Drug Safety最新文献

{"title":"Retraction Note: Hydroxyethylstarch as a Risk Factor for Acute Renal Failure : Is a Change of Clinical Practice Indicated?","authors":"Joachim Boldt","doi":"10.1007/s40264-026-01701-6","DOIUrl":"10.1007/s40264-026-01701-6","url":null,"abstract":"","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"1111"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148410717","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":"Theoretical Framework and Key Considerations for Time-to-Onset Analysis in Spontaneous Reporting Systems.","authors":"Yoshihiro Noguchi, Yoko Ino, Satoshi Yokoyama, Tomoya Tachi, Tomoaki Yoshimura","doi":"10.1007/s40264-026-01677-3","DOIUrl":"10.1007/s40264-026-01677-3","url":null,"abstract":"<p><p>Spontaneous reporting databases play a central role in pharmacovigilance for monitoring the safety of drugs and vaccines. Conventional statistical signal detection has relied primarily on disproportionality analyses based on reporting frequencies, whereas information on the timing of adverse event onset has not been fully exploited. Time to onset (TTO), defined as the interval between the initiation of drug administration and the occurrence of an adverse event, provides complementary information that captures temporal patterns of event manifestation beyond simple occurrence counts. This review summarizes the definition, calculation, characteristics, and limitations of TTO analyses in spontaneous reporting databases and provides an overview of statistical signal detection methods incorporating TTO information. In particular, nonparametric distribution-comparison approaches, such as the Kolmogorov-Smirnov and Anderson-Darling tests, are well suited to spontaneous reporting data, in which the underlying population and exposure size are unknown. These methods enable the detection of abnormalities in the temporal structure of adverse event onset that may not be identifiable through frequency-based analyses alone. Furthermore, disproportionality analysis and TTO-based approaches are not competing methods but complementary strategies that capture different dimensions of safety signals-reporting frequency and temporal patterns-and their combined use may improve both sensitivity and interpretability of signal detection. The review also discusses survival analysis-based methods and Weibull modeling for TTO data, outlining their theoretical background and applications while emphasizing their inherent limitations when applied to spontaneous reporting systems. Because of reporting bias, incomplete time information, and the absence of non-event cases, such methods should not be used to estimate population-level risks or to infer causality. In conclusion, TTO analyses using spontaneous reporting databases should be positioned as exploratory tools for characterizing onset patterns, generating hypotheses, and informing the design of subsequent epidemiological and safety studies, rather than as a direct basis for clinical or regulatory decision making.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"1009-1021"},"PeriodicalIF":5.9,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147873838","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":"Predictive Safety in the Era of Artificial Intelligence: Promises, Limits, and the Case for an Enterprise Lifecycle Evidence Ecosystem.","authors":"Tarek A Hammad, Justine Rochon, Kate Gofman, Gianluca Trifirò, Andrew Bate","doi":"10.1007/s40264-026-01719-w","DOIUrl":"https://doi.org/10.1007/s40264-026-01719-w","url":null,"abstract":"<p><p>Drug safety remains central to patient benefit, as maximizing the value of beneficial therapies requires recognition, appropriate characterization, and effective mitigation of treatment-related adverse drug reactions (ADRs). These challenges, particularly with the advent of artificial intelligence (AI) tools, have increased interest in predictive safety as a lifecycle scientific capability that seeks to anticipate plausible harm in a timely fashion and translate evolving evidence into more informed decisions across acquisitions, clinical development, and postmarketing use. In this article, predictive safety is used primarily to mean product-level and population- or subgroup-level anticipation of plausible treatment-related harm, rather than an autonomous patient-level clinical decision-making approach. Recent advances in AI, human genetics, mechanistic modeling, translational biomarkers, and real-world data have strengthened the scientific basis for this approach. However, predictive safety should never be viewed as a promise to eliminate ADRs or as a substitute for clinical judgment. Its value would be in improving prospective ADR characterization, supporting portfolio prioritization, and enabling timely and more targeted mitigation. In some settings, notably prospective genotype-based screening before exposure, it might prevent the reaction from occurring. This article argues for the development of a governed AI-supported predictive safety ecosystem. It outlines the reasons predictive safety is needed and the context in which it is most likely to add value. It also discusses principal implementation risks, including fragmented data, unstable phenotypes, model drift, transportability failure, and misuse of probabilistic outputs, together with practical mitigation strategies and leading indicators for early governance response.