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Drug-induced autoimmunity (DIA) is a non-IgE immune-related adverse drug reaction that poses substantial challenges in predictive toxicology due to its idiosyncratic nature, complex pathogenesis, and diverse clinical manifestations. To address these challenges, we developed InterDIA, an interpretable machine learning framework for predicting DIA toxicity based on molecular physicochemical properties. Multi-strategy feature selection and advanced ensemble resampling approaches were integrated to enhance prediction accuracy and overcome data imbalance. The optimized Easy Ensemble Classifier achieved robust performance in both 10-fold cross-validation (AUC value of 0.8836 and accuracy of 82.81 %) and external validation (AUC value of 0.8930 and accuracy of 85.00 %). Paired case studies of hydralazine/phthalazine and procainamide/N-acetylprocainamide demonstrated the model's capacity to discriminate between structurally similar compounds with distinct immunogenic potentials. Mechanistic interpretation through SHAP (SHapley Additive exPlanations) analysis revealed critical physicochemical determinants of DIA, including molecular lipophilicity, partial charge distribution, electronic states, polarizability, and topological features. These molecular signatures were mechanistically linked to key processes in DIA pathogenesis, such as membrane permeability and tissue distribution, metabolic bioactivation susceptibility, immune protein recognition and binding specificity. SHAP dependence plots analysis identified specific threshold values for key molecular features, providing novel insights into structure-toxicity relationships in DIA. To facilitate practical application, we developed an open-access web platform enabling batch prediction with real-time visualization of molecular feature contributions through SHAP waterfall plots. This integrated framework not only advances our mechanistic understanding of DIA pathogenesis from a molecular perspective but also provides a valuable tool for early assessment of autoimmune toxicity risk during drug development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2025 Elsevier B.V. All rights reserved.)

The FDA Adverse Event Reporting System (FAERS) is a large-scale repository of reports concerning adverse drug events (ADEs). The same published clinical study or report may be reviewed by multiple companies or healthcare professionals and reported separately to the FDA, leading to a significant presence of duplicate reports in FAERS. These duplicate records can result in the identification of false associations between a given drug and an ADE. In this study, we first assessed the consistency of drug and ADE information in FAERS reports from Alzheimer's disease patients. Our findings showed greater congruence in drug-related information compared to ADE-related information, likely due to the greater heterogeneity and variety of terms or phrases used to describe ADEs. We then demonstrated that text comparison methods are effective in identifying duplicate records based on literature citations, testing 10 different comparison functions for their overall efficacy. Token-based methods (such as COSINE, QGRAM, and JACCARD), edit-based approaches (including OSA, LV, and DL), and sequence-based techniques like LCS have proven highly effective in accurately detecting identical publications within free text, demonstrating both high sensitivity and specificity. These results offer valuable insights for identifying duplicate FAERS reports and improving the reliability of detected associations between drugs and ADEs., Competing Interests: The authors declare there are no competing interests.

Introduction: Drug-related hospital admissions (DRAs) can account for 5%-40% of total hospital admissions in older adults, with a significant proportion deemed preventable. To increase the detection of DRAs, in 2021, a revised trigger tool listing 21 frequent causes of admissions and medications at risk was proposed. This study aimed to describe DRAs using this trigger tool in a French acute geriatric ward and to assess the performance of the tool., Methods: This was a retrospective cohort study in a 20-bed geriatric unit including all patients hospitalised in 2023. During the first quarter of 2024, each patient's chart was adjudicated by using a two-step standardised review procedure to assess whether the admission was a DRA. The potentially at cause medications and reasons for admission were also assessed., Results: During the study period, 483 patients were hospitalised in the acute-care geriatric ward (mean age 86.7 ± 6.15 years). After adjudication, 207 admissions (43%) were identified as DRAs; 70% were considered preventable. The main causes of DRAs were falls/fractures (33%), bleeding (23%) and delirium (14%). The drugs most frequently responsible were diuretics (21%), renin-angiotensin system inhibitors (20%) and direct oral anticoagulants (15%). The overall sensitivity and specificity of the tool for detecting DRAs was 90% (95% CI 88-93) and 72% (68-76), respectively. After adjudication, the trigger tool helped detect 83% more DRAs as compared with the attending geriatrician., Conclusion: DRAs are frequent in a geriatric population and often preventable. Their detection may be improved by the use of a trigger tool., (© The Author(s) 2025. Published by Oxford University Press on behalf of the British Geriatrics Society. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: Only limited data are available on trough plasma voriconazole concentration in elderly patients. This study aimed to assess the association between voriconazole concentration and adverse drug reactions (ADR)., Materials and Methods: A retrospective analysis was conducted to investigate the correlation between voriconazole trough concentration and adverse reactions among elderly patients admitted between October 1, 2017, and September 30, 2020., Results: In total 147 patients were included in this study, with 248 measurements of voriconazole concentrations available. ADR occurred in 75 patients after drug therapy, with liver injury showing the highest incidence (n = 37, 49.33%), followed by hypokalemia (n = 27, 36%), and neurological disorder (n = 20, 26.67%). 121 measurements (48.79%) in 82 patients (55.78%) showed that the monitored trough concentration did not match the occurrence of ADR, mainly including hyponatremia, hypokalemia, liver injury, and kidney injury., Conclusion: The accuracy of predicting the trough concentration of voriconazole as recommended by current guidelines for elderly patients might be limited. Consequently, it is advisable to establish a tailored treatment range of voriconazole concentration specifically catered to this patient demographic.

Introduction: Due to its role in absorption and metabolism, the kidney is an important target for drug toxicity. Drug-induced nephrotoxicity (DIN) presents a significant challenge in clinical practice and drug development. Conventional methods for assessing nephrotoxicity have limitations, highlighting the need for innovative approaches. In recent years, in silico methods have emerged as promising tools for predicting DIN., Areas Covered: A literature search was performed using PubMed and Web of Science, from 2013 to February 2023 for this review. This review provides an overview of the current progress and pitfalls in the in silico prediction of DIN, which discusses the principles and methodologies of computational models., Expert Opinion: Despite significant advancements, this review identified issues accentuates the pivotal imperatives of data fidelity, model optimization, interdisciplinary collaboration, and mechanistic comprehension in sculpting the vista of DIN prediction. Integration of multiple data sources and collaboration between disciplines are essential for improving predictive models. Ultimately, a holistic approach combining computational, experimental, and clinical methods will enhance our understanding and management of DIN.

Background: Liver injury from drug-drug interactions (DDIs), notably with anti-tuberculosis drugs such as isoniazid, poses a significant safety concern. Electronic medical records contain comprehensive clinical information and have gained increasing attention as a potential resource for DDI detection. However, a substantial portion of adverse drug reaction (ADR) information is hidden in unstructured narrative text, which has yet to be efficiently harnessed, thereby introducing bias into the research. There is a significant need for an efficient framework for the DDI assessment., Methods: Using a Chinese natural language processing (NLP) model, we extracted 25,130 adverse drug reaction (ADR) records, dividing them into sets for training an automated normalization model. The trained models, in conjunction with liver function laboratory tests, were used to thoroughly and efficiently identify liver injury cases. Ultimately, we applied a case-control study design to detect DDI signals increasing isoniazid's liver injury risk., Results: The Logistic Regression model demonstrated stable and superior performance in classification task. Based on laboratory criteria and NLP, we identified 128 liver injury cases among a cohort of 3,209 patients treated with isoniazid. Preliminary screening of 113 drug combinations with isoniazid highlighted 20 potential signal drugs, with antibacterials constituting 25%. Sensitivity analysis confirmed the robustness of signal drugs, especially in cardiac therapy and antibacterials., Conclusion: Our NLP and machine learning approach effectively identifies isoniazid-related DDIs that increase the risk of liver injury, identifying 20 signal drugs, mainly antibacterials. Further research is required to validate these DDI signals., Competing Interests: Declarations. Ethics approval and consent to participate: This retrospective study was conducted under the Declaration of Helsinki. Ethical approval was not required for this study as it was conducted using anonymized electronic medical records. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Purpose: Toxicity to systemic cancer treatment represents a major anxiety for patients and a challenge to treatment plans. We aimed to develop machine learning algorithms for the upfront prediction of an individual's risk of experiencing treatment-relevant toxicity during the course of treatment., Methods: Clinical records were retrieved from a single-center, consecutive cohort of patients who underwent neoadjuvant treatment for early breast cancer. We developed and validated machine learning algorithms to predict grade 3 or 4 toxicity (anemia, neutropenia, deviation of liver enzymes, nephrotoxicity, thrombopenia, electrolyte disturbance, or neuropathy). We used 10-fold cross-validation to develop two algorithms (logistic regression with elastic net penalty [GLM] and support vector machines [SVMs]). Algorithm predictions were compared with documented toxicity events and diagnostic performance was evaluated via area under the curve (AUROC)., Results: A total of 590 patients were identified, 432 in the development set and 158 in the validation set. The median age was 51 years, and 55.8% (329 of 590) experienced grade 3 or 4 toxicity. The performance improved significantly when adding referenced treatment information (referenced regimen, referenced summation dose intensity product) in addition to patient and tumor variables: GLM AUROC 0.59 versus 0.75, P = .02; SVM AUROC 0.64 versus 0.75, P = .01., Conclusion: The individual risk of treatment-relevant toxicity can be predicted using machine learning algorithms. We demonstrate a promising way to improve efficacy and facilitate proactive toxicity management of systemic cancer treatment.

