Drug Safety最新文献

{"title":"Choosing an Index Date for Untreated Patients in External Comparator Studies.","authors":"Luis Antunes, Gerd Rippin, Eleanor Ralphs, Artis Luguzis, Kellyn Arnold, Hopin Lee","doi":"10.1007/s40264-025-01613-x","DOIUrl":"https://doi.org/10.1007/s40264-025-01613-x","url":null,"abstract":"<p><strong>Background and objective: </strong>Selecting an index date (also called time zero or baseline) can be challenging for External Comparator (EC) studies when comparing against untreated patients. Existing literature addresses methods for defining an index date for untreated patients in observational studies generally, but not for EC studies specifically, which are likely to benefit from customized approaches.</p><p><strong>Methods: </strong>A simulation study was performed to assess different index date assignments and analytical approaches in terms of bias and other performance characteristics: The first approach took the time from a major clinical event (say, diagnosis date) to treatment start as observed in the treated cohort and randomly assigned these times to the untreated cohort to derive the index dates. This approach was applied without and with the condition that the emulated index dates in the untreated cohort needed to be before the observed event times (index date emulation [IDE] and modified index date emulation approach [mIDE]). The second approach was to start the follow-up period at the diagnosis date (early index date approach [EID]) and to perform an analysis according to a time-dependent Cox model (or its generalization, e.g., a Marginal Structural Cox Model). This model was applied both in a traditional but also in a modified manner (modified early index date approach, mEID), where the modified model coded the treatment cohorts before the true (treated patients) and emulated (untreated patients, using IDE) treatment start dates to belong to a third treatment category. This allowed the treatment comparison of interest to be restricted to the time after the true and emulated treatment start dates.</p><p><strong>Results: </strong>The IDE and mEID approaches were shown to be unbiased with identical performance, while mIDE and EID exhibited significant bias.</p><p><strong>Conclusions: </strong>We showed that our EC analysis approach based on emulated index dates for untreated patients constitutes a valid concept, which may be advantageous for many external comparator studies.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145184931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on: Association of GLP1-Receptor Agonists with Risk of Hepatocellular Carcinoma: A Retrospective Cohort Study.","authors":"Chien-Hsiang Weng, Philip A Chan, Joseph Magagnoli, Charles L Bennett","doi":"10.1007/s40264-025-01617-7","DOIUrl":"10.1007/s40264-025-01617-7","url":null,"abstract":"","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Authors' Response to Weng et al.'s Comment on \"Association of GLP1-Receptor Agonists with Risk of Hepatocellular Carcinoma: A Retrospective Cohort Study\".","authors":"Ishak A Mansi, Moheb Boktor","doi":"10.1007/s40264-025-01616-8","DOIUrl":"https://doi.org/10.1007/s40264-025-01616-8","url":null,"abstract":"","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Content Analysis of Promotional Materials for Prescription Drugs Authorized Under Emergency Use Authorization.","authors":"Bridget Kelly, Kathryn J Aikin, Helen W Sullivan, Gabe Madson, Anne-Celine Jeffroy-Menard, Diamond Hawkins, Kathy Vu, Shirley Liu, Lauren McCormack, Sandra Crouse Quinn","doi":"10.1007/s40264-025-01610-0","DOIUrl":"https://doi.org/10.1007/s40264-025-01610-0","url":null,"abstract":"<p><strong>Introduction: </strong>Under Section 564 of the Federal Food, Drug and Cosmetic Act, the United States (US) Food and Drug Administration (FDA) may, pursuant to a declaration by the US Department of Health and Human Services Secretary, based on one of four types of determinations, authorize an unapproved product or unapproved uses of an approved product for emergency use. Although sponsors are not prohibited from promoting products with Emergency Use Authorizations (EUAs), little is known about how they promote these products.</p><p><strong>Objectives: </strong>The aim of this study was to investigate how EUAs are being described in promotional materials disseminated to health care providers (HCPs) and consumer audiences.