Safety analysis of Oseltamivir and Baloxavir Marboxil after market approval: a pharmacovigilance study based on the FDA adverse event reporting system.

BACKGROUND AND OBJECTIVES: Amidst limited influenza treatment options, evaluating the safety of Oseltamivir and Baloxavir Marboxil is crucial, particularly given their comparable efficacy. This study investigates post-market safety profiles, exploring adverse events (AEs) and their drug associations to provide essential clinical references. METHODS: A meticulous analysis of FDA Adverse Event Reporting System (FAERS) data spanning the first quarter of 2004 to the fourth quarter of 2022 was conducted. Using data mining techniques like reporting odds ratio (ROR), proportional reporting ratio, Bayesian Confidence Propagation Neural Network, and Multiple Gamma Poisson Shrinkage, AEs related to Oseltamivir and Baloxavir Marboxil were examined. Venn analysis compared and selected specific AEs associated with each drug. RESULTS: Incorporating 15,104 Oseltamivir cases and 1,594 Baloxavir Marboxil cases, Wain analysis unveiled 21 common AEs across neurological, psychiatric, gastrointestinal, dermatological, respiratory, and infectious domains. Oseltamivir exhibited 221 significantly specific AEs, including appendicolith [ROR (95% CI), 459.53 (340.88 ∼ 619.47)], acne infantile [ROR (95% CI, 368.65 (118.89 ∼ 1143.09)], acute macular neuroretinopathy [ROR (95% CI), 294.92 (97.88 ∼ 888.64)], proctitis [ROR (95% CI), 245.74 (101.47 ∼ 595.31)], and Purpura senile [ROR (95% CI), 154.02 (81.96 ∼ 289.43)]. designated adverse events (DMEs) associated with Oseltamivir included fulminant hepatitis [ROR (95% CI), 12.12 (8.30-17.72), n=27], ventricular fibrillation [ROR (95% CI), 7.68 (6.01-9.83), n=64], toxic epidermal necrolysis [ROR (95% CI), 7.21 (5.74-9.05), n=75]. Baloxavir Marboxil exhibited 34 specific AEs, including Melaena [ROR (95% CI), 21.34 (14.15-32.18), n = 23], cystitis haemorrhagic [ROR (95% CI), 20.22 (7.57-54.00), n = 4], ileus paralytic [ROR (95% CI), 18.57 (5.98-57.71), n = 3], and haemorrhagic diathesis [ROR (95% CI), 16.86 (5.43-52.40)), n = 3]. DMEs associated with Baloxavir Marboxil included rhabdomyolysis [ROR (95% CI), 15.50 (10.53 ∼ 22.80), n = 26]. CONCLUSION: Monitoring fulminant hepatitis during Oseltamivir treatment, especially in patients with liver-related diseases, is crucial. Oseltamivir's potential to induce abnormal behavior, especially in adolescents, necessitates special attention. Baloxavir Marboxil, with lower hepatic toxicity, emerges as a potential alternative for patients with liver diseases. During Baloxavir Marboxil treatment, focused attention on the occurrence of rhabdomyolysis is advised, necessitating timely monitoring of relevant indicators for those with clinical manifestations. The comprehensive data aims to provide valuable insights for clinicians and healthcare practitioners, facilitating an understanding of the safety profiles of these influenza treatments in real-world scenarios.

Trends in Adverse Event Reporting Before and After the Introduction of the Med Safety App in Nigeria.