Background: The process of identifying drug-related hospitalisations is subjective and time-consuming. Assessment tool for identifying hospital admissions related to medications (AT-HARM10) was developed to simplify and objectify this process. AT-HARM10 has not previously been externally validated, thus the predictive precision of the tool is uncertain., Aim: To externally validate AT-HARM10 in adult patients admitted to the emergency department (ED)., Method: This retrospective cross-sectional study investigated 402 patients admitted to the ED, Diakonhjemmet Hospital, Oslo, Norway. A trained 5th-year pharmacy student used AT-HARM10 to assess all patients and to classify their ED visits as possibly or unlikely drug-related. Assessment of the same patients by an interdisciplinary expert panel acted as the gold standard. The external validation was conducted by comparing AT-HARM10 classifications with the gold standard., Results: According to AT-HARM10 assessments, 169 (42%) patients had a possible drug-related ED visit. Calculated sensitivity and specificity values were 95% and 71%, respectively. Further, positive and negative predictive values were 46% and 98%, respectively. Adverse effects/over-treatment and suboptimal treatment were the issues most frequently overestimated by AT-HARM10 compared with the gold standard., Conclusion: AT-HARM10 identifies drug-related ED visits with high sensitivity. However, the low positive predictive value indicates that further review of ED visits classified as possible drug-related by AT-HARM10 is necessary. AT-HARM10 can serve as a useful first-step screening that efficiently identifies unlikely drug-related ED visits, thus only a smaller proportion of the patients need to be reviewed by an interdisciplinary expert panel., Competing Interests: Conflicts of interest The authors have no conflict of interest to declare., (© 2024. The Author(s).)

Many drug labels contain precautions of use in G6PD-deficient patients due to hemolytic concerns, but much of this is based on scarce clinical, epidemiological, or structural data. In this real-world study, we aimed to examine if the administration of presumably risky medications for G6PD-deficient patients was followed by hemolysis. The study is based on data from Clalit Health Services database that provides inclusive health care for more than half of the Israeli population (~ 4.7 million). Within the database, we identified all G6PD-deficient patients by G6PD <6 U/g Hb. Within the G6PD-deficient cohort, we identified all hospitalizations with a discharge diagnosis of hemolysis (January 1, 2010 to December 31, 2022), validated the cases, and identified the culprit event. For the rest of the G6PD-deficient patients with no-hemolysis, we recorded filled prescriptions of medications listed as presumably risky. We identified 31,962 G6PD-deficient patients. Within the cohort, there were 71 cases of major hemolysis requiring hospitalization (0.2% of the cohort), of whom 51 (71.8%) had been caused by ingestion of fava beans, six (8.5%) were associated with an infection, and three (4.2%) suggested to be associated with medications (nitrofurantoin, phenazopyridine, and a "pain killer"). Within the 31,875 patients with no major hemolysis, nitrofurantoin has been prescribed safely to 1,366 G6PD-deficient males and females; hundreds/thousands of G6PD-deficient patients had been prescribed safely ciprofloxacin, glibenclamide, ofloxacin, phenazopyridine, sulfamethoxazole/cotrimoxazole, sulfasalazine, hydroxychloroquine, glimepiride, mesalazine, and sulfacetamide. In this real-world study, we are showing that a list of medications, suspected previously as carrying risks for hemolysis in G6PD-deficient patients, have been prescribed safely to G6PD-deficient patients, providing reassurance to patients, prescribers, and regulators., (© 2024 The Author(s). Clinical Pharmacology & Therapeutics published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Introduction: Identification and monitoring of adverse drug reactions (ADRs) and interventions to reduce ADRs are essential for patient safety in hospitals. Causality analysis (CA) is an approach that helps to determine a causal link between medication and patient harm (i.e. an ADR). While numerous CA tools exist, there is no gold standard., Areas Covered: Five online databases were searched to identify studies that evaluated the potential clinical utility of CA tools for ADRs. CA tools were mapped against the Bradford Hill (BH) criteria and included if they adhered to the first seven criteria proposed by BH. Upon the database search, 550 studies were identified, with 41 studies being selected that looked at tools mapped to BH. Thirty-four different CA tools were identified in the included studies., Expert Opinion: Naranjo and WHO-UMC were the most reported CA tools for studies examining inter-rater and intra-rater reliability. Naranjo commonly received a 'fair' agreement level while WHO-UMC received a 'substantial' agreement level between raters. Along with kappa statistics, time using the CA tool was also analyzed, with WHO-UMC being the most time-efficient. There does not appear to be one CA tool that can be applied universally to pharmacovigilance efforts in hospital in-patient settings.

Background: Social networking services (SNS) closely reflect the lives of individuals in modern society and generate large amounts of data. Previous studies have extracted drug information using relevant SNS data. In particular, it is important to detect adverse drug reactions (ADRs) early using drug surveillance systems. To this end, various deep learning methods have been used to analyze data in multiple languages in addition to English., Objective: A cautionary drug that can cause ADRs in older patients was selected, and Korean SNS data containing this drug information were collected. Based on this information, we aimed to develop a deep learning model that classifies drug ADR posts based on a recurrent neural network., Methods: In previous studies, ketoprofen, which has a high prescription frequency and, thus, was referred to the most in posts secured from SNS data, was selected as the target drug. Blog posts, café posts, and NAVER Q&A posts from 2005 to 2020 were collected from NAVER, a portal site containing drug-related information, and natural language processing techniques were applied to analyze data written in Korean. Posts containing highly relevant drug names and ADR word pairs were filtered through association analysis, and training data were generated through manual labeling tasks. Using the training data, an embedded layer of word2vec was formed, and a Bidirectional Long Short-Term Memory (Bi-LSTM) classification model was generated. Then, we evaluated the area under the curve with other machine learning models. In addition, the entire process was further verified using the nonsteroidal anti-inflammatory drug aceclofenac., Results: Among the nonsteroidal anti-inflammatory drugs, Korean SNS posts containing information on ketoprofen and aceclofenac were secured, and the generic name lexicon, ADR lexicon, and Korean stop word lexicon were generated. In addition, to improve the accuracy of the classification model, an embedding layer was created considering the association between the drug name and the ADR word. In the ADR post classification test, ketoprofen and aceclofenac achieved 85% and 80% accuracy, respectively., Conclusions: Here, we propose a process for developing a model for classifying ADR posts using SNS data. After analyzing drug name-ADR patterns, we filtered high-quality data by extracting posts, including known ADR words based on the analysis. Based on these data, we developed a model that classifies ADR posts. This confirmed that a model that can leverage social data to monitor ADRs automatically is feasible., (© Chung-Chun Lee, Seunghee Lee, Mi-Hwa Song, Jong-Yeup Kim, Suehyun Lee. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Background: Adverse drug reactions are more prevalent in geriatric patients and are frequently associated with a range of polypharmacy-related issues as well as some physiological aging-related alterations. These affect the pharmacokinetic and pharmacodynamic properties of drugs. This study aimed to assess the magnitude of ADRs and their contributing factors among geriatric patients admitted at Comprehensive Specialized Hospitals of the Amhara Region., Methods: A multicenter prospective cohort study was carried out from May 2023 to August 2023 on geriatric patients admitted to four randomly selected comprehensive hospitals in the Amhara region. We used logistic regression to find the factors influencing the occurrence of ADRs. A P value of less than 0.05 was deemed statistically significant., Results: During the study's follow-up period, 373 patients in total were included. An incidence rate of 31.10% (95% CI: 26.38-35.82) was obtained from the identification of 121 ADRs in total. The organ most frequently affected by ADRs was the gastrointestinal tract (28.92%), followed by the cardiovascular system (19.01%), and the drug class most often implicated in ADRs was antibiotics (21.49%), then anticoagulants (12.40%). ADRs were substantially linked to being overweight (P < 0.001), having been hospitalized in the previous six months (P = 0.000), and hyperpolypharmacy (p = 0.047). 93.39% of all ADRs received the interventions. 85.12% of the adverse drug reactions were successfully resolved., Conclusions: This study found that over one-third of older people and individuals admitted to the hospital experienced ADRs. Overweight, hyperpolypharmacy, and patients who had previously been admitted during the preceding six months were significantly linked with the occurrence of ADRs. Improving the drug safety of elderly patients, particularly those who are admitted, should be a greater priority for healthcare professionals., Competing Interests: Declarations Ethics approval and consent to participate The ethical review committee of Debre Tabor University’s College of Health Science, Department of Pharmacy has approved ethical authorization with reference number 058/2023. A support letter was sent to each of the four comprehensive hospitals. The directors of those healthcare organizations were then asked to sign a letter approving the usage of patient records as a source of data. Participants were informed about the purpose and design of the study before data collection. Furthermore, they provided their official consent to participate in the research. Written informed consent was obtained from all the study participants. Patients’ medical registration numbers (MRNs) were swapped out for new codes during the data collecting and entry process. Besides, to protect its confidentiality, the gathered data was similarly kept in a locked cabinet and on a computer with a strong password. The study followed Helsinki legislation in terms of doing it in a properly anonymous and confidential manner. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Purpose: The prevalence of type 2 diabetes mellitus (T2DM) in cancer patients is high. During the medication review process, clinical pharmacists could detect and manage drug-related problems (DRP) to optimize pharmacotherapy but there is a need to standardize pharmacists' interventions (PI) especially for T2DM-related DRP. The present study aims to describe DRP in cancer patients with T2DM undergoing anticancer treatment (AT) and to propose related preliminary guidelines to manage T2DM-related DRP., Methods: The study was conducted in one oncology outpatient hospital where a clinical pharmacy team performs medication reviews to detect and manage DRP by performing PI in cancer patients undergoing AT. All the data from November 23rd, 2015 to November 23rd, 2019 were extracted and demographic, clinical, oncological, and biological data were collected and analyzed. Based on these results and a literature review, a working group (2 pharmacists and one diabetologist) was constituted to propose a first set of preliminary guidelines clinical pharmacists that were then reviewed using the Delphi method by an expert panel of oncologists and pharmacists., Results: A total of 161 T2DM cancer patients were included in the study (19.7% of all cancer patients screened). Overall, 152 DRP (mean 1.67/patient) were detected (49.3% drug interaction) and 152 PI were performed by clinical pharmacists, mainly drug monitoring (48.0) and drug discontinuation (25.7%). Specifically, there was 24 T2DM-related DRP (including 29.2% drug interactions). Twenty-four DRPs were directly related to T2DM status. The five proposed guidelines reached a consensus after revisions and a sixth has been added., Conclusion: DRPs were frequent among cancer patients with T2DM and we hypothesize that the preliminary guidelines should improve the detection of DRPs directly related to T2DM. The implementation of the preliminary guidelines should now be assessed in clinical practice., Competing Interests: Declarations Ethics approval The study was conducted in accordance with the Helsinki declaration. Patients’ records were anonymized prior to analysis. A database from ONCOPTIMAL (F20220817142044) was created in accordance with the reference methodology MR004 of the National Commission of Liberties and Informatics (no. MR00414012022). As per French regulations about retrospective study, no informed consent and additional ethical committee review were required. Competing interests The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

