Medical Dictionary For Regulatory Activities Terms Research Articles

Lessons learned from annotation of VAERS reports on adverse events following influenza vaccination and related to Guillain-Barré syndrome

BackgroundVaccine Adverse Events ReportingSystem (VAERS) is a promising resource of tracking adverse events following immunization. Medical Dictionary for Regulatory Activities (MedDRA) terminology used for coding adverse events in VAERS reports has several limitations. We focus on developing an automated system for semantic extraction of adverse events following vaccination and their temporal relationships for a better understanding of VAERS data and its integration into other applications. The aim of the present studyis to summarize the lessons learned during the initial phase of this project in annotating adverse events following influenza vaccination and related to Guillain-Barré syndrome (GBS). We emphasize on identifying the limitations of VAERS and MedDRA.ResultsWe collected 282 VAERS reports documented between 1990 and 2016 and shortlisted those with at least 1,100 characters in the report. We used a subset of 50 reports for the preliminary investigation and annotated all adverse events following influenza vaccination by mapping to representative MedDRA terms. Associated time expressions were annotated when available. We used 16 System Organ Class (SOC) level MedDRA terms to map GBS related adverse events and expanded some SOC terms to Lowest Level Terms (LLT) for granular representation. We annotated three broad categories of events such as problems, clinical investigations, and treatments/procedures. The inter-annotator agreement of events achieved was 86%. Incomplete reports, typographical errors, lack of clarity and coherence, repeated texts, unavailability of associated temporal information, difficulty to interpret due to incorrect grammar, use of generalized terms to describe adverse events / symptoms, uncommon abbreviations, difficulty annotating multiple events with a conjunction / common phrase, irrelevant historical events and coexisting events were some of the challenges encountered. Some of the limitations we noted are in agreement with previous reports.ConclusionsWe reported the challenges encountered and lessons learned during annotation of adverse events in VAERS reports following influenza vaccination and related to GBS. Though the challenges may be due to the inevitable limitations of public reporting systems and widely reported limitations of MedDRA, we emphasize the need to understand these limitations and extraction of other supportive information for a better understanding of adverse events following vaccination.

A Semi-Automated Term Harmonization Pipeline Applied to Pulmonary Arterial Hypertension Clinical Trials.

Data harmonization is essential to integrate individual participant data from multiple sites, time periods, and trials for meta-analysis. The process of mapping terms and phrases to an ontology is complicated by typographic errors, abbreviations, truncation, and plurality. We sought to harmonize medical history (MH) and adverse events (AE) term records across 21 randomized clinical trials in pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension. We developed and applied a semi-automated harmonization pipeline for use with domain-expert annotators to resolve ambiguous term mappings using exact and fuzzy matching. We summarized MH and AE term mapping success, including map quality measures, and imputation of a generalizing term hierarchy as defined by the applied Medical Dictionary for Regulatory Activities (MedDRA) ontology standard. Over 99.6% of both MH (N = 37,105) and AE (N = 58,170) records were successfully mapped to MedDRA low-level terms. Automated exact matching accounted for 74.9% of MH and 85.5% of AE mappings. Term recommendations from fuzzy matching in the pipeline facilitated annotator mapping of the remaining 24.9% of MH and 13.8% of AE records. Imputation of the generalized MedDRA term hierarchy was unambiguous in 85.2% of high-level terms, 99.4% of high-level group terms, and 99.5% of system organ class in MH, and 75% of high-level terms, 98.3% of high-level group terms, and 98.4% of system organ class in AE. This pipeline dramatically reduced the burden of manual annotation for MH and AE term harmonization and could be adapted to other data integration efforts.

Drug-induced disseminated intravascular coagulation: a pharmacovigilance study on World Health Organization's database.