INTRODUCTION: Spontaneous reporting of adverse events (AEs) is a mainstay of pharmacovigilance, and an ongoing challenge is how to ensure that more high-quality reports are collected for comprehensive information provision. The Med Safety App, a smartphone-based application, was launched in Nigeria in November 2020 to provide an electronic platform for users to seamlessly report AEs. There has been a paucity of evidence on the use of this application or other mobile applications for reporting adverse drug reactions/AEs following immunization in the Nigerian environment. OBJECTIVE: The aim of this study was to evaluate the trends in adverse event reporting before and after the introduction of the Med Safety App in Nigeria. METHODS: This was a retrospective, observational study using data from the VigiFlow database to compare adverse event reporting in Nigeria before and after the deployment of the Med Safety App. The baseline period was 1st April 2019 to 30th October 2020 and the comparison period was 1st November 2020 to 31st May 2022. We used Vigilance Hub, the back-end system for the Med Safety App, to extract data on App downloads and de-identified user statistics. Data were summarized using descriptive statistics, frequencies and proportions. Quality was assessed by assigning a completeness score to each individual case safety report. The Kruskal-Wallis test was used to test for differences in medians between groups. RESULTS: Following deployment of the App, the Nigerian National Pharmacovigilance Centre recorded an increase in the total number of adverse event reports received in VigiFlow, from 2051 in the baseline period to 18,995 following deployment of the App, with 81.7% of those reported via the Med Safety App. There was a reduction in the proportion of paper-based reporting from 98.4 to 15.7% post-deployment, and direct reporting by consumers increased from 2.7 to 17.6%. Of the 15,526 reports submitted via the App, 15,111 (97.3%) had a completeness score above 70% and 6993 (45%) had a completeness score of 100%. The median completeness score of adverse event reports on the Med Safety App was 6 out of 7. On bivariate analysis using the Kruskal-Wallis test, there was an association between means of reporting and completeness score, and this association was significant, with a p value of 0.0001, which may reflect the validation rules that are applied within the App. CONCLUSION: Deployment of the Med Safety App increased both the number and quality of adverse event reports; however, more awareness and capacity building are needed to strengthen and sustain reporting on the tool by all categories of healthcare professionals and consumers/patients.

An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records.

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.

The Reporting of a Disproportionality Analysis for Drug Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Development and Statement.

BACKGROUND AND AIM: Disproportionality analyses using reports of suspected adverse drug reactions are the most commonly used quantitative methods for detecting safety signals in pharmacovigilance. However, their methods and results are generally poorly reported in published articles and existing guidelines do not capture the specific features of disproportionality analyses. We here describe the development of a guideline (REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance [READUS-PV]) for reporting the results of disproportionality analyses in articles and abstracts. METHODS: We established a group of 34 international experts from universities, the pharmaceutical industry, and regulatory agencies, with expertise in pharmacovigilance, disproportionality analyses, and assessment of safety signals. We followed a three-step process to develop the checklist: (1) an open-text survey to generate a first list of items; (2) an online Delphi method to select and rephrase the most important items; (3) a final online consensus meeting. RESULTS: Among the panel members, 33 experts responded to round 1 and 30 to round 2 of the Delphi and 25 participated to the consensus meeting. Overall, 60 recommendations for the main body of the manuscript and 13 recommendations for the abstracts were retained by participants after the Delphi method. After merging of some items together and the online consensus meeting, the READUS-PV guidelines comprise a checklist of 32 recommendations, in 14 items, for the reporting of disproportionality analyses in the main body text and four items, comprising 12 recommendations, for abstracts. CONCLUSIONS: The READUS-PV guidelines will support authors, editors, peer-reviewers, and users of disproportionality analyses using individual case safety report databases. Adopting these guidelines will lead to more transparent, comprehensive, and accurate reporting and interpretation of disproportionality analyses, facilitating the integration with other sources of evidence.

The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration.

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance.

Unveiling the potential of antisense oligonucleotides: Mechanisms, therapies, and safety insights.

Antisense oligonucleotides (ASOs) are short, synthetic, single-stranded deoxynucleotide sequences composed of phosphate backbone-connected sugar rings. Designing of those strands is based on Watson-Crick hydrogen bonding mechanism. Thanks to rapidly advancing medicine and technology, evolving of the gene therapy area and ASO approaches gain attention. Considering the genetic basis of diseases, it is promising that gene therapy approaches offer more specific and effective options compared to conventional treatments. The objective of this review is to explain the mechanism of ASOs and discuss the characteristics and safety profiles of therapeutic agents in this field. Pharmacovigilance for gene therapy products is complex, requiring accurate assessment of benefit-risk balance and evaluation of adverse effects.