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148850084","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":"Artificial Intelligence in Pharmacovigilance: Guiding Principles from the CIOMS Working Group XIV.","authors":"G Niklas Norén, Julie Durand, Vijay Kara, Elizabeth MacEntee Pileggi, Hua Carroll, Sanna Hill, Manfred Hauben, Taxiarchis Botsis, Lembit Rägo","doi":"10.1007/s40264-026-01708-z","DOIUrl":"https://doi.org/10.1007/s40264-026-01708-z","url":null,"abstract":"<p><p>Artificial intelligence (AI) is increasingly being explored to support pharmacovigilance activities including processes involving individual case safety reports. These technologies may improve efficiency and enable new analytical capabilities, but they also raise important questions regarding the responsible use of these systems in contexts that directly support regulatory decision-making and patient safety. In response to these opportunities and challenges, the Council for International Organizations of Medical Sciences (CIOMS) set up Working Group XIV on Artificial Intelligence in Pharmacovigilance to establish and promote principles and guidance for this area. The resulting CIOMS report was published in December 2025 and presents seven guiding principles: risk-based approach, human oversight, validity and robustness, transparency, data privacy, fairness and equity, and governance and accountability. To maximise stakeholder awareness and understanding, this article provides an overview of the CIOMS XIV guidance and illustrates how these principles may be applied in practice through selected use cases. The report aims to support stakeholders in developing and deploying AI systems in a manner that maintains scientific rigor, transparency, and public trust while enabling innovation to support safe use of medicines worldwide.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148826238","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":"Antiemetic Combination Use During the First Trimester and Adverse Circulatory Outcomes in Newborn Infants: A Data-Mining Analysis.","authors":"Thuy N Thai, Judith C Maro, Joshua Brown, Stephan Schmidt, Sonja A Rasmussen, Almut G Winterstein","doi":"10.1007/s40264-026-01714-1","DOIUrl":"https://doi.org/10.1007/s40264-026-01714-1","url":null,"abstract":"<p><strong>Background: </strong>Pregnant women sometimes need antiemetic combination therapy to control severe nausea and vomiting during pregnancy. Because promethazine, ondansetron, and metoclopramide have QT-prolonging effects, combination use may potentiate arrhythmic effects in mothers and cause circulatory conditions in infants, with promethazine combinations expected to exert stronger effects than metoclopramide combinations.</p><p><strong>Objective: </strong>We implemented tree-based scan statistics to screen for signals of adverse circulatory-related conditions in newborn infants following maternal exposure to promethazine-ondansetron combination compared to metoclopramide-ondansetron combination.</p><p><strong>Methods: </strong>We used Merative® Marketscan® Commercial Claims 2005-19 and Medicaid Analytic eXtract data 2005-15 to identify pregnant women aged 12-55 years with a live birth. To define combination use, pregnant women had to fill a prescription for a second antiemetic during the active days' supply of the first antiemetic and then refill the first antiemetic during the active days' supply of the second antiemetic. Our outcomes included circulatory diseases, individually and clustered within a hierarchical tree structure. We adjusted for potential confounders using propensity score quartile stratification and used Poisson tree-based scan statistics to screen for signals (p < 0.05).</p><p><strong>Results: </strong>A total of 6865 and 4186 live birth deliveries with first-trimester exposure to promethazine-ondansetron combination or metoclopramide-ondansetron combination, respectively, met our inclusion criteria. After multiplicity adjustment for over 300 outcome clusters, we found three significant signals: unspecified hypotension (relative risk = 3.71, p < 0.001), chest pain (relative risk = 2.01, p < 0.001), and essential hypertension (relative risk = 1.76, p = 0.002) among infants with maternal promethazine-ondansetron combination exposure. Unspecified hypotension likely reflects neonatal conditions, whereas unspecified chest pain and essential hypertension may represent maternal conditions based on the claims origin.