Characteristics of drugs safety signals that predict safety related product information update

Adverse Drug Reactions Related with Antibiotic Medicines in Malawi: A Retrospective Analysis of Prevalence and Associated Factors

Objective

We aimed to assess the occurrence and characteristics of antibiotic-associated adverse drug reactions (ADRs) in Malawi.

Methods

We retrospectively reviewed 304 patient records from medical wards in three hospitals in Southern Malawi. A global trigger tool was applied for the detection of suspected ADRs, and we used the Naranjo scale, the World Health Organization classification and the Schumock and Thornton scale for causality, seriousness and preventability assessment respectively. ADRs were also further characterized according to anatomical systems. Statistical analysis was done in STATA 14.1. The Chi-square test was used to determine the association between categorical variables and logistic regression analysis was used to measure the strength of the association between various independent variables and the occurrence of ADRs.

Results

Suspected ADRs were detected in 24% (73/304) of patients, of which 1.4% were definite, 15.1% were probable and 83.6% were possible ADRs. Most of the sADRs were gastrointestinal events (42.5%), followed by: musculoskeletal (26.3%); cardiovascular (16.3%); central nervous system (13.8%; and urinary events (1.3%). About 27% of the sADRs were serious events such as convulsions. The geriatric age group (≥65 years) was more likely to experience sADRs as compared to the younger age group, with an adjusted odds ratio (aOR) of 4.53, 95% CI (2.21-9.28), P<0.001. Patients taking more than one antibiotic medicine had a higher risk of developing sADRs as compared to patients who were administered one type of antibiotic medicine, aOR 2.14, 95% CI (1.18-3.90), p < 0.012. A long hospital stay of >3days was associated with a higher risk of sADRs with aOR of 5.11, 95% CI (2.47-10.55), p < 0.001 than those who stayed ≤ 3 days in the hospital.

Conclusion

We found a higher prevalence of serious sADRs associated with antibiotic medicines than reported elsewhere. This may, among others, contribute to high patient mortality, poor treatment adherence, antibiotic resistance and increased cost of care.

The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance.

The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement

BACKGROUND AND AIM

Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts.

METHODS

We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting.

RESULTS

Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts.

CONCLUSIONS

The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence.

Drug Repurposing Using FDA Adverse Event Reporting System (FAERS) Database

Drug repurposing is an emerging approach to reassigning existing pre-approved therapies for new indications. The FDA Adverse Event Reporting System (FAERS) is a large database of over 28 million adverse event reports submitted by medical providers, patients, and drug manufacturers and provides extensive drug safety signal data. In this review, four common drug repurposing strategies using FAERS are described, including inverse signal detection for a single disease, drug-drug interactions that mitigate a target ADE, identifying drug-ADE pairs with opposing gene perturbation signatures and identifying drug-drug pairs with congruent gene perturbation signatures. The purpose of this review is to provide an overview of these different approaches using existing successful applications in the literature. With the fast expansion of adverse drug event reports, FAERS-based drug repurposing represents a promising strategy for discovering new uses for existing therapies.

Mapping Strategies to Assess and Increase the Validity of Published Disproportionality Signals: A Meta-Research Study

BACKGROUND AND AIM

Disproportionality analysis is traditionally used in spontaneous reporting systems to generate working hypotheses about potential adverse drug reactions: the so-called disproportionality signals. We aim to map the methods used by researchers to assess and increase the validity of their published disproportionality signals.

METHODS

From a systematic literature search of published disproportionality analyses up until 1 January 2020, we randomly selected and analyzed 100 studies. We considered five domains: (1) rationale for the study, (2) design of disproportionality analyses, (3) case-by-case assessment, (4) use of complementary data sources, and (5) contextualization of the results within existing evidence.

