Drug Safety

BackgroundUsing the FDA Adverse Event Reporting System (FAERS) database, this pharmacovigilance investigation systematically assessed the adverse events (AEs) associated with Fedratinib use in real-world clinical practice. MethodsUsing the FAERS database, we performed a disproportionality analysis incorporating four distinct signal detection methodologies: ROR, PRR, BCPNN, and MGPS. Subgroup analyses were conducted to evaluate the effects of age and gender on Fedratinib-associated AEs. Furthermore, a time-to-onset analysis was performed to characterize the temporal patterns of AEs. ResultsThe FAERS database comprised 10,011,422 individual case safety reports, of which 1,284 were classified as Fedratinib-related AEs, encompassing 38 significant preferred terms (PTs). The most frequently reported adverse reactions included diarrhoea, nausea, vomiting, constipation, abdominal discomfort, fatigue, anaemia, platelet count decreased, and Wernickes encephalopathy. New AEs emerged from the study, such as blood potassium increased, hyperkalaemia, gout, hypocalcaemia, renal failure, renal disorder, and gallbladder disorder. Higher rates were observed in males over 65 years of age. Most cases occurred within the first month of Fedratinib treatment, with the incidence of related AEs decreasing over time. ConclusionThis current study marks the debut investigation regarding Fedratinibs safety in actual clinical practice, which providing substantive evidence to inform future pharmacovigilance investigations of this drug.

BackgroundPost-marketing surveillance is essential for complementing the safety profiles of medicinal products, especially for populations generally excluded from clinical trials such as pregnant individuals. However, the absence of a standardised pregnancy indicator in the electronic transmissions of adverse event reports hampers their correct identification in pharmacovigilance databases and complicates the study of safety concerns related to pregnancy exposures. Three recently developed rule-based algorithms with the common aim to systematically retrieve pregnancy-related reports differ in scope and are tailored to different databases (A. FAERS, B. EudraVigilance, C. VigiBase). AimTo compare the design and outputs of the three pregnancy algorithms. MethodsThis study was a collaboration among the authors of the three pregnancy algorithms. We harmonised their rules, implemented them in an R package to enable execution in both VigiBase and FAERS, and analysed key characteristics of reports flagged by each algorithm. ResultsThe pregnancy algorithms A, B, and C flagged 235653, 279515, and 446957 reports respectively in VigiBase, and 265015, 260734, 350479 in FAERS. Reports exclusively retrieved by each algorithm (994, 3248, and 142324 in VigiBase, and 1528, 1100, and 59643 in FAERS) were mostly explained by Algorithm A having no age restriction, Algorithm B excluding normal pregnancy and ineffective contraception, and Algorithm C excluding paternal exposure. ConclusionsDifferences in flagging were largely related to varying scopes. Understanding commonalities and differences is crucial for empowering professionals working with pregnancy-related pharmacovigilance to select and use the most appropriate algorithm for their specific needs. Key pointsO_LIThree independently developed algorithms were designed to retrieve pregnancy-related adverse event reports and support research into pregnancy-specific safety concerns. C_LIO_LIBy applying these algorithms to VigiBase and FAERS, we highlighted overlaps and differences in the reports they flag, reflecting heterogeneous scope and implementation. C_LIO_LIAwareness of these distinctions is essential to select and apply the most suitable algorithm for their specific needs. C_LI