This study aimed to measure relative telomere length (RTL) in blood leukocytes of advanced-stage NSCLC patients either with or without Osimertinib-induced ADRs and determine whether RTL could serve as a biomarker of Osimertinib-induced ADRs. Blood leukocytes RTL were measured in 63 advanced-stage NSCLC patients and 62 age-matched healthy controls using real-time polymerase chain reaction. In patients with advanced-stage NSCLC, RTL was significantly shorter than that in healthy controls (P < 0.001). Compared to patients without ADRs and those with mild/moderate ADRs, patients with severe ADRs exhibited significantly decreased RTL (P < 0.001, P < 0.001, respectively). ROC curve analysis uncovered a diagnostic value of RTL as a biomarker of Osimertinib-induced ADRs (AUC = 1.000, P < 0.001). Kaplan-Meier analysis revealed a significant association between shorter RTL and increased cumulative incidence of Osimertinib-induced ADRs in patients with advanced-stage NSCLC (P < 0.001). Shorter RTL in blood leukocytes would reflect the occurrence of Osimertinib-induced ADRs and might emerge as a promising biomarker for identifying advanced-stage NSCLC patients who are at risk of experiencing Osimertinib-induced ADRs, particularly those with severe ADRs., (© 2024. The Author(s).)

The NCCN Guidelines for the Management of Immunotherapy-Related Toxicities are intended to provide oncology practitioners with guidance on how to manage the wide-ranging and potentially fatal toxicities that may occur with cancer immunotherapy. The guidelines address immune-related adverse events related to immune checkpoint inhibitors, CAR T-cell therapies, and lymphocyte engagers (which include T-cell-engaging bispecific antibodies). These NCCN Guidelines Insights highlight recent guideline updates pertaining to the management of emerging toxicities related to cancer immunotherapy.

Introduction: There is a need to strengthen the evidence base regarding medication use during pregnancy and to facilitate the early detection of safety signals. EudraVigilance (EV) serves as the primary system for managing and analysing information concerning suspected adverse drug reactions (ADRs) within the European Economic Area. Despite its various functionalities, the current format for electronic submissions of safety reports lacks a specific data element indicating medicine exposure during pregnancy., Objective: This paper aims to address the limitations of existing approaches by developing a rule-based algorithm in EV that more reliably identifies cases that are truly representative of an ADR during pregnancy., Methods: The study utilised the standardised MedDRA query (SMQ) 'Pregnancy and neonatal topics' (PNT) as a benchmark for comparison. Recognising that the SMQ PNT also retrieves healthy pregnancy outcomes, contraceptive failure, failed abortifacients as well as ADRs not associated with pregnancy, a novel algorithm was tailored to improve the accuracy of identifying suspected ADRs occurring during pregnancy., Results: Upon testing, the algorithm demonstrated superior performance, correctly predicting 90% of cases reporting an ADR during pregnancy, compared to 54% achieved by the SMQ PNT. The implementation of the algorithm in EV led to the retrieval of 202,426 cases., Conclusion: The development and successful testing of the novel algorithm represents a step forward in pregnancy-specific signal detection in EV. Because signals associated with pregnancy may be diluted in a large database such as EV, this study lays the groundwork for future research to evaluate the effectiveness of disproportionality methods on a more refined subset of pregnancy-related ADR reports., (© 2024. The Author(s).)

Background: In randomised controlled trials with efficacy-related primary outcomes, adverse events are collected to monitor potential intervention harms. The analysis of adverse event data is challenging, due to the complex nature of the data and the large number of unprespecified outcomes. This is compounded by a lack of guidance on best analysis approaches, resulting in widespread inadequate practices and the use of overly simplistic methods; leading to sub-optimal exploitation of these rich datasets. To address the complexities of adverse events analysis, statistical methods are proposed that leverage existing structures within the data, for instance by considering groupings of adverse events based on biological or clinical relationships., Methods: We conducted a methodological scoping review of the literature to identify all existing methods using structures within the data to detect signals for adverse reactions in a trial. Embase, MEDLINE, Scopus and Web of Science databases were systematically searched. We reviewed the analysis approaches of each method, extracted methodological characteristics and constructed a narrative summary of the findings., Results: We identified 18 different methods from 14 sources. These were categorised as either Bayesian approaches (n=11), which flagged events based on posterior estimates of treatment effects, or error controlling procedures (n=7), which flagged events based on adjusted p-values while controlling for some type of error rate. We identified 5 defining methodological characteristics: the type of outcomes considered (e.g. binary outcomes), the nature of the data (e.g. summary data), the timing of the analysis (e.g. final analysis), the restrictions on the events considered (e.g. rare events) and the grouping systems used., Conclusions: We found a large number of analysis methods that use the group structures of adverse events. Continuous methodological developments in this area highlight the growing awareness that better practices are needed. The use of more adequate analysis methods could help trialists obtain a better picture of the safety-risk profile of an intervention. The results of this review can be used by statisticians to better understand the current methodological landscape and identify suitable methods for data analysis - although further research is needed to determine which methods are best suited and create adequate recommendations., (© 2024. The Author(s).)

Background: Antimicrobials are widely used in hospitals and are often associated with adverse drug reactions (ADRs). The objective of this study was to determine the incidence of ADRs caused by antimicrobials and classify them according to the type of reaction, the class of antimicrobials used, causality, severity and avoidability., Methods: A prospective cohort study was carried out with paediatric patients for 6 months. Causality was verified using the Naranjo and Liverpool algorithms, the severity was verified with the adapted scale of Hartwig and the avoidability was verified with the Liverpool Avoidability Assessment Tool., Results: A total of 303 patients were followed, and 18.2% (55/303) of them had one or more ADRs during the hospital stay. Just over half of the patients (28/55) had diarrhea. The most used antimicrobials were beta-lactams and second-generation cephalosporins. Suspicions were classified mainly as possible 78.6% (55/70) according to the Naranjo algorithm, and as probable 48.6% (34/70) according to the Liverpool algorithm. The antimicrobial most involved with ADRs was cefepime. The risk of manifesting ADR was greater with the use of some antimicrobials such as clindamycin (relative risk (RR) 3.0, CI 1.67 to 5.4), as well as with the increase in hospitalisation days (OR 1.022, CI 1.008 to 1.036) and in the number of antimicrobials prescribed (OR 1.649, CI 1.360 to 2.001)., Conclusion: ADRs were observed in approximately one-fifth of patients and were mostly gastrointestinal, moderate, unavoidable and with variable causality, depending on the algorithm used., Competing Interests: Competing interests: None declared., (© European Association of Hospital Pharmacists 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

The concept of time toxicity in oncology refers to the presence of frequent healthcare-related interactions that can interfere with patient well-being. In this review, we examine several manifestations of time toxicity in non-Hodgkin lymphoma and multiple myeloma and discuss their impact on decision-making with patients. For example, time toxicity may influence the choice of chemoimmunotherapy versus lenalidomide-rituximab in follicular lymphoma. In myeloma, it may inform the optimal dosing schedule for proteasome inhibitors and bisphosphonates. In both malignancies, varying time toxicity profiles are a key distinction between chimeric antigen receptor T-cell therapies and bispecific antibodies. We outline the challenges with measuring time toxicity as a trial endpoint but discuss its importance as a consideration for patient care, both in standard-of-care settings and in clinical trials. Throughout the review, we highlight strategies to lower the time toxicity of therapies in lymphoma and myeloma without compromising their efficacy or patient safety.