RESULTS

Among the articles, multiple strategies were adopted to assess and enhance the results validity. The rationale, in 95 articles, was explicitly referred to the accrued evidence, mostly observational data (n = 46) and regulatory documents (n = 45). A statistical adjustment was performed in 34 studies, and specific strategies to correct for biases were implemented in 33 studies. A case-by-case assessment was complementarily performed in 35 studies, most often by investigating temporal plausibility (n = 26). Complementary data sources were used in 25 articles. In 78 articles, results were contextualized using accrued evidence from the literature and regulatory documents, the most important sources being observational (n = 45), other disproportionalities (n = 37), and case reports (n = 36).

CONCLUSIONS

This meta-research study highlighted the heterogeneity in methods and strategies used by researchers to assess the validity of disproportionality signals. Mapping these strategies is a first step towards testing their utility in different scenarios and developing guidelines for designing future disproportionality analysis.

Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review

BACKGROUND

Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains.

OBJECTIVE

This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data.

METHODS

The Arksey and O'Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines.

RESULTS

A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic.

CONCLUSIONS

This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/22505.

A New Drug Safety Signal Detection and Triage System Integrating Sequence Symmetry Analysis and Tree-Based Scan Statistics with Longitudinal Data

Purpose

Development and evaluation of a drug-safety signal detection system integrating data-mining tools in longitudinal data is essential. This study aimed to construct a new triage system using longitudinal data for drug-safety signal detection, integrating data-mining tools, and evaluate adaptability of such system.

Patients and Methods

Based on relevant guidelines and structural frameworks in Taiwan's pharmacovigilance system, we constructed a triage system integrating sequence symmetry analysis (SSA) and tree-based scan statistics (TreeScan) as data-mining tools for detecting safety signals. We conducted an exploratory analysis utilizing Taiwan's National Health Insurance Database and selecting two drug classes (sodium-glucose co-transporter-2 inhibitors (SGLT2i) and non-fluorinated quinolones (NFQ)) as chronic and episodic treatment respectively, as examples to test feasibility of the system.

Results

Under the proposed system, either cohort-based or self-controlled mining with SSA and TreeScan was selected, based on whether the screened drug had an appropriate comparator. All detected alerts were further classified as known adverse drug reactions (ADRs), events related to other causes or potential signals from the triage algorithm, building on existing drug labels and clinical judgement. Exploratory analysis revealed greater numbers of signals for NFQ with a relatively low proportion of known ADRs; most were related to indication, patient characteristics or bias. No safety signals were found. By contrast, most SGLT2i signals were known ADRs or events related to patient characteristics. Four were potential signals warranting further investigation.

Conclusion

The proposed system facilitated active and systematic screening to detect and classify potential safety signals. Countries with real-world longitudinal data could adopt it to streamline drug-safety surveillance.

Signals of Adverse Drug Reactions Communicated by Pharmacovigilance Stakeholders: A Scoping Review of the Global Literature

INTRODUCTION AND OBJECTIVE

Signals of adverse drug reactions (ADRs) can be supported by reports of ADRs and by interventional and non-interventional studies. The evidence base and features of ADR reports that are used to support signals remain to be comprehensively described. To this end, we have undertaken a scoping review.

METHODS

We searched the following databases: PubMed, EMBASE, PsycINFO, Web of Science, and Google Scholar, without language or time restrictions. We also hand searched the bibliographies of relevant studies. We included studies of any design if the results were described as signals. We assessed the levels of evidence using the Oxford Centre for Evidence-Based Medicine (OCEBM) criteria and coded features of reports of ADRs using the Bradford Hill guidelines.