Disseminated intravascular coagulation (DIC) occurs in several clinical conditions, including drug therapy. We aim to investigate the association between the administration of several drug classes and the onset of DIC by using the reports of Adverse Drug Reactions (ADR) collected in Vigibase, the World Health Organization (WHO) database of ADR. We collected reports of drug-related DIC from 1968 to September 2015, classified in Vigibase according to the MedDRA (Medical Dictionary for Regulatory Activities) term "Disseminated intravascular coagulation". A disproportionality analysis using Reporting Odds Ratio (ROR) with 95% Confidence Interval (CI95%) was performed. Overall, 4653 reports of drug-associated DIC were retrieved and the 75.9% of them was serious according to WHO seriousness criteria. DIC was significantly (ROR > 1, lower limit of CI95% > 1) associated with 88 drugs, mainly antineoplastic agents, antithrombotic agents and antibacterials for systemic use. Among of the most frequently reported individual drugs we found dabigatran (94 reports) ROR = 1.34 (CI95% 1.08-1.67), oxaliplatin and bevacizumab both with 75 reports and ROR = 1.77 (1.38-2.27) and 2.02 (1.57-2.61), respectively. A substantial number of drugs, widely used in the clinical practice, may be associated with the potential occurrence of DIC. For many of these drugs, the ADR is not acknowledged in the corresponding Summary of Product Characteristics. The high number of drugs involved underlines the importance of evaluate this condition such as an ADR that might occur during drug therapy.

The risk of respiratory tract infections and symptoms in psoriasis patients treated with interleukin 17 pathway–inhibiting biologics: A meta-estimate of pivotal trials relevant to decision making during the COVID-19 pandemic

To the Editor: Biologic agents have revolutionized psoriasis treatment.1Menter A. Strober B.E. Kaplan D.H. et al.Joint AAD-NPF guidelines of care for the management and treatment of psoriasis with biologics.J Am Acad Dermatol. 2019; 80: 1029-1072Abstract Full Text Full Text PDF PubMed Scopus (364) Google Scholar However, they are considered “immunosuppressive,” and thus, safety assessments focus on infection, particularly those that are serious or opportunistic, or both. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has focused attention on respiratory track infections (RTIs).2Lebwohl M. Rivera-Oyola R. Murrell D.F. Should biologics for psoriasis be interrupted in the era of COVID-19?.J Am Acad Dermatol. 2020; 82: 1217-1218Abstract Full Text Full Text PDF PubMed Scopus (120) Google Scholar The conceptual model of COVID-19 is that immunosuppression early in disease may be harmful, yet may be helpful in “late” severe COVID-19 illness; which may be mediated by a dysregulated hyperimmune response characterized by proinflammatory cytokines including interleukin 17 (IL-17).3Schett G. Sticherling M. Neurath M.F. COVID-19: risk for cytokine targeting in chronic inflammatory diseases?.Nat Rev Immunol. 2020; 20: 271-272Crossref PubMed Scopus (233) Google Scholar The effect of IL-17 inhibitors on COVID-19 is unknown, neither the risk of initial infection nor the risk of progression to worse disease. Current understanding of viral immunology suggests that IL-17 is not a dominant cytokine in viral immunity; however, IL-17 is important to mucosal immunity, raising the hypothesis that biologics targeting IL-17 could potentially increase RTI risk.4Das S. Khader S. Yin and yang of interleukin-17 in host immunity to infection.F1000Res. 2017; 6: 741Crossref PubMed Scopus (53) Google Scholar To test this hypothesis, we calculated a meta-estimate from the placebo-controlled period of phase 3 pivotal IL-17 trials of terms consistent with RTI of secukinumab, ixekizumab, and brodalumab abstracted from United States Food and Drug Administration prescribing information. RTI is a broad term classified by clinical judgment. The Medical Dictionary for Regulatory Activities (MedDRA), used to classify adverse events (AEs), has multiple terms for RTIs. To assess for RTIs, we summed the number of AEs that are associated with RTIs, divided by the total number of subjects in each study, and then calculated a meta-estimate. We found an increased risk of RTIs in the groups receiving IL-17 inhibitors compared with placebo (odds ratio, 1.56; 95% confidence interval, 1.04-2.33; Fig 1). Because prescribing information is not inclusive of all respiratory AEs from the pivotal trials that supported approval of IL-17 inhibitors, we conducted a summary risk estimate using data from the placebo-controlled period of these studies obtained from clinicaltrials.gov. This more detailed analysis yielded similar findings to our meta-estimate of prescribing information data (odds ratio, 1.31; 95% confidence interval, 1.05-1.62; Fig 2). Sensitivity analyses varying the terms analyzed yielded similar findings but with loss of statistical significance. Evaluating the risk of RTI in clinical trials is difficult because the diagnosis is made clinically without objective testing, and therefore, the etiology of these symptoms, be they viral, bacterial, fungal, or allergic, is unknown. Furthermore, there is substantial variation in the rates of RTIs in the placebo groups across the trials, demonstrating a lack of precision in measuring this outcome. For example, rates of “upper RTI” ranged from 0.0% to 7.44% in the placebo groups evaluated. In addition, owing to variation in reporting of MedDRA terms, the events were unevenly pooled because terms are reported inconsistently. It is also possible that patients may have had more than one RTI event, which could impact our estimates. These findings highlight the need for more meticulous evaluation of the impact of IL-17 inhibitors on RTIs in the setting of the novel coronavirus pandemic. Nevertheless, our meta-estimate demonstrates a potential safety signal for RTI associated with IL-17 inhibition and supports guidance issued by American Academy of Dermatology that clinicians should use their clinical judgment to continue or discontinue patients on these drugs in patients who have not tested positive or exhibited symptoms of COVID-19 and to discontinue these agents in patients who test positive for COVID-19 symptoms.5American Academy of Dermatology AssociationGuidance on the use of immunosuppressive agents.https://www.aad.org/member/practice/coronavirus/clinical-guidance/biologicsDate accessed: April 24, 2020Google Scholar Reply to research letterJournal of the American Academy of DermatologyVol. 83Issue 6PreviewTo the Editor: In light of the current coronavirus disease 2019 (COVID-19) pandemic, scientists have debated the safety of immunosuppressive biologic therapies. We read with interest the letter by Wan et al,1 “The risk of respiratory tract infections and symptoms in psoriasis patients treated with interleukin 17-pathway inhibiting biologics: A meta-estimate of pivotal trials relevant to decision-making during the COVID-19 pandemic.” We agree that caution should be used when exposing patients to systemic biologic agents in the setting of not only COVID-19 but also any acute or chronic infection, as Kaushik and Lebwohl2 have previously emphasized. Full-Text PDF

