Drug Safety最新文献

{"title":"Sequential Epidemiological Analyses of Real-World Data: A Tool for Prospective Drug Safety Surveillance from the Rofecoxib Example.","authors":"Saad Hanif Abbasi, Lars Christian Lund, Jesper Hallas, Anton Pottegård","doi":"10.1007/s40264-024-01512-7","DOIUrl":"https://doi.org/10.1007/s40264-024-01512-7","url":null,"abstract":"<p><strong>Introduction: </strong>Large administrative healthcare databases can be used for near real-time sequential safety surveillance of drugs as an alternative approach to traditional reporting-based pharmacovigilance. The study aims to build and empirically test a prospective drug safety monitoring setup and perform a sequential safety monitoring of rofecoxib use and risk of cardiovascular outcomes.</p><p><strong>Methods: </strong>We used Danish population-based health registers and performed sequential analysis of rofecoxib use and cardiovascular outcomes using case-time-control and cohort study designs from January 2000 to September 2004. Each monitoring period added 6 months of data until the end of the study period. In the case-time-control study, incident cases of myocardial infarction (MI) and ischemic stroke were identified and matched with up to five time controls on age, sex, and calendar time. Exposure status on the date of diagnosis was assessed using a 60-day focal window, with reference windows 120, 180, and 240 days prior to the diagnoses. In the cohort study, incident users of rofecoxib were matched up to 1:4 with ibuprofen users (active comparators) using high-dimensional disease risk scores and were followed for 60 days.</p><p><strong>Results: </strong>The earliest association between rofecoxib use and the risk of MI was seen in study period 2 for case-time-control design (OR 1.42, 95% CI 1.04-1.93) and in study period 7 for the cohort study design (RR 1.22; 95% CI 1.02-1.47).</p><p><strong>Conclusions: </strong>Our prospective drug safety monitoring setup showed that the risk of MI could have been detected 3.5 years before the ultimate market withdrawal of rofecoxib. However, further research is needed to validate this approach.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143045976","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":"Antiretroviral Use for HIV Prevention During Pregnancy: The Need to Strengthen Regulatory and Surveillance Systems in Africa.","authors":"Robin Schaefer, L Donaldson, A Chigome, M Escudeiro Dos Santos, S Lamprianou, N Ndembi, J I Nwokike, P Nyambayo, V Palmi, F Renaud, M Gonzalez Tome, V Miller","doi":"10.1007/s40264-024-01494-6","DOIUrl":"https://doi.org/10.1007/s40264-024-01494-6","url":null,"abstract":"<p><p>HIV-prevention efforts focusing on women of child-bearing potential are needed to end the HIV epidemic in the African region. The use of antiretroviral drugs as pre-exposure prophylaxis (PrEP) is a critical HIV prevention tool. However, safety data on new antiretrovirals during pregnancy are often limited because pregnant people are excluded from drug development studies. Calls from communities, healthcare professionals, and regulators to improve the information supporting decision-making around the use of medical products during pregnancy have been increasing. Post-marketing safety surveillance is an essential tool for detecting adverse outcomes and evaluating real-world, longer-term effects of drugs. Detecting and evaluating uncommon pregnancy outcomes requires large sample sizes, highlighting the benefits of and need for safety surveillance. Surveillance systems vary widely across Africa, and the need for enhanced surveillance of PrEP use during pregnancy highlights the limitations of current regulatory and surveillance systems. Challenges include weak regulation and insufficient resources. Pooling of resources and regulatory harmonization could address resource challenges. The African Medicines Agency, as a specialized agency of the African Union, has the potential to improve African medical product regulation, including post-marketing safety surveillance. This can strengthen regulation and ensure that market authorization holders meet their responsibility to invest in post-marketing surveillance systems, such as pregnancy registries. At the same time, independent post-marketing studies are needed to ensure generation of essential safety data. The Forum for Collaborative Research has initiated a project to facilitate interactions between regulators in Africa, the USA, and Europe, as well as other stakeholders, and to work toward consensus on safety data generation from PrEP during pregnancy before and after marketing authorization.