Adverse events with risankizumab in the real world: postmarketing pharmacovigilance assessment of the FDA adverse event reporting system.

Background: Risankizumab, a humanized IgG1 monoclonal antibody that selectively inhibits IL-23, is currently approved for the treatment of moderate-to-severe plaque psoriasis and Crohn's disease. The real-world safety study of risankizumab in a large- sample population is currently lacking. The aim of this study was to evaluate risankizumab-associated adverse events (AEs) and characterize the clinical priority through the data mining of the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). Methods: Disproportionality analyses were performed by calculating the reporting odds ratios (RORs), deemed significant when the lower limit of the 95% confidence interval was greater than 1, to quantify the signals of risankizumab-related AEs from the second quarter (Q2) of 2019 to 2022 Q3. Serious and non-serious cases were compared, and signals were prioritized using a rating scale. Results: Risankizumab was recorded in 10,235 reports, with 161 AEs associated with significant disproportionality. Of note, 37 PTs in at least 30 cases were classified as unexpected AEs, which were uncovered in the drug label, such as myocardial infarction, cataract, pancreatitis, diabetes mellitus, stress, and nephrolithiasis. 74.68%, 25.32%, and 0% PTs were graded as weak, moderate, and strong clinical priorities, respectively. A total of 48 risankizumab-related AEs such as pneumonia, cerebrovascular accident, cataract, loss of consciousness, cardiac disorder, hepatic cirrhosis, and thrombosis, were more likely to be reported as serious AEs. The median TTO of moderate and weak signals related to risankizumab was 115 (IQR 16.75-305) and 124 (IQR 29-301) days, respectively. All of the disproportionality signals had early failure type features, indicating that risankizumab-associated AEs gradually decreased over time. Conclusion: Our study found potential new AE signals and provided valuable evidence for clinicians to mitigate the risk of risankizumab-associated AEs based on an extensive analysis of a large-scale postmarketing international safety database.

Safety of COVID-19 Vaccines Among the Paediatric Population: Analysis of the European Surveillance Systems and Pivotal Clinical Trials.

BACKGROUND AND OBJECTIVES: The European Medicine Agency extended the use of Comirnaty, Spikevax, and Nuvaxovid in paediatrics; thus, these vaccines require additional real-world safety evidence. Herein, we aimed to monitor the safety of COVID-19 vaccines through Covid-19 Vaccine Monitor (CVM) and EudraVigilance surveillance systems and the published pivotal clinical trials. METHODS: In a prospective cohort of vaccinees aged between 5 and 17 years, we measured the frequency of commonly reported (local/systemic solicited) and serious adverse drug events (ADRs) following the first and second doses of COVID-19 vaccines in Europe using data from the CVM cohort until April 2022. The results of previous pivotal clinical trials and data in the EudraVigilance were also analysed. RESULTS: The CVM study enrolled 658 first-dose vaccinees (children aged 5-11 years; n = 250 and adolescents aged 12-17 years; n = 408). Local/systemic solicited ADRs were common, whereas serious ADRs were uncommon. Among Comirnaty first and second dose recipients, 28.8% and 17.1% of children and 54.2% and 52.2% of adolescents experienced at least one ADR, respectively; injection-site pain (29.2% and 20.7%), fatigue (16.1% and 12.8%), and headache (22.1% and 19.3%) were the most frequent local and systemic ADRs. Results were consistent but slightly lower than in pivotal clinical trials. Reporting rates in Eudravigilance were lower by a factor of 1000. CONCLUSIONS: The CVM study showed high frequencies of local solicited reactions after vaccination but lower rates than in pivotal clinical trials. Injection-site pain, fatigue, and headache were the most commonly reported ADRs for clinical trials, but higher than spontaneously reported data.

Is PEGylation of Drugs Associated with Hypersensitivity Reactions? An Analysis of the Italian National Spontaneous Adverse Drug Reaction Reporting System.