RESULTS

Overall, 1974 publications reported 2421 studies of signals; 1683/2421 were clinical assessments of anecdotal reports of ADRs, but only 225 (13%) of these included explicit judgments on which features of the ADR reports were supportive of a signal. These 225 studies yielded 228 signals; these were supported by features, which were: 'experimental evidence' (i.e., positive dechallenge or rechallenge, 154 instances [68%]), 'temporality' (i.e., time to onset, 130 [57%]), 'exclusion of competing causes' (49 [21%]), and others (40 [17%]). Positive dechallenge/rechallenge often co-occurred with temporality (77/228). OCEBM 4 (i.e., case series and case-control studies) was the most frequent level of evidence (2078 studies). Between 2013 and 2019, there was a three-fold increase in clinical assessments of reports of ADRs compared with a less than two-fold increase in studies supported by higher levels of evidence (i.e., OCEBM 1-3). We identified an increased rate between 2013 and 2019 in disproportionality analyses (about 15 studies per year), mostly from academia.

CONCLUSIONS

Most signals were supported by temporality and dechallenge/rechallenge, but clear reporting of judgments on causality remains infrequent. The number of studies supported only by anecdotal reports of ADRs increased from year to year. The impact of a growing number of signals of disproportionate reporting communicated without an accompanying clinical assessment should be evaluated.

Development of a multivariate prediction model to identify individual case safety reports which require clinical review

BACKGROUND

The number of Individual Case Safety Reports (ICSRs) in pharmacovigilance databases are rapidly increasing world-wide. The majority of ICSRs at the Netherlands Pharmacovigilance Centre Lareb is reviewed manually to identify potential signal triggering reports (PSTR) or ICSRs which need further clinical assessment for other reasons.

OBJECTIVES

To develop a prediction model to identify ICSRs that require clinical review, including PSTRs. Secondly, to identify the most important features of these reports.

METHODS

All ICSRs (n = 30 424) received by Lareb between October 1, 2017 and February 26, 2021 were included. ICSRs originating from marketing authorisation holders and ICSRs reported on vaccines were excluded. The outcome was defined as PSTR (yes/no), where PSTR 'yes' was defined as an ICSR discussed at a signal detection meeting. Nineteen features were included, concerning structured information on: patients, adverse drug reactions (ADR) or drugs. Data were divided into a training (70%) and test set (30%) using a stratified split to maintain the PSTR/no PSTR ratio. Logistic regression, elastic net logistic regression and eXtreme Gradient Boosting models were trained and tuned on a training set. Random down-sampling of negative controls was applied on the training set to adjust for the imbalanced dataset. Final models were evaluated on the test set. Model performances were assessed using the area under the curve (AUC) with 95% confidence interval of a receiver operating characteristic (ROC), and specificity and precision were assessed at a threshold for perfect sensitivity (100%, to not miss any PSTRs). Feature importance plots were inspected and a selection of features was used to re-train and test model performances with fewer features.

RESULTS

1439 (4.7%) of reports were PSTR. All three models performed equally with a highest AUC of 0.75 (0.73-0.77). Despite moderate model performances, specificity (5%) and precision (5%) were low. Most important features were: 'absence of ADR in the Summary of product characteristics', 'ADR reported as serious', 'ADR labelled as an important medical event', 'ADR reported by physician' and 'positive rechallenge'. Model performances were similar when using only nine of the most important features.

CONCLUSIONS

We developed a prediction model with moderate performances to identify PSTRs with nine commonly available features. Optimisation of the model using more ICSR information (e.g., free text fields) to increase model precision is required before implementation.

Evolution of adverse drug reactions reporting systems: paper based to software based

OBJECTIVE

Adverse Drug Reactions (ADR) add a significant clinical and economic burden to the healthcare system of a country. We present an overview of the different approaches of ADR reporting systems worldwide and their evolution over time.

METHODS

A systematic review of the literature was made based on PubMed and the Cochrane database of systematic reviews. The articles searched for included original articles, WHO and FDA reports and institute of medicine reports. Reporting ADRs is the cornerstone of detecting uncommon ADRs once the drugs are on the market. In many countries, ADR reporting is regulated by national regulatory bodies and various methods are employed to report ADRs. Direct reporting by healthcare professionals has been adopted by many developed and developing countries. With emerging new technologies in the field of medicine, there is a great potential to develop better ADR reporting systems in the countries where they have poor reporting.