Machine learning-based identification and rule-based normalization of adverse drug reactions in drug labels

BackgroundUse of medication can cause adverse drug reactions (ADRs), unwanted or unexpected events, which are a major safety concern. Drug labels, or prescribing information or package inserts, describe ADRs. Therefore, systematically identifying ADR information from drug labels is critical in multiple aspects; however, this task is challenging due to the nature of the natural language of drug labels.ResultsIn this paper, we present a machine learning- and rule-based system for the identification of ADR entity mentions in the text of drug labels and their normalization through the Medical Dictionary for Regulatory Activities (MedDRA) dictionary. The machine learning approach is based on a recently proposed deep learning architecture, which integrates bi-directional Long Short-Term Memory (Bi-LSTM), Convolutional Neural Network (CNN), and Conditional Random Fields (CRF) for entity recognition. The rule-based approach, used for normalizing the identified ADR mentions to MedDRA terms, is based on an extension of our in-house text-mining system, SciMiner. We evaluated our system on the Text Analysis Conference (TAC) Adverse Drug Reaction 2017 challenge test data set, consisting of 200 manually curated US FDA drug labels. Our ML-based system achieved 77.0% F1 score on the task of ADR mention recognition and 82.6% micro-averaged F1 score on the task of ADR normalization, while rule-based system achieved 67.4 and 77.6% F1 scores, respectively.ConclusionOur study demonstrates that a system composed of a deep learning architecture for entity recognition and a rule-based model for entity normalization is a promising approach for ADR extraction from drug labels.

Standardizing Safety Assessment and Reporting for Neonatal Clinical Trials

Standardizing Safety Assessment and Reporting for Neonatal Clinical Trials

Information on adverse drug reactions—Proof of principle for a structured database that allows customization of drug information

