therefore the operational considerations of turning a revised document in an expedited fashion are often challenging. 

Awareness of the CTR and its guidance are critical to mitigating the risk of an RFI. Per the CTR guidance, when deferring publication of documents, redactions should be minimal. Further, sponsors should be redacting ‘for publication’ documents based on what is anticipated to be confidential at the time of publication (not at the time of upload to CTIS).

Examples of information that may be deemed acceptable to redact as CCI are listed below (note: this is not an exhaustive list). It is recommended you check the guidance to confirm your suggested CCI data for your study meet the necessary criteria.

• Exploratory endpoints
• Biomarkers
• Manufacturing site and vendor details
• 3rd party questionnaires 
• Formulations and unique processes 

Link to more information: EMA CCI CTIS Guidance

Are Member States reviewing public version documents in CTIS and could my ‘for publication’ document redactions result in an RFI?

The guidance from EMA has been consistent: if you are deferring, ‘for publication’ version documents should contain minimal if any redactions upon upload to CTIS. Further, the guidance states that Sponsors should not defer and redact.

At the start of the CTIS launch in 2022 it became clear that Member States were not reviewing ‘for publication’ documents to ensure conformance to this expectation - although it was expected that study sponsors adhere to the guidance at all times. However, more recently, multiple study sponsors have begun to receive RFIs as a result of heavy redaction in ‘for publication’ documents. It is important for all study sponsors to remember that when deferring, only commercially sensitive information at the time of publication (following the deferral period, if applicable) should be redacted. EMA is expecting all ‘for publication’ documents to be meaningful for the public.

Link to Sponsor Handbook with guidance regarding deferral and ‘for publication’ document redaction.
Link to EMA CTR Questions & Answers Version 6.5, see Question 6.5, paragraphs 259-261.

My organization considers the dosing information for my CTIS project as Confidential (CCI). However, CTIS requires metadata fields be populated with dosing information. How can a Sponsor approach the protection of this information?

Depending on the phase of the trial, Sponsors can protect this information from becoming public despite certain CTIS fields requiring an input. Below is EMA’s guidance from section 3.3 “Q&A on the protection of Commercially Confidential Information and Personal Data while using CTIS”:

How can dose details be protected from disclosure from CTIS for certain trials category falling within category 2?
The main characteristics of medicinal products used in clinical trials falling under category 2 are subject to publication rules after a decision on the clinical trial application is issued by the first Member State concerned.

These main characteristics also include structured data fields on daily dose allowed and maximum dose allowed for the medicinal product under investigation. In some instances, depending on the trial development phase, dose details may be considered to be CCI. In such instances, sponsors can include ‘dummy data’ (e.g. 00 digits) in the related structured data field(s) of CTIS.

The full information on the posology should, however, be provided to the Member States for assessment in the document version ‘not for publication’ and can be redacted in the corresponding documents to be published.

This approach would be acceptable only on justified grounds, i.e. when the sponsor proves that the specific information on the posology is not in the public domain and constitutes patentable matter, the disclosure of which before a patent application is filed (typically, after the completion of the trial and during the trial readout) would jeopardize its protection.

This might be applicable for example to integrated phase I/phase II trials that are to be marked in CTIS as category 2 trials. The grounds for considering dose details as CCI should be clearly documented in the cover letter of the application. Link to additional Guidance

With the primary aim of fostering research innovation and improving transparency of the clinical trial process in Europe, the Clinical Trials Regulation (CTR) was implemented in January of 2022. To facilitate the implementation and management of the CTR, the EMA simultaneously launched a digital portal known as Clinical Trials Information System (CTIS). Beginning January 31, 2023, all new clinical trial applications must be submitted via the CTIS.

These changes are leading a cultural shift in clinical trial transparency  [See: A Cultural Shift Is Happening] and the operational impact of this change needs to be discussed in further detail.        

Why adopt transparency as a critical component in the clinical trial lifecycle?   

The EMA has elevated transparency and disclosure of in-process and approved trials to another level. With the CTR, transparency has become an integral part of the clinical trial process in Europe. Using the CTIS, a publicly accessible database, anyone interested in the clinical trials conducted in Europe can search to obtain useful information. Interested parties may be current patients being treated for a disease or chronic illness, participants of a clinical trial seeking detailed information regarding his or her trial, researchers who are seeking details about past and in-process trials and their results, and everyday citizens seeking knowledge of a disease or treatment.

Increased transparency will help to mitigate redundancy or duplication of clinical trial initiatives. If a clinical trial had negative or inconclusive results, the CTIS will make  this trial information available to researchers and individuals. For pharmaceutical companies, this visibility will help in avoiding the repetition of unsuccessful study designs. Increased transparency also helps in improving the quality of data. In other words, the structure and workflow that the CTIS requires is providing clarity and consistency in what stakeholders can expect when querying clinical trials in Europe. 

In a nutshell, although these increased transparency requirements brought about by the CTR will trigger changes to pharmaceutical company’s current workflow, embracing transparency as a high value component in clinical trials will help to expedite research in a given therapy area while building trust and confidence with the public who seek to learn and understand about the trial itself.