</p><p><strong>Conclusions: </strong>By scanning more than 300 infant circulatory-related outcomes, we identified unspecified hypotension as a potential safety signal among newborns whose mothers were exposed to promethazine-ondansetron combination rather than metoclopramide-ondansetron combination during the first trimester. This finding warrants further investigation and illustrates the utility of tree-based scan statistics as a hypothesis-generating tool for pregnancy medication safety research.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148789631","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":"Harmonising Pharmacovigilance and Pharmacoepidemiology: Lessons from Europe for Cohesive Medicines Safety Science.","authors":"Deborah Layton, Pav Rishiraj","doi":"10.1007/s40264-026-01713-2","DOIUrl":"https://doi.org/10.1007/s40264-026-01713-2","url":null,"abstract":"","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148789537","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":"Optimising Pharmacovigilance Efficiency with MLIT (Machine Learning for Intelligent Triage): A Tool for Statistical Safety Alerts.","authors":"Luciano Ciccarelli, Olivia Mahaux, Christie Roshan, Ami Fofana, Anna Kawka, Emilia Occhipinti, Mariapia Possidente, Silvia Cenci, Jeffery L Painter, Andrew Bate","doi":"10.1007/s40264-026-01696-0","DOIUrl":"https://doi.org/10.1007/s40264-026-01696-0","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background and aim: &lt;/strong&gt;Pharmacovigilance is essential to ensuring patient safety by enabling timely identification of adverse reactions in increasingly complex and voluminous data. Routine quantitative signal detection methods generate statistical alerts for product-event pairs based on predefined criteria; however, most alerts do not warrant further investigation, creating inefficiencies and significant time demands for pharmacovigilance teams. Manual triage of these alerts is often resource-intensive, prone to variability, and challenging to audit, highlighting the need for more reliable, transparent and efficient triage strategies. This study aimed to design, develop and prospectively evaluate an explainable Machine Learning for Intelligent Triage (MLIT) tool to assist pharmacovigilance teams in reviewing statistical alerts for vaccine and drug portfolios. The objective was to enhance signal detection performance without increasing the risk of missing signals, improving operational efficiency and maintaining decision traceability and regulatory compliance.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Alert and individual case safety report data were retrieved from the company's safety and signal management databases. Feature selection was guided by prior experience with a published case completeness tool, called Clinical Utility Score for Prioritisation (CUSP), and expert input. Of several ML methods explored, eXtreme Gradient Boosting (XGBoost) emerged as the optimal algorithm, with models trained and tested using a 75/25 split dataset. Iterative model refinement was conducted using Shapley Additive Explanations analyses to ensure explainability and alignment with safety reviewers' decision-making processes. Refined models underwent prospective validation in two four-month prospective validation studies, covering over 20 products across vaccine and drug portfolios. The prospective validations assessed concordance between model predictions and reviewers' decision under real-world conditions, as well as estimated time savings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The vaccine model demonstrated robust predictive performance, achieving a weighted-average F1 score of 0.81 and an accuracy of 0.79. In the prospective validation phase, 92% of vaccine alerts were closed in alignment with the model's top-ranked prediction, while 98% were closed within the top 3 predictions. The MLIT tool also identified inconsistencies and human errors in manual triage, highlighting its potential role as a quality-control mechanism. Safety reviewers reported a 24% reduction in time spent on triage activities, and explainability analyses confirmed that the model's decision-making was conceptually aligned with safety reviewers' logic. Comparable results were observed for the drug portfolio.