BackgroundThe drug information most commonly requested by patients is to learn more about potential adverse drug reactions (ADRs) of their drugs. Such information should be customizable to individual information needs. While approaches to automatically aggregate ADRs by text-mining processes and establishment of respective databases are well known, further efforts to map additional ADR information are sparse, yet crucial for customization. In a proof-of-principle (PoP) study, we developed a database format demonstrating that natural language processing can further structure ADR information in a way that facilitates customization. MethodsWe developed the database in a 3-step process: (1) initial ADR extraction, (2) mapping of additional ADR information, and (3) review process. ADRs of 10 frequently prescribed active ingredients were initially extracted from their Summary of Product Characteristics (SmPC) by text-mining processes and mapped to Medical Dictionary for Regulatory Activities (MedDRA) terms. To further structure ADR information, we mapped 7 additional ADR characteristics (i.e. frequency, organ class, seriousness, lay perceptibility, onset, duration, and management strategies) to individual ADRs. In a PoP study, the process steps were assessed and tested. Initial ADR extraction was assessed by measuring precision, recall, and F1-scores (i.e. harmonic mean of precision and recall). Mapping of additional ADR information was assessed considering pre-defined parameters (i.e. correctness, errors, and misses) regarding the mapped ADR characteristics. ResultsOverall the SmPCs listed 393 ADRs with an average of 39.3 ± 18.1 ADRs per SmPC. For initial ADR extraction precision was 97.9% and recall was 93.2% leading to an F1-score of 95.5%. Regarding mapping of additional ADR information, the frequency information of 28.6 ± 18.4 ADRs for each SmPC was correctly mapped (72.8%). Overall 77 ADRs (20.6%) of the correctly extracted ADRs did not have a concise frequency stated in the SmPC and were consequently mapped with ‘frequency not known’. Mapping of remaining ADR characteristics did not result in noteworthy errors or misses. ConclusionADR information can be automatically extracted and mapped to corresponding MedDRA terms. Additionally, ADR information can be further structured considering additional ADR characteristics to facilitate customization to individual patient needs.

Ontological and Non-Ontological Resources for Associating Medical Dictionary for Regulatory Activities Terms to SNOMED Clinical Terms With Semantic Properties.

Background: Formal definitions allow selecting terms (e.g., identifying all terms related to “Infectious disease” using the query “has causative agent organism”) and terminological reasoning (e.g., “hepatitis B” is a “hepatitis” and is an “infectious disease”). However, the standard international terminology Medical Dictionary for Regulatory Activities (MedDRA) used for coding adverse drug reactions in pharmacovigilance databases does not beneficiate from such formal definitions. Our objective was to evaluate the potential of reuse of ontological and non-ontological resources for generating such definitions for MedDRA. Methods: We developed several methods that collectively allow a semiautomatic semantic enrichment of MedDRA: 1) using MedDRA-to-SNOMED Clinical Terms (SNOMED CT) mappings (available in the Unified Medical Language System metathesaurus or other mapping resources, e.g., the MedDRA preferred term “hepatitis B” is associated to the SNOMED CT concept “type B viral hepatitis”) to extract term definitions (e.g., “hepatitis B” is associated with the following properties: has finding site liver structure, has associated morphology inflammation morphology, and has causative agent hepatitis B virus); 2) using MedDRA labels and lexical/syntactic methods for automatic decomposition of complex MedDRA terms (e.g., the MedDRA systems organ class “blood and lymphatic system disorders” is decomposed in blood system disorders and lymphatic system disorders) or automatic suggestions of properties (e.g., the string “cyclic” in preferred term “cyclic neutropenia” leads to the property has clinical course cyclic). Results: The Unified Medical Language System metathesaurus was the main ontological resource reusable for generating formal definitions for MedDRA terms. The non-ontological resources (another mapping resource provided by Nadkarni and Darer in 2010 and MedDRA labels) allowed defining few additional preferred terms. While the Ci4SeR tool helped the curator to define 1,935 terms by suggesting potential supplemental relations based on the parents’ and siblings’ semantic definition, defining manually all MedDRA terms remains expensive in time. Discussion: Several ontological and non-ontological resources are available for associating MedDRA terms to SNOMED CT concepts with semantic properties, but providing manual definitions is still necessary. The ontology of adverse events is a possible alternative but does not cover all MedDRA terms either. Perspectives are to implement more efficient techniques to find more logical relations between SNOMED CT and MedDRA in an automated way.

Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA

BackgroundAdverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern.ResultsThis study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression).ConclusionsThis study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

Patient free text reporting of symptomatic adverse events in cancer clinical research using the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE).