Background: Natural language processing (NLP) techniques can be used to analyze large amounts of electronic health record texts, which encompasses various types of patient information such as quality of life, effectiveness of treatments, and adverse drug event (ADE) signals. As different aspects of a patient's status are stored in different types of documents, we propose an NLP system capable of processing 6 types of documents: physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes., Objective: This study aimed to investigate the system's performance in detecting ADEs by evaluating the results from multitype texts. The main objective is to detect adverse events accurately using an NLP system., Methods: We used data written in Japanese from 2289 patients with breast cancer, including medication data, physician progress notes, discharge summaries, radiology reports, radioisotope reports, nursing records, and pharmacist progress notes. Our system performs 3 processes: named entity recognition, normalization of symptoms, and aggregation of multiple types of documents from multiple patients. Among all patients with breast cancer, 103 and 112 with peripheral neuropathy (PN) received paclitaxel or docetaxel, respectively. We evaluate the utility of using multiple types of documents by correlation coefficient and regression analysis to compare their performance with each single type of document. All evaluations of detection rates with our system are performed 30 days after drug administration., Results: Our system underestimates by 13.3 percentage points (74.0%-60.7%), as the incidence of paclitaxel-induced PN was 60.7%, compared with 74.0% in the previous research based on manual extraction. The Pearson correlation coefficient between the manual extraction and system results was 0.87 Although the pharmacist progress notes had the highest detection rate among each type of document, the rate did not match the performance using all documents. The estimated median duration of PN with paclitaxel was 92 days, whereas the previously reported median duration of PN with paclitaxel was 727 days. The number of events detected in each document was highest in the physician's progress notes, followed by the pharmacist's and nursing records., Conclusions: Considering the inherent cost that requires constant monitoring of the patient's condition, such as the treatment of PN, our system has a significant advantage in that it can immediately estimate the treatment duration without fine-tuning a new NLP model. Leveraging multitype documents is better than using single-type documents to improve detection performance. Although the onset time estimation was relatively accurate, the duration might have been influenced by the length of the data follow-up period. The results suggest that our method using various types of data can detect more ADEs from clinical documents., (©Tomohiro Nishiyama, Ayane Yamaguchi, Peitao Han, Lis Weiji Kanashiro Pereira, Yuka Otsuki, Gabriel Herman Bernardim Andrade, Noriko Kudo, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki, Masahiro Takada, Masakazu Toi. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 24.09.2024.)

Introduction: The Medication Review in the Pharmacotherapeutic Follow-up Service (PFS) seems to be an effective method to study long-term drug safety in the outpatient setting. The adverse drug reactions (ADRs) that are not immediately obvious are difficult to identify and sometimes can be confused with a more common condition. Misdiagnosis by not associating the symptoms of AMR to its pharmacological cause causes its masking and hinders its detection., Objective: Detect in the SFT service the diagnostic errors related to the non-detection of adverse reactions to statins., Material and Methods: The data obtained from the medication review at the Pharmacotherapeutic Follow-up Service (PFS) were pooled for analysis. The patients who received the service were selected with the "DLGM screening" tool, an acronym "Diagnosis load Generated by Medications", that allows us to describe adverse drug reactions (ADRs), when their symptoms are attributed to a pathology, without considering medication as a possible underlying cause.Only the results of patients over 60 years of age, who after a prolonged period of statin use gradually presented musculoskeletal disorders (MSD) and other symptoms theoretically described as possible ADRs, are shown., Results: In 66 % of the cases, corresponding to 14 patients out of a total of 21 studied, the physician modified the treatment and in 92% of these cases there was improvement and a decrease of the consumption of analgesics drug, anti-inflammatory and other drugs used to treat ADR symptoms., Conclusion: DLGM screening identified hidden AMRs in 62 % of patients., (Copyright SEFAC. Sociedad Española de Farmacia Clínica, Familiar y Comunitaria. This article is available from url https://www.farmaceuticoscomunitarios.org/.)

Aims: This systematic review aimed to investigate the occurrence of moderate and severe adverse drug reactions (ADRs) to antimicrobials among hospitalized children., Methods: The PubMed/Medline, Cochrane Library, Embase, Web of Science, Scopus, Lilacs and CINAHL databases were searched in April 2023 to systematically review the published data describing the characteristics of moderate and severe ADRs to antimicrobials among hospitalized children. The search was carried out without date restrictions, up to the search date (April, 2023)., Results: At the end of the selection process, 30 articles met the inclusion criteria. Cutaneous reactions were the primary serious clinical manifestations in most articles (19/30), followed by erythema multiforme (71 cases), Stevens-Johnson syndrome (72 cases), and toxic epidermal necrolysis (22 cases). The main antimicrobials involved in moderate and severe ADRs were penicillins, cephalosporins and sulfonamides. Regarding the primary outcomes, 30% (9/30) of the articles reported deaths, and 46.7% (14/30) of studies reported increased lengths of hospital stay, need for intensive care, and transfer to another hospital. Regarding the main interventions, 10% (3/30) of the articles mentioned greater monitoring, suspension, medication substitution or prescription of specific medications for the symptomatology., Conclusions: The findings of this review could be used to identify areas for improvement and help health professionals and policymakers develop strategies. In addition, we emphasize the importance of knowing about ADRs so that there is adequate management to avoid undesirable consequences., (© 2024 British Pharmacological Society.)

Immune checkpoint inhibitors (ICI) represent a breakthrough in cancer therapy. They are effective in various tumor entities and can be used in more and more treatment settings. This leads to an increase in the number and complexity of cases with immune-related adverse events (irAE). The most common irAE are cutaneous, gastrointestinal and endocrine side effects, whereas less common irAE include pneumonitis, nephritis, myocarditis or neurological reactions. IrAE can usually be successfully treated, mainly with corticosteroids or other immunosuppressants, but they can also result in long-term sequelae or death. The optimal management of patients with steroid-refractory or steroid-dependent side effects still remains unclear. Broad awareness of these irAE across specialties is therefore of crucial importance to ensure early diagnosis and to improve irAE management., (© 2024. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Purpose: The magnitude of repeat exposures to culprit medications after hospital discharge is not well studied. We combined prospective cohort data with administrative health data to understand the frequency of repeat exposures to culprit medications after discharge and the risk factors for their occurrence., Methods: This was a retrospective analysis of three prospective cohorts of patients who presented to the hospital with an adverse drug event in British Columbia, from 2008 to 2015 (n = 849). We linked prospectively identified adverse drug events to administrative data to examine patterns of redispensing of culprit medications. We used Cox regression to assess risk factors for re-exposure, and conducted subgroup analyses for essential vs. nonessential medications., Results: Among 849 diagnosed adverse drug events, 45.2% had subsequent culprit medication redispensing within a year of hospital discharge. The factors associated with re-exposures included atrial fibrillation, adverse drug event type (e.g. adverse reaction), culprit medication type, and longer historical duration of medication use., Conclusions: Re-exposures to culprit medications occurred in almost half of the adverse drug events diagnosed in emergency departments. Many of these were appropriate re-exposures to essential medications for indications in which the risk of uncontrolled disease likely outweighed the risk of a repeat adverse event. More research is needed to understand re-exposures to nonessential medications or medications with safer alternatives., (© 2024 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Background: Immune-checkpoint inhibitors (ICIs) often cause immune-related adverse events (irAEs). The spectrum of irAEs and their managements has been partially clarified, however the knowledge on time-course of irAEs is not well understood., Methods: A retrospective study based on the medical record was performed. The study subjects were consisting of patients with various types of solid tumors for whom ICIs (nivolumab, pembrolizumab, durvalumab, atezolizumab, nivolumab plus ipilimumab) were used between April 2016 and October 2021. We focused on irAEs developed more than 1-year after commencement ICIs (delayed irAE group) and compared with irAEs developed within 1-year (non-delayed irAE group) in terms of types and severity of irAEs., Results: A total of 336 patients were enrolled in the study. Eighty-eight patients (26.2%) developed irAEs and 248 did not. Most of the patients developing irAEs were treated using PD-L1/PD-1 inhibitors. Eighty-one patients (24.1%) in non-delayed irAE group and 7 patients (2.1%) in delayed irAE group developed irAEs. The median onset of irAEs in the delayed irAE group was 18.6 months (range: 13.5-24.3). The types of irAEs observed in delayed irAE group were dermatitis (2 cases), pneumonitis (2 cases), nephritis (1 case), arthritis (1 case), and gastritis (1 case). The severity of irAEs was almost mild (≤G2), but one patient (.3%) developed G3 nephritis., Conclusion: PD-L1/PD-1 inhibitors frequently caused various irAEs but their severities were mostly tolerable. Few patients developed delayed irAE with mild toxities., (© 2024 John Wiley & Sons Australia, Ltd.)