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;This study highlights the potential of ML-based tools to improve pharmacovigilance by enhancing signal detection performance, reducing the likelihood","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148701005","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":"AS01_E-Adjuvanted Respiratory Syncytial Virus Prefusion F Protein Vaccine (Adjuvanted RSVPreF3): Review of the First 2 Years of Post-Marketing Safety Surveillance.","authors":"Catherine Tregunno, Quentin Deraedt, Susanna Cuadripani, Céline Verheust, Philip Bryan","doi":"10.1007/s40264-026-01698-y","DOIUrl":"https://doi.org/10.1007/s40264-026-01698-y","url":null,"abstract":"<p><strong>Background: </strong>The AS01_E-adjuvanted respiratory syncytial virus prefusion F protein vaccine (adjuvanted RSVPreF3) received its first marketing authorization in May 2023 for the prevention of respiratory syncytial virus lower respiratory tract disease in individuals aged ≥ 60 years.</p><p><strong>Methods: </strong>We conducted a review of post-marketing safety surveillance data following adjuvanted RSVPreF3 administration. Spontaneous adverse events (AEs) that were reported worldwide since the first approval (3 May, 2023) to 2 May, 2025 in the GSK global safety database were analyzed.</p><p><strong>Results: </strong>Up to 2 May, 2025, 13.4 million doses of adjuvanted RSVPreF3 were distributed globally. A total of 2861 spontaneous reports, including 7012 AEs (serious: 11.0%), were received. The most frequent AEs consisted mainly of non-serious reactogenic events. Of all reports, 1164 described vaccination errors, with most frequent errors consisting of either wrong product administration (N = 243) or administration to patients of an inappropriate age (N = 204). The majority (90.3%) of vaccination error reports were not associated with any other AEs. Adjuvanted RSVPreF3 was erroneously administered to pregnant individuals in 415 cases; most reports (95.2%) occurred without associated AEs. Observed-to-expected analysis of reports of Guillain-Barré syndrome (N = 37) did not suggest an increased risk compared to background incidence of this event and the reports of atrial fibrillation (N = 29) did not indicate an excess risk.</p><p><strong>Conclusions: </strong>Review of the post-marketing surveillance data from 2 years of exposure to adjuvanted RSVPreF3 showed that the safety profile of the vaccine is consistent with that observed in clinical trials supporting an unchanged and favorable benefit-risk profile for the vaccine. Additional post-marketing surveillance is ongoing to further evaluate a potential risk of rare AEs, such as Guillain-Barré syndrome, following adjuvanted RSVPreF3 vaccination.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":5.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148653301","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":"Liver Safety of the Dual Neurokinin-1/-3 Receptor Antagonist Elinzanetant.","authors":"James H Lewis, Raúl J Andrade, Dominique Larrey, Yves Horsmans, Richard A Anderson, Mila Trajanovic, Esther Groettrup-Wolfers, Lineke Zuurman","doi":"10.1007/s40264-026-01663-9","DOIUrl":"10.1007/s40264-026-01663-9","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Elinzanetant is a selective dual neurokinin (NK)-1/-3 receptor antagonist for the treatment of moderate-to-severe vasomotor symptoms associated with menopause or caused by endocrine therapy. Elinzanetant has been approved by health authorities in several countries. The occurrence of elevated liver enzymes has been observed in clinical use with NK-3 receptor antagonists, e.g., fezolinetant and pavinetant, with recommendations for liver monitoring in the fezolinetant labeling information. Therefore, a thorough evaluation of the liver safety data from clinical trials with elinzanetant was undertaken.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To summarize available hepatic safety data from the clinical development program of elinzanetant.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;The potential risk of drug-induced liver injury (DILI) associated with elinzanetant was evaluated on the basis of the analysis of 24 phase 1 studies, three phase 2 studies (RELENT-1, SWITCH-1, NIRVANA), and four randomized, placebo-controlled phase 3 studies (OASIS 1‒4). Liver safety assessments were conducted throughout the studies as per protocol. In the phase 3 studies, a liver safety monitoring board (LSMB) performed an independent and blinded review of cases meeting close liver observation criteria, in alignment with US Food and Drug Administration DILI Guidance and the Council for International Organizations of Medical Sciences 2020 consensus on DILI. An independent data and safety monitoring board monitored the general safety of the participants.