How Do You Currently Tag Literature ICSRs Across Databases?

Introduction

In pharmacovigilance, literature-monitoring is essential. Among the outputs of literature surveillance are Literature Individual Case Safety Reports (literature ICSRs) — adverse event reports coming from scientific literature, rather than spontaneous reporting. Correctly identifying, tagging, classifying, and managing these literature ICSRs across multiple literature databases is critical for regulatory compliance, patient safety, and signal detection.

But how is this done today, what are the challenges, what are best practices, and how can tools like Tesserblu help?

What Is a Literature ICSR and Why Tag It Correctly

A literature ICSR refers to an ICSR derived from scientific literature (journal articles, case reports, systematic reviews, abstracts, etc.) rather than spontaneous or solicited reporting. It must satisfy certain minimum criteria—for example:

• Identifiable reporter or author

• Identifiable patient (or enough patient detail)

• At least one suspect medicinal product

• Clear adverse event description and other metadata (onset, outcome, etc.)

These are required before a valid literature ICSR is created.

Tagging literature ICSRs means classifying them properly: marking them as literature source, tagging whether they are serious, suspect product, reporter, etc.; linking them with metadata (pharmacovigilance attributes like product, country, severity, outcome). Also timing (“Day 0”) matters because regulatory timelines begin once the minimum info is known.

How Tagging Is Typically Done Across Databases

Here’s a breakdown of how organizations today commonly tag literature ICSRs across databases — what works, what is laborious, where there is variation.

Tagging literature ICSRs across databases is a structured, multi-step process involving continuous monitoring, assessment, and classification of safety data from scientific publications. It begins with literature surveillance, where companies search databases like PubMed, Embase, and local journals using predefined drug names and adverse event terms. This step ensures wide coverage but faces challenges such as high article volume, inconsistent indexing, and restricted access to full-text papers, especially from regional sources.

Next comes screening for relevance, where pharmacovigilance reviewers identify whether an article meets regulatory ICSR criteria — identifiable patient, reporter, suspect drug, and adverse event. Although this ensures quality, manual screening is time-consuming and prone to subjectivity, making automation increasingly important.

In the metadata extraction and tagging phase, reviewers capture key details like product name, event type, seriousness, and outcome, then enter them into safety databases. Inconsistent article formats, missing data, and translation needs often complicate this process. Standardized tagging frameworks and use of controlled vocabularies like MedDRA help improve accuracy and regulatory compliance.

Determining “Day 0” — when minimum information for an ICSR is available — is crucial for setting reporting timelines. EMA guidelines define this as the date the literature is screened or the organization becomes aware of the case. Variability in internal policies or full-text access can make this date difficult to establish, underscoring the need for precise tracking.

During classification and tagging, literature ICSRs are categorized by seriousness, listedness, expectedness, and product type. Consistent tagging supports data mining and regulatory reporting, but differences across teams or systems can lead to inconsistencies and compliance risks.

Duplicate detection follows to ensure the same case is not reported multiple times. Because the same patient report may appear in various journals, linking duplicates is vital to avoid data inflation. Automated duplicate-checking systems in regulatory databases like EudraVigilance help mitigate this issue.

Finally, validated ICSRs move to reporting and submission, formatted according to E2B R2/R3 standards for authorities such as EMA or FDA. Manual data entry at this stage can introduce errors, making automation and structured workflows essential for timely compliance.

Overall, tagging literature ICSRs requires precision, consistency, and adherence to regulations. Manual methods, though common, are increasingly inefficient. Advanced tools like Tesserblu now play a key role in automating detection, tagging, and submission — improving accuracy, speeding up timelines, and ensuring global pharmacovigilance compliance.

Common Pain Points in Tagging Literature ICSRs

• High Volume & Scaling: Thousands of articles every month; screening, extracting, tagging manually is time-intensive.

• Inconsistent Metadata: Variation in how literature reports supply data. Missing reporter or patient Identifiable info; ambiguous or conflicting information.

• Regulatory Differences & Ambiguity: Different requirements by region (EMA, FDA, other authorities) about when “Day 0” starts; what constitutes valid ICSR; what is required in literature reports.

• Local/Non-indexed Journals: Harder to access; more likely to have incomplete metadata; less consistent indexing or visibility.