Data mining and safety analysis of avatrombopag: a retrospective pharmacovigilance study based on the US food and drug administration's adverse event reporting system.

With its increasing use in the treatment of thrombocytopenia, avatrombopag's associated adverse events (AEs) pose a major challenge to its clinical application. This study aims to comprehensively study AEs associated with avatrombopag by using real-world evidence. We curated AE reports for avatrombopag from the first quarter of 2018 to the fourth quarter of 2023 in the US Food and Drug Administration's Adverse Event Reporting System (FAERS) database. AEs were coded using the Medical Dictionary for Regulatory Activities of Preferred Terms and System Organ Classes. The reporting odds ratio, proportional reporting ratio, Bayesian confidence propagation neural network, and multi-item Gamma-Poisson Shrinker were used to investigate the relationship between avatrombopag and AE reports. Among 9,060,312 reported cases in the FAERS database, 1211 reports listed avatrombopag as "primary suspected" drug. Disproportionality analysis identified 44 preferred terms across 17 organ systems met the criteria for at least one of the four algorithms. The most commonly reported AEs were platelet count decreased (20.2%), headache (16.7%), platelet count increased (11.9%), platelet count abnormal (6.3%), contusion (2.7%), pulmonary embolism (2.3%), and deep vein thrombosis (2.1%). Unexpected AEs such as seasonal allergy, rhinorrhea, antiphospholipid syndrome, ear discomfort, and photopsia were also observed. Excluding the other serious outcomes, hospitalization (34.6%) was the most frequently reported serious outcome, followed by death (15.4%). Most reported AEs occurred within the first 2 days of initiating avatrombopag therapy, and the median onset time was 60 days. We identified new and unexpected AEs with clinical use of avatrombopag, and our results may provide valuable information for clinical monitoring and identifying risks associated with avatrombopag.

A real-world pharmacovigilance study of FDA adverse event reporting system events for Capmatinib.

Capmatinib is a potent selective mesenchymal-epithelial transition inhibitor approved in 2020 for the treatment of metastatic non-small cell lung cancer. As real-world evidence is very limited, this study evaluated capmatinib-induced adverse events through data mining of the FDA Adverse Event Reporting System database. Four disproportionality analysis methods were employed to quantify the signals of capmatinib-related adverse events. The difference in capmatinib-associated adverse event signals was further investigated with respect to sex, age, weight, dose, onset time, continent, and concomitant drug. A total of 1518 reports and 4278 adverse events induced by capmatinib were identified. New significant adverse event signals emerged, such as dysphagia, dehydration, deafness, vocal cord paralysis, muscle disorder, and oesophageal stenosis. Notably, higher risk of alanine aminotransferase and aspartate aminotransferase increases were observed in females, especially when capmatinib was combined with immune checkpoint inhibitors. Compared with Europeans and Asians, Americans were more likely to experience peripheral swelling, especially in people > 65 years of age. Renal impairment and increased blood creatinine were more likely to occur with single doses above 400 mg and in Asians. This study improves the understanding of safety profile of capmatinib.

The Association between PDE5 Inhibitors and Aneurysm/Arterial Dissection:A Pharmacovigilance Study Using WHO Safety Database.

Aneurysm and arterial dissection have been reported as adverse drug events, associated with angiogenesis inhibitors and fluoroquinolones. Specifically, several cases of severe arterial disease following cGMP-specific phosphodiesterase type 5 (PDE5) inhibitors usage have recently been reported. It is necessary to ascertain the risks of serious adverse events caused by PDE5 inhibitors. We aimed to evaluate the association of aneurysm and artery dissection with PDE5 inhibitors using VigiBase, which is a World Health Organization database of spontaneously reported adverse events, for explorative hypothesis-generating analysis. We performed disproportionality analysis using a dataset from inception in 1967 to December 2022 and calculated reporting odds ratios (ROR) between PDE5 inhibitors and arterial diseases. We extracted 195,839 reports on PDE5 inhibitors with 254 reports of arterial disease as adverse events from VigiBase. Disproportionality analysis showed disproportional signals for PDE5 inhibitors (ROR, 2.30;95% confidence intervals, 2.04-2.61);disproportional signals were detected in analyses restricting the lesion site to the aorta or cerebral arteries. From stratified analysis, disproportional signals were noted in females, as well as males, generally recognized as a risk factor for artery diseases. This real-world data analysis suggests that PDE5 inhibitors may play a role in the development of lethal arterial disease. J. Med. Invest. 71 : 134-140, February, 2024.