BACKGROUND AND OBJECTIVE: Evidence highlights the allergenic potential of PEGylated drugs because of the production of anti-polyethylene glycol immunoglobulins. We investigated the risk of hypersensitivity reactions of PEGylated drugs using the Italian spontaneous adverse drug reaction reporting system database. METHODS: We selected adverse drug reaction reports attributed to medicinal products containing PEGylated active substances and/or PEGylated liposomes from the Italian Spontaneous Reporting System in the period between its inception and March 2021. As comparators, we extracted adverse drug reaction reports of medicinal products containing the same non-PEGylated active substances and/or non-PEGylated liposomes (or compounds belonging to the same mechanistic class). A descriptive analysis of reports of hypersensitivity reactions was performed. Reporting rates and time to onset of hypersensitivity reactions were also calculated in the period between January 2009 and March 2021. As a measure of disproportionality, we calculated the reporting odds ratio. RESULTS: Overall, 3865 adverse drug reaction reports were related to PEGylated medicinal products and 11,961 to their non-PEGylated comparators. Around two-thirds of patients were female and reports mostly concerned patients aged between 46 and 64 years. The frequency of hypersensitivity reactions reporting was higher among PEGylated versus non-PEGylated medicinal products (11.7% vs 9.4%, p < 0.0001). The hypersensitivity reaction reporting rates were higher for PEGylated medicinal products versus non-PEGylated medicinal products, with reporting rate ratios that ranged from 1.4 (95% confidence interval 0.8-2.5) for pegfilgrastim versus filgrastim to 20.0 (95% confidence interval 2.8-143.5) for peginterferon alpha-2a versus interferon alpha-2a. The median time to onset of hypersensitivity reactions was 10 days (interquartile range: 0-61) for PEGylated medicinal products, and 36 days (interquartile range: 3-216) for non-PEGylated comparators. Statistically significant reporting odds ratios were observed when comparing the reporting of hypersensitivity reactions for PEGylated versus non-PEGylated medicinal products (reporting odds ratio: 1.3; 95% confidence interval 1.1-1.4). However, when using all other drugs as comparators, the disproportionality analysis showed no association with hypersensitivity reactions for PEGylated nor non-PEGylated medicinal products, thus suggesting that many other triggers of drug-induced hypersensitivity reactions play a major role. CONCLUSIONS: The findings of this analysis of the Italian spontaneous adverse drug reaction database suggest a potential involvement for PEGylation in triggering drug-related hypersensitivity reactions, especially clinically relevant reactions. However, when comparing both PEGylated and non-PEGylated drugs under study to all other drugs no disproportionate reporting of hypersensitivity reactions was observed, probably due to a masking effect owing to the presence in the same database of other medicinal products increasing the threshold required to highlight a safety signal when the entire database is used as a reference.

The Evolving Profile of Idiosyncratic Drug-Induced Liver Injury.

Idiosyncratic drug-induced liver injury (DILI) is an infrequent but important cause of liver disease. Newly identified causes of DILI include the COVID vaccines, turmeric, green tea extract, and immune checkpoint inhibitors. DILI is largely a clinical diagnosis of exclusion that requires evaluation for more common causes of liver injury and a compatible temporal association with the suspect drug. Recent progress in DILI causality assessment includes the development of the semi-automated revised electronic causality assessment method (RECAM) instrument. In addition, several drug-specific HLA associations have been identified that can help with the confirmation or exclusion of DILI in individual patients. Various prognostic models can help identify the 5%-10% of patients at highest risk of death. Following suspect drug cessation, 80% of patients with DILI fully recover, whereas 10%-15% have persistently abnormal laboratory studies at 6 months of follow-up. Hospitalized patients with DILI with an elevated international normalized ratio or mental status changes should be considered for N-acetylcysteine therapy and urgent liver transplant evaluation. Selected patients with moderate to severe drug reaction with eosinophilia and systemic symptoms or autoimmune features on liver biopsy may benefit from short-term corticosteroids. However, prospective studies are needed to determine the optimal patients and dose and duration of steroids to use. LiverTox is a comprehensive, freely accessible Web site with important information regarding the hepatotoxicity profile of more than 1000 approved medications and 60 herbal and dietary supplement products. It is hoped that ongoing "omics" studies will lead to additional insight into DILI pathogenesis, improved diagnostic and prognostic biomarkers, and mechanism-based treatments.

Evaluation of the effectiveness of electronic prescription in reducing medical and medical errors (systematic review study).