• Duplication / Oversight Risks: Same case reported multiple times, duplicate tagging; or missing cases due to gaps in search strategies.

• Manual Costs & Errors: Human resources, time; risk of missing items, mis-tagging, inconsistent categories; delays that might put regulatory compliance at risk.

  
  

Best Practices / Emerging Standards

To mitigate challenges, many organizations adopt some or all of the following:

1. Well-Designed Search Strategy: Use controlled vocabularies, synonyms of drug names, adverse event terms; frequent searches (e.g. weekly) per EMA GVP guidance. Include non-indexed literature if local/regional journals matter.

   
   

2. Pre-defined Tagging Taxonomies: E.g. seriousness, listedness, expectedness, product vs concomitant, patient demographics; standard product dictionaries (INN, brand); use of MedDRA for adverse event terms.

   
   

3. Automated / Semi-automated Tools: Tools that can pre-screen articles, detect mentions of adverse events, products, etc.; assist in extraction of metadata; speed up manual review. Use of AI/NLP.

   
   

4. Full-text Access & Procurement: Ensuring access to full text so sufficient information is available; having systems/tools to request and track article procurement.

   
   

5. Clear Definition of “Day 0” and Awareness Date: Having company policies aligned to regulatory guidance so that as soon as minimum criteria are met, timelines begin. Documenting when awareness occurred.

   
   

6. Duplicate Detection: Both across literature, and between literature and spontaneous reports. Use of unique identifiers, matching by patient data, event date, author etc.

   
   

7. Quality Checks and Oversight: Medical review, QA of data entry; cross-team harmonisation (e.g. between PV, medical info, literature teams). Use of validation, peer review.

   
   

8. Documentation & Audit Readiness: Keeping logs of what searches were done, what articles reviewed, decisions (why something is/not an ICSR), handling missing data. That’s essential for regulatory inspections.

   
   

How Databases Typically Handle Tagging

• Indexing Databases (Embase, MEDLINE etc.) provide metadata like journal, authors, abstract; but often don’t have ICSR tags. That must be applied downstream.

• Company / Marketing Authorization Holder (MAH) Safety Databases maintain ICSR processing & tagging; these are internal systems.

• Regulatory Databases like EudraVigilance, FDA's safety databases, require submission in a specific format (e.g. E2B XML) with required fields. Tagging must correspond to those formats.

• Literature Management Tools / Platforms / Outsourced Services that provide screening, filtering, sometimes tagging or even full ICSR identification from literature for clients.

  
  

Where Variability Enters

• Criteria for Valid ICSR: slight differences in interpreting minimal required information.

• Search frequency / scope: Some companies search weekly, others less frequently; some include all regional journals, many don’t.

• Tag Definitions: What counts as “listed” vs “unexpected”; seriousness; suspect vs concomitant; how to define reporter identity (author vs actual reporter) etc.

• Automation vs Manual: Some organisations use AI/NLP tools to pre-tag or assist; others fully manual.

• Policies for Day 0 “Awareness” of literature differ by region and MAH internal policy.

  
  

How Tesserblu Helps with Tagging Literature ICSRs

Tesserblu is a suite of software tools for pharmacovigilance and drug safety, and several of its products are directly relevant to literature ICSRs. I’ll describe how Tesserblu can help streamline, automate, and improve accuracy of tagging literature ICSRs.

Tesserblu Products Relevant to Literature ICSRs

• Crypta: A Literature Management Platform that detects the ICSR in literature and automatically generates E2B R2/R3 XML.

• Salvus: The Drug Safety Database which ensures automated intake and handling of case reports from pre- and post-market sources.

• Other supporting tools (for example for document generation, translation) in the Tesserblu suite help with downstream tasks once the ICSR is identified.

  
  

How Tesserblu Addresses Each Step in Tagging

Tesserblu helps streamline the tagging of literature ICSRs across every stage of the pharmacovigilance workflow. During the surveillance and detection phase, its module Crypta continuously monitors global and regional literature databases, using advanced AI and natural language processing (NLP) algorithms to identify and flag articles that mention adverse events, suspect medicinal products, or other key safety elements. This automation reduces manual effort and accelerates detection. When screening for relevance, Crypta employs intelligent classification models that categorize literature into groups such as “likely ICSR,” “needs more information,” or “non-relevant,” minimizing false positives and allowing reviewers to focus their time efficiently on meaningful reports.