CONCLUSION

Development and acquisition of newer technologies to promote ADR monitoring and reporting is a necessity for an effective pharmacovigilance system in a country.

Comprehensive evaluation of post-approval regulatory actions during the drug lifecycle - a focus on benefits and risks

Background: Prior studies investigated regulatory actions that reflected a negative impact on drug risks. We aimed to evaluate occurrence of regulatory actions that reflected a negative or positive impact on benefits or risks, as well as relations between them.Research design and methods: We followed EMA-approved innovative drugs from approval (2009-2010) until July 2020 or withdrawal to identify regulatory actions. We assessed these for impact on benefits or risks and relations between actions. Additionally, we scrutinized drug lifecycles for time-variant characteristics that may contribute to specific patterns of regulatory actions.Results: We identified 14 letters and 361 label updates for 40 drugs. Of the label updates, 85 (24%) reflected a positive impact, mostly concerning indications, and 276 (76%) a negative impact, mostly adverse drug reactions. Many updates (54%) occurred simultaneously with other updates, also if these reflected a different impact. Furthermore, levels of patient exposure, innovativeness, needs for regulatory learning and unexpected risks may contribute to patterns of regulatory actions.Conclusions: Almost a quarter of regulatory actions reflected a positive impact on benefits and risks. Also, simultaneous learning about benefits and risks suggests an important role for drug development in risk characterization. These findings may impact regulatory analyses and decision-making.

Signal Detection of Adverse Drug Reactions of Cephalosporins Using Data from a National Pharmacovigilance Database

This case-non-case study aims to detect signals not currently listed on cephalosporin drug labels. From 2009 to 2018, adverse event (AE) reports concerning antibacterial drugs (anatomical therapeutic chemical (ATC) code J01) in the Korea Adverse Events Reporting System (KAERS) database were examined. For signal detection, three indices of disproportionality, proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC), were calculated. The list of signals was compared with ADRs on the drug labels from the United States, United Kingdom, Japan, and South Korea. A total of 163,800 cephalosporin-AE combinations and 72,265 all other J01-AE combinations were analyzed. This study detected 472 signals and 114 new signals that are not included on the drug labels. Cefatrizine-corneal edema (PRR, 440.64; ROR, 481.67; IC, 3.84) and cefatrizine-corneal ulceration (PRR, 346.22; ROR, 399.70; IC, 4.40) had the highest PRR, ROR, and IC among all signals. Additionally, six serious AEs that were not listed on drug labels such as cefaclor-induced stupor (ten cases) and cefaclor-induced respiratory depression (four cases) were found. Detecting signals using a national pharmacovigilance database is useful for identifying unknown ADRs. This study identified signals of cephalosporins that warrant further investigation.

Signals from the Dutch national spontaneous reporting system: Characteristics and regulatory actions

PURPOSE

The aim of the study is to characterise safety signals based on the Dutch spontaneous reporting system (SRS) and to investigate the association between signal characteristics and Product Information (PI) update stratified by approval type: centrally authorised products (CAPs) versus nationally and decentralised authorised products (NAPs).

METHODS

This study evaluates the full cohort of signals disseminated from the Dutch SRS in the period from 2008 to 2017. Each retrieved signal was characterised on a number of aspects. The signal management process from signal generation to a potential PI update was analysed in four steps: (1) signal characterisation; (2) proposed actions by the Dutch national competent authority (NCA) for the signals; (3) presence of PI update (yes/no) and association with signal characteristics; (4) timing from the moment the signal was issued to PI update. For step 1-3 we stratified products in CAPs and NAPs.

RESULTS

Of all signals, 88.7% led to a proposed regulatory action by the NCA. Signals from the Dutch SRS for CAPs versus NAPs more often concerned biologicals, important medical events, class effects and shorter periods since marketing authorization. We detected PI updates for 26.2% of CAP signals and 61.3% of NAP signals.