INTRODUCTION: The use of electronic systems in prescription is considered as the final solution to overcome the many problems of the paper transcription process, especially with the outbreak of Coronavirus needs more attention than before. But despite the many advantages, its implementation faces many challenges and obstacles. Therefore, the present study was conducted to review the effectiveness of computerized physician order entry systems (CPOE) on relative risk reduction on medication error and adverse drug events (ADE). METHOD: This study is one of the systematic review studies that was conducted in 2021. In this study, searching for keywords such as E-Electronic Prescription, Patient safety, Medication Errors prescription, Drug Interactions, orginal articles from 2000 to October-2020 in the valid databases such as ISI web of Science PubMed Embase, Scopus and search engines like google was done. The included studies were based on the main objectives of the study and based on the inclusion criteria after several stages of review and quality evaluation. In fact, the main criteria for selecting articles were studies that compared the rate of medication errors with or without assessing the associated harms (real or potential) before and after the implementation of EMS. RESULTS: Out of 110 selected studies after initial screening, only 16 articles were selected due to their relevance. Among the final studies, there was a significant heterogeneity. Only 6 studies were of good quality. Of the 10 studies prescribing error rates, 9 reported reductions, but variable denominators prevented meta-analysis. Twelve studies provided specific examples of systemic drug errors. 5 cases reported their occurrence slightly. Out of 9 cases that analyzed the effects on drug error rate, 7 cases showed a significant relative reduction between 13 and 99%. Four of the six studies that analyzed the effects on potential ADEs showed a significant relative reduction of between 35 and 98%. Two of the four studies that analyzed the effect of ADEs showed a relative reduction of between 30 and 84%. CONCLUSION: Finally, e-prescribing seems to reduce the risk of medication errors and ADE. However, the studies differed significantly in terms of setting, design, quality and results. More randomized controlled trials (RCTs) are needed to further improve the evidence of health informatics information.

Automatic Identification of Self-Reported COVID-19 Vaccine Information from Vaccine Adverse Events Reporting System.

BACKGROUND: The short time frame between the coronavirus disease 2019 (COVID-19) pandemic declaration and the vaccines authorization led to concerns among public regarding the safety and efficacy of the vaccines. The Food and Drug Administration uses the Vaccine Adverse Events Reporting System (VAERS) where general population can report their vaccine side effects in the text box. This information could be utilized to determine self-reported vaccine side effects. OBJECTIVE: To develop a supervised and unsupervised natural language processing (NLP) pipeline to extract self-reported COVID-19 vaccination side effects, location of the side effects, medications, and possibly false/misinformation seeking further investigation in a structured format for analysis and reporting. METHODS: We utilized the VAERS dataset of COVID-19 vaccine reports from November 2020 to August 2022 of 725,246 individuals. We first developed a gold-standard (GS) dataset of randomly selected 1,500 records. Second, the GS was split into training, testing, and validation sets. The training dataset was used to develop the NLP applications (supervised and unsupervised) and testing and validation datasets were used to test the performances of the NLP application. RESULTS: The NLP application automatically extracted vaccine side effects, body locations of the side effects, medication, and possibly misinformation with moderate to high accuracy (84% sensitivity, 82% specificity, and 83% F-1 measure). We found that 23% people (386,270) faced arm soreness, 31% body swelling (226,208), 23% fatigue/body weakness (168,160), and 22% (159,873) cold/flue-like symptoms. Most of the complications occurred in the body locations such as the arm, back, chest, neck, face, and head. Over-the-counter pain medications such as Tylenol and Ibuprofen and allergy medication like Benadryl were most reported self-reported medications. Death due to COVID-19, changes in the DNA, and infertility were possible false/misinformation reported by people. CONCLUSION: Some self-reported side effects such as syncope, arthralgia, and blood clotting need further clinical investigations. Our NLP application may help in extracting information from big free-text electronic datasets to help policy makers and other researchers with decision making.

Tree-based data mining for safety assessment of first COVID-19 booster doses in the Vaccine Safety Datalink.