Operationally, based on the information that is required in CTIS about a particular clinical trial,  study sponsors need to assume all trial documents and information will become public, with few exceptions. All functional teams must understand that disclosure is now an integral part of the clinical process and prepare submission documents accordingly.

How can trial sponsors manage this change?

Over the next several years, as the industry embraces this change, the resulting impact to clinical teams and risks brought about  with the disclosure of clinical documents  and Commercially Confidential Information (CCI) must be managed carefully. Study sponsors may need to conduct a review of existing processes such as medical writing practices and awareness training regarding transparency of confidential information. A gap analysis of roles, responsibilities, tasks, internal policies and procedures may be required. Study sponsors may choose to start with foundational aspects such as how end-to-end CTR information flow will be managed in addition to the preparation of public version documents. Further, a risk-based approach to anonymizing personal data will be a necessary step in the process before sharing trial results and patient narratives.

As of January 31, 2023, the use of CTIS for new trial applications is mandatory. Study sponsors need to assess their internal capabilities, map their updated business processes, prioritize action items and seek support from vendors who specialize in identification of personal data and can support the redaction and anonymization of this information in a timely manner.

Is there any support available to navigate these changes?

Transparency requirements have indeed disrupted the historical workflows previously implemented by study sponsors and CROs. Study sponsors are looking for more guidance during these times of change. The EMA has provided guidance documentation for study sponsors which is a good place to start. Additionally, to cope with the increased workload related to standardized document templates and document redactions, some companies are choosing to collaborate with external software and service providers such as Real Life Sciences (RLS).

Redactions can be less stressful purpose built digital tools are used to complete the document redaction and anonymization tasks. At RLS,  solutions such as RLS Protect can help to automate the identification of personal data and confidential information and prepare public version documents. However, minimizing the need for redactions is one strategy that study sponsors can adopt. In the next blog of this series, let’s delve deeper into “lean” authoring...

Prior to Health Canada’s anonymization guidelines issued in 2019, subjective input from researchers helped in maintaining data utility, and the process was primarily conducted at the clinical level. At present, preserving data utility during the anonymization process must involve quantitative measurements at the document/data level. Similarly, it must include a well-defined and precise implementation of the selected rules to prevent over-redaction or over-anonymization. Health Canada provided these guidelines to maximize the release of analytically-valuable information.

Sponsors and Clinical Research Organizations (CROs) often consult experts at data transformation or anonymization such as Real Life Sciences (RLS) to conduct this process. At RLS, we follow a quantitative and metrics-driven process for preserving data utility for mandatory disclosures. This process is facilitated by the RLS PROTECT solution that is designed to balance the protection of patient identity and data utility. The solution supports and expedites regulatory submissions and further facilitates voluntary data-sharing projects.

Using this solution, RLS data anonymization specialists preserve data utility by taking a methodical approach that has six distinct steps. 

First, they determine the set of possible transformations across all identifiers to meet the minimum 0.09 threshold in line with the EMA/HC guidance on anonymization techniques. This enables the data to preserve the clinical or research value after transformation and it aligns with guidelines issued by the European Medicine Agency (EMA) 2018 guidelines. Regulatory authorities such as Health Canada prefer anonymization of data instead of redactions. RLS has demonstrated its thought leadership by adopting this process early on. Overall the preference is to employ transformations that allow for a higher level of granularity over outright redaction of data. This process yields multiple possible transformation scenarios across indirect identifiers.

The next step is to measure the ‘Risk of Re-identification’ (ROR) across all possible transformation scenarios. This ROR metric is used to filter transformation scenarios that meet the risk threshold. In this step, several options for a number of variables or identifiers are considered. (Health Canada requires a 0.09 threshold but it can be customized to meet thresholds recommended by other regulatory authorities as necessary.)

In the third step, we prioritize transformation scenarios by the ‘Information Loss’ (IL) metric. These metrics are used to ensure the optimal anonymization solution with minimal loss of data quality. The metric guides the next steps in the process such as optimization and transformation. The IL metric is also used to rank the different transformation scenarios in terms of data utility/data loss.   

For the fourth step, input from the client team plays a critical role. In this step, we optimize transformation options in consultation with clinical scientists, who independently prioritize quasi-identifiers and evaluate the Clinical Utility (CU) based on the context of the drug or condition in study. At this point, clinical scientists from the client team also further review the risk associated with medical events, including minor adverse events, found in the documents.

Then we focus on selecting the optimal transformation scenario using optimal ROR, IL, and CU trade-offs. This selection requires a thoughtful approach as too stringent criteria will suppress data from too many patients. If new information is found that was previously missed, the process may be updated to incorporate new measurements if necessary.

The last step is to measure implementation risks post-transformation, i.e. the precision of implementation to ensure over-anonymization has not occurred. Anonymization is an iterative process that means fine-tuning may be necessary upon manual review. Our data scientists and client team work together to ensure regulatory requirements are adequately met. This step-by-step approach ensures that the anonymization process does not have a detrimental effect on data utility and any undesirable impacts on data can be caught in a timely manner.   