</p><p><strong>Methods: </strong>A content analysis was conducted on promotional materials for drugs authorized under an EUA that were submitted to the FDA between April 2020 and April 2023. Each material was coded for the presence or absence and location of certain words, phrases, or resources relating to EUAs and product risk information. Statistical analyses include descriptive statistics and bivariate analyses comparing materials created for consumer and HCP audiences. Readability statistics were also conducted for consumer materials.</p><p><strong>Results: </strong>The sample included 423 promotional materials. Most materials included risk information; however, few included a formal definition of an EUA. Materials for HCPs were more likely to contain links to fact sheets and other information and resources related to EUAs. The reading level of consumer materials was very difficult (requiring graduate-level education).</p><p><strong>Conclusion: </strong>Although most of the materials contained risk and benefit information in promotional materials about EUAs, improvements could be made through the inclusion of a specific definition of \"EUA\" and more prominent information about limitations of use in consumer materials. Readability could also be improved for consumer materials by applying plain language principles.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145148419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Charting and Sidestepping the Pitfalls of Disproportionality Analysis.","authors":"Michele Fusaroli, Daniele Sartori, Eugène P van Puijenbroek, G Niklas Norén","doi":"10.1007/s40264-025-01604-y","DOIUrl":"https://doi.org/10.1007/s40264-025-01604-y","url":null,"abstract":"<p><p>Disproportionality analysis is used by many pharmacovigilance organizations for detecting and assessing signals of potential adverse drug reactions. However, its goal is often misunderstood and the approach misapplied, leading to erroneous conclusions due to neglected violated assumptions. In this paper we illustrate how simplistic use and interpretation of disproportionality analysis can lead to incorrect conclusions. Using VigiBase, the WHO global database of adverse event reports, and the Information Component disproportionality metric, we provide selected examples to highlight common sources of error that can introduce spurious disproportionalities or lead to missing important signals: confounding (by age, sex, indication, comedication), effect modification (by age), notoriety bias, masking, misclassification (by miscoding, incomplete or imprecise event retrieval), neglecting report utility, and violated independence assumption. Additionally, we present how sophisticated analyses may introduce new biases or amplify existing ones, such as collider bias or masking amplification. Due to its pitfalls, disproportionality analysis plays a supportive rather than decisive role in signal detection and assessment. Careful design and interpretation of disproportionality analysis, with appropriate subgrouping and clinical assessment, are essential. While subgrouping can mitigate some pitfalls, it reduces sample size and may introduce or amplify existing biases and needs to be used with care. Further development of tools to detect and mitigate biases in disproportionality analyses, and to assess their risk of bias, is needed.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implied ADR-Admissions: A Cohort Study Introducing a Novel Administrative Data Approach for Identifying Drug-Related Hospitalisations.","authors":"Miriam Schechner, Marietta Rottenkolber, Clara Weglage, Vita Brišnik, Annette Haerdtlein, Bruce Guthrie, Ulrich Jaehde, Eva Grill, Tobias Dreischulte","doi":"10.1007/s40264-025-01614-w","DOIUrl":"https://doi.org/10.1007/s40264-025-01614-w","url":null,"abstract":"<p><strong>Background: </strong>Adverse drug reactions (ADRs) are a key contributor to unplanned hospitalisations, particularly in patients with polypharmacy. Traditional detection methods, such as expert reviews or diagnostic coding, are limited in scalability and sensitivity.</p><p><strong>Objective: </strong>This study introduces and evaluates a novel scalable method, implied ADR-admissions, that links drug exposures to adverse events using administrative data to improve the detection of plausible drug-related hospitalisations.</p><p><strong>Methods: </strong>A retrospective cohort study was conducted using linked health data from 123,662 individuals aged ≥ 40 years with polypharmacy in two Scottish health boards. Implied ADR-admissions were defined as emergency hospitalisations with one of 15 adverse events plausibly linked to drug exposure (based on a structured consensus process) within the prior 90 days. Incidence was compared with three existing approaches: adverse event-admissions (regardless of drug exposure), explicit ADR-admissions (explicitly coded as ADRs) and preventable ADR-admissions (with prior medication error). Multivariate logistic regression was used to identify predictors of implied ADR-admissions.