BACKGROUND: The Centers for Disease Control and Prevention's Vaccine Safety Datalink (VSD) has been performing safety surveillance for COVID-19 vaccines since their earliest authorization in the United States. Complementing its real-time surveillance for pre-specified health outcomes using pre-specified risk intervals, the VSD conducts tree-based data-mining to look for clustering of a broad range of health outcomes after COVID-19 vaccination. This study's objective was to use this untargeted, hypothesis-generating approach to assess the safety of first booster doses of Pfizer-BioNTech (BNT162b2), Moderna (mRNA-1273), and Janssen (Ad26.COV2.S) COVID-19 vaccines. METHODS: VSD enrollees receiving a first booster of COVID-19 vaccine through April 2, 2022 were followed for 56 days. Incident diagnoses in inpatient or emergency department settings were analyzed for clustering within both the hierarchical ICD-10-CM code structure and the follow-up period. The self-controlled tree-temporal scan statistic was used, conditioning on the total number of cases for each diagnosis. P-values were estimated by Monte Carlo simulation; p = 0.01 was pre-specified as the cut-off for statistical significance of clusters. RESULTS: More than 2.4 and 1.8 million subjects received Pfizer-BioNTech and Moderna boosters after an mRNA primary series, respectively. Clusters of urticaria/allergy/rash were found during Days 10-15 after the Moderna booster (p = 0.0001). Other outcomes that clustered after mRNA boosters, mostly with p = 0.0001, included unspecified adverse effects, common vaccine-associated reactions like fever and myalgia, and COVID-19. COVID-19 clusters were in Days 1-10 after booster receipt, before boosters would have become effective. There were no noteworthy clusters after boosters following primary Janssen vaccination. CONCLUSIONS: In this untargeted data-mining study of COVID-19 booster vaccination, a cluster of delayed-onset urticaria/allergy/rash was detected after the Moderna booster, as has been reported after Moderna vaccination previously. Other clusters after mRNA boosters were of unspecified or common adverse effects and COVID-19, the latter evidently reflecting immunity to COVID-19 after 10 days.

Post-Marketing Surveillance of CAR-T-Cell Therapies: Analysis of the FDA Adverse Event Reporting System (FAERS) Database.

INTRODUCTION: As chimeric antigen receptor T-cell therapies are becoming increasingly available in the armamentarium of the hematologist, there is an emerging need to monitor post-marketing safety. OBJECTIVE: We aimed to better characterize their safety profile by focusing on cytokine release syndrome and identifying emerging signals. METHODS: We queried the US Food and Drug Administration Adverse Event Reporting System (October 2017-September 2020) to analyze suspected adverse drug reactions to tisagenlecleucel (tisa-cel) and axicabtagene ciloleucel (axi-cel). Disproportionality analyses (reporting odds ratio) were performed by comparing chimeric antigen receptor T-cell therapies with (a) all other drugs (reference group 1) and (b) other onco-hematological drugs with a similar indication, irrespective of age (reference group 2), or (c) restricted to adults (reference group 3). Notoriety was assessed through package inserts and risk management plans. Adverse drug reaction time to onset and cytokine release syndrome features were investigated. RESULTS: Overall, 3225 reports (1793 axi-cel; 1433 tisa-cel) were identified. The reported toxicities were mainly: cytokine release syndrome (52.2%), febrile disorders (27.7%), and neurotoxicity (27.2%). Cytokine release syndrome and neurotoxicity were often co-reported and 75% of the events occurred in the first 10 days. Disproportionalities confirmed known adverse drug reactions and showed unexpected associations: for example, axi-cel with cardiomyopathies (reporting odds ratio = 2.3; 95% confidence interval 1.2-4.4) and gastrointestinal perforations (2.9; 1.2-7.3), tisa-cel with hepatotoxicity (2.5; 1.1-5.7) and pupil disorders (15.3; 6-39.1). CONCLUSIONS: Our study confirms the well-known adverse drug reactions and detects potentially emerging safety issues specific for each chimeric antigen receptor T-cell therapy, also providing insights into a stronger role for tisa-cel in inducing some immunodeficiency-related events (e.g., hypogammaglobulinemia, infections) and coagulopathies, and for axi-cel in neurotoxicity.

Sales of "COVID kit" drugs and adverse drug reactions reported by the Brazilian Health Regulatory Agency.

Off-label use of azithromycin, hydroxychloroquine, and ivermectin (the "COVID kit") has been suggested for COVID-19 treatment in Brazil without clinical or scientific evidence of efficacy. These drugs have known adverse drug reactions (ADR). This study aimed to analyze if the sales of drugs in the "COVID kit" are correlated to the reported number of ADR after the COVID-19 pandemic began. Data was obtained from the Brazilian Health Regulatory Agency (Anvisa) website on reported sales and ADRs for azithromycin, hydroxychloroquine, and ivermectin for all Brazilian states. The period from March 2019 to February 2020 (before the pandemic) was compared to that from March 2020 to February 2021 (during the pandemic). Trend adjustment was performed for time series data and cross-correlation analysis to investigate correlation between sales and ADR within the same month (lag 0) and in the following months (lag 1 and lag 2). Spearman's correlation coefficient was used to assess the magnitude of the correlations. After the pandemic onset, sales of all investigated drugs increased significantly (69.75% for azithromycin, 10,856,481.39% for hydroxychloroquine, and 12,291,129.32% for ivermectin). ADR levels of all medications but azithromycin were zero before the pandemic, but increased after its onset. Cross-correlation analysis was significant in lag 1 for all drugs nationwide. Spearman's correlation was moderate for azithromycin and hydroxychloroquine but absent for ivermectin. Data must be interpreted cautiously since no active search for ADR was performed. Our results show that the increased and indiscriminate use of "COVID kit" during the pandemic correlates to an increased occurrence of ADRs.