CONCLUSIONS

The Dutch SRSs remains an important source of signals. There are some notable differences in the characteristics of signals for CAPs versus NAPs. Signals for NAPs more frequently led to PI updates.

Factors Influencing Regulatory Decision-Making in Signal Management: Analysis Based on the Signals Identified from the FAERS

PURPOSE

This study aimed to identify factors that influence the decision to take safety regulatory actions in routine signal management based on spontaneous reports. For this purpose, we analyzed the safety signals identified from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and related information.

METHOD

From the signals that the FDA identified in the FAERS between 2008 1Q and 2014 4Q, we selected 216 signals for which regulatory action was or was not taken. Characteristics of the signals were extracted from the FAERS quarterly reports that give information about what signals were identified from the FAERS and what actions were taken for them, and the FAERS data released in the same quarter when the signal was published. Univariate and multivariable logistic regression analysis was used to assess the relationship between the characteristics of each of the signals and the decision on regulatory action.

RESULT

As a result of the univariate logistic regression analysis, we selected 5 factors (positive rechallenge, number of cases accumulated in the last one-year period before the signal indication, previous awareness, serious outcome, risk for special populations) to include in the multivariable logistic regression model (p < 0.2). The multivariate logistic regression analysis showed that the number of cases accumulated in the last one-year period before the signal indication and previous awareness were associated with the regulatory action (p < 0.05).

CONCLUSION

The present study showed that number of cases accumulated in the last one-year period before the signal indication and previous awareness potentially associated with the United States regulatory action. When assessing safety signals, we should be careful of the adverse events with a large number of cases accumulated rapidly in a short period. In addition, we should pay attention to new information on not only unknown risks but also previously identified and potential risks.

Signals of adverse drug reactions communicated by pharmacovigilance stakeholders: protocol for a scoping review of the global literature

BACKGROUND

Signals of adverse drug reactions (ADRs) form the basis of some regulatory risk-minimization actions in pharmacovigilance. Reviews of limited scope have highlighted that such signals are mostly supported by reports of ADRs or multiple types of evidence. The time that elapses between a report of a suspected ADR and the communication of a signal has not been systematically characterized. Neither has the features of reports of suspected ADRs that authors used to support putative causal relationships, although difficulties with establishing causal relationships between medicinal products and adverse events have been highlighted. The objectives of this study will be to describe the evidence underpinning signals in pharmacovigilance, the features of reports of ADRs supporting signals, and the time that it takes to communicate a signal.

METHODS

We shall retrieve records from PubMed, EMBASE, Web of Science, and PsycINFO (from inception onwards), without language/design restrictions, and apply backward citation screening. We shall hand-search the websites of 35 regulatory agencies/authorities, restricted publications from the Uppsala Monitoring Centre, and drug bulletins. Signals will be requested from the competent stakeholder, if absent from websites. We shall use VigiBase, the World Health Organization's Global Individual Case Safety Report database, to determine the dates on which ADRs were reported. We shall manage records using EndNote (v. 8.2); one reviewer will screen titles/abstracts and full texts, a second will cross-validate the findings, and a third will arbitrate disagreements. Data will be charted via the Systematic Reviews Data Repository, following the same procedures as for data retrieval. Evidence will be categorized according to the Oxford Centre for Evidence-Based Medicine Levels of Evidence. Features of reports of ADRs will be coded. Tables will display frequencies of types of evidence and features of reports of ADRs. We shall use plots or pictograms (if appropriate) to represent the time from the first report of a suspected ADR to a signal.

DISCUSSION

We expect the findings from this review will allow a better understanding of global patterns of similarities or differences in terms of supporting evidence and timing of communications and identify relevant research questions for future systematic reviews. SYSTEMATIC REVIEW REGISTRATION: osf.io/a4xns.