Drug-induced skin disease or cutaneous adverse drug reactions (CADRs) are terms that encompass the clinical manifestations of the skin, mucosae and adnexa induced by a drug or its metabolites. The skin is the organ most frequently affected by drug reactions, which may affect up to 10% of hospitalized patients and occur in 1-3% of multimedicated patients. Most CADRs are mild or self-resolving conditions; however, 2-6.7% of could develop into potentially life-threatening conditions. CADRs represent a heterogeneous field and can be diagnostically challenging as they may potentially mimic any dermatosis. Currently, there are between 29-35 different cutaneous drug-reaction patterns reported ranging from mild dermatitis to an extensively burnt patient. The most frequently reported are maculopapular rash, urticaria/angioedema, fixed drug eruption and erythema multiforme. Less common but more severe patterns include erythroderma, drug reaction with eosinophilia and systemic symptoms, and Stevens-Johnson syndrome/toxic epidermal necrolysis spectrum. Almost any drug can induce a CADR, but antibiotics, nonsteroidal anti-inflammatory drugs and antiepileptics are the most frequently involved. Different mechanisms are involved in the pathogenesis of CADRs, although in some cases, these remain still unknown. CADRs could be classified in different ways: (i) type A (augmented) or type B (bizarre); (ii) immediate or delayed; (iii) immune-mediated or nonimmune-mediated; (iv) nonsevere or life-threatening; and (v) by their phenotype, including exanthematous, urticarial, pustular and blistering morphology. Recognizing a specific CADR will mostly depend on the ability of the physician to perform a detailed clinical examination, the proper description of the morphology of the skin lesions and supporting laboratory and/or skin biopsy findings., (© 2022 British Pharmacological Society.)

Background: Falls are a significant public health problem and constitute a major cause of injuries and mortality. Risk factors for falls are multifactorial and include medication use., Aim: To develop and investigate the content validity of the Medication-Related fall (MRF) screening and scoring tool., Method: The MRF tool was developed from clinical practice guidelines addressing medication-related problems, and additional medications identified by specialist pharmacists across a region of the United Kingdom (Northern Ireland). Medication classes were categorised according to their 'potential to cause falls' as: high-risk (three points), moderate-risk (two points) or low-risk (one point). The overall medication-related falls risk for the patient was determined by summing the scores for all medications. The MRF was validated using Delphi consensus methodology, whereby three iterative rounds of surveys were conducted using SurveyMonkey ® . Twenty-two experts from 10 countries determined their agreement with the falls risk associated with each medication on a 5-point Likert scale. Only medications with at least 75% of respondents agreeing or strongly agreeing were retained in the next round., Results: Consensus was reached for 19 medications/medication classes to be included in the final version of the MRF tool; ten were classified as high-risk, eight as moderate-risk and one as low-risk., Conclusion: The MRF tool is simple and has the potential to be integrated into medicines optimisation to reduce falls risk and negative fall-related outcomes. The score from the MRF tool can be used as a clinical parameter to assess the need for medication review and clinical interventions., (© 2024. The Author(s).)

Aims: While diagnostic codes from administrative health data might be a valuable source to identify adverse drug events (ADEs), their ability to identify unintended harms remains unclear. We validated claims-based diagnosis codes for ADEs based on events identified in a prospective cohort study and assessed whether key attributes predicted their documentation in administrative data., Methods: This was a retrospective analysis of 3 prospective cohorts in British Columbia, from 2008 to 2015 (n = 13 969). We linked prospectively identified ADEs to administrative insurance data to examine the sensitivity and specificity of different diagnostic code schemes. We used logistic regression to assess which key attributes (e.g., type of event, symptoms and culprit medications) were associated with better documentation of ADEs in administrative data., Results: Among 1178 diagnosed events, the sensitivity of the diagnostic codes in administrative data ranged from 3.4 to 52.6%, depending on the database and codes used. We found that documentation was worse for certain types of ADEs (dose-related: odds ratio [OR]: 0.32, 95% confidence interval [CI]: 0.15, 0.69; nonadherence events (OR: 0.35, 95% CI: 0.20, 0.62), and better for those experiencing arrhythmias (OR: 4.19, 95% CI: 0.96, 18.28)., Conclusion: ADEs were not well documented in administrative data. Alternative methods should be explored to capture ADEs for health research., (© 2024 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Admissions of the elderly related to medication errors are frequent in hospital, more than half would be avoidable, but there is currently no validated method in French to identify them. The objective of this work was to validate the French version of the AT-HARM10 tool in order to use it for patients admitted in our healthcare facilities. The tool has 10 questions. A positive response to any of the first 3 questions identify admissions that are unlikely to be drug-related. A positive response to one of the following 7 questions identify possible medication-related admissions. For semantic and linguistic validation, we performed cross-validation with forward-backward translation. To clinically validate the method, we conducted a retrospective study including patients over 65 admitted to short-stay units (UHCD) and to orthopedic surgery units in two French hospitals. Two hundred and sixty-six (266) patients were included ; 166 patients admitted to UHCD (mean age 86.0±5.7 years; sex ratio 0.66; mean number of drugs prescribed 7.7±3.8) and 100 patients admitted to orthopedic units (mean age 85.2±6.1 years; sex ratio 0.43; mean number of prescribed drugs 6.4±3.6). We identified 55 % of admissions probably related to medication in UHCD and 76 % in orthopedic units (p<0.05). The most represented item was P5 in both groups (Might [side] effects of the medications the patient was taking [prescribed or not prescribed] prior to hospitalization have caused the admission [including over-treatment] ? The validated AT-HARM10 tool is now integrated into our clinical pharmacy practices and medication reviews are offered as a priority to patients admitted for iatrogenic reasons., (Copyright © 2023 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved.)

Objective: To identify trigger tools applied to detect adverse drug events (ADEs) in older people and describe their utility and performance., Methods: A systematic review was conducted in the PubMed, Lilacs, and Scopus databases (January 2024). Studies that developed, applied, or validated trigger tools and evaluated their utility and/or performance for detecting ADEs in older people were considered. Direct proportion meta-analyses using the inverse-variance method were performed for prevalence of ADEs and positive predictive value (PPV)., Results: Twenty-four studies (25 publications) were included. Twelve trigger tools were identified, of which six were developed for detecting ADEs in older population, four developed for general population and modified for older people, and two developed for general population. No tools for detecting ADEs in older people receiving palliative care or hospitalized in intensive or surgical care units were found. The performance of triggers was presented through PPV (11.5-71%), negative predictive values (83.3%), and sensitivity (30-94.8%). The overall PPV was 33.3% (95%CI: 32.5-34.2%). Triggers with good performance were changes in plasma levels of digoxin, glucose, and potassium; changes in international normalized ratio; abrupt medication stop; hypotension; and constipation. The prevalence of ADEs ranged from 2.8 to 66%, with overall prevalence of ADEs of 20% (95%CI: 19.3-20.8%). Preventability ranged from 8.4 to 94.4%. Metabolic or electrolyte disturbances induced by diuretics, constipation induced by opioids, and falls and delirium induced by benzodiazepines were the most prevalent ADEs., Conclusion: The trigger tools are flexible and easy to apply, and they can contribute to the detection of ADEs, their associated risk factors, the level of harm, and preventability in different health settings. However, there is no consensus on good or poor values of PPV, which indicate the performance of triggers. Furthermore, there is limited evidence regarding the evaluation of performance through negative predictive value, sensitivity, and specificity., Prospero: CRD42022379893., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Machine learning may assist with the identification of potentially inappropriate penicillin allergy labels. Strategies to improve the performance of existing models for this task include the use of additional training data, synthetic data and transfer learning., Aims: The aims of this study were to investigate the use of additional training data and novel machine learning strategies, namely synthetic data and transfer learning, to improve the performance of penicillin adverse drug reaction (ADR) machine learning classification., Methods: Machine learning natural language processing was applied to free-text penicillin ADR data extracted from a public health system electronic health record (EHR). The models were developed by training on various labelled data sets. ADR entries were split into training and testing data sets and used to develop and test a variety of machine learning models. The effect of training on additional data and synthetic data versus the use of transfer learning was analysed., Results: Following the application of these techniques, the area under the receiver operator curve of best-performing models for the classification of penicillin allergy (vs intolerance) and high-risk allergy (vs low-risk allergy) improved to 0.984 (using the artificial neural network model) and 0.995 (with the transfer learning approach) respectively., Conclusions: Machine learning models demonstrate high levels of accuracy in the classification and risk stratification of penicillin ADR labels using the reaction documented in the EHR. The model can be further optimised by incorporating additional training data and using transfer learning. Practical applications include automating case detection for penicillin allergy delabelling programmes., (© 2024 Royal Australasian College of Physicians.)