IntroductionPharmaceutical innovation can contribute to reducing the burden of disease in human populations. This research considers whether products approved by the US Food and Drug Administration (FDA) 2010-2019 and policies for expedited review of products for serious disease were aligned with the US or global burden of disease. MethodsCross-sectional study of products approved 2010-2019, their first approved indications, designations for expedited review, the burden of disease (DALYs), years of life lost (YLL), and years of life lived with disability (YLD) for 122 WHO Global Health Estimates (GHE) conditions. Statistical analyses of associations between drug approvals, disease burden of conditions comprising first approved indications, and designations for expedited review. ResultsThe FDA approved 387 drugs 2010-2019 with lead indications for 59/122 GHE conditions. Conditions with at least one new drug had greater US DALYs (U=1193, p=0.001), US YLL (U=1144, p<0.001), global DALYs (U=1436, p=0.030), and global YLL (U=1304, p=0.004) but not US YLD (U=1583, p=0.158) or global YLD (U=1777, p=0.676). Most approvals were for conditions in the top quartiles of US DALYs or YLL, but <27% were for conditions in the top quartile of global DALYs or YLL. The likelihood of a drug having one or more expedited review designations was negatively associated (odds ratio <1) with US DALYs, US YLD, and global YLD. There was a weak negative association with global DALYs and a weak positive association (odds ratio >1) with US and global YLL. ConclusionsDrug approvals 2010-2019 were more strongly aligned with US than global disease burden and more strongly associated with YLL than YLD. Expedited review pathways were not aligned with the US or global burdens of disease and prioritize YLL over YLD. These results may inform policies to incentivize pharmaceutical innovation better aligned with global burden of disease. KEY QUESTIONS What is already known on this topicPharmaceutical innovation is strongly influenced by (US) market opportunities and poorly aligned with the global burden of disease. Previous studies have suggested that regulatory policies designed to expedite development of products for serious disease could promote better alignment between pharmaceutical innovation and global disease burdens. What this study addsDrug approvals by the US Food and Drug Administration 2010-2019 were more strongly associated with the US than global burden of disease and were disproportionately focused on disorders contributing to premature death as opposed to disability. The odds of a product being designated for expedited review was negatively associated with the burden of disease and measures of disability but positively associated with years of life lost to disease. How this study might affect research, practice, or policyThis work demonstrates a persistent failure of drug development for conditions that contribute the most to the global burden of disease and disabilities that is not addressed with policies for expedited review. This analysis may inform new policy explicitly designed to promote innovation for indications associated with the greatest disease burden and, specifically, the burden associated with disabilities.

ImportanceDrug shortages leave affected patients in a vulnerable position. ObjectiveTo describe incidence and prevalence of use for medicines with suggested shortages in at least one European country, as announced by the European Medicines Agency, and to characterise the users of these drugs including the indication of use, duration of use, and dosage. DesignWe performed a descriptive cohort study from 2010 and up to 2024 in a network of databases which have mapped their data to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). SettingSettings included primary care, secondary care, claims and various disease registries. ParticipantsWe included all patients with at least 365 days of history on the database. ExposuresAll medicines with a suggested shortage in at least one European country for more than 365 days (n=18). We also assessed their key alternatives (n=39). Main outcomes and measuresWe estimated annual incidence rates and period prevalence. A drop in incidence or prevalence of >33% after the shortage was announced was considered confirmation of a shortage. ResultsAmong 52 databases from Europe and the United States, we observed shortages according to decreased incidence of use for 8 drugs and shortages according to prevalence of use for 9 drugs. The drugs varenicline and amoxicillin alone or plus clavulanate were in shortage in the most number of countries. Conclusion and relevanceWe compiled and analysed data of annual incidence and prevalence of use plus information on patient characteristics, indication, and dose for 57 medicines among 52 databases in Europe and the United States between 2010 and 2024. We detected shortages and observed a change in the users characteristics for several drugs. We have described timely real-world scenarios of drug shortages and those unobserved in various health care settings and countries which helps to better understand how drug shortages play out in real life.

This study explores the potential of Reddit as a pharmacovigilance data source by comparing its adverse event reports related to mental health drugs with those from the FDA Adverse Event Reporting System (FAERS). Using data mining techniques, we annotated 1,000 Reddit posts to identify drug-adverse event pairs, which were then compared to FAERS data. Significant differences were noted in the frequency and types of adverse events reported on each platform. For instance, Reddit showed higher reports of sexual dysfunction and cognitive disorders for certain drugs, which were less frequently reported in FAERS. This disparity suggests that Reddit could capture a different demographic or more candid discussions, potentially influenced by its pseudonymous nature. The findings indicate that social media platforms like Reddit can provide valuable insights into real-world drug effects, complementing traditional pharmacovigilance systems. This study highlights the need for further research to integrate social media data to enhance drug safety monitoring.