</p><p><strong>Results: </strong>Over 1 year, 2.6% experienced an implied ADR-admission, compared with 5.7% with adverse event-admissions, and 0.4% with explicit ADR-admissions. For gastrointestinal bleeding, the implied ADR-admission incidence was 20 times higher than the preventable ADR-admission incidence. Key predictors for implied ADR-admissions included prior hypokalaemia-related hospitalisation and use of potentially inappropriate medications.</p><p><strong>Conclusions: </strong>The implied ADR-admission approach has improved specificity relative to broad adverse event definitions while enhancing sensitivity beyond methods that rely solely on explicit ADR codes or pre-specified medication errors. It offers a scalable automated tool for pharmacovigilance, though further validation is needed prior to routine use in medication safety monitoring.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Characterizing the FDA Adverse Event Reporting System (FAERS) as a Network to Improve Pattern Discovery.","authors":"Raechel Davis, Oanh Dang, Suranjan De, Robert Ball","doi":"10.1007/s40264-025-01609-7","DOIUrl":"https://doi.org/10.1007/s40264-025-01609-7","url":null,"abstract":"<p><strong>Introduction: </strong>In drug-safety monitoring systems, adverse events (AEs) associated with the use of medical products often consist of complex patterns of clinical events. Network analysis (NA) was used for pattern recognition and characterizing the Vaccine Adverse Event Reporting System (VAERS), but limited applications of NA to the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) left its network description incomplete.</p><p><strong>Methods: </strong>In this analysis, the network properties of FAERS were characterized and leveraged to facilitate pattern discovery. Reported AE information in FAERS is represented using preferred terms (PTs) in Medical Dictionary for Regulatory Activities terminology. The FAERS subsets were analyzed with drugs and PTs as nodes and interconnections as edges. Global characteristics, like the scale-free nature of the distribution, were examined to explore theoretical and structural considerations. Metrics that assess connectivity and edge weighting algorithms based on report co-occurrence or clustering were applied.</p><p><strong>Results: </strong>Serious AE reports from 2016 to 2023 (2,062,099) were represented as a network of 20,965 nodes (16,847 PTs and 4116 drugs) with more than four million interconnections. Characteristics of FAERS subnetworks were determined with heavy-tailed degree distributions, high local clustering, and low diameters. Complexities related to structural and evolutionary characteristics were revealed as the log-normal model fits the degree distribution better than the power law.</p><p><strong>Conclusions: </strong>Network-based techniques identified clinically relevant patterns and clustering patterns representative of known adverse drug reactions. Comparisons to VAERS reveal similarities in networks of AE reporting systems. This initial systematic application of NA to FAERS describes the overall network characteristics of the FAERS database and provides insight into the use of network applications in drug safety research.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Managing Drug Interactions with Enzalutamide in Patients with Prostate Cancer: A Podcast.","authors":"Alicia K Morgans, Brooke Looney, Jesse Mack, Judeth Bianco","doi":"10.1007/s40264-025-01600-2","DOIUrl":"https://doi.org/10.1007/s40264-025-01600-2","url":null,"abstract":"<p><p>Enzalutamide is an oral androgen receptor signaling inhibitor used in the treatment of prostate cancer. Elderly patients with prostate cancer commonly have age-related comorbidities that require concomitant, active treatment. As a moderate inducer of the cytochrome P450 (CYP) enzymes CYP2C9 and CYP2C19, and a strong inducer of CYP3A4, there is potential for drug-drug interactions (DDIs) when enzalutamide is coadministered with other drugs that are CYP3A4 substrates-resulting in loss of efficacy or increased risk of unintended drug-related adverse events. In this podcast, we describe enzalutamide including its dosing, pharmacokinetics, and potential for interaction with coadministered drugs using several hypothetical patient cases with real-world clinical implications. Discussion of each patient case will highlight management strategies and illustrate that nearly all enzalutamide drug-drug interactions can be effectively managed with appropriate knowledge of which drugs pose interaction risks, when dose adjustments are indicated, and when alternative drugs can be substituted. Supplementary file1 (MP4 192541 KB).