Background/aim: A few case reports of central nervous system (CNS) symptoms caused by amantadine intoxication have been published, detailing various types of symptoms and differing times to onset. We encountered a patient who developed CNS symptoms with amantadine. This prompted us to investigate the types, time to onset, and outcome of CNS adverse reactions to amantadine by analyzing data from a pharmacovigilance database., Patients and Methods: The patient was evaluated at Chutoen General Hospital, Shizuoka, Japan. Analysis was performed using the Japanese Adverse Drug Event Report (JADER) database., Results: In our case, the amantadine blood concentration was 4,042 ng/ml, i.e., in the toxic range. The time to onset was 26 days for dyskinesia and 90 days for depressed level of consciousness. Symptoms resolved when amantadine was discontinued. The JADER database contained 974 cases of adverse reactions to amantadine. The most frequently reported CNS adverse reaction was hallucination, with a reporting odds ratio of 64.28 (95% confidence interval=52.67-78.46). Positive signals were detected for all CNS adverse reactions. For all CNS reactions, clinical outcomes were poor in a comparatively low percentage of cases. Most CNS reactions occurred soon after administration of amantadine, usually within approximately one month., Conclusion: Because most CNS adverse reactions to amantadine usually occur within approximately one month of initiating treatment, healthcare providers should exercise heightened vigilance in monitoring patients for such reactions during this period., (Copyright © 2024, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.)

Background: Following a national multicentre study, two emergency department (ED) screening tools were developed to determine risk of medication-related problems; one for use at ED presentation and another at ED discharge to the community. This study aimed to determine the inter-rater reliability amongst ED health professionals when applying these screening tools to a series of case scenarios., Methods: A prospective, cross-sectional study was undertaken in the ED of a major metropolitan hospital. Twelve case scenarios were developed following ED observation of a range of patients, which were incorporated into a questionnaire and distributed to 50 health professionals. Inter-rater reliabilities of each explanatory variable of the screening tools and overall assessment were calculated using Fleiss' multi-rater kappa., Results: The questionnaire was completed by 15 doctors, 19 nurses and 16 pharmacists. Fleiss' kappa showed an overall inter-rater reliability for the ED presentation tool of 0.83 (95% CI 0.83-0.84), indicating near perfect agreement. Fleiss' kappa for the ED discharge tool was 0.83 (95% CI 0.83-0.85), which also showed near perfect agreement., Conclusions: The screening tools produced favourable inter-rater reliability amongst ED health professionals. These results have important implications for ensuring consistency of ED decision-making in screening patients at risk of developing medication-related problems., Competing Interests: Declaration of Competing Interest All authors wish to confirm there are no known actual or potential conflicts of interest in relation to this paper or the research conducted in relation to this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Outpatient clinical notes are a rich source of information regarding drug safety. However, data in these notes are currently underutilized for pharmacovigilance due to methodological limitations in text mining. Large language models (LLMs) like Bidirectional Encoder Representations from Transformers (BERT) have shown progress in a range of natural language processing tasks but have not yet been evaluated on adverse event (AE) detection. We adapted a new clinical LLM, University of California - San Francisco (UCSF)-BERT, to identify serious AEs (SAEs) occurring after treatment with a non-steroid immunosuppressant for inflammatory bowel disease (IBD). We compared this model to other language models that have previously been applied to AE detection. We annotated 928 outpatient IBD notes corresponding to 928 individual patients with IBD for all SAE-associated hospitalizations occurring after treatment with a non-steroid immunosuppressant. These notes contained 703 SAEs in total, the most common of which was failure of intended efficacy. Out of eight candidate models, UCSF-BERT achieved the highest numerical performance on identifying drug-SAE pairs from this corpus (accuracy 88-92%, macro F1 61-68%), with 5-10% greater accuracy than previously published models. UCSF-BERT was significantly superior at identifying hospitalization events emergent to medication use (P < 0.01). LLMs like UCSF-BERT achieve numerically superior accuracy on the challenging task of SAE detection from clinical notes compared with prior methods. Future work is needed to adapt this methodology to improve model performance and evaluation using multicenter data and newer architectures like Generative pre-trained transformer (GPT). Our findings support the potential value of using large language models to enhance pharmacovigilance., (© 2024 The Authors. Clinical Pharmacology & Therapeutics © 2024 American Society for Clinical Pharmacology and Therapeutics.)

Immune checkpoint inhibitors (ICIs) have emerged as crucial therapeutic agents for various malignancies by activating the host immune system against tumor cells. However, many different types of skin adverse reactions may occur during its use, including eruption, pruritus, blistering, hypopigmentation, alopecia, and even severe cases, Stevens-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN). These cutaneous immune-related adverse events (cirAEs) had a high incidence, which seriously affected patients' quality of life and antitumor treatment decisions. Some severe cutaneous adverse reactions (SCARs) even endanger patients' lives. Therefore, the Chinese Society of Dermatology, the Chinese Dermatologist Association of the Chinese Medical Doctor Association, the Dermatology Division of the Chinese Geriatrics Society, and other relevant experts jointly discussed and formulated the 'Chinese Expert Consensus on the Diagnosis and Treatment of Immune Checkpoint Inhibitor-Related Cutaneous Adverse Reactions'. This consensus covers the name, epidemiology, pathogenesis, clinical features, classification and grading of cirAEs, principles of management and the re-initiation of ICIs. It aims to provide a more scientific and authoritative reference for the diagnosis and treatment of cirAEs in China in the future.

Aims: Medication non-adherence is a type of adverse drug event that can lead to untreated and exacerbated chronic illness, and that drives healthcare utilization. Research using medication claims data has attempted to identify instances of medication non-adherence using the proportion of days covered or by examining gaps between medication refills. We sought to validate these measures compared to a gold standard diagnosis of non-adherence made in hospital., Methods: This was a retrospective analysis of adverse drug events diagnosed during three prospective cohorts in British Columbia between 2008 and 2015 (n = 976). We linked prospectively identified adverse drug events to medication claims data to examine the sensitivity and specificity of typical non-adherence measures., Results: The sensitivity of the non-adherence measures ranged from 22.4% to 37.5%, with a proportion of days covered threshold of 95% performing the best; the non-persistence measures had sensitivities ranging from 10.4% to 58.3%. While a 7-day gap was most sensitive, it classified 61.2% of the sample as non-adherent, whereas only 19.6% were diagnosed as such in hospital., Conclusions: The methods used to identify non-adherence in administrative databases are not accurate when compared to a gold standard diagnosis by healthcare providers. Research that has relied on administrative data to identify non-adherent patients both underestimates the magnitude of the problem and may label patients as non-adherent who were in fact adherent., (© 2024 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Cancer immunotherapy is revolutionary for survival but has complications due to immunogenicity with unpredictable and potentially long-lasting autoimmune side effects known as immune-related adverse events (irAEs). Currently, treatment beyond corticosteroids can be complicated by the diversity of providers who are needed across a variety of clinical settings to manage irAEs. We outline the role of critical players in the management of irAEs, discuss the current limitations that exist, and propose various methodologies that can be adapted across clinical settings to tackle these needs. We aim to better understand who can be affected by irAEs and tailor diagnostics and therapeutics appropriately., Competing Interests: Disclosure P. Reid has a patent pending regarding the use of interleukin 6-axis inhibitors for viral infection-associated pneumonitis. COVID-19 Funds to Retain Clinical Scientists by the SECURED (Supporting Early Career University Researchers to Excel through Disruptions) Steering Committee as well as the University of Chicago Institute of Translational Medicine Clinical and Translational Science Award K12/KL2 Grant 5KL2TR002387-05., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Determining the causality of Adverse Drug Reactions (ADRs) is essential for management and prevention of future occurrences. The WHO-Uppsala Monitoring Centre (UMC) system is recommended under the Pharmacovigilance Program of India whereas Naranjo's algorithm is commonly utilized by clinicians, but their agreement remains a subject of investigation. This study aims to compare the inter-rater agreement between these two scales for causality assessment of ADRs. In this cross-sectional study, two groups of pharmacovigilance experts were given a set of total 399 anonymized individual case safety reports, collected over six months. The raters were blinded to each other's assessments and applied the WHO-UMC system and Naranjo algorithm to each case independently. Inter-rater agreement was then evaluated utilizing Cohen's kappa. The suspected ADRs were also comprehensively analysed on parameters like age, sex, route of administration, speciality, organ system affected, most common drug categories and individual drugs, outcome of ADRs. Analysis of 399 suspected ADRs revealed that mean age of patients was 36.8 ± 18.0 years, females were more frequently affected, highest proportion of reports were from psychiatry inpatients, seen with antipsychotic drugs, involved the central nervous system, with oral administration, and 91% resolved. On causality assessment by the WHO-UMC system, 53.3% were "Certain" whereas Naranjo's algorithm categorized 96.74% of ADRs as "Probable". Cohen's kappa showed a "Minimal" agreement (0.22) between WHO-UMC and Naranjo system of causality assessment. The considerable lack of agreement between the two commonly employed systems of causality assessment of ADRs warrants further investigation into specific factors influencing the disagreement to improve the accuracy of causality assessments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Inc.)