Objectives. To present DrugSet, a validated R Shiny application supporting the construction medicinal products codelists based on the Anatomical Therapeutic Chemical (ATC) system and their mapping to Clinical Practice Research Datalink (CPRD) Aurum prodcodes within a single interactive workflow. Materials and Methods. DrugSet comprises four modules: data preparation, ATC-based hierarchical code selection, string-based CPRD Aurum prodcodes mapping, and codelist export. Validation was conducted against World Health Organization (WHO) ATC reference codelists and manually curated prodcodes mappings across three drug classes: metformin, beta-blocking agents, and topical salicylic acid. Sensitivity, specificity, and Positive Predictive Values (PPV) were calculated for ATC codelist generation. Agreement proportions (overlapping against total identified codes) were calculated for prodcodes mapping. Time needed for codelist construction using DrugSet was recorded and compared to manual approaches. Results. DrugSet ATC codelist generation against WHO manual reference achieved 100% sensitivity, specificity, and PPV across all medicinal products. Prodcodes mapping agreement ranged from 89.2% to 98.3% with discrepancies due to missing data in the prodcodes input vocabulary. DrugSet completed codelist construction in 9 minutes compared to 3 hours and 10 minutes manually, across all medicinal products classes. Discussion. DrugSet provides a unified workflow that runs directly on ATC and source CPRD Aurum vocabulary files. The reduction in codelist construction time and export of the generated codelists supports reproducibility in pharmacoepidemiologic studies where codelist creation can represent a significant proportion of study setup time. Conclusion. DrugSet is an open-source, validated tool that improves accuracy, and efficiency of codelist construction for medicinal products based on ATC codes towards CPRD Aurum prodcodes.

Withdrawal StatementThe authors have withdrawn this manuscript because We request withdrawal as the current version is incomplete and requires additional experimental data. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

BackgroundThe circulation of substandard and falsified (SF) medical products, including medicines, vaccines, and medical devices, continues to impact health of populations worldwide. Meanwhile, surveillance of SF products and data sharing are limited in most of the world, and incidents are rarely published in accessible databases. We describe here the Medicine Quality Monitoring Globe system (MQM Globe) for capturing, curating, characterising, and disseminating online media reports relating to SF products. The system consists of the curation environment and the Web application. MethodsOnline reports are acquired from search engines, direct feeds, and targeted extraction, in five languages. They then pass a series of automated content characterisation steps to filter and label them by relevance, location, and product. Reports are then reviewed by a trained curator staff, before being made available through a visualisation Web application. FindingsOver the study period from July 2018 through December 2024, we identified 11,000 relevant distinct SF incidents, across a broad range of geography, products, and categories. The automated relevance classifier exhibited precision of 37%, recall 90%, and F1-score 52%, for English-language content. InterpretationThe information captured by the MQM Globe is publicly available and can help inform public health authorities as situations emerge. It is the only publicly available repository of open source intelligence specifically on SF medical products. It can be used as an informational tool to trigger intelligence-led activities across law enforcement, customs and Medicine Regulatory Authorities. FundingThis research was funded in whole, or in part, by the Wellcome Trust [202935/Z/16/Z and 106698/Z/14/Z]. The authors have applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. The Gates Foundation supported the development of the Regulatory and Alerts webpage and the adaptation of the Globe to respond to SF COVID-19 medical products. Research In ContextO_ST_ABSEvidence before this studyC_ST_ABSThe World Health Organization estimated that approximately 10.5% of medicines in low- and middle-income countries are substandard or falsified (SF). However, there is limited evidence on the extent of the problem, due to its nature, the weak regulation capacity in many poor-resourced countries, the complexity of global supply chains, and lack of awareness on the issue. To date there has been little analysis of publicly available online media reporting on events related to SF medical products. Added value of this studyThis article describes the development and assessment of a publicly available online platform, the Medicine Quality Monitoring Globe, for monitoring news media and regulatory agency information related to SF medical products events. This active and timely system curates and organises reports in five languages. Implications of all the available evidenceThis article introduces a tool that offers a new source of data for better understanding the global challenges related to SF medicinal products, and serves as an early warning system for incidents that may require investigation and action from regulatory authorities, health professionals, researchers and the public.