Once a potential literature ICSR is identified, Tesserblu automatically extracts the relevant metadata — including details like the suspect product, adverse event terms, seriousness, outcome, patient demographics, and source publication data. These data elements are mapped directly to the structured fields required by regulatory standards such as E2B R2/R3, enabling seamless transition to case processing and submission. Through this automation, Crypta can even generate E2B XML files, reducing manual entry errors and expediting regulatory compliance.

Tesserblu also assists with Day 0 and regulatory timelines by capturing the exact date when a valid literature ICSR is detected. Because the system automates detection and tracking, the “awareness date” is accurately documented, ensuring compliance with regional reporting timelines such as those defined by EMA and FDA. In terms of tagging and classification, Tesserblu supports customizable taxonomies — covering seriousness, listedness, expectedness, and product identification using standard dictionaries and MedDRA terms. Its AI-driven consistency ensures that every case follows the same logical framework, eliminating the variability often seen in manual processes.

For duplicate detection and linking, Tesserblu intelligently scans for overlapping or identical content across multiple literature sources to identify whether the same case has been reported more than once. By automatically linking duplicates, it prevents redundant ICSR entries, improving data quality and reducing regulatory risk. Finally, during the reporting and submission stage, Tesserblu’s automated generation of fully compliant E2B XML files ensures that all extracted and tagged data are correctly formatted for electronic submission to global regulatory databases. This eliminates the need for repetitive manual reformatting, minimizes human error, and shortens turnaround times.

Overall, Tesserblu transforms literature ICSR tagging from a fragmented, manual, and error-prone process into a highly automated, standardized, and audit-ready workflow — ensuring faster case identification, consistent data tagging, and reliable compliance with global pharmacovigilance requirements.

Additional Benefits

• Faster turnaround: Because many steps are automated, identification, tagging and preparation of literature ICSRs can be much faster. This helps with regulatory compliance (observing timelines) and earlier detection of safety signals.

• Better consistency and quality: With AI/NLP and structured flows, fewer manual mistakes; consistency across reviewers; uniform tagging taxonomy.

• Scalability: As the volume of literature increases, Tesserblu scales without proportional increase in human resourcing. Handling regional/local non-indexed literature becomes more feasible.

• Regulatory compliance and audit readiness: Because of structured capture, metadata, versioning, and possibly tracking of “awareness” and “Day 0”, the system better supports audit trails and inspection readiness.

• Integration: Tesserblu’s tools can integrate with safety databases, case management systems, allowing metadata and tagged ICSRs to flow into the existing safety / regulatory workflows.

  
  

Potential Challenges and How to Mitigate Them

Even with tools like Tesserblu helping, there are still things to watch out for:

• AI Misclassification / False Positives / Negatives: No automated tool is perfect. Review workflows with human-in-the-loop oversight are essential.

• Incomplete / Ambiguous Literature: Even when an article is flagged, metadata may be missing; procuring full text or translations might still be needed.

• Local / Hard-to-Access Literature: Regional journals, languages, non-digitized content remain a challenge. The tool may need connectors to full coverage.

• Change Management & Validation: Ensuring internal teams accept automation; validating AI models; adjusting search/tag taxonomies; regulatory acceptability.

• Data Privacy / IP / Copyright: Extracting or quoting text must be done in compliance with copyright, privacy laws etc., especially when generating submissions.

Tesserblu likely has built-in measures for some of these (e.g. automatic detection, but human review, etc.), but organizations must still govern the process well.

Conclusions

Tagging literature ICSRs across databases is a complex, multi-step task involving literature surveillance, screening, metadata extraction, classification, duplicate detection, regulatory timing considerations, and reporting.

Currently, many organizations do much of this manually or with semi-automated tools; variability in quality, time lags, inconsistency, and resource demands are common. Best practices are well known, but not always fully implemented due to tooling or resource constraints.

Tesserblu (especially with solutions like Crypta and its suite) offers a way to streamline this process: automating detection, tagging, metadata extraction, generating formatted submissions, ensuring consistency, and reducing delays. This can lead to more efficient pharmacovigilance operations, better compliance, and ultimately, better patient safety. Book a meeting if you are interested to discuss more.