A cross-sectional study of COVID-19 vaccination patterns among patients with epilepsy in Hong Kong.

OBJECTIVE: As Hong Kong faced the 5th wave of the COVID-19 pandemic, the facilitators and hurdles toward effective vaccination is important for healthcare professionals to understand the vaccination gap among patients with epilepsy. METHODS: A cross-sectional, pragmatic study of COVID-19 vaccination was performed at a tertiary epilepsy center with regards to patterns of vaccination and any unusually high rate of adverse events. Patients having recent visits at the epilepsy center (4 months) had their anonymized electronic linkage records examined 12 months after the inception of vaccination program for types of vaccines, seizure demographics, and adverse events following immunization (AEFI). RESULTS: A total of 200 patients with epilepsy and their anonymized data were analyzed. The vaccine uptake was approximately 60% of that of the general population. Twice as many patients with epilepsy chose to receive mRNA vaccine as compared with inactivated vaccine. The proportion of patients who kept up-to-date with all available dosing was 7%. Patients with epilepsy with genetic etiology were least likely to receive vaccination (13/38, 34%, P = .02). There was no unreasonably high rate of unacceptable side effects after vaccination among patients with epilepsy. Only 3 patients reported worsening of seizures without meeting the criteria for AEFI. Refractory epilepsy, allergy to antiseizure medications and elder age (≥65) did not confer any significant difference in vaccination patterns or adverse effects. SIGNIFICANCE: A vaccination gap exists among epilepsy patients which calls for actionable strategies for improving vaccine uptake, including education and outreach programs.

Adverse effects following COVID-19 vaccination in Iran.

BACKGROUND: Vaccination is a key intervention to prevent COVID-19. Many vaccines are administered globally, yet there is not much evidence regarding their safety and adverse effects. Iran also faces this challenge, especially as data regarding the Sputnik V vaccine is sparse. Therefore, the aim of this study is to determine the adverse effects of the most commonly used vaccines in Iran. METHODS: Using a retrospective cohort study design, 6600 subjects aged 18 years or older who had received two doses of any of the three COVID-19 vaccines (Sinopharm, AstraZeneca, and Sputnik V) were selected using a random sampling method between March and August 2021. Subjects were asked about any adverse effects of the vaccines by trained interviewers via telephone interview. Vaccine-related adverse effects in individuals during the first 72 h and subsequently following both doses of the vaccines were determined. The demographic variables, type of administered vaccine, adverse effects, and history of the previous infection with COVID-19 were collected. Descriptive statistics (mean, standard deviation) and analytical statistics (Chi-squared and Wilcoxon tests) were performed at a 95% significance level using STATA software version 15 (STATA Corp, College Station, TX, USA). RESULTS: From 6600 participants, 4775 responded (response rate = 72.3%). Of the participants, 1460 (30.6%) received the AstraZeneca vaccine, 1564 (32.8%) received the Sinopharm vaccine and 1751 (36.7%) received the Sputnik V vaccine. 2653 participants (55.56%) reported adverse effects after the first dose and 1704 (35.7%) after the second dose. Sputnik V caused the most adverse effects with 1449 (82.7%) vaccine recipients reporting symptoms after the first or second dose, compared with 1030 (70.5%) for AstraZeneca and only 585 (37.4%) for the Sinopharm vaccine. The most common adverse effects after the first dose were fatigue (28.37%), chill/fever (26.86%), and skeletal pain (22.38%). These three adverse effects were the same for the second dose, although their prevalence was lower. CONCLUSIONS: In this study, we demonstrate that the Sputnik V vaccine has the highest rate of adverse effects, followed by the AstraZeneca and Sinopharm vaccines. COVID-19 vaccines used in Iran are safe and there were no reports of serious adverse effects.

Detecting Drug-Drug Interactions in COVID-19 Patients.