Each clinical trial or program tries to solve a specific clinical problem, and as such, no two clinical trials are alike. A cookie-cutter approach to data transformation is oftentimes not the best way to go. RLS data scientists work closely with the client team and customize data solutions for queries at hand to meet quantitative regulatory requirements. This approach ensures quick approvals from the regulatory authorities and helps complete clinical trials within a given timeframe. Our specialists are available to further discuss data-related challenges and propose solutions.

The rollout of the Clinical Trial Information System (CTIS) has resulted in a cultural shift in the ways in which study sponsor leaders and operational teams think about clinical trial transparency and how this impacts their planning and operations. A primary aim of the Clinical Trial Regulation (CTR) EU No 536/2014, improving clinical trial transparency, will help to enhance trust and confidence with the public and provide material benefit in aiding and improving research. However, the operational impacts of CTIS and how clinical trials are managed in conformance with the Regulation in Europe is igniting changes that are far reaching within the organization. These changes will be adopted through well defined SOPs and a clear understanding of roles and responsibilities for clinical teams and their counterparts. Study sponsors that choose to embrace the change holistically by building a transparency minded culture throughout the organization will wind up on top. 

What History Tells Us?

Transparency requirements have evolved and progressed for years. In 2017, the European Medicines Agency (EMA) published external guidance for Policy 0070, and in 2019, Health Canada published a Public Release of Clinical Information (HC PRCI) guidance. The pharma industry has gradually become accustomed to disclosing trial results. Some have progressed to sharing beyond what is mandated by regional health authorities. The documents for sharing results may include trial synopsis, layperson summaries, and Clinical Study Reports (CSRs), for example. 

In 2022, the EMA launched the CTIS, and in 2023, its use will become mandatory for new clinical trials. The transparency requirements implemented as a result of the CTR has brought several questions and issues to the forefront.

What are the Aims of the Clinical Trial Regulation?

Few regulations have impacted pharmaceutical manufacturers more than Regulation (EU) No 536/2014, otherwise known as The Clinical Trials Regulation or CTR. The CTR aims to centralize the regulatory submission and review process for all trials conducted in the twenty-seven (27) European Economic Union countries. The intent is to position Europe as a favorable region to conduct clinical trials while increasing the transparency of clinical trials information to the public at large.

The CTIS is the secure online portal that is used for the implementation and operation of the CTR. This portal facilitates interactions between study sponsors, researchers, regulatory bodies and ethics committees throughout the lifecycle of a clinical trial. The public can also access a subset of trial information from the portal. The effort to centralize and streamline the clinical trial process in Europe using the CTIS has had a widespread impact on the operational teams that coordinate and manage clinical trials. To provide further clarity, leading up to the launch of CTIS, the EMA published guidance to use the CTIS, which has completely changed current conversations around clinical trial transparency. 

Why is the cultural shift happening? 

Apart from the use of CTIS, the transparency requirements alone have caused manufacturers to rethink their approach to clinical transparency and the ways their cross-functional teams think about and manage the regulatory process. For example, CTR’s transparency requirements have brought changes to what trial information and documentation is disclosed to the public and when. Five important needs draw our attention now: 

• The need to share detailed information about the trial as it is happening rather than after the trial results are analyzed. 

• The need to adjust processes, roles and responsibilities, standard operating procedures, and critical operational decision-making to ensure the trial adheres to the regulation’s detailed expectations. 

• The need to rethink regulatory document authoring practices to minimize document redaction workload and drive consistency within and across trials. 

• The need to protect and anonymize personal data and Commercially Confidential Information published via the CTIS while remaining in compliance with GDPR.

• The need to organize cross-functional teams to respond quickly to Member State questions in accordance with the regulation which may include updates to core regulatory documents that require redaction and local language translation.

This combination of requirements has triggered study sponsors to rethink how their teams work together to achieve this new set of expectations. As a result, the importance of a common understanding of what clinical transparency means, how it’s implemented and supported, and ultimately how it can be embraced must be tackled not only through roles, procedures, and processes but also how it is weaved in the cultural fabric of the organization.   

How do we embrace this cultural shift? 

Pharma leaders and regulatory affairs teams are looking for solutions that will help them adapt to the current evolving landscape and seamlessly integrate CTIS into their internal day-to-day workflow. These solutions include digital tools, streamlined processes, and expert teams that can help manage the transition. Real Life Sciences (RLS) has been at the forefront of developing and implementing solutions for current clinical transparency challenges.

This 6-part blog series will focus on the high-impact areas being realized by pharmaceutical manufacturers today. Our experts will share their perspectives and observations on how study sponsors can implement changes to improve operational efficiency and employee satisfaction while embracing the new world of clinical transparency we find ourselves in. Together, these focus areas make up the challenges and opportunities that are upon us in clinical transparency.