Repurposing of drugs against bacterial infections: A pharmacovigilance-based data mining approach.

The World Health Organization (WHO) has published a list of priority pathogens that urgently require research to develop new antibiotics. The main aim of the current study is to identify potential marketed drugs that can be repurposed against bacterial infections. A pharmacovigilance-based drug repurposing approach was used to identify potential drugs. OpenVigil 2.1 tool was used to query the FDA Adverse Event Reporting System database. The reporting odds ratio (ROR) < 1, ROR95CI upper bound <1, and no. of cases ≥30 were used for filtering and sorting of drugs. Sunburst plot was used to represent drugs in a hierarchical order using the Anatomical Therapeutic Chemical classification. Molecular docking and dynamics were performed using the Maestro and Desmond modules of Schrodinger 2023 software respectively. A total of 40 drugs with different classes were identified based on the pharmacovigilance approach which has antibacterial potential. The molecular docking results have shown energetically favored binding conformation of lisinopril against 3-deoxy-manno-octulosonate cytidylyltransferase, UDP-2,3-diacylglucosamine hydrolase, and penicillin-binding protein 3 (PBP3) of Pseudomonas aeruginosa; olmesartan, atorvastatin against lipoteichoic acids flippase LtaA and rosiglitazone and varenicline against  d-alanine ligase of Staphylococcus aureus; valsartan against peptidoglycan deacetylase (SpPgdA) and atorvastatin against CDP-activated ribitol for teichoic acid precursors of Streptococcus pneumoniae. Further, molecular dynamic results have shown the stability of identified drugs in the active site of bacterial targets except lisinopril with PBP3. Lisinopril, olmesartan, atorvastatin, rosiglitazone, varenicline, and valsartan have been identified as potential drugs for repurposing against bacterial infection.

Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.

BACKGROUND: Adverse drug reactions (ADRs), which are the phenotypic manifestations of clinical drug toxicity in humans, are a major concern in precision clinical medicine. A comprehensive evaluation of ADRs is helpful for unbiased supervision of marketed drugs and for discovering new drugs with high success rates. OBJECTIVE: In current practice, drug safety evaluation is often oversimplified to the occurrence or nonoccurrence of ADRs. Given the limitations of current qualitative methods, there is an urgent need for a quantitative evaluation model to improve pharmacovigilance and the accurate assessment of drug safety. METHODS: In this study, we developed a mathematical model, namely the Adverse Drug Reaction Classification System (ADReCS) severity-grading model, for the quantitative characterization of ADR severity, a crucial feature for evaluating the impact of ADRs on human health. The model was constructed by mining millions of real-world historical adverse drug event reports. A new parameter called Severity_score was introduced to measure the severity of ADRs, and upper and lower score boundaries were determined for 5 severity grades. RESULTS: The ADReCS severity-grading model exhibited excellent consistency (99.22%) with the expert-grading system, the Common Terminology Criteria for Adverse Events. Hence, we graded the severity of 6277 standard ADRs for 129,407 drug-ADR pairs. Moreover, we calculated the occurrence rates of 6272 distinct ADRs for 127,763 drug-ADR pairs in large patient populations by mining real-world medication prescriptions. With the quantitative features, we demonstrated example applications in systematically elucidating ADR mechanisms and thereby discovered a list of drugs with improper dosages. CONCLUSIONS: In summary, this study represents the first comprehensive determination of both ADR severity grades and ADR frequencies. This endeavor establishes a strong foundation for future artificial intelligence applications in discovering new drugs with high efficacy and low toxicity. It also heralds a paradigm shift in clinical toxicity research, moving from qualitative description to quantitative evaluation.