COVID-19 patients with multiple comorbid illnesses are more likely to be using polypharmacy to treat their COVID-19 disease and comorbid conditions. Previous literature identified several DDIs in COVID-19 patients; however, various DDIs are unrecognized. This study aims to discover novel DDIs by conducting comprehensive research on the FDA Adverse Event Reporting System (FAERS) data from January 2020 to March 2021. We applied seven algorithms to discover DDIs. In addition, the Liverpool database containing DDI confirmed by clinical trials was used as a gold standard to determine novel DDIs in COVID-19 patients. The seven models detected 2,516 drug-drug pairs having adverse events (AEs), 49 out of which were confirmed by the Liverpool database. The remaining 2,467 drug pairs tested to be significant by the seven models can be candidate DDIs for clinical trial hypotheses. Thus, the FAERS database, along with informatics approaches, provides a novel way to select candidate drug-drug pairs to be examined in COVID-19 patients.

A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network.

Recently Artificial Intelligence(AI) has not only been used to diagnose the disease but also to cure the disease. Researchers started using AI for drug discovery. Predicting the Adverse Drug Reactions(ADRs) caused by the drug in the manufacturing stage or in the clinical trial stage is a very important problem in drug discovery. ADRs have become a major concern resulting in injuries and also becoming fatal sometimes. Drug safety has gained much importance over the years propelling to the forefront investigation of predicting the ADRs. Although prior studies have queried diverse approaches to predict ADRs, very few were found to be effective. Also, the problem of having fewer reports makes the prediction of ADRs more difficult. To tackle this problem effectively, a novel method has been proposed in this paper. The proposed method is based on Knowledge Graph(KG) embedding. Using the KG embedding, we designed and trained a custom-made Deep Neural Network(DNN) called KGDNN(Knowledge Graph DNN) for predicting the ADRs. A KG has been constructed with 6 types of entities: drugs, ADRs, target proteins, indications, pathways, and genes. Using the Node2Vec algorithm, each node has been embedded into a feature space. Using those embeddings, the ADRs are classified by the KGDNN model. The proposed method has obtained an AUROC score of 0.917 and significantly outperformed the existing methods. Two case studies on drugs causing liver injury and COVID-19 recommended drugs have been performed to illustrate the model efficacy.

Prevalence of COVID-19 Vaccine Side Effects among Early-Vaccinated Healthcare Workers in Eastern Ethiopia.

Background: The Ministry of Health of Ethiopia launched the COVID-19 vaccination campaign in March 2021, with frontline healthcare workers as first-round recipients and a goal of vaccinating 20% of the population by the end of 2021. The study aims to estimate the prevalence of COVID-19 vaccination side effects among early vaccinated healthcare workers in Adama hospital medical college. Methods: A cross-sectional study was carried out between March and June 2021, following the vaccination of COVID-19 vaccine among healthcare workers in Adama hospital medical college. The study used a structured self-administered questionnaire and additional telephone surveys on items covering the participants' demographic data, local and systemic manifestations after vaccination. Results: A total of 540 health care workers and supportive staff were enrolled in this study. The overall any-symptom report after the first dose of ChAdOx1 nCoV- 19 vaccine was 84.3%. The majority (39.6%) of participants had both systemic and local symptoms and 25.7% had only local and 18.9% had only systemic symptoms. Injection site pain was the most prevalent side effect symptom (64.1%), followed by fatigue (35.7%), headache (28.9%), joint pain (26.5%), and muscle pain (21.5%). Conclusion: Vaccine side effects were common and found to be well-tolerated among the recipients of the first dose of ChAdOx1 nCoV-19 at Adama hospital medical college healthcare workers. The side effects were mainly mild to moderate. More side-effect profiles should be studied and disseminated to detect rare adverse reactions.

Adverse events of alpelisib: A postmarketing study of the World Health Organization pharmacovigilance database.

AIMS: To explore and describe the adverse reaction signals in the safety reporting for alpelisib. METHODS: We performed a disproportionality analysis of the World Health Organization's VigiBase pharmacovigilance database from 1 January 2019 to 30 June 2021. Disproportionality analysis by information components (ICs) were used to evaluate the potential association between adverse events (AEs) and alpelisib. RESULTS: A total of 33 327 reports were extracted, 5695 of them were chosen with alpelisib as the suspected drug. After combining the same ID, 687 cases remained. The 45-64-years group had the most cases (n = 203, 29.55%). There were 129 Preferred Terms with significant signals. Hyperglycaemia (IC025 = 6.74), breast cancer metastatic (IC025 = 5.85) and metastases to liver (IC025 = 4.70) were the AEs with the strongest signal. AEs with the most cases were hyperglycaemia (n = 595), rash (n = 535) and diarrhoea (n = 475). CONCLUSION: We established a comprehensive list of AEs potentially associated with alpelisib. AEs with the most significant signals were hyperglycaemia, breast cancer metastatic, metastases to liver. The AEs with the most cases were hyperglycaemia, rash, diarrhoea, blood glucose increase and nausea.

