Frontiers in drug safety and regulation最新文献

{"title":"Exploring the scientific underpinnings of investigating safety signals: analytical insights in deciphering drug safety evidence.","authors":"Tarek A Hammad, Simon Davis, Salman Afsar","doi":"10.3389/fdsfr.2024.1445998","DOIUrl":"10.3389/fdsfr.2024.1445998","url":null,"abstract":"<p><p>Ensuring the safety of drugs is a critical aspect of healthcare. Accurate interpretation of drug safety evidence is vital to understand the safety profile and to evaluate the benefits and risks of the medicinal product. However, validity of this evidence has numerous challenges that must be carefully considered, highlighting the need for a heightened appreciation of data interpretation pitfalls. This paper aims to delve into the intricacies of analytic considerations in drug safety data interpretation aiming at providing insights into the safety profile of pharmaceutical interventions. The applicability of these analytic considerations extends to controlled and observational data as well as spontaneously reported individual case reports. Increasing the understanding of scientific underpinnings of evidence interpretation empowers Pharmacovigilance professionals to grasp fundamental concepts, use appropriate terminology, engage in meaningful discussions with colleagues conducting analyses, and critically evaluate evidence. These skills and knowledge enable them to make informed decisions and enhance their expertise in drug safety. By correct utilization of analytic approaches while appreciating their strengths and limitations, one can advance drug safety and benefit-risk research using evidence-based decision-making and ultimately ensure better healthcare outcomes for patients.</p>","PeriodicalId":521057,"journal":{"name":"Frontiers in drug safety and regulation","volume":"4 ","pages":"1445998"},"PeriodicalIF":0.0,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cohort event monitoring of safety of COVID-19 vaccines: the Italian experience of the \"ilmiovaccinoCOVID19 collaborating group\".","authors":"Nicoletta Luxi, Chiara Bellitto, Francesco Ciccimarra, Emiliano Cappello, Luca L'Abbate, Marco Bonaso, Chiara Ajolfi, Paolo Baldo, Roberto Bonaiuti, Claudio Costantino, Giovambattista De Sarro, Cristina Di Mauro, Giuseppina Fava, Marina Ferri, Alberto Firenze, Fabiana Furci, Luca Gallelli, Luca Leonardi, Giovanna Negri, Fabio Pieraccini, Elisabetta Poluzzi, Chiara Sacripanti, Elisa Sangiorgi, Ester Sapigni, Ilenia Senesi, Roberto Tessari, Luigia Trabace, Alfredo Vannacci, Francesca Venturini, Francesco Vitale, Donatella Zodda, Marco Tuccori, Gianluca Trifirò","doi":"10.3389/fdsfr.2024.1363086","DOIUrl":"10.3389/fdsfr.2024.1363086","url":null,"abstract":"<p><p><b>Introduction:</b> In 2021, the European Medicines Agency supported the \"Covid Vaccine Monitor (CVM),\" an active surveillance project spanning 13 European countries aimed at monitoring the safety of COVID-19 vaccines in general and special populations (i.e., pregnant/breastfeeding women, children/adolescents, immunocompromised people, and people with a history of allergies or previous SARS-CoV-2 infection). Italy participated in this project as a large multidisciplinary network called the \"ilmiovaccinoCOVID19 collaborating group.\" <b>Methods:</b> The study aimed to describe the experience of the Italian network \"ilmiovaccinoCOVID19 collaborating group\" in the CVM context from June 2021 to February 2023. Comprising about 30 partners, the network aimed to facilitate vaccinee recruitment. Participants completed baseline and follow-up questionnaires within 48 h from vaccination over a 6-month period. Analyses focused on those who completed both the baseline and the first follow-up questionnaire (Q1), exploring temporal trends, vaccination campaign correlation, and loss to follow-up. Characteristics of recruited vaccinees and vaccinee-reported adverse drug reactions (ADRs) were compared with passive surveillance data in Italy. <b>Results:</b> From June 2021 to November 2022, 22,384,663 first doses and 38,207,452 booster doses of COVID-19 vaccines were administered in Italy. Simultaneously, the study enrolled 1,229 and 2,707 participants for the first and booster doses, respectively. Of these, 829 and 1,879 vaccinees, respectively, completed both baseline and at least Q1 and were included in the analyses, with