The study sought to describe patient-entered supplemental information on symptomatic adverse events (AEs) in cancer clinical research reported via a National Cancer Institute software system and examine the feasibility of mapping these entries to established terminologies. Patients in 3 multicenter trials electronically completed surveys during cancer treatment. Each survey included a prespecified subset of items from the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Upon completion of the survey items, patients could add supplemental symptomatic AE information in a free text box. As patients typed into the box, structured dropdown terms could be selected from the PRO-CTCAE item library or Medical Dictionary for Regulatory Activities (MedDRA), or patients could type unstructured free text for submission. Data were pooled from 1760 participants (48% women; 78% White) who completed 8892 surveys, of which 2387 (26.8%) included supplemental symptomatic AE information. Overall, 1024 (58%) patients entered supplemental information at least once, with an average of 2.3 per patient per study. This encompassed 1474 of 8892 (16.6%) dropdowns and 913 of 8892 (10.3%) unstructured free text entries. One-third of the unstructured free text entries (32%) could be mapped post hoc to a PRO-CTCAE term and 68% to a MedDRA term. Participants frequently added supplemental information beyond study-specific survey items. Almost half selected a structured dropdown term, although many opted to submit unstructured free text entries. Most free text entries could be mapped post hoc to PRO-CTCAE or MedDRA terms, suggesting opportunities to enhance the system to perform real-time mapping for AE reporting. Patient reporting of symptomatic AEs using a text box functionality with mapping to existing terminologies is both feasible and informative.

Validating a Framework for Coding Patient-Reported Health Information to the Medical Dictionary for Regulatory Activities Terminology: An Evaluative Study.

BackgroundThe availability of and interest in patient-generated health data (PGHD) have grown steadily. Patients describe medical experiences differently compared with how clinicians or researchers would describe their observations of those same experiences. Patients may find nonserious, known adverse drug events (ADEs) to be an ongoing concern, which impacts the tolerability and adherence. Clinicians must be vigilant for medically serious, potentially fatal ADEs. Having both perspectives provides patients and clinicians with a complete picture of what to expect from drug therapies. Multiple initiatives seek to incorporate patients’ perspectives into drug development, including PGHD exploration for pharmacovigilance. The Food and Drug Administration (FDA) Adverse Event Reporting System contains case reports of postmarketing ADEs. To facilitate the analysis of these case reports, case details are coded using the Medical Dictionary for Regulatory Activities (MedDRA). PatientsLikeMe is a Web-based network where patients report, track, share, and discuss their health information. PatientsLikeMe captures PGHD through free-text and structured data fields. PatientsLikeMe structured data are coded to multiple medical terminologies, including MedDRA. The standardization of PatientsLikeMe PGHD enables electronic accessibility and enhances patient engagement.ObjectiveThe aim of this study is to retrospectively review PGHD for symptoms and ADEs entered by patients on PatientsLikeMe and coded by PatientsLikeMe to MedDRA terminology for concordance with regulatory-focused coding practices.MethodsAn FDA MedDRA coding expert retrospectively reviewed a data file containing verbatim patient-reported symptoms and ADEs and PatientsLikeMe-assigned MedDRA terms to determine the medical accuracy and appropriateness of the selected MedDRA terms, applying the International Council for Harmonisation MedDRA Term Selection: Points to Consider (MTS:PTC) guides.ResultsThe FDA MedDRA coding expert reviewed 3234 PatientsLikeMe-assigned MedDRA codes and patient-reported verbatim text. The FDA and PatientsLikeMe were concordant at 97.09% (3140/3234) of the PatientsLikeMe-assigned MedDRA codes. The 2.91% (94/3234) discordant subset was analyzed to identify reasons for differences. Coding differences were attributed to several reasons but mostly driven by PatientsLikeMe’s approach of assigning a more general MedDRA term to enable patient-to-patient engagement, while the FDA assigned a more specific medically relevant term.ConclusionsPatientsLikeMe MedDRA coding of PGHD was generally comparable to how the FDA would code similar data, applying the MTS:PTC principles. Discordant coding resulted from several reasons but mostly reflected a difference in purpose. The MTS:PTC coding principles aim to capture the most specific reported information about an ADE, whereas PatientsLikeMe may code patient-reported symptoms and ADEs to more general MedDRA terms to support patient engagement among a larger group of patients. This study demonstrates that most verbatim reports of symptoms and ADEs collected by a PGHD source, such as the PatientsLikeMe platform, could be reliably coded to MedDRA terminology by applying the MTS:PTC guide. Regarding all secondary use of novel data, understanding coding and standardization principles applied to these data types are important.