ObjectivesTo describe the differences between European Medicine Agency (EMA) Good Clinical Practice (GCP) inspection reports and medical literature on drugs with withdrawn or refused applications. DesignA retrospective study comparing studies included in European Public Assessment Reports (EPARs) and the corresponding articles published in the medical literature. Data sourcesWe screened all EPARs released by the EMA from inception to April 2024 for drugs that were refused or had a withdrawn application. In those EPARs, we looked for mentions of GCP inspections and details about them, then we searched for related publications on the inspected studies on bibliographic databases. Eligibility criteriaAll EPARs mentioning good clinical practice inspection were included in this survey. Data extractedTwo reviewers independently gathered information on the GCP inspections and their findings, including the EMAs opinion on their impact on the study data. The reviewers checked related publications for mentions of the inspection and any subsequent correction, retraction, or expressions of concern related to its findings. Main outcome measuresThe main outcome was the mention of the GCP inspection findings in the publication of the inspected studies. We also assessed whether there was any mention of these findings in a correction, retraction, or expression of concern. ResultsOut of 285 EPARs screened, 57 (20%) mentioned a GCP inspection. 58 distinct studies with inspections had 61 publications. For 17 publications the inspection occurred after the publication, for 20 the inspection happened before the publication, and for 24 the date of the inspection was unknown. Only 1 publication (2%) addressed the inspection findings. Moreover, there were no corrections, retractions, or expressions of concern related to inspection findings. Among the 61 publications, 26 (43%) were related with 24 distinct studies that had an inspection that casted doubts on data reliability, but none mentioned the inspections at or after the time of publication. ConclusionsThis meta-research survey indicates that health authorities GCP inspections are not reflected in the published literature, even when the inspections have put the data reliability in doubt. Journals should clearly specify which aspects of those studies are trustworthy and which ones are not. Trial registrationosf.io/pa9fq/

Physicians have the freedom to prescribe medicines outside the list of approved indications, to treat mild to life-threatening clinical conditions and diseases, particularly when conventional treatments fail or are lacking. Off-label drug usage is more frequently observed in specific populations not often represented in clinical trials, e.g., pediatric, geriatric, or pregnant patients. Despite conflicting reports on patient safety, exploring alternative treatment options in medical practice promotes innovation and extends the applicability of current medicines. This process can be significantly improved by properly documenting and discussing off-label usage. This paper aims to document off-label uses discussed in less conventional sources, such as the r/medicine Reddit subforum. We identified 66 "Reddit off-label uses" (ROLUs) not captured in our reference database, DrugCentral (https://drugcentral.org/), for a set of 40 drugs. These drugs are associated with 209 on-label drug indications (INDs) and 58 "non-Reddit" off-label uses (NROLUs). Most of these drugs are relatively old (approved before 2000) and act on the nervous system, many with psychiatric applications. However, ROLUs are distinct from INDs and NROLUs. An automated scientific literature query showed that 90% of the ROLUs are linked to 4 scientific publications or more, with 80% linked to at least 10. A further search in the clinical trials database revealed 46 ROLUs mentioned; 39 are in phase 3 trials. These results indicate that most off-label uses discussed on the Reddit forum are supported by scientific evidence. We conclude that medical social media channels can provide a valuable source of alternative drug applications and should be scientifically explored and evaluated in the future.

BackgroundCodeine is a widely available opioid medicine that is on the World Health Organisations list of essential medicines. However, widespread access to codeine has led to its misuse, abuse, dependence, and deaths. Some countries, including France and Australia, have successfully reclassified codeine to prescription only, which is encouraging other regulators, including the UK, to reconsider the status of codeine. However, little is known about how much codeine is sold in the UK. AimTo assess national trends in sales and expenditure of codeine-containing products sold over the counter (OTC) between 2013 and 2019. MethodsWe conducted a retrospective observational study using electronic point-of-sales data from the human data science company IQVIA and population statistics from the UKs Office of National Statistics (ONS). Descriptive statistics were used to examine the quantity, trends over time, and types of OTC codeine-containing products sold. Results4.75 billion dosage units of codeine were sold OTC in the UK between April 2013 and March 2019, an average of 72 dosage units per UK resident. Over time, sales of codeine fell by 8%, from 12.54 dosage units per resident in 2013 to 11.48 dosage units per resident in 2019. Codeine was often sold in combination with other analgesics, amounting to 1711 tonnes of paracetamol and 96 tonnes of ibuprofen. The public spent {pound}638 million on OTC codeine-containing products, which increased by 12% over the study period. There were 83 different types of codeine-containing products sold. ConclusionLarge volumes of codeine-containing products were purchased OTC in the UK in 2013-19. To improve the safety of opioids, OTC codeine sales data should be made accessible for public health surveillance. The trends presented in this study should inform policy for the future status of codeine availability in the UK. Principal Investigator statementThe authors confirm that the Principal Investigator for this paper is Dr Georgia Richards and that they had direct responsibility for the conduct of this study.