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;In the clinical program (phases 1‒3), there was no imbalance between the elinzanetant and placebo groups in alanine aminotransferase (ALT) elevations up to 5×, 10×, or 20× the upper limit of normal (ULN). In the pooled studies that included postmenopausal women, during weeks 1‒52 of treatment, ALT or aspartate transaminase (AST) elevations ≥ 3× ULN were observed in 1.2% of participants treated with elinzanetant 120 mg and in 1.0% of participants treated with placebo. In addition, there were no cases meeting Hy's law, and no indication of cholestatic injury. The LSMB assessed two cases on elinzanetant (2 out of 1697 participants, 0.12%) and one case on placebo (1 out of 1028, 0.10%) as probably related to study drug. For the cases on elinzanetant, one participant had a peak ALT level 7.4× ULN and the other had a peak ALT 3.5× ULN. Alkaline phosphatase and total bilirubin levels remained within reference range; both cases were mild and reversible. The peak ALT for the participant on placebo was 3.6× ULN. No case was classified as highly likely or definite.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;The aggregated phase 1‒3 clinical data, supported by preclinical toxicology and individual case adjudications of participants with elevated liver enzymes, suggest that there is a low risk of liver toxicity for elinzanetant. On the basis of these data, liver test monitoring was considered unneces","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"985-1002"},"PeriodicalIF":5.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13354706/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147480116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"International Medicinal Product Information Documents: A Quantitative Content Analysis of Instructions for Preventing, Mitigating, and Monitoring Adverse Drug Reactions.","authors":"Daniele Sartori, Michele Fusaroli, Jeffrey K Aronson, G Niklas Norén, Igho J Onakpoya","doi":"10.1007/s40264-026-01666-6","DOIUrl":"10.1007/s40264-026-01666-6","url":null,"abstract":"<p><strong>Introduction: </strong>Medicinal product information documents (PIDs) detail clinical characteristics and instructions for monitoring, preventing, and mitigating adverse drug reactions (ADRs). They vary across countries, but there have been no recent international comparisons. We have therefore quantified and compared the completeness of the information given in drug labelling from different countries.</p><p><strong>Methods: </strong>From the websites of 35 regulatory agencies, we retrieved the PIDs of medicinal products that were involved in signals communicated by regulators in 2014-2019. We developed a data extraction framework based on the Dose-relatedness, Time course, Susceptibility (DoTS) clinical classification of ADRs and used its implications for prevention and mitigation to score the completeness of related instructions in PIDs. To extract and classify monitoring instructions, we used a modified Systematic Instructions for Monitoring (SIM) method. PIDs had sufficiently complete instructions for prevention when the DoTS score was ≥ 5/12, and sufficiently complete monitoring instructions when the SIM score was ≥ 3/6. We used proportions of PIDs having a score ≥ 1 to determine the relative availability of clinical characteristics or instructions in a country, compared with all other countries. We quantified their pairwise disagreements using Jaccard's distance and identified clusters with similar patterns of completeness using agglomerative hierarchical clustering.</p><p><strong>Results: </strong>PIDs were available on the websites of 18 of 35 regulatory agencies. They concerned 364 distinct medicinal products, which were involved in 627 signals. Across all countries, the instructions for prevention or mitigation met sufficient completeness for a median of 30% of PIDs (IQR 28-33%), while instructions for monitoring were sufficiently complete for a median of 22% (IQR 19-25%). The information given by the European Union (EU) and Canada had the highest relative availability of clinical characteristics and prevention or mitigation instructions, with a proportion of 0.86. Canadian and EU PIDs also had the highest relative availability of monitoring instructions, with proportions of 0.79 and 0.70. Two clusters of countries showed low disagreements: Malaysia and Singapore; Australia and New Zealand.</p><p><strong>Conclusions: </strong>This study suggests that PIDs often do not contain complete instructions for prevention, mitigation, and monitoring of ADRs. Extending existing regulatory cooperation globally would enable regulators to access clinical characteristics and instructions from different regions.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"941-954"},"PeriodicalIF":5.9,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13354609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147493773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}