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143051984","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":"Empowering African Expertise: Enhancing Safety Data Integration and Signal Detection for COVID-19 Vaccines Through the African Union Smart Safety Surveillance Joint Signal Management Group.","authors":"Victoria Prudence Nambasa, Hannah May Gunter, Modupe Bamidele Adeyemo, Neetesh Yanish Bhawaneedin, Marc Blockman, George Tsey Sabblah, John Owusu Gyapong, Eric Muriithi Guantai, Tamrat Abebe, Workeabeba Abebe, Henry Jeremy Lawson, Mercedes Chawada Leburu, Abdullahi Mohammed, Kwame Amponsa-Achiano, Mafora Florah Matlala, Uchenna Geraldine Elemuwa, Hudu Mogtari, Alexander Kwadwo Nyarko, Marione Schönfeldt, Mercy Kamupira, Kerrigan McCarthy, Yohannes Lakew Tefera, Asnakech Alemu, Kabir Mawashi Yusuf, Obi Emelife, Ladji Sidibe, Kudakwashe Dandajena, Kenneth Onu, Mojisola Christianah Adeyeye, Delese Mimi Darko, Heran Gerba, Boitumelo Semete, Fred Siyoi, Aggrey Ambali, Johanna Catharina Meyer","doi":"10.1007/s40264-024-01493-7","DOIUrl":"https://doi.org/10.1007/s40264-024-01493-7","url":null,"abstract":"<p><strong>Introduction: </strong>The COVID-19 pandemic accelerated new vaccine development. Limited safety data necessitated robust global safety surveillance to accurately identify and promptly communicate potential safety issues. The African Union Smart Safety Surveillance (AU-3S) program established the Joint Signal Management (JSM) group to support identification of potential vaccine safety concerns in five pilot countries (Ethiopia, Ghana, Kenya, Nigeria, South Africa), accounting for approximately 35% of the African population.</p><p><strong>Objective: </strong>Our objective was to provide an overview of the JSM group's role in supporting signal management activities for the AU-3S program during the COVID-19 pandemic.</p><p><strong>Methods: </strong>Spontaneous, electronically reported COVID-19 vaccine adverse events following immunization (AEFI) from each country's safety data were integrated into the interim Data Integration and Signal Detection system. Statistical disproportionality methods were used to identify and review vaccine-event combinations (VECs) for potential safety concerns. The JSM group-which comprised pharmacovigilance and subject matter experts from National Medicine Regulatory Authorities, Expanded Programs on Immunization, and vaccine safety committees-conducted signal detection activities on cross-country safety data and provided recommendations.</p><p><strong>Results: </strong>From April 2021 to December 2023, a total of 48,294 spontaneously reported AEFI were analyzed for six COVID-19 vaccines (NRVV Ad [ChAdOx1 nCoV-19]; Ad26.COV2.S; Elasomeran; Tozinameran; Covid-19 vaccine [Vero Cell], Inactivated; NRVV Ad26 [Gam-Covid-Vac]) administered in Ethiopia (34.6%), Nigeria (30.3%), South Africa (16.9%), Ghana (13.5%), and Kenya (4.7%). Overall, 2,742 VECs were validated. A causal association between the COVID-19 vaccines and the reported AEFI cannot be inferred, as data were reported spontaneously. JSM group recommendations included monitoring for further evidence, no immediate action required, engaging marketing authorization holder(s) for additional information, or sensitizing healthcare providers and/or the public about events. Although no new safety signals were identified, nine safety-related recommendations were issued, including patient and healthcare provider education.</p><p><strong>Conclusions: </strong>The JSM group established a scalable and replicable model for future signal management of other priority health products in low- and middle-income countries, fostering ongoing collaboration and capacity building. Knowledge and experience gained from this pilot initiative will guide stakeholders in future safety surveillance initiatives within the African continent.