IntroductionProduct information is intended to be a reference for healthcare professionals to verify instructions for monitoring and preventability of adverse drug reactions (ADRs), among other things. International comparisons of these documents, using the Systematic Information for Monitoring (SIM) method, have highlighted discrepancies in the instructions for monitoring, but there has been no comparison of preventability instructions. ObjectivesTo quantify and compare, across different countries, the completeness of instructions for monitoring and preventability provided to healthcare professionals in medicinal product information. MethodsWe shall retrieve information included with medicinal products that have been involved in signals communicated by regulators, in 2014-2019 and based on clinical assessments of reports of ADRs, from the websites of 35 regulatory agencies. We shall evaluate the completeness of instructions for monitoring using a modified version of the SIM method; a score of 67% will qualify a monitoring instruction as sufficiently complete. To evaluate the completeness of instructions for preventability, we have derived a framework from the Dose-responsiveness-Temporality-Susceptibility (DoTS) classification of ADRs and related implications, comprising domains and items/implications. We shall iteratively develop a threshold to define the sufficiency of completeness of instructions based on data distribution across DoTS domains. We shall present descriptive statistics by country for each item of the framework and by total scores, using tables, or figures where necessary. OutcomesOur target audience is regulators, and the results should highlight gaps in the level of information available to healthcare professionals. This study may also provide some insights into how suspicions of causality that arise from clinical assessments of reports of ADRs translate into actionable recommendations in clinical practice.

Introduction: Adverse drug withdrawal events (ADWEs) are a key safety concern with deprescribing but are infrequently reported in trials. Although pharmacovigilance systems have advanced our understanding of medication-related harms, it is unclear how extensively these systems have been used for ADWEs. Objectives: To examine the reporting patterns of ADWEs for all drugs recorded in United States and European pharmacovigilance databases between 2004 and 2023. Methods: A retrospective study was conducted using two pharmacovigilance databases, the publicly available FDA-FAERS dataset and EMA-EV Level 2A (individual-level) dataset. ADWE cases were identified using relevant MedDRA preferred terms. Data on patient characteristics, reporter type, drugs, indication, ADWE outcomes, dechallenge/rechallenge, seriousness criteria, time to onset, duration, and causality were summarised. Results: A total of 158,505 ADWE reports were analysed (FDA-FAERS: 145,514; EMA-EV: 12,987), with mean ages of 46.1 (FDA; 55.3% female) and 45.5 years (EMA; 57.1% female). The frequently reported drug classes were opioids (FDA: oxycodone, 29.8%; EMA: buprenorphine, 19%), antidepressants (FDA: duloxetine, 32%; EMA: venlafaxine, 25.9%) and gabapentinoids (FDA: pregabalin, 6.7%; EMA: pregabalin, 6.0%). The most common adverse outcomes were other serious medical conditions (FDA=63.9%; EMA=46.0%), hospitalisation (FDA=15.9%; EMA=28.3%), and disability (FDA=13.3%; EMA=6.2%) and these outcomes varied significantly based on sex and age group (p<0.05). Conclusions: This study provides novel evidence of reporting patterns and characteristics of ADWEs across drugs in pharmacovigilance data. These findings emphasise that adverse drug reaction reporting systems need to accommodate ADWEs (i.e., clarity on terminologies, dechallenge/rechallenge, causality assessment) to effectively capture ADWE-related data to support evidence-based deprescribing practices for better patient safety