National pharmacovigilance programs in Arab countries: A quantitative assessment study

PURPOSES

The aim of the pharmacovigilance (PV) process is to bring together all the much-needed information about various aspects of product safety or, in particular, the safety and vigilance of drugs as pharmaceutical products. This study aimed to investigate and provide an overview on the current situation and activities of the national PV centers in Arab countries.

METHODS

A cross sectional study was conducted between March and May in 2018. The current survey was adopted and modified from a study that used the questionnaire designed by the World Health Organization Collaborating Centre for International Drug Monitoring. The national PV centers of 22 Arab countries were invited to participate in this study. Descriptive analyses were conducted utilizing the analysis services provided by SurveyMonkey.

RESULTS

In total of, 15 countries responded to our invitation (response rate: 68%). Most Arab countries started their PV program in the last decade, with Palestine implementing its program in 2017. Among the respondents, nine (60%) were members of the WHO International Drug Monitoring Program and were all users of the software provided by the WHO Uppsala Monitoring Center (VigiFlow or VigiBase), except Sudan. In 2017, a total of 27 502 reports were received by the centers in the studied countries, ranging from three reports received in Lebanon to a total of 7362 reports received by the national program of Algeria.

CONCLUSIONS

An improvement was noticed among the national PV programs in the Arab countries. However, a considerable difference still exists among the countries in terms of the implementation and practice of PV.

Uses of pharmacovigilance databases: An overview

Over the past decades, assessment of drug safety and of their benefits harms balance has been profoundly modified by the availability of large databases and computerized automated statistical approaches. Improvement of digital data storage capacity has been applied to pharmacovigilance reports. VigiBase, the international pharmacovigilance database, is now aggregating over 21 million individual case safety reports in 2020. Identification and investigation of drug safety signals - concerning notably rare and unknown adverse drug reactions - is one of the major tasks in pharmacovigilance that can be amplified by automated signal detection. Several quantitative statistical methods exist, each with its own strengths and limits. Integrating signal detection, pharmacovigilance databases can be used for a wide variety of retrospective observational studies illustrated here by concrete examples. Confirming these signals by orthogonal validation using pre-clinical platforms and prospective trials is helpful. Pharmacovigilance databases represent a considerable source of information. However, the quality of signal detection and of pharmacoepidemiology studies in the field of adverse drug reaction closely depends on the quality of the individual data recorded.

Characteristics of drugs safety signals that predict safety related product information update

Purpose

Investigation of drug safety signals is one of the major tasks in pharmacovigilance. Among many potential signals identified, only a few reflect adverse drug reactions requiring regulatory actions, such as product information (PI) update. Limited information is available regarding the signal characteristics that might predict PI update following signal evaluation. The objective of this study was to identify signal characteristics associated with PI updates following signal evaluation by the European Medicines Agency Pharmacovigilance Risk Assessment Committee during 2012 to 2016.

Methods

A comparative study was performed based on data from 172 safety signals. Characteristics of signals were extracted from the European Pharmacovigilance Issues Tracking Tool database. Multivariable logistic regression analysis was used to assess the relationship between signal characteristics and the decision to update the PI.

Results

Multivariable logistic regression analysis showed that the presence of evidence in multiple types of data sources (adjusted odds ratio [OR] 7.8 95% CI [1.5, 40.1]); mechanistic plausibility of the drug‐event association (adjusted OR 3.9 95% CI [1.9, 8.0]); seriousness of the event (adjusted OR 4.2 95% CI [1.3, 13.9]); and age of drugs ≤5 years (adjusted OR 3.9 95% CI [1.2, 12.7]) were associated with the decision to change the PI (P < 0.05).

Conclusions

This study identified 4 characteristics of drug safety signals that have shown to be associated with PI changes as outcome of signal evaluation. These characteristics may be used as criteria for selection and prioritization of potential signals that are more likely to necessitate product information updates.