MedDRA (Medical Dictionary for Regulatory Activities)

MedDRA is improved by ICH (international conference on hormonisation). It is defined as clinically validated international terminology for medical products used by regulatory authorities. It is a dictionary of adverse events. It is classified into 5 levels totaling over 81, 000 terms. When compared with the world health organization-adverse reaction terminology (WHO-ART) MedDRA is much larger dictionary. The main purpose of MedDRA is to provide uniform consistent, reliable methodology. Between the process of premarketing to the post marketing MedDRA terminologies are used. ICH recognized the different terminologies at different stages to make easy to analyze data. MedDRA was developed based on UK earlier version drug regulatory agency. For monitoring, product evaluation, electronic records exchange, communication, and oversight Medical dictionary for regulatory activities (MedDRA) became an important tool. In the MedDRA terms for diseases, signs, indications, medication errors, investigations, some terms for social medical history and some quality term procedures are included. In MedDRA drugs, advanced therapies and some device information was covered. To handle the information regarding clinical about biologics, pharmaceuticals, drug-device combinations and vaccines for entire life span of products MedDRA was created. Hardware or software are not required for MedDRA. specific functional system or version of a functional system is not required. Finally, MedDRA went live in early 1999.

OntoADR a semantic resource describing adverse drug reactions to support searching, coding, and information retrieval

IntroductionEfficient searching and coding in databases that use terminological resources requires that they support efficient data retrieval. The Medical Dictionary for Regulatory Activities (MedDRA) is a reference terminology for several countries and organizations to code adverse drug reactions (ADRs) for pharmacovigilance. Ontologies that are available in the medical domain provide several advantages such as reasoning to improve data retrieval. The field of pharmacovigilance does not yet benefit from a fully operational ontology to formally represent the MedDRA terms. Our objective was to build a semantic resource based on formal description logic to improve MedDRA term retrieval and aid the generation of on-demand custom groupings by appropriately and efficiently selecting terms: OntoADR. MethodsThe method consists of the following steps: (1) mapping between MedDRA terms and SNOMED-CT, (2) generation of semantic definitions using semi-automatic methods, (3) storage of the resource and (4) manual curation by pharmacovigilance experts. ResultsWe built a semantic resource for ADRs enabling a new type of semantics-based term search. OntoADR adds new search capabilities relative to previous approaches, overcoming the usual limitations of computation using lightweight description logic, such as the intractability of unions or negation queries, bringing it closer to user needs. Our automated approach for defining MedDRA terms enabled the association of at least one defining relationship with 67% of preferred terms. The curation work performed on our sample showed an error level of 14% for this automated approach. We tested OntoADR in practice, which allowed us to build custom groupings for several medical topics of interest. DiscussionThe methods we describe in this article could be adapted and extended to other terminologies which do not benefit from a formal semantic representation, thus enabling better data retrieval performance. Our custom groupings of MedDRA terms were used while performing signal detection, which suggests that the graphical user interface we are currently implementing to process OntoADR could be usefully integrated into specialized pharmacovigilance software that rely on MedDRA.