AimsTo correlate potential links between the suspected adverse drug reaction (ADR) profile of licensed non-steroidal androgen receptor antagonists (NSARA) with their unique chemical properties and known off-target polypharmacology. MethodsPhysiochemical and polypharmacology data was curated from the Electronic Medicines Compendium, FDA New Drug Applications documents, and ChEMBL databases. System organ class (SOC, MedDRA) suspected ADRs and fatalities were curated from the United Kingdom Medicines and Healthcare products Regulatory Authority (MHRA) Yellow card spontaneous reporting scheme for their respective prescribing period; apalutamide (Jan 2019-), bicalutamide (Aug 2018-), enzalutamide (Aug 2018-), flutamide (Aug 2018-) and darolutamide (March 2019-) until Oct 2023. The number of daily doses (dd) was extracted from OpenPrescribing and NHS Digital secondary care medicines data. Data was standardised before comparison to suspected ADRs and fatality reports per 100,000 dd. ResultsA total of n = 2,480 suspected ADRs were associated with 42,903,000 dd of NSARAs in the United Kingdom. The highest number of ADRs were associated with enzalutamide (n = 1,091) and bicalutamide (n = 749). Enzalutamide was found to have the most off-target pharmacological interactions of the NSARAs studied (n = 5) including potent inhibition of {gamma}-aminobutyric acid, GABA receptor (IC50 = 2.6 {micro}M vs Cmax = 7.7 {micro}M) associated with nervous system disorders (n = 72, accounting for 73% of all NSARA ADRs in this SOC). Apalutamide, the only other GABA inhibitor (IC50 = 3 {micro}M vs Cmax = 2.9 {micro}M) had the highest relative rate of suspected nervous system ADRs at 1.08 per 100,000 dd. Apalutamide was also a modest inhibitor of the human Ether-a-go-go-Related Gene (hERG) ion channel (IC50 = 6 {micro}M vs Cmax = 2.9 {micro}M) and had the highest rate of suspected cardiac arrhythmia ADRs, 30-fold over, enzalutamide, a significantly weaker hERG inhibitor (15.7 {micro}M vs Cmax = 7.7 {micro}M). Darolutamide was the only NSARA to show effects at 5-HT (serotonin) receptor at < 10 {micro}M but did not translate to psychiatric disorders due to low clinical BBB penetration but a an association with hepatobiliary and cardiac disorders was identified based on this inhibitory axis. Suspected skin and subcutaneous SOC ADRs was associated with all NSARAs (except flutamide) but did not reach statistical significance (P = .25). A rationale for epidermis reactions relating to apalutamide containing a masked arylamine was explored but molecular matched pair (MMP) analysis with enzalutamide suggests it may not be a chemical cause. Statistical significance (P < .05) was identified in reported fatalities associated with NSARAs, flutamide had n = 24 or 897.5 fatalities per 100,000 dd which was likely due to both the indication and the small number of dd (n = 3,000) during the time period of the study. ConclusionsAn investigation of suspected ADRs, standardised to the number of dd for the novel NSARA drug class identified SOCs of potential interest. The highest number of reports related to enzalutamide and bicalutamide. Suspected skin and subcutaneous ADRs approached statistical significance and was interrogated for chemical and pharmacological connections for the first time with the aid of MMP analysis. A potential correlation to nervous system disorders and cardiac arrhythmia for the GABA and hERG inhibitors, enzalutamide and apalutamide, respectively was identified. Darolutamides interaction with 5-HT may influence ADRs associated with cardiac and hepatobiliary SOCs. Statistically significant number of suspected fatalities with flutamide was identified.

ObjectiveHarnessing drug-related data posted on social media in real time can offer insights into how the pandemic impacts drug use and monitor misinformation. This study developed a natural language processing (NLP) pipeline tailored for the analysis of social media discourse on COVID-19 related drugs. MethodsThis study constructed a full pipeline for COVID-19 related drug tweet analysis, utilizing pre-trained language model-based NLP techniques as the backbone. This pipeline is architecturally composed of four core modules: named entity recognition (NER) and normalization to identify medical entities from relevant tweets and standardize them to uniform medication names, target sentiment analysis (TSA) to reveal sentiment polarities associated with the entities, topic modeling to understand underlying themes discussed by the population, and drug network analysis to potential adverse drug reactions (ADR) and drug-drug interactions (DDI). The pipeline was deployed to analyze tweets related to COVID-19 and drug therapies between February 1, 2020, and April 30, 2022. ResultsFrom a dataset comprising 2,124,757 relevant tweets sourced from 1,800,372 unique users, our NER model identified the top five most-discussed drugs: Ivermectin, Hydroxychloroquine, Remdesivir, Zinc, and Vitamin D. Sentiment and topic analysis revealed that public perception was predominantly shaped by celebrity endorsements, media hotspots, and governmental directives rather than empirical evidence of drug efficacy. Co-occurrence matrices and complex network analysis further identified emerging patterns of DDI and ADR that could be critical for public health surveillance like better safeguarding public safety in medicines use. ConclusionThis study evidences that an NLP-based pipeline can be a robust tool for large-scale public health monitoring and can offer valuable supplementary data for traditional epidemiological studies concerning DDI and ADR. The framework presented here aspires to serve as a cornerstone for future social media-based public health analytics.