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143022752","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":"Clinical Relatedness and Stability of vigiVec Semantic Vector Representations of Adverse Events and Drugs in Pharmacovigilance.","authors":"Nils Erlanson, Joana Félix China, Henric Taavola, G Niklas Norén","doi":"10.1007/s40264-024-01509-2","DOIUrl":"https://doi.org/10.1007/s40264-024-01509-2","url":null,"abstract":"<p><strong>Introduction: </strong>Individual case reports are essential to identify and assess previously unknown adverse effects of medicines. On these reports, information on adverse events (AEs) and drugs are encoded in hierarchical terminologies. Encoding differences may hinder the retrieval and analysis of clinically related reports relevant to a topic of interest. Recent studies have explored the use of data-driven semantic vector representations to support analysis of pharmacovigilance data.</p><p><strong>Objective: </strong>This study aims to evaluate the stability and clinical relatedness of vigiVec, a semantic vector representation for codes of AEs and drugs.</p><p><strong>Methods: </strong>vigiVec is a published adaptation to pharmacovigilance of the publicly available Word2Vec model, applied to structured data instead of free text. It provides vector representations for MedDRA^® Preferred Terms and WHODrug Global active ingredients, learned from reporting patterns in VigiBase, the WHO global database of adverse event reports for medicines and vaccines. For this study, a 20-dimensional Skip-gram architecture with window size 250 was used. Our evaluation focused on nearest neighbors identified by the cosine similarity of vigiVec vector representations. Clinical relatedness was measured through term intruder detection, whereby a medical doctor was tasked to identify a randomly selected term-the intruder-included among the four nearest neighbors to a specific AE or drug. Stability was measured as the average overlap in the ten nearest neighbors for each AE or drug, in repeated fittings of vigiVec.</p><p><strong>Results: </strong>Among the ten nearest neighbors, 1.8 AEs on average belonged to the same MedDRA High Level Term (HLT; e.g., coagulopathies), and 1.3 drugs belonged to the same Anatomical Therapeutic Chemical level 3 (ATC-3; e.g., opioids). In the intruder detection task, when neighbors and intruders were both chosen from the same HLT, the intruder detection rate was 46%. When selected from different HLTs, it was 79%. By random chance, we should expect 20% (1 in 5). Corresponding rates for drugs were 42% in same ATC-3 and 65% in different ATC-3. The stability of nearest neighbors was 80% for AEs and 64% for drugs.</p><p><strong>Conclusion: </strong>Nearest neighbors identified with vigiVec are stable and show high level of clinical relatedness. They are often from different parts of the existing hierarchies and complement these.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143002041","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":"Multi-Stakeholder Call to Action for the Future of Vaccine Post-Marketing Monitoring: Proceedings from the First Beyond COVID-19 Monitoring Excellence (BeCOME) Conference.","authors":"Vincent Bauchau, Kaatje Bollaerts, Phil Bryan, Jim Buttery, Kourtney Davis, Robert T Chen, Daniel R Feikin, Antonella Fretta, Sarah Frise, Sonja Gandhi-Banga, Hector S Izurieta, Corinne Jouquelet-Royer, Alena Khromava, Lin Li, Raj Long, Sarah MacDonald, Lydie Marcelon, Robert Massouh, Wilhelmine Meeraus, Flor M Munoz, Karen Naim, Dale Nordenberg, Hanna Nohynek, Heather Rubino, Daniel A Salmon, Sarah Sellers, Laurence Serradell, Laurence Torcel-Pagnon, Jamie Wilkins","doi":"10.1007/s40264-024-01510-9","DOIUrl":"https://doi.org/10.1007/s40264-024-01510-9","url":null,"abstract":"","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946472","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":"Principles and Practice of Pharmacovigilance and Drug Safety : Jimmy Jose, Anthony R. Cox, Vibhu Paudyal, editors. Springer, 2024. Hardcover ISBN 978-3-031-51088-5, Softcover ISBN 978-3-031-51091-5, eBook ISBN 978-3-031-51089-2.","authors":"Ian W Boyd","doi":"10.1007/s40264-024-01508-3","DOIUrl":"https://doi.org/10.1007/s40264-024-01508-3","url":null,"abstract":"","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946485","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":"Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.","authors":"Su Golder, Dongfang Xu, Karen O'Connor, Yunwen Wang, Mahak Batra, Graciela Gonzalez Hernandez","doi":"10.1007/s40264-024-01505-6","DOIUrl":"https://doi.org/10.1007/s40264-024-01505-6","url":null,"abstract":"<p><strong>Background: </strong>Natural language processing (NLP) and machine learning (ML) techniques may help harness unstructured free-text electronic health record (EHR) data to detect adverse drug events (ADEs) and thus improve pharmacovigilance. However, evidence of their real-world effectiveness remains unclear.