Validation of Artificial Intelligence to Support the Automatic Coding of Patient Adverse Drug Reaction Reports, Using Nationwide Pharmacovigilance Data.

INTRODUCTION: Adverse drug reaction reports are usually manually assessed by pharmacovigilance experts to detect safety signals associated with drugs. With the recent extension of reporting to patients and the emergence of mass media-related sanitary crises, adverse drug reaction reports currently frequently overwhelm pharmacovigilance networks. Artificial intelligence could help support the work of pharmacovigilance experts during such crises, by automatically coding reports, allowing them to prioritise or accelerate their manual assessment. After a previous study showing first results, we developed and compared state-of-the-art machine learning models using a larger nationwide dataset, aiming to automatically pre-code patients' adverse drug reaction reports. OBJECTIVES: We aimed to determine the best artificial intelligence model identifying adverse drug reactions and assessing seriousness in patients reports from the French national pharmacovigilance web portal. METHODS: Reports coded by 27 Pharmacovigilance Centres between March 2017 and December 2020 were selected (n = 11,633). For each report, the Portable Document Format form containing free-text information filled by the patient, and the corresponding encodings of adverse event symptoms (in Medical Dictionary for Regulatory Activities Preferred Terms) and seriousness were obtained. This encoding by experts was used as the reference to train and evaluate models, which contained input data processing and machine-learning natural language processing to learn and predict encodings. We developed and compared different approaches for data processing and classifiers. Performance was evaluated using receiver operating characteristic area under the curve (AUC), F-measure, sensitivity, specificity and positive predictive value. We used data from 26 Pharmacovigilance Centres for training and internal validation. External validation was performed using data from the remaining Pharmacovigilance Centres during the same period. RESULTS: Internal validation: for adverse drug reaction identification, Term Frequency-Inverse Document Frequency (TF-IDF) + Light Gradient Boosted Machine (LGBM) achieved an AUC of 0.97 and an F-measure of 0.80. The Cross-lingual Language Model (XLM) [transformer] obtained an AUC of 0.97 and an F-measure of 0.78. For seriousness assessment, FastText + LGBM achieved an AUC of 0.85 and an F-measure of 0.63. CamemBERT (transformer) + Light Gradient Boosted Machine obtained an AUC of 0.84 and an F-measure of 0.63. External validation for both adverse drug reaction identification and seriousness assessment tasks yielded consistent and robust results. CONCLUSIONS: Our artificial intelligence models showed promising performance to automatically code patient adverse drug reaction reports, with very similar results across approaches. Our system has been deployed by national health authorities in France since January 2021 to facilitate pharmacovigilance of COVID-19 vaccines. Further studies will be needed to validate the performance of the tool in real-life settings.

Monitoring User Opinions and Side Effects on COVID-19 Vaccines in the Twittersphere: Infodemiology Study of Tweets.

BACKGROUND: In the current phase of the COVID-19 pandemic, we are witnessing the most massive vaccine rollout in human history. Like any other drug, vaccines may cause unexpected side effects, which need to be investigated in a timely manner to minimize harm in the population. If not properly dealt with, side effects may also impact public trust in the vaccination campaigns carried out by national governments. OBJECTIVE: Monitoring social media for the early identification of side effects, and understanding the public opinion on the vaccines are of paramount importance to ensure a successful and harmless rollout. The objective of this study was to create a web portal to monitor the opinion of social media users on COVID-19 vaccines, which can offer a tool for journalists, scientists, and users alike to visualize how the general public is reacting to the vaccination campaign. METHODS: We developed a tool to analyze the public opinion on COVID-19 vaccines from Twitter, exploiting, among other techniques, a state-of-the-art system for the identification of adverse drug events on social media; natural language processing models for sentiment analysis; statistical tools; and open-source databases to visualize the trending hashtags, news articles, and their factuality. All modules of the system are displayed through an open web portal. RESULTS: A set of 650,000 tweets was collected and analyzed in an ongoing process that was initiated in December 2020. The results of the analysis are made public on a web portal (updated daily), together with the processing tools and data. The data provide insights on public opinion about the vaccines and its change over time. For example, users show a high tendency to only share news from reliable sources when discussing COVID-19 vaccines (98% of the shared URLs). The general sentiment of Twitter users toward the vaccines is negative/neutral; however, the system is able to record fluctuations in the attitude toward specific vaccines in correspondence with specific events (eg, news about new outbreaks). The data also show how news coverage had a high impact on the set of discussed topics. To further investigate this point, we performed a more in-depth analysis of the data regarding the AstraZeneca vaccine. We observed how media coverage of blood clot-related side effects suddenly shifted the topic of public discussions regarding both the AstraZeneca and other vaccines. This became particularly evident when visualizing the most frequently discussed symptoms for the vaccines and comparing them month by month. CONCLUSIONS: We present a tool connected with a web portal to monitor and display some key aspects of the public's reaction to COVID-19 vaccines. The system also provides an overview of the opinions of the Twittersphere through graphic representations, offering a tool for the extraction of suspected adverse events from tweets with a deep learning model.