The Health and Environmental Sciences Institute (HESI) Cardiac Safety Committee designed and created a publicly accessible database with an initial set of 128 pharmacologically defined pharmaceutical agents, many with known cardiotoxic properties. The database includes specific information about each compound that could be useful in evaluating hypotheses around mechanisms of drug-induced cardiac toxicity or for development of novel cardiovascular safety assays. Data on each of the compounds was obtained from published literature and online sources (e.g., DrugBank.ca and International Union of Basic and Clinical Pharmacology (IUPHAR) / British Pharmacological Society (BPS) Guide to PHARMACOLOGY) and was curated by 10 subject matter experts. The database includes information such as compound name, pharmacological mode of action, characterized cardiac mode of action, type of cardiac toxicity, known clinical cardiac toxicity profile, animal models used to evaluate the cardiotoxicity profile, routes of administration, and toxicokinetic parameters (i.e., Cmax). Data from both nonclinical and clinical studies are included for each compound. The user-friendly web interface allows for multiple approaches to search the database and is also intended to provide a means for the submission of new data/compounds from relevant users. This will ensure that the database is constantly updated and remains current. Such a data repository will not only aid the HESI working groups in defining drugs for use in any future studies, but safety scientists can also use the database as a vehicle of support for broader cardiovascular safety studies or exploring mechanisms of toxicity associated with certain pharmacological modes of action., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Inc.)

Purpose: We present and validate a rule-based algorithm for the detection of moderate to severe liver-related immune-related adverse events (irAEs) in a real-world patient cohort. The algorithm can be applied to studies of irAEs in large data sets., Methods: We developed a set of criteria to define hepatic irAEs. The criteria include: the temporality of elevated laboratory measurements in the first 2-14 weeks of immune checkpoint inhibitor (ICI) treatment, steroid intervention within 2 weeks of the onset of elevated laboratory measurements, and intervention with a duration of at least 2 weeks. These criteria are based on the kinetics of patients who experienced moderate to severe hepatotoxicity (Common Terminology Criteria for Adverse Events grades 2-4). We applied these criteria to a retrospective cohort of 682 patients diagnosed with hepatocellular carcinoma and treated with ICI. All patients were required to have baseline laboratory measurements before and after the initiation of ICI., Results: A set of 63 equally sampled patients were reviewed by two blinded, clinical adjudicators. Disagreements were reviewed and consensus was taken to be the ground truth. Of these, 25 patients with irAEs were identified, 16 were determined to be hepatic irAEs, 36 patients were nonadverse events, and two patients were of indeterminant status. Reviewers agreed in 44 of 63 patients, including 19 patients with irAEs (0.70 concordance, Fleiss' kappa: 0.43). By comparison, the algorithm achieved a sensitivity and specificity of identifying hepatic irAEs of 0.63 and 0.81, respectively, with a test efficiency (percent correctly classified) of 0.78 and outcome-weighted F1 score of 0.74., Conclusion: The algorithm achieves greater concordance with the ground truth than either individual clinical adjudicator for the detection of irAEs.

Background: Immune checkpoint inhibitors (ICIs) have significantly improved outcomes in various cancers. ICI treatment is associated with the incidence of immune-related adverse events (irAEs) which can affect any organ. Data on irAEs occurrence in relation to sex- differentiation and their association with gender-specific factors are limited., Aims: The primary objective of the G-DEFINER study is to compare the irAEs incidence in female and male patients who undergo ICI treatment. Secondary objectives are: to compare the irAEs incidence in pre- and postmenopausal female patients; to compare the irAEs incidence in female and male patients according to different clinical and gender-related factors (lifestyle, psychosocial, and behavioral factors). Exploratory objectives of the study are to compare and contrast hormonal, gene-expression, SNPs, cytokines, and gut microbiota profiles in relation to irAEs incidence in female and male patients., Methods and Results: The patients are recruited from Fondazione IRCCS Istituto Nazionale dei Tumori, Italy, St Vincent's University Hospital, Ireland, Oslo University Hospital, Norway, and Karolinska Insitutet/Karolinska University Hospital, Sweden. The inclusion of patients was delayed due to the Covid pandemic, leading to a total of 250 patients recruited versus a planned number of 400 patients. Clinical and translational data will be analyzed., Interpretation: The expected outcomes are to improve the management of cancer patients treated with ICIs, leading to more personalized clinical approaches that consider potential toxicity profiles. The real world nature of the trial makes it highly applicable for timely irAEs diagnosis.

Purpose: Combination therapies are a promising approach for improving cancer treatment, but it is challenging to predict their resulting adverse events in a real-world setting., Experimental Design: We provide here a proof-of-concept study using 15 million patient records from the FDA Adverse Event Reporting System (FAERS). Complex adverse event frequencies of drugs or their combinations were visualized as heat maps onto a two-dimensional grid. Adverse event frequencies were shown as colors to assess the ratio between individual and combined drug effects. To capture these patterns, we trained a convolutional neural network (CNN) autoencoder using 7,300 single-drug heat maps. In addition, statistical synergy analyses were performed on the basis of BLISS independence or χ2 testing., Results: The trained CNN model was able to decode patterns, showing that adverse events occur in global rather than isolated and unique patterns. Patterns were not likely to be attributed to disease symptoms given their relatively limited contribution to drug-associated adverse events. Pattern recognition was validated using trial data from ClinicalTrials.gov and drug combination data. We examined the adverse event interactions of 140 drug combinations known to be avoided in the clinic and found that near all of them showed additive rather than synergistic interactions, also when assessed statistically., Conclusions: Our study provides a framework for analyzing adverse events and suggests that adverse drug interactions commonly result in additive effects with a high level of overlap of adverse event patterns. These real-world insights may advance the implementation of new combination therapies in clinical practice., (©2024 The Authors; Published by the American Association for Cancer Research.)

Purpose: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, yet their use is associated with immune-related adverse events (irAEs). Estimating the prevalence and patient impact of these irAEs in the real-world data setting is critical for characterizing the benefit/risk profile of ICI therapies beyond the clinical trial population. Diagnosis codes, such as International Classification of Diseases codes, do not comprehensively illustrate a patient's care journey and offer no insight into drug-irAE causality. This study aims to capture the relationship between ICIs and irAEs more accurately by using augmented curation (AC), a natural language processing-based innovation, on unstructured data in electronic health records., Methods: In a cohort of 9,290 patients treated with ICIs at Mayo Clinic from 2005 to 2021, we compared the prevalence of irAEs using diagnosis codes and AC models, which classify drug-irAE pairs in clinical notes with implied textual causality. Four illustrative irAEs with high patient impact-myocarditis, encephalitis, pneumonitis, and severe cutaneous adverse reactions, abbreviated as MEPS-were analyzed using corticosteroid administration and ICI discontinuation as proxies of severity., Results: For MEPS, only 70% (n = 118) of patients found by AC were also identified by diagnosis codes. Using AC models, patients with MEPS received corticosteroids for their respective irAE 82% of the time and permanently discontinued the ICI because of the irAE 35.9% (n = 115) of the time., Conclusion: Overall, AC models enabled more accurate identification and assessment of patient impact of ICI-induced irAEs not found using diagnosis codes, demonstrating a novel and more efficient strategy to assess real-world clinical outcomes in patients treated with ICIs.

Background: The causality assessment tool can be utilized to assist patients in identifying adverse drug reactions (ADRs)., Aim: To evaluate the accuracy of the causality assessment tool for patients identifying ADRs compared to assessments made by pharmacists, and to explore how patients recall and recognize symptoms as ADRs., Method: Mixed methods study consisting of self-administered questionnaires (phase 1) and semi-structured, face-to-face interviews (phase 2) with patients who had experienced ADRs in the past year at a tertiary care hospital in Thailand., Results: Out of 769 questionnaires, 716 were returned and 622 of these were both valid and had at least one ADR (86.8%). Classification of patient-reported symptoms using the causality assessment tool found 12 (1.9%) highly-probable ADRs, 399 (64.1%) probable ADRs, 207 (33.3%) possible ADRs, and 4 (0.6%) that were not classified as ADRs. There was fair agreement between patient-assessed and pharmacist-assessed causality classifications using the Naranjo algorithm (K = 0.268) and the World Health Organization Uppsala Monitoring Centre (WHO-UMC) criteria (K = 0.373). The timing relationship between the occurrence of symptoms and administration of a suspected drug was the most frequently mentioned reason that patients gave for recalling and recognizing suspected ADRs., Conclusion: Promoting the causality assessment tool for use by patients in collaboration with healthcare professionals is likely to enhance patients' ability to correctly identify ADRs and ultimately contribute to increased medication safety., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Antibiotics are drugs widely used all around the world. Central nervous system adverse drug reactions (CNS ADRs) are mostly under-suspected with antibiotics. Nevertheless, these ADRs could lead to severe complications such as encephalopathy. To illustrate the clinical patterns of these off-target ADRs, we here present data from pharmacovigilance system, through different populations and points of view (worldwide, French population, vulnerable population and individual). These data could help clinicians to better know about CNS ADRs with antibiotics, to better identify risk factors and vulnerable patients and to highlight the importance to set up the right diagnostic explorations in the best timing to avoid complications. Clinicians should request a pharmacological opinion from pharmacologist (biologists and pharmacovigilance clinicians) in front of vulnerable population before or during antibiotics. Pharmacovigilance advice could help clinicians in the diagnosis and the management of an ADR. Therapeutic drug monitoring is particularly contributive to adjust doses of antibiotics administered in vulnerable patients. Pharmacovigilance advice and TDM are essential to perform personalized medicine, and contribute to the proper use of drugs., (Copyright © 2023 Société française de pharmacologie et de thérapeutique. Published by Elsevier Masson SAS. All rights reserved.)