</p><p><strong>Objective: </strong>To summarise the evidence on the effectiveness of NLP/ML in detecting ADEs from unstructured EHR data and ultimately improve pharmacovigilance in comparison to other data sources.</p><p><strong>Methods: </strong>A scoping review was conducted by searching six databases in July 2023. Studies leveraging NLP/ML to identify ADEs from EHR were included. Titles/abstracts were screened by two independent researchers as were full-text articles. Data extraction was conducted by one researcher and checked by another. A narrative synthesis summarises the research techniques, ADEs analysed, model performance and pharmacovigilance impacts.</p><p><strong>Results: </strong>Seven studies met the inclusion criteria covering a wide range of ADEs and medications. The utilisation of rule-based NLP, statistical models, and deep learning approaches was observed. Natural language processing/ML techniques with unstructured data improved the detection of under-reported adverse events and safety signals. However, substantial variability was noted in the techniques and evaluation methods employed across the different studies and limitations exist in integrating the findings into practice.</p><p><strong>Conclusions: </strong>Natural language processing (NLP) and machine learning (ML) have promising possibilities in extracting valuable insights with regard to pharmacovigilance from unstructured EHR data. These approaches have demonstrated proficiency in identifying specific adverse events and uncovering previously unknown safety signals that would not have been apparent through structured data alone. Nevertheless, challenges such as the absence of standardised methodologies and validation criteria obstruct the widespread adoption of NLP/ML for pharmacovigilance leveraging of unstructured EHR data.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946471","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":"Pharmacovigilance in the Community: A Special-Interest Group of the International Society of Pharmacovigilance.","authors":"Mohamed A Elhawary, Rebecca Noss, Loubna Alj, Manal Younus, Mayada Alkhakany, Hadir Rostom, Angela Caro-Rojas, Thamir M Alshammari","doi":"10.1007/s40264-024-01513-6","DOIUrl":"https://doi.org/10.1007/s40264-024-01513-6","url":null,"abstract":"","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946484","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":"Disproportionality Analysis and Characterisation of Medication Errors in EudraVigilance: Exploring Findings on Sexes and Age Groups.","authors":"Victor Pera, Jan A Kors, Erik M van Mulligen, Marcel de Wilde, Peter R Rijnbeek, Katia M C Verhamme","doi":"10.1007/s40264-024-01478-6","DOIUrl":"10.1007/s40264-024-01478-6","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;While medication errors (MEs) have been studied in the European Medicines Agency's EudraVigilance, extensive characterisation and signal detection based on sexes and age groups have not been attempted.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;The aim of this study was to characterise all ME-related individual case safety reports in EudraVigilance and explore notable signals of disproportionate reporting (SDRs) among sexes and age groups for the 30 most frequently reported drugs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Individual case safety reports were used from EudraVigilance reported between 2002 and 2021. An ME was defined as any Preferred Term from the narrow Standardised Medical Dictionary for Regulatory Activities&lt;sup&gt;®&lt;/sup&gt; Query. Signals of disproportionate reporting were selected based on a lower boundary of the 95% confidence interval ≥ 1 of the reporting odds ratio, and at least 3 individual case safety reports. Analysed subgroups were female individuals, male individuals, and age groups 0-1 month, 2 months to 2 years, 3-11 years, 12-17 years, 18-64 years, 65-85 years, and &gt;85 years. Heatmaps were utilised as a visual aid to identify striking SDRs.