Identification of Drug Interaction Adverse Events in Patients With COVID-19: A Systematic Review.

Importance: During the COVID-19 pandemic, urgent clinical management of patients has mainly included drugs currently administered for other diseases, referred to as repositioned drugs. As a result, some of these drugs have proved to be not only ineffective but also harmful because of adverse events associated with drug-drug interactions (DDIs). Objective: To identify DDIs that led to adverse clinical outcomes and/or adverse drug reactions in patients with COVID-19 by systematically reviewing the literature and assessing the value of drug interaction checkers in identifying such events. Evidence Review: After identification of the drugs used during the COVID-19 pandemic, the drug interaction checkers Drugs.com, COVID-19 Drug Interactions, LexiComp, Medscape, and WebMD were consulted to analyze theoretical DDI-associated adverse events in patients with COVID-19 from March 1, 2020, through February 28, 2022. A systematic literature review was performed by searching the databases PubMed, Scopus, and Cochrane for articles published from March 1, 2020, through February 28, 2022, to retrieve articles describing actual adverse events associated with DDIs. The drug interaction checkers were consulted again to evaluate their potential to assess such events. Findings: The DDIs identified in the reviewed articles involved 46 different drugs. In total, 575 DDIs for 58 drug pairs (305 associated with at least 1 adverse drug reaction) were reported. The drugs most involved in DDIs were lopinavir and ritonavir. Of the 6917 identified studies, 20 met the inclusion criteria. These studies, which enrolled 1297 patients overall, reported 115 DDI-related adverse events: 15 (26%) were identifiable by all tools analyzed, 29 (50%) were identifiable by at least 1 of them, and 14 (24%) remained nonidentifiable. Conclusions and Relevance: The main finding of this systematic review is that the use of drug interaction checkers could have identified several DDI-associated adverse drug reactions, including severe and life-threatening events. Both the interactions between the drugs used to treat COVID-19 and between the COVID-19 drugs and those already used by the patients should be evaluated.

Healthcare costs and mortality associated with serious fluoroquinolone-related adverse reactions.

The aim of this study was to estimate healthcare costs and mortality associated with serious fluoroquinolone-related adverse reactions in Finland from 2008 to 2019. Serious adverse reaction types were identified from the Finnish Pharmaceutical Insurance Pool's pharmaceutical injury claims and the Finnish Medicines Agency's Adverse Reaction Register. A decision tree model was built to predict costs and mortality associated with serious adverse drug reactions (ADR). Severe clostridioides difficile infections, severe cutaneous adverse reactions, tendon ruptures, aortic ruptures, and liver injuries were included as serious adverse drug reactions in the model. Direct healthcare costs of a serious ADR were based on the number of reimbursed fluoroquinolone prescriptions from the Social Insurance Institution of Finland's database. Sensitivity analyses were conducted to address parameter uncertainty. A total of 1 831 537 fluoroquinolone prescriptions were filled between 2008 and 2019 in Finland, with prescription numbers declining 40% in recent years. Serious ADRs associated with fluoroquinolones lead to estimated direct healthcare costs of 501 938 402 €, including 11 405 ADRs and 3,884 deaths between 2008 and 2019. The average mortality risk associated with the use of fluoroquinolones was 0.21%. Severe clostridioides difficile infections were the most frequent, fatal, and costly serious ADRs associated with the use of fluoroquinolones. Although fluoroquinolones continue to be generally well-tolerated antimicrobials, serious adverse reactions cause long-term impairment to patients and high healthcare costs. Therefore, the risks and benefits should be weighed carefully in antibiotic prescription policies, as well as with individual patients.