a significant proportion of them (57.8%/34.3%) belonging to special cohorts. Most vaccinees included in the analyses were women. Comirnaty^® (69%) and Spikevax^® (29%) were the most frequently administered vaccines. ADR rates following Comirnaty^® and Spikevax^® were higher after the second dose, particularly following Spikevax^®. Serious ADRs were infrequent. Differences were observed in ADR characteristics between CVM and Italian passive surveillance. <b>Conclusion:</b> This study confirmed the favorable safety profile of COVID-19 vaccines, with findings consistent with pivotal clinical trials of COVID-19 vaccines, although different proportions of serious ADRs compared to spontaneous reporting were observed. Continuous evaluation through cohort event monitoring studies provides real-time insights crucial for regulatory responses. Strengthening infrastructure and implementing early monitoring strategies are essential to enhance vaccine safety assessment and prepare for future pandemics.</p>","PeriodicalId":521057,"journal":{"name":"Frontiers in drug safety and regulation","volume":"4 ","pages":"1363086"},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12445166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the performance of large language models in literature screening for pharmacovigilance: a comparative study.","authors":"Dan Li, Leihong Wu, Mingfeng Zhang, Svitlana Shpyleva, Ying-Chi Lin, Ho-Yin Huang, Ting Li, Joshua Xu","doi":"10.3389/fdsfr.2024.1379260","DOIUrl":"10.3389/fdsfr.2024.1379260","url":null,"abstract":"<p><p>Pharmacovigilance plays a crucial role in ensuring the safety of pharmaceutical products. It involves the systematic monitoring of adverse events and the detection of potential safety concerns related to drugs. Manual literature screening for pharmacovigilance related articles is a labor-intensive and time-consuming task, requiring streamlined solutions to cope with the continuous growth of literature. The primary objective of this study is to assess the performance of Large Language Models (LLMs) in automating literature screening for pharmacovigilance, aiming to enhance the process by identifying relevant articles more effectively. This study represents a novel application of LLMs including OpenAI's GPT-3.5, GPT-4, and Anthropic's Claude2, in the field of pharmacovigilance, evaluating their ability to categorize medical publications as relevant or irrelevant for safety signal reviews. Our analysis encompassed N-shot learning, chain-of-thought reasoning, and evaluating metrics, with a focus on factors impacting accuracy. The findings highlight the promising potential of LLMs in literature screening, achieving a reproducibility of 93%, sensitivity of 97%, and specificity of 67% showcasing notable strengths in terms of reproducibility and sensitivity, although with moderate specificity. Notably, performance improved when models were provided examples consisting of abstracts, labels, and corresponding reasoning explanations. Moreover, our exploration identified several potential contributing factors influencing prediction outcomes. These factors encompassed the choice of key words and prompts, the balance of the examples, and variations in reasoning explanations. By configuring advanced LLMs for efficient screening of extensive literature databases, this study underscores the transformative potential of these models in drug safety monitoring. Furthermore, these insights gained from this study can inform the development of automated systems for pharmacovigilance, contributing to the ongoing efforts to ensure the safety and efficacy of pharmacovigilance products.</p>","PeriodicalId":521057,"journal":{"name":"Frontiers in drug safety and regulation","volume":"4 ","pages":"1379260"},"PeriodicalIF":0.0,"publicationDate":"2024-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443093/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progressive multifocal leukoencephalopathy reports in rheumatoid arthritis concerning different treatment patterns-an exploratory assessment using the food and drug administration adverse event reporting system.","authors":"Takeshi Honma, Kenji Onda, Koichi Masuyama","doi":"10.3389/fdsfr.2024.1334468","DOIUrl":"10.3389/fdsfr.2024.1334468","url":null,"abstract":"<p><p><b>Introduction:</b> Progressive multifocal leukoencephalopathy (PML) is