BackgroundFollowing the mass recall of valsartan products with nitrosamine impurities in July 2018, the number of patients exposed to these valsartan products, the duration of exposure, and the potential for cancer remains unknown. Therefore, we assessed the extent and duration of use of valsartan products with a nitrosamine impurity in the US, Canada, and Denmark. MethodsWe conducted a retrospective cohort study using administrative healthcare data from the US FDA Sentinel System, four Canadian provinces that contribute to the Canadian Network for Observational Drug Effect Studies (CNODES), and the Danish National Prescription Registry. Patients, 18 years and older between May 2012 and December 2020 with a valsartan dispensing were identified in each database. Patients were followed from the date of valsartan dispensing until discontinuation. We defined four valsartan exposure categories based on nitrosamine impurity status; recalled generic products with confirmed NDMA/NDEA levels (recalled-tested); recalled generic products that were not tested (recalled); non-recalled generic and non-recalled branded products. In Denmark, recalled-tested category was not included due to absence of testing data. The proportion and duration of use of valsartan episodes stratified by nitrosamine-impurity status. ResultsWe identified 3.3 and 2.8 million (US) and 51.3 and 229 thousand (Canada) recalled-tested and recalled valsartan exposures. In Denmark, where valsartan exposure was generally low, there were 10,747 recalled exposures. Immediately after the recall notices were issued, there was increased rates of switching to a non-valsartan ARB. The mean duration of use of the recalled-tested products was 167({+/-}223.1) and 146({+/-}255.8) days in the US and Canada respectively. For the recalled products, mean cumulative duration of use was 178({+/-}249.6), 269({+/-}397.3) and 166({+/-}251.0) days in the US, Canada, and Denmark, respectively. ConclusionIn this cohort study, despite widespread use of recalled generic valsartan between 2012 and 2018, the duration of use was relatively short and likely did not pose an elevated risk of nitrosamine-induced cancer. However, since products with nitrosamine impurity could have been on the market over a six-year period, patients potentially exposed to these products for longer duration could have a different risk of cancer.

BackgroundPost-market pharmacovigilance relies predominantly on single-database disproportionality analysis of spontaneous adverse event reports, which lacks corroboration across independent evidence streams and cannot integrate randomised trial evidence. No publicly accessible platform has previously combined European national pharmacovigilance registries, the US FDA Adverse Event Reporting System (FAERS), and clinical trial meta-analyses into a unified, continuously scored signal detection framework. MethodsWe describe the Signal Consensus Index (SCI), a composite 0-100 pharmacovigilance signal score integrating disproportionality evidence from the Danish National Pharmacovigilance Database, the UK MHRA Yellow Card scheme, and FAERS, with DerSimonian-Laird meta-analytic risk ratios from ClinicalTrials.gov, across 6,905,874 drug-adverse event pairs. Each source contributes a continuous score derived from the lower bounds of three complementary disproportionality metrics (ROR, PRR, IC025) for spontaneous reporting sources, and from the pooled risk ratio lower confidence bound for clinical trials. The SCI is publicly accessible via the Adverse Event Atlas (aeatlas.com). We report reference set validation against the EU-ADR reference standard, a single-source comparison with discordance characterisation, temporal stability analysis across eight cumulative data windows (2015-2023), and a weight sensitivity analysis across seven pre-specified weighting schemes. ResultsThe SCI generated 129,176 Moderate-or-Strong signals (SCI [&ge;] 50, confidence [&ge;] 50) and 7,290 Strong signals (SCI [&ge;] 70, confidence [&ge;] 70). Reference set validation against 88 classifiable drug-event pairs (44 positive controls, 44 negative controls) yielded 18 true positives, 0 false positives, 44 true negatives, and 26 false negatives (sensitivity 40.9%, specificity 100.0%, PPV 100.0%, NPV 62.9%). Zero false positives were observed across all 44 classifiable negative controls, with five false negatives attributable to the confidence gate correctly suppressing single-source signals pending multi-source corroboration. Single-source comparison demonstrated that FAERS alone generated 1,438,246 disproportionality signals, of which 94.8% were not confirmed by the SCI, while 54,184 SCI-detected signals were absent from FAERS, of which 8.3% involved drugs absent from the US reporting system. Discordance analysis showed that 99.8% of Danish non-confirmation reflected data availability constraints. Temporal stability was high: 98.5% of pairs received identical classifications across all seven weight scenarios, and 57.0% of final Strong signals were already detectable as Moderate or Strong in the earliest data window (2015-2016). Strong classifications were stable across weight scenarios (94.0% of Strong observations remaining Strong). ConclusionsThe SCI provides a transparent, openly accessible framework for cross-source pharmacovigilance signal prioritisation with 100% specificity and PPV against an established reference standard and stable classifications across weighting schemes. Progressive signal emergence through the Moderate tier supports its use as an early detection layer. The platform is available at aeatlas.com.