</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145069260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Development and Use of Office of New Drugs Custom Medical Queries for Safety Analyses of Clinical Trial Data.","authors":"Scott Proestel, Vaishali Popat, Ellis F Unger, Linda J B Jeng","doi":"10.1007/s40264-025-01582-1","DOIUrl":"https://doi.org/10.1007/s40264-025-01582-1","url":null,"abstract":"<p><p>The evaluation of safety data by the US Food and Drug Administration (FDA) is a critical step in the review of marketing applications for drugs and biologics. It can be difficult to identify a safety signal, and important signals can be missed if not evaluated comprehensively. Adverse events reported by study participants constitute a major source of safety data, and while previously established standard term groupings have been useful for analysis (e.g., Standardised MedDRA^® Queries), the Office of New Drugs (OND) at the FDA determined a need for more clinically meaningful groupings specifically designed for use in premarket drug safety evaluation. To improve safety signal detection and analyses of adverse reactions, OND developed standard groupings of adverse event terms known as OND Custom Medical Queries (OCMQs). OCMQs are intended to capture clinically meaningful groupings (i.e., safety signals) represented in premarketing data. OND has seen great utility in OCMQs during premarket drug safety evaluations, as they have improved OND's ability to detect safety signals and to distinguish and quantify adverse reactions in clinical trial data.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145063831","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Algorithms to Identify Major Congenital Malformations in Routinely Collected Healthcare Data: A Systematic Review.","authors":"Melanie H Jacobson, Meritxell Sabidó, Ana Sofia Afonso, Adebola Ajao, Eman A Alghamdi, Susan E Andrade, Dimitri Bennett, Vineetkumar Kharat, Marie-Laure Kürzinger, Maryline Le Noan-Lainé, Ditte Mølgaard-Nielsen, Gayle Murray, Elena Rivero-Ferrer, Sandra Lopez-Leon","doi":"10.1007/s40264-025-01606-w","DOIUrl":"https://doi.org/10.1007/s40264-025-01606-w","url":null,"abstract":"<p><strong>Introduction: </strong>Major congenital malformations (MCMs) are a primary outcome of interest in pregnancy safety studies.</p><p><strong>Objective: </strong>This study aimed to identify and summarize algorithms used to identify MCMs in routinely collected healthcare data sources in the USA, Canada, and Europe by conducting a systematic literature review.</p><p><strong>Methods: </strong>We developed a search strategy to identify studies containing algorithms for MCMs from January 1, 2010, to April 11, 2025. Search terms included those related to MCMs as an outcome, routinely collected healthcare data, epidemiologic designs likely to incorporate algorithms, and pregnant individuals and/or infants. Study review and data extraction was conducted in duplicate using a standardized data collection form.</p><p><strong>Results: </strong>Among the initially identified 2242 studies, 974 were selected for full-text review. Of these, 70.3% were excluded, leaving 289 studies. Over half (58.1%) of the included studies were from Europe, predominantly from Nordic countries using national register data (N = 135; 80.4%). Studies using claims (18.0%) or hospital discharge data (16.3%) were also common. Although there was heterogeneity in the timing of MCM assessment, 55.7% of studies collected MCMs through the infant's first year of life. Overall, algorithms varied across data source type and geography in the codes specified, rules, utilization of maternal versus infant records, and coding system. There were 27 (9.3%) validation studies, 70.4% of which were based on claims and/or electronic health record data only. Most had positive predictive values >70%, though this varied according to MCM type or anatomical site.</p><p><strong>Conclusion: </strong>We provide the first comprehensive systematic literature review of algorithms used to identify MCMs in routinely collected healthcare data, aiding researchers in their ability to generate reliable evidence in pregnancy safety pharmacoepidemiology.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145052430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}