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Of the 9,662,345 EudraVigilance reports, 267,262 (2.8%) contained at least one ME, with a total of 300,324 MEs, for 429,554 drugs. The most reported ME was \"Inappropriate schedule of product administration\" (52,646; 17.5%), followed by \"Incorrect dose administered\" (32,379; 10.8%) and \"Wrong technique in product usage process\" (26,831; 8.9%). Individual case safety reports with MEs were most frequently related to female individuals (148,009; 55.4%), most often submitted by healthcare professionals (155,711; 58.3%), originated predominantly from the USA (98,716; 36.9%), followed by France (26,678; 10.0%), and showed a median reported age of 50 years (interquartile range: 26-68). Most ME individual case safety reports (158,991; 59.5%) were associated with a serious health outcome. A total of 847 SDRs were identified, based on the entire EudraVigilance database; for subgroups, the number of SDRs ranged from 84 for the age group 0-1 month to 749 for female individuals. Signals of disproportionate reporting for female individuals and male individuals were very similar. Most MEs were reported for the vaccine against human papillomavirus (Anatomical Therapeutic Chemical [ATC]: J07BM01; 11,086 MEs, 57% being \"inappropriate schedule of product administration\"), with reporting odds ratios that range from 1.5 to 47.0 among age groups. The SDR for the live-attenuated vaccine against herpes zoster (ATC: J07BK02) had a reporting odds ratio that ranged from 26.6 to 78.1 among all subgroups. Signals of disproportionate reporting for oxycodone (ATC: N02AA05; 847 cases of \"Accidental overdose\", 35%), risperidone (ATC: N05AX08; 469 cases \"Inappropriate schedule of product administration\", 22.3%) and rivaroxaban (ATC: B01AF01; 1,377 cases of \"Incorrect dose ad","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"59-74"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11711134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142282082","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":"An Integrated Approach for Representing Knowledge on the Potential of Drugs to Cause Acute Kidney Injury.","authors":"Daniel Fernández-Llaneza, Romy M P Vos, Joris E Lieverse, Helen R Gosselt, Sandra L Kane-Gill, Teun van Gelder, Joanna E Klopotowska","doi":"10.1007/s40264-024-01474-w","DOIUrl":"10.1007/s40264-024-01474-w","url":null,"abstract":"<p><strong>Introduction and objective: </strong>The recent rise in acute kidney injury (AKI) incidence, with approximately 30% attributed to potentially preventable adverse drug events (ADEs), poses challenges in evaluating drug-induced AKI due to polypharmacy and other risk factors. This study seeks to consolidate knowledge on the drugs with AKI potential from four distinct sources: (i) bio(medical) peer-reviewed journals; (ii) spontaneous reporting systems (SRS); (iii) drug information databases (DIDs); and (iv) NephroTox website. By harnessing the potential of these underutilised sources, our objective is to bridge gaps and enhance the understanding of drug-induced AKI.</p><p><strong>Methods: </strong>By searching Medline, studies with lists of drugs with AKI potential established through consensus amongst medical experts were selected. A final list of 63 drugs was generated aggregating the original studies. For these 63 drugs, the AKI reporting odds ratios (RORs) using three SRS databases, the average frequency of ADEs from four different DIDs and the number of published studies identified via NephroTox was reported.</p><p><strong>Results: </strong>Drugs belonging to the antivirals, antibacterials, and non-steroidal anti-inflammatory pharmacological classes exhibit substantial consensus on AKI potential, which was also reflected in strong ROR signals, frequent to very frequent AKI-related ADEs and a high number of published studies reporting adverse kidney events as identified via NephroTox. Renin-angiotensin aldosterone system inhibitors and diuretics also display comparable signal strengths, but this can be attributed to expected haemodynamic changes. More variability is noted for proton-pump inhibitors.</p><p><strong>Conclusions: </strong>By integrating four disjointed sources of knowledge, we have created a novel, comprehensive resource on drugs with AKI potential, contributing to kidney safety improvement efforts.</p>","PeriodicalId":11382,"journal":{"name":"Drug Safety","volume":" ","pages":"43-58"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11711143/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142343747","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}