HPV Vaccination and Complex Regional Pain Syndrome: Lack of Evidence

HPV Vaccination and Complex Regional Pain Syndrome: Lack of Evidence

Attractive tools for systematic case accumulation

As Yoshizato et al. [1] mentioned in the recent International Journal of Hematology issue, to establish a causal relationship between a drug candidate and a suspected adverse reaction, it is insufficient only to assess individual adverse drug reaction (ADR) cases; evaluation of accumulated ADR cases is required. We point out that there are potentially effective database systems to support accumulated case review, such as the post-marketing safety database maintained by the market authorization holders (MAH) according to Japanese regulation. A subset of the data is open to the public through the Pharmaceutical and Medical Devices Agency (PMDA) (http://www.info.pmda. go.jp/fukusayou/menu_fukusayou_attention.html). Table 1 shows the number of reported cases for all ADR and for hypothyroidism. Supplemental data can be downloaded from the website, with more details for the reported ADR cases including year reported, age, sex, outcome, suspected drug, and ADR. The reported proportion of hypothyroidism among all ADRs in using sunitinib appeared to be more than for other tyrosine kinase inhibitors. This tendency appears to be compatible with what Yoshizato et al. mentioned in their article. Thus, reporting tendency in MAH’s safety databases may reflect facts to some extent. In fact, the PMDA’s data has been used to analyze other ADRs in limited cases [2, 3]. Even though the MAH’s safety databases have potentials for various applications, serious problems remain, as the MAH’s safety database includes spontaneously generated safety reports. First of all, as the MAH’s safety databases collect only ADR cases, they contain no information on others who are treated with a drug of interest, but do not experience any ADR. In other words, since the safety database consists nearly entirely of ADR reports, there are no denominators for calculating incidence. Second, the ADR term may vary between reporters. The MAH’s safety database usually registers ADR term based on the reporting physician’s diagnosis. Since these diagnoses are not based on identical criteria, the ADR terms registered in the MAH’s safety database may vary among reporting physicians, even though the ADR terms are coded according to the MedDRA (the Medical Dictionary for Regulatory Activities) terms, which is a standard term system designed to handle ADR in safety databases. Third, the MAH’s safety databases usually collect spontaneous reports. This means if physicians who experienced ADR do not report to the MAH, nothing is registered into the database. Even given these serious problems regarding spontaneous safety reports, however, seven of the 11 safety signals that led to medical product withdrawals from the US and UK markets between 1999 and 2000 were based on spontaneous reports [4], suggesting that such reports may nonetheless be useful for detecting safety signals. Moreover, since there are conditions for approval in the Japanese market for secondary tyrosine kinase inhibitors, including nilotinib and dasatinib, which require the registration of all patients to MAHs, MAHs can be expected to record the number of patients treated with such tyrosine kinase inhibitors, and to collect all adverse events (so long as the attending physicians report all adverse event occurring in the registered patients). Thus, the first and third problems should not affect the use of such databases in monitoring ADR associated with the use of secondary tyrosine kinase inhibitors. Electronic supplementary material The online version of this article (doi:10.1007/s12185-011-0893-9) contains supplementary material, which is available to authorized users.

Invasive and In Situ Cervical Cancer Reported to the Vaccine Adverse Event Reporting System (VAERS)

The quadrivalent human papillomavirus (HPV) vaccine was recommended in 2006 for routine vaccination of 11 or 12-year-old girls, with catchup through age 26 years, for the prevention of genital HPV-related diseases. The Vaccine Adverse Event Reporting System (VAERS) is a national spontaneous surveillance system of adverse events following vaccination in the United States. The objective of this study was to identify and review VAERS reports of invasive and in situ cervical cancer in women immunized with the quadrivalent HPV vaccine. A VAERS database search was performed to identify such cases reported in the United States from January 1, 2006, through April 9, 2009. Medical Dictionary for Regulatory Activities (MedDRA) search terms used were "cervix carcinoma," "cervix carcinoma stage 0," "cervix carcinoma stage III," "carcinoma in situ," and "cervical dysplasia." Case inclusion required a report to contain a clear statement of a cervical carcinoma or carcinoma in situ diagnosis on any screening or diagnostic test after at least one dose of the HPV vaccine. All reports were reviewed by two investigators. Four VAERS reports for MedDRA term "cervix carcinoma," one for "cervix carcinoma stage 0," none for "cervix carcinoma stage III," three for "carcinoma in situ," and 53 for "cervical dysplasia" were identified. Of these, three cases of carcinoma in situ and one case of microinvasive cervical cancer met study inclusion criteria. Cases of cervical cancer and precancers are not unexpected in vaccinated women. Cervical cancer screening continues to be important, even for women who have received the HPV vaccine.

Building an ontology of adverse drug reactions for automated signal generation in pharmacovigilance

Automated signal generation in pharmacovigilance implements unsupervised statistical machine learning techniques in order to discover unknown adverse drug reactions (ADR) in spontaneous reporting systems. The impact of the terminology used for coding ADRs has not been addressed previously. The Medical Dictionary for Regulatory Activities (MedDRA) used worldwide in pharmacovigilance cases does not provide formal definitions of terms. We have built an ontology of ADRs to describe semantics of MedDRA terms. Ontological subsumption and approximate matching inferences allow a better grouping of medically related conditions. Signal generation performances are significantly improved but time consumption related to modelization remains very important.