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Introduction: Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain. Method: Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection. Result: The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. Discussion: This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.

Adverse events associated with immune checkpoint inhibitors in non-small cell lung cancer: a safety analysis of clinical trials and FDA pharmacovigilance system.

Objectives: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC). However, the application of ICIs can also cause treatment-related adverse events (trAEs) and immune-related adverse events (irAEs). This study was to evaluate both the irAEs and trAEs of different ICI strategies for NSCLC based on randomized clinical trials (RCTs). The study also examined real-world pharmacovigilance data from the Food and Drug Administration Adverse Event Reporting System (FAERS) regarding claimed ICI-associated AEs in clinical practice. Methods: Based on Pubmed, Embase, Medline, and the Cochrane CENTRAL, we retrieved RCTs comparing ICIs with chemotherapy drugs or with different ICI regimens for the treatment of NSCLC up to October 20, 2023. Bayesian network meta-analysis (NMA) was performed using odds ratios (ORs) with 95% credible intervals (95%CrI). Separately, a retrospective pharmacovigilance study was performed based on FAERS database, extracting ICI-associated AEs in NSCLC patients between the first quarter (Q1) of 2004 and Q4 of 2023. The proportional reports reporting odds ratio was calculated to analyze the disproportionality. Results: The NMA included 51 RCTs that involved a total of 26,958 patients with NSCLC. Based on the lowest risk of any trAEs, cemiplimab, tislelizumab, and durvalumab were ranked as the best. Among the agents associated with the lowest risk of grades 3-5 trAEs, tislelizumab, avelumab, and nivolumab were most likely to rank highest. As far as any or grades 3-5 irAEs are concerned, atezolizumab plus bevacizumab plus chemotherapy is considered the most safety option. However, it is associated with a high risk of grades 3-5 trAEs. As a result of FAERS pharmacovigilance data analysis, 9,420 AEs cases have been identified in 7,339 NSCLC patients treated with ICIs, and ICIs were related to statistically significant positive signal with 311 preferred terms (PTs), and comprehensively investigated and identified those AEs highly associated with ICIs. In total, 152 significant signals were associated with Nivolumab, with malignant neoplasm progression, death, and hypothyroidism being the most frequent PTs. Conclusion: These findings revealed that ICIs differed in their safety profile. ICI treatment strategies can be improved and preventive methods can be developed for NSCLC patients based on our results.

Evaluation of the knowledge of and attitudes towards pharmacovigilance among healthcare students in China: a cross-sectional study.

BACKGROUND: Knowledge of pharmacovigilance (PV) and adverse drug reactions (ADRs) are the core competencies that healthcare students should acquire during their studies. The objective of this study was to assess attitudes towards and knowledge of PV and ADRs among healthcare students in China. METHODS: An online, cross-sectional survey was conducted nationally among healthcare students in China from April through October 2023. Knowledge of PV and ADRs was assessed using a questionnaire based on current PV guidelines. We performed logistic regression analysis to determine the potential factors related to knowledge of and attitudes towards PV and ADRs. RESULTS: A total of 345 students were included in the analysis. Among the healthcare students who participated in the survey, 225 (65.22%) students correctly defined PV, while only 68 (19.71%) had a correct understanding of ADRs. Among all respondents included in the analysis, only 71 (20.58%) reported having taken a PV course. Pharmacy students were more likely to have taken PV courses at a university and to demonstrate superior knowledge compared to other healthcare students. The logistic regression model revealed that the significant predictors of a higher level of PV knowledge were being female (odds ratio [OR]: 1.76; 95% confidence interval (CI): 1.06-2.92; P value: 0.028) and having previously taken PV-related courses (OR: 2.00; 95% CI: 1.06-3.80; P value: 0.034). CONCLUSIONS: This study revealed that healthcare students' knowledge of PV and ADRs is unsatisfactory. However, there were a limited number of universities providing PV education. Given the vital role of healthcare professionals in identifying and reporting ADRs, our findings raise significant concerns. Hence, more efforts should be made to enhance PV education for future healthcare professionals.