a rare but potentially life-threatening brain infection caused by the John Cunningham virus. PML is a known adverse effect associated with molecular-targeted drugs and immunosuppressive agents. Recent concerns have emerged regarding the link between methotrexate (MTX) and PML. However, limited information exists on the influence of concomitant drug use in rheumatoid arthritis (RA) treatment, where various medications are often used together. <b>Methods:</b> To explore treatment patterns and patient background that affect PML reporting in RA, we analyzed data on RA cases from the Food and Drug Administration Adverse Event Reporting System (FAERS; JAPIC AERS) database between 1997 and 2019. <b>Results and Discussion:</b> Our analysis revealed significantly elevated crude and adjusted reporting odds ratios (aROR) for MTX, rituximab (RIT), azathioprine, and cyclophosphamide. When considering treatment patterns, the concomitant use of MTX and RIT showed a higher aROR than using MTX or RIT alone. Additional TNF-α inhibitors or glucocorticoids did not increase PML reports. Moreover, male sex and older age were associated with increased PML reports. While limitations are inherent in studies using spontaneous reporting data, our exploratory assessment suggests an association between PML and the combination of MTX and RIT and a higher risk in men and older patients. These findings help enhance our understanding of PML risk factors in the context of RA treatment.</p>","PeriodicalId":521057,"journal":{"name":"Frontiers in drug safety and regulation","volume":"4 ","pages":"1334468"},"PeriodicalIF":0.0,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443095/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum: An industry perspective on the use of machine learning in drug and vaccine safety.","authors":"Jeffery L Painter, Raymond Kassekert, Andrew Bate","doi":"10.3389/fdsfr.2023.1244115","DOIUrl":"10.3389/fdsfr.2023.1244115","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fdsfr.2023.1110498.].</p>","PeriodicalId":521057,"journal":{"name":"Frontiers in drug safety and regulation","volume":"3 ","pages":"1244115"},"PeriodicalIF":0.0,"publicationDate":"2023-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drug target, class level, and PathFX pathway information share utility for machine learning prediction of common drug-induced side effects.","authors":"Han Jie Liu, Jennifer L Wilson","doi":"10.3389/fdsfr.2023.1287535","DOIUrl":"10.3389/fdsfr.2023.1287535","url":null,"abstract":"<p><p><b>Introduction:</b> Development of drugs often fails due to toxicity and intolerable side effects. Recent advancements in the scientific community have rendered it possible to leverage machine learning techniques to predict individual side effects with domain knowledge features (i.e., drug classification). While several factors can be used to anticipate drug effects including their targets, pathways, and drug classes, it is unclear which domain knowledge is most predictive and whether certain domain knowledge is more important than others for different side effects. <b>Methods:</b> The goal of this project is to understand the predictive values of drug targets, drug classification (i.e., level 2 ATC codes), and protein-protein interaction networks (i.e., PathFX targets and network proteins) for machine learning prediction of 30 frequently occurring drug-induced side effects. <b>Results:</b> We compared the prediction accuracy for individual side effects of trained models across five domain knowledge combinations and discovered that level 2 ATC codes have the highest predictive value across the domain knowledge features. Logistic regression coefficient analyses further suggest that side effects are more dependent on drug targets and drug classes, and less so on PathFX targets and network proteins. <b>Discussion:</b> Our quantitative assessments may inform the development of safe and effective drugs by understanding the domain knowledge features underlying frequently occurring drug-induced side effects.</p>","PeriodicalId":521057,"journal":{"name":"Frontiers in drug safety and regulation","volume":"3 ","pages":"1287535"},"PeriodicalIF":0.0,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12443107/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145116108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}