Appraisal of the MedDRA Conceptual Structure for Describing and Grouping Adverse Drug Reactions

Computerised queries in spontaneous reporting systems for pharmacovigilance require reliable and reproducible coding of adverse drug reactions (ADRs). The aim of the Medical Dictionary for Regulatory Activities (MedDRA) terminology is to provide an internationally approved classification for efficient communication of ADR data between countries. Several studies have evaluated the domain completeness of MedDRA and whether encoded terms are coherent with physicians' original verbatim descriptions of the ADR. MedDRA terms are organised into five levels: system organ class (SOC), high level group terms (HLGTs), high level terms (HLTs), preferred terms (PTs) and low level terms (LLTs). Although terms may belong to different SOCs, no PT is related to more than one HLT within the same SOC. This hierarchical property ensures that terms cannot be counted twice in statistical studies, though it does not allow appropriate semantic grouping of PTs. For this purpose, special search categories (SSCs) [collections of PTs assembled from various SOCs] have been introduced in MedDRA to group terms with similar meanings. However, only a small number of categories are currently available and the criteria used to construct these categories have not been clarified. The objective of this work is to determine whether MedDRA contains the structural and terminological properties to group semantically linked adverse events in order to improve the performance of spontaneous reporting systems. Rossi Mori classifies terminological systems in three categories: first-generation systems, which represent terms as strings; second-generation systems, which dissect terminological phrases into a set of simpler terms; and third-generation systems, which provide advanced features to automatically retrieve the position of new terms in the classification and group sets of meaning-related terms. We applied Cimino's desiderata to show that MedDRA is not compatible with the properties of third-generation systems. Consequently, no tool can help for the automated positioning of new terms inside the hierarchy and SSCs have to be entered manually rather than automatically using the MedDRA files. One solution could be to link MedDRA to a third-generation system. This would allow the current MedDRA structure to be kept to ensure that end users have a common view on the same data and the addition of new computational properties to MedDRA.

Prescription-event monitoring in Japan (J-PEM).

In prescription-event monitoring in Japan (J-PEM), patients are identified by prescriptions in individual pharmacies where drugs are dispensed. The methodology is somewhat different to that used by the Drug Safety Research Unit in the UK, in that two questionnaires, one to the pharmacist and the other to the doctor are sent for each patient and the method of concurrent control is employed in J-PEM. In the data analysis, the list of events reported as a suspected reaction or a reason for stopping the drug is made to generate a signal. In addition, a signal may be generated for some events with the statistically significant difference of crude rates followed by the regression analysis or a follow-up study. In J-PEM, Medical Dictionary for Regulatory Activities (MedDRA) terminology is used for data entry and data analysis. Lowest level terms (LLTs) in MedDRA are used in data entry while a signal is generated using preferred terms (PTs). However, to generate a signal effectively, some PTs may be grouped as one term. In addition, if two terms are so similar, it may be instructed that one of those two terms is normally selected in data entry to avoid confusion. Many more PEM studies could be undertaken to determine if MedDRA can be used for effective signal generation, but the usefulness of MedDRA in J-PEM is still to be determined.

The medical dictionary for regulatory activities (MedDRA).

The International Conference on Harmonisation has agreed upon the structure and content of the Medical Dictionary for Regulatory Activities (MedDRA) version 2.0 which should become available in the early part of 1999. This medical terminology is intended for use in the pre- and postmarketing phases of the medicines regulatory process, covering diagnoses, symptoms and signs, adverse drug reactions and therapeutic indications, the names and qualitative results of investigations, surgical and medical procedures, and medical/social history. It can be used for recording adverse events and medical history in clinical trials, in the analysis and tabulations of data from these trials and in the expedited submission of safety data to government regulatory authorities, as well as in constructing standard product information and documentation for applications for marketing authorisation. After licensing of a medicine, it may be used in pharmacovigilance and is expected to be the preferred terminology for international electronic regulatory communication. MedDRA is a hierarchical terminology with 5 levels and is multiaxial: terms may exist in more than 1 vertical axis, providing specificity of terms for data entry and flexibility in data retrieval. Terms in MedDRA were derived from several sources including the WHO's adverse reaction terminology (WHO-ART), Coding Symbols for a Thesaurus of Adverse Reaction Terms (COSTART), International Classification of Diseases (ICD) 9 and ICD9-CM. It will be maintained, further developed and distributed by a Maintenance Support Services Organisation (MSSO). It is anticipated that using MedDRA will improve the quality of data captured on databases, support effective analysis by providing clinically relevant groupings of terms and facilitate electronic communication of data, although as a new tool, users will need to invest time in gaining expertise in its use.