Global and regional burden of vaccine-associated facial paralysis, 1967-2023: Findings from the WHO international pharmacovigilance database.

The scarce and conflicting data on vaccine-associated facial paralysis limit our understanding of vaccine safety on a global scale. Therefore, this study aims to evaluate the global burden of vaccine-associated facial paralysis and to identify the extent of its association with individual vaccines, thereby contributing to the development of a more effective vaccination program. We used data on vaccine-associated facial paralysis from 1967 to 2023 (total reports, n = 131 255 418 418) from the World Health Organization International Pharmacovigilance Database. Global reporting counts, reported odds ratios (ROR), and information components (ICs) were computed to elucidate the association between the 16 vaccines and the occurrence of vaccine-associated facial paralysis across 156 countries. We identified 26 197 reports (men, n = 10 507 [40.11%]) of vaccine-associated facial paralysis from 49 537 reports of all-cause facial paralysis. Vaccine-associated facial paralysis has been consistently reported; however, a pronounced increase in reported incidence has emerged after the onset of the coronavirus disease 2019 (COVID-19) pandemic, which is attributable to the COVID-19 mRNA vaccine. Most vaccines were associated with facial paralysis, with differing levels of association, except for tuberculosis vaccines. COVID-19 mRNA vaccines had the highest association with facial paralysis reports (ROR, 28.31 [95% confidence interval, 27.60-29.03]; IC, 3.37 [IC0.25, 3.35]), followed by encephalitis, influenza, hepatitis A, papillomavirus, hepatitis B, typhoid, varicella-zoster, meningococcal, Ad-5 vectored COVID-19, measles, mumps and rubella, diphtheria, tetanus toxoids, pertussis, polio, and Hemophilus influenza type b, pneumococcal, rotavirus diarrhea, and inactivated whole-virus COVID-19 vaccines. Concerning age- and sex-specific risks, vaccine-associated facial paralysis was more strongly associated with older age groups and males. The serious adverse outcome and death rate of vaccine-associated facial paralysis were extremely low (0.07% and 0.00%, respectively). An increase in vaccine-induced facial paralysis, primarily owing to COVID-19 mRNA vaccines, was observed with most vaccines, except tuberculosis vaccines. Given the higher association observed in the older and male groups with vaccine-associated facial paralysis, close monitoring of these demographics when administering vaccines that are significantly associated with adverse reactions is crucial.

Navigating the Complexities of Artificial Intelligence-Enabled Real-World Data Collection for Oncology Pharmacovigilance.

This new editorial discusses the promise and challenges of successful integration of natural language processing methods into electronic health records for timely, robust, and fair oncology pharmacovigilance.

A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches.

INTRODUCTION: The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans. METHODS: Drug X, a mature product with post-marketing data, and Drug Y, a recently approved drug in another therapeutic area, were selected. Gradient boosting-based ML approaches (e.g., XGBoost) were applied as the main modeling strategy. RESULTS: For Drug X, eight true signals were present in the test set. Among 12 potential new signals generated, four were true signals with a 50.0% sensitivity rate and a 33.3% positive predictive value (PPV) rate. Among the remaining eight potential new signals, one was confirmed as a signal and detected six months earlier than humans. For Drug Y, nine true signals were present in the test set. Among 13 potential new signals generated, five were true signals with a 55.6% sensitivity rate and a 38.5% PPV rate. Among the remaining eight potential new signals, none were confirmed as true signals upon human review. CONCLUSION: This model demonstrated acceptable accuracy for safety signal detection and potential for earlier detection when compared to humans. Expert judgment, flexibility, and critical thinking are essential human skills required for the final, accurate assessment of adverse event cases.

Global burden of vaccine-associated multiple sclerosis, 1967-2022: A comprehensive analysis of the international pharmacovigilance database.

Vaccine-associated multiple sclerosis (MS) is rare, with insufficient evidence from case reports. Given the scarcity of large-scale data investigating the