Background and objective: Upadacitinib is indicated for diseases affecting persons of childbearing potential including rheumatoid arthritis, psoriatic arthritis, axial spondyloarthritis, atopic dermatitis, Crohn's disease, and ulcerative colitis; however, teratogenicity was observed in animal studies. Given the potential for human fetal risk, pregnancy avoidance measures were required during clinical trials. This analysis describes pregnancy outcomes in patients exposed to upadacitinib during pregnancy.
Methods: Clinical trial and postmarketing cases of in utero exposure to upadacitinib were identified in AbbVie's safety database through 25 April, 2023. Analysis of clinical trial cases and postmarketing reports are presented separately; prospective and retrospectively reported pregnancy outcomes are integrated for each. Descriptive rates are presented to summarize outcomes.
Results: There were 128 maternal upadacitinib-exposed pregnancies with known outcomes identified; 80 and 48 pregnancies were reported in clinical trials and the postmarketing setting, respectively. In clinical trials (mean in utero exposure of 5 weeks, 3 days), live births (54%), spontaneous abortions (24%), elective terminations (21%), and ectopic pregnancy (1%) were reported. There was one report of a congenital malformation: a 35-week infant with an atrial septal defect. In postmarketing cases, live births (46%), spontaneous abortions (38%), elective terminations (15%), and ectopic pregnancy (2%) were reported.
Conclusions: As the data are limited for in utero exposure to upadacitinib, definitive conclusions cannot be drawn regarding the effect of upadacitinib on pregnancy outcomes. Rates of adverse pregnancy outcomes with upadacitinib exposure were comparable to rates observed in the general population or patients with autoimmune inflammatory diseases. To date, no apparent evidence of teratogenicity exists in the analyses of human pregnancies exposed to upadacitinib during the first trimester.
Background: Immunocompromised individuals are at high risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and subsequent severe or fatal coronavirus disease 2019 (COVID-19), yet they have suboptimal responses to mRNA and inactivated COVID-19 vaccines. The efficacy of tixagevimab-cilgavimab in reducing symptomatic SARS-CoV-2 infection was demonstrated in phase III clinical trials. Nevertheless, real-world data on the effectiveness and safety of tixagevimab-cilgavimab remain limited.
Objective: The aim was to evaluate the effectiveness and safety of tixagevimab-cilgavimab among immunocompromised individuals.
Methods: Adults who were immunocompromised or receiving immunosuppressive therapies were included in this target trial emulation using territory-wide electronic health records in Hong Kong. A sequential trial emulation approach was adopted to compare effectiveness and safety outcomes between individuals who received tixagevimab-cilgavimab and individuals who did not.
Results: A total of 746 tixagevimab-cilgavimab recipients and 2980 controls were included from 1 May 2022 to 30 November 2022. Tixagevimab-cilgavimab significantly reduced the risk of COVID-19 infection (hazard ratio [HR] 0.708, 95% confidence interval [CI] 0.527-0.951) during a median follow-up of 60 days. No significant difference was observed in the risk of COVID-19-related hospitalisation. Zero versus eight COVID-19 mortality cases and zero versus two severe COVID-19 cases were observed in tixagevimab-cilgavimab recipients and controls, respectively. Notably, significant risk reduction in COVID-19 infection was also observed among immunocompromised individuals who had been previously vaccinated with three or more doses of COVID-19 vaccine, or had no prior COVID-19 infection history.
Conclusions: Tixagevimab-cilgavimab was effective in reducing COVID-19 infection among immunocompromised patients during the Omicron wave. Findings were consistent among individuals who previously received three or more doses of COVID-19 vaccine, or had no previous history of COVID-19 infection.
Introduction: Preventable medication errors have been proven to cause significant public health burden, and ePrescription is a key part of the process where medication errors and adverse effects could be prevented. Information systems and "intelligent" computational approaches could provide a valuable tool to prevent such errors with profound impact in clinical practice.
Objectives: The PrescIT platform is a Clinical Decision Support System (CDSS) that aims to facilitate the prevention of adverse drug reactions (ADRs) and drug-drug interactions (DDIs) in the phase of ePrescription in Greece. The proposed platform could be relatively easily localized for use in other contexts too.