ObjectivesTo conduct weighted itemset analysis to identify patterns of polysubstance use from National Survey on Drug Use and Health (NSDUH) data. MethodsWe computed weighted support for every combination of one or more substances, termed as a drugset, used by individuals in the nationally representative NSDUH data over 5 decades (1965-2014). A computational framework for efficient representation and search of patterns of association between drugsets and demographics of user groups over time was developed. A new method for mining rules of transition between pairs of substances used within a time-interval was given. ResultsWe identified the frequent drugsets from individual substance use data, and determined their representation among different demographic groups at different intervals. An interesting pattern of use of pain relievers and tranquilizers was detected for the age-group of 26-34 years. Transition rules for heroin use in the last decade (2004-2015) of data were mined. ConclusionsComputation of weighted supports over time for every possible drugset in the data and their association with specific user groups produced a framework for generation and testing new and interesting hypotheses. The framework is useful to explore different combinations of substances used among diverse demographic groups including those that have received less attention in this problem.

Background: Retrieval of adverse event reports based on coded drug-event co-occurrence enables large-scale pharmacovigilance analyses, but yields candidate reports rather than validated cases, risking misinterpretation if used alone. Aim: To develop and apply a framework for identification and characterization of clinically meaningful case series in pharmacovigilance. Methods: We conducted two case studies. The first developed and refined the framework in an information-rich setting, focusing on drug-induced impulsivity across selected drugs; the second tested its applicability in a more routine, information-poor setting, focusing on drug-induced suicidality. Results: In Case 1, non-relevant reports were frequent for drugs with uncertain evidence and negative controls ({approx}20-40%) compared to drugs with established causal roles (4%). The emerging framework assessed relevance based on exposure, event, drug-event relationship, and population. For suspected adverse drug reactions, relevant reports were further characterized by reporter suspicion and evidentiary qualifiers supporting or refuting causality; higher suspicion was associated with more supportive qualifiers. Applied to Case 2, the framework ruled out 69% of reports as non-relevant but highlighted substantial non-assessability (17%). Conclusions: In pharmacovigilance, retrieval is not equivalent to case identification. Relevance is question-specific and shaped by how reports are captured, processed, and retrieved. This can be especially critical for emerging or bias-prone safety questions. Transparent and reproducible case definition and adjudication are essential for interpretable analyses.

When a new medical product is released to the market, a continuous pharmacovigilance process is initiated to guarantee patient safety by collecting and analyzing adverse drug reaction (ADR) reports. These ADRs are noxious and unintended responses to a medicine and are collected and analyzed in databases like EudraVigilance to determine safety profiles of the products and signal detection. The spontaneous reporting of suspected ADR could be performed by both health care workers and patients/consumers. However, the under-reporting is well known. Different initiatives have been developed to encourage reporting by health professionals, however, further work is required to support patients in taking a more active role in reporting adverse drug reactions. In this context, we will conduct a Scoping Review of the literature to inquire about what is known about actions or strategies to improve pharmacovigilance engagement by patients. We will conduct searches in MEDLINE/PubMed, Scielo, Latindex, DOAJ, CINAHL, LILACS, IAM, IMEMR, IMSEAR, WPRO, and Cochrane Library databases. Two reviewers will screen all identified records for relevance. Conflicts between reviewers will be solved by consensus. We will chart the data using data-charting forms. Findings will be reported according to PRISMA for Scoping Reviews (PRISMA-ScR). No quality assessment will be performed.