Aims: The Medicines and Healthcare products Regulatory Agency Yellow Card scheme (YCS) is the UK's system that collects spontaneous reports about suspected adverse drug reactions (ADRs). Reporting of suspected ADRs by young people (age <19 years) in the UK is extremely uncommon, driving efforts to improve awareness and reporting., Methods: Quality improvement project, using an anonymous online survey about updated information for young people, distributed through school pupils (age 13-18 years) across the UK through the Alder Hey Research Ambassador programme., Results: Research Ambassadors were recruited in 21 schools and colleges, generating 2933 responses (15 November 2022-08 April 2023); 6.3% of respondents had heard of the YCS, and 0.8% had previously reported a Yellow Card. There were 307 suspected drug-event combinations reported, 36 of which required attendance at hospital. The updated YCS reporting guide was understood by 92.8% of young people, and 90.8% reported knowing more about ADRs after reading the guide. The percentage of young people 'Not comfortable' reporting a suspected ADR decreased from 13.3% (before reading) to 4.1% after reading (P < .000001), and 84.5% of young people reported willingness to report a side effect in the future. The most common comments regarding further improvement of the information were content, or length of the text could be altered in some way (n = 543, 26.1%) and graphic design could be improved (n = 357, 17.2%)., Conclusions: The age-appropriate information provided met many of their needs, increasing willingness to report. Integration into existing education curricula in the UK would facilitate knowledge transfer and improve reporting., (© 2023 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.)

Timely identification and discontinuation of culprit-drug is the cornerstone of clinical management of drug-induced acute pancreatitis (AP), but the comprehensive landscape of AP culprit-drugs is still lacking. To provide the current overview of AP culprit-drugs to guide clinical practice, we reviewed the adverse event (AE) reports associated with AP in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database from 2004 to 2022, and summarized a potential AP culprit-drug list and its corresponding AE report quantity proportion. The disproportionality analysis was used to detect adverse drug reaction (ADR) signals for each drug in the drug list, and the ADR signal distribution was integrated to show the risk characteristic of drugs according to the ADR signal detection results. In the FAERS database, a total of 62,206 AE reports were AP-related, in which 1,175 drugs were reported as culprit-drug. On the whole, metformin was the drug with the greatest number of AE reports, followed by quetiapine, liraglutide, exenatide, and sitagliptin. Drugs used in diabetes was the drug class with the greatest number of AE reports, followed by immunosuppressants, psycholeptics, drugs for acid-related disorders, and analgesics. In disproportionality analysis, 595 drugs showed potential AP risk, whereas 580 drugs did not show any positive ADR signal. According to the positive-negative distribution of the ADR signal for drug classes, the drug class with the greatest number of positive drugs was antineoplastic agents. In this study, we provided the current comprehensive landscape of AP culprit-drugs from the pharmacovigilance perspective, which can provide reference information for clinical practice., (© 2023 The Authors. Clinical Pharmacology & Therapeutics © 2023 American Society for Clinical Pharmacology and Therapeutics.)

Aims: Neonates hospitalized in neonatal intensive care units (NICUs) commonly experience adverse drug reactions (ADRs). Thus, we aimed to develop and validate a tool for predicting ADRs in neonates hospitalized in NICUs., Methods: A nested case-control study in an open cohort with neonates admitted to the NICU of a maternity hospital in Natal, Brazil was conducted from January 2019 to January 2022 [Correction added on 4 December 2023, after first online publication: 2023 has been changed to 2019 in the preceding sentence.]. Neonates with ADR were randomly paired with 2 controls. For the development of the tool, a multivariate logistic regression was applied on 2/3 of the sample (cases with respective controls). The model's fit was evaluated using the Hosmer-Lemeshow test for calibration and the Brier score for performance assessment. Validation of the tool was performed by determining the area under the receiver operating characteristic curve with bootstrap adjusted c-statistics., Results: In all, 450 neonates (150 cases and 300 controls) were included in the study. We identified 5 independent risk factors for ADR, 4 related to the neonate (current mechanical ventilation, heart rate ≥178 beats/min, intravenous medications, ≥5 prescription medications) and 1 to the mother (gestational hypertension). The tool had a classification cut-off point of ≥15, and its total score ranged from 0 to 34. In validation, the tool had an area under the receiver operating characteristic curve of 0.74 (95% confidence interval [CI] 0.66-0.81) with sensitivity of 52.02% (95% CI 47.40-56.64) and specificity of 81.35% (95% CI 77.75-84.95)., Conclusion: The tool demonstrated adequate discriminative ability and utilized 5 commonly monitored variables in the NICU., (© 2023 British Pharmacological Society.)

Background: A series of signal detection methods have been developed to detect adverse drug reaction (ADR) signals in spontaneous reporting system. However, different signal detection methods yield quite different signal detection results, and we do not know which method has the best detection performance. How to choose the most suitable signal detection method is an urgent problem to be solved. In this study, we systematically reviewed the characteristics and application scopes of current signal detection methods, with the goal of providing references for the optimization selection of signal detection methods in spontaneous reporting system., Methods: We searched six databases from inception to January 2023. The search strategy targeted literatures regarding signal detection methods in spontaneous reporting system. We used thematic analysis approach to summarize the advantages, disadvantages, and application scope of each signal detection method., Results: A total of 93 literatures were included, including 27 reviews and 66 methodological studies. Moreover, 31 signal detection methods were identified in these literatures. Each signal detection method has its inherent advantages and disadvantages, resulting in different application scopes of these methods., Conclusion: Our systematic review finds that there are variabilities in the advantages, disadvantages, and application scopes of different signal detection methods. This finding indicates that the most suitable signal detection method varies across different drug safety scenarios. Moreover, when selecting signal detection method in a particular drug safety scenario, the following factors need to be considered: purpose of research, database size, drug characteristics, adverse event characteristics, and characteristics of the relations between drugs and adverse events., (© 2024 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Objectives: Prior data indicate a very rare risk of serious adverse drug reaction (ADR) to ultrasound enhancement agents (UEAs). We sought to evaluate the frequency of ADR to UEA administration in contemporary practice., Methods: We retrospectively reviewed 4 US health systems to characterize the frequency and severity of ADR to UEA. Adverse drug reactions were considered severe when cardiopulmonary involvement was present and critical when there was loss of consciousness, loss of pulse, or ST-segment elevation. Rates of isolated back pain and headache were derived from the Mayo Clinic Rochester stress echocardiography database where systematic prospective reporting of ADR was performed., Results: Among 26,539 Definity and 11,579 Lumason administrations in the Mayo Clinic Rochester stress echocardiography database, isolated back pain or headache was more frequent with Definity (0.49% vs 0.04%, P < .0001) but less common with Definity infusion versus bolus (0.08% vs 0.53%, P = .007). Among all sites there were 201,834 Definity and 84,943 Lumason administrations. Severe and critical ADR were more frequent with Lumason than with Definity (0.0848% vs 0.0114% and 0.0330% vs 0.0010%, respectively; P < .001 for each). Among the 3 health systems with >2,000 Lumason administrations, the frequency of severe ADR with Lumason ranged from 0.0755% to 0.1093% and the frequency of critical ADR ranged from 0.0293% to 0.0525%. Severe ADR rates with Definity were stable over time but increased in more recent years with Lumason (P = .02). Patients with an ADR to Lumason since the beginning of 2021 were more likely to have received a COVID-19 vaccination compared with matched controls (88% vs 75%; P = .05) and more likely to have received Moderna than Pfizer-Biotech (71% vs 26%, P < .001)., Conclusion: Severe and critical ADR, while rare, were more frequent with Lumason, and the frequency has increased in more recent years. Additional work is needed to better understand factors, including associations with recently developed mRNA vaccines, which may be contributing to the increased rates of ADR to UEA since 2021., Competing Interests: Conflicts of Interest None., (Copyright © 2023 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved.)