Methods: The PrescIT platform is based on the use of Knowledge Engineering (ΚΕ) approaches, i.e., the use of Ontologies and Knowledge Graphs (KGs) developed upon openly available data sources. Open standards (i.e., RDF, OWL, SPARQL) are used for the development of the platform enabling the integration with already existing IT systems or for standalone use. The main KG is based on the use of DrugBank, MedDRA, SemMedDB and OpenPVSignal. In addition, the Business Process Management Notation (BPMN) has been used to model long-term therapeutic protocols used during the ePrescription process. Finally, the produced software has been pilot tested in three hospitals by 18 clinical professionals via in-person think-aloud sessions.
Results: The PrescIT platform has been successfully integrated in a transparent fashion in a proprietary Hospital Information System (HIS), and it has also been used as a standalone application. Furthermore, it has been successfully integrated with the Greek National ePrescription system. During the pilot phase, one psychiatric therapeutic protocol was used as a testbed to collect end-users' feedback. Summarizing the feedback from the end-users, they have generally acknowledged the usefulness of such a system while also identifying some challenges in terms of usability and the overall user experience.
Conclusions: The PrescIT platform has been successfully deployed and piloted in real-world environments to evaluate its ability to support safer medication prescriptions.
This article reflects on the 2010 pharmacovigilance legislation of the European Union (EU). Its legislative aim of better patient and public health protection through new responsibilities for pharmaceutical companies and regulatory bodies is considered to have been achieved and is well supported by the good pharmacovigilance practices 'EU-GVP'. For future progress, we set out a vision for high-quality pharmacovigilance in a world of ongoing medical, technological and social changes. To deliver this vision, four principles are proposed to guide actions for further progressing the EU pharmacovigilance system: synergistic interactions with healthcare systems; trustworthy evidence for regulatory decisions; adaptive process efficiency; and readiness for emergency situations (the 'STAR principles'). Like a compass, these principles should guide actions for building capacity, technology and methods; improving regulatory processes; and expanding policies, frameworks and research agendas. Fit for the future, the EU system should achieve further improved outputs in terms of safe, effective and trusted use of medicines and positive health outcomes within patient-centred healthcare.
Background: The attribution of drug-induced liver injury (DILI) to specific herbal and dietary supplements (HDS) is confounded by inaccurate labels and undisclosed ingredients. The US Drug-Induced Liver Injury Network (DILIN) determines the attribution of injury to an agent through its structured expert opinion causality assessment process, but without the use of chemical analysis data of HDS. We aimed to determine the impact of chemical analysis of HDS products on prior causality assessment scores.
Methods: Obtained samples of HDS consumed by DILIN-enrolled patients were analyzed by high-performance liquid chromatography-mass spectrometry (HPLC-MS). Chemical analysis data were compared to label accuracy and detect whether the product contained botanical and non-botanical compounds. A comparison of the causality scores reassessed with chemical analysis was compared with the original scores.
Results: A total of 54 previously adjudicated cases with chemical analysis available were reassessed for causality with chemical analysis data; reviewers were blinded to original causality scores. Using the chemical analysis data, 37% (n = 20) of the 54 cases were scored with a higher likelihood of DILI compared with the original causality scores; 14 of the 20 (70%) moved from probable to highly likely; 52% had no change in causality score; and 11% of cases were scored as a lower likelihood of DILI.
Conclusions: Our study demonstrates that there is value in using HDS chemical analysis data in the causality assessment process for DILI. In more than a third of cases, chemical analysis of products led to an increased confidence in DILI attribution to HDS. These findings suggest that chemical analysis is an important tool in causality assessment for HDS agents, specifically in challenging situations, and further studies are needed to confirm its applicability in clinical practice.
Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artificial intelligence in pharmacovigilance raises ethical concerns related to fairness, non-discrimination, compliance, and responsibility as the central ethical principles in risk assessment and regulatory requirements. This paper explores these concerns and provides a roadmap to how to address these challenges by considering data collection, privacy protection, transparency and accountability, model training, and explainability in artificial intelligence decision making for drug safety surveillance. A number of responsible approaches have been identified including an ethics framework and best practices to enhance artificial intelligence use in healthcare. The document also recognizes some initiatives that have demonstrated the importance of ethics in artificial intelligence pharmacovigilance. Nevertheless, the major needs mentioned in this paper are transparency, accountability, data protection, and fairness, which stress the necessity of collaboration to construct a cognitive framework aimed at integrating ethical artificial intelligence into pharmacovigilance. In conclusion, innovation should be balanced with ethical responsibility to enhance public health outcomes as well as patient safety.
Introduction and objective: 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.
Methods: 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.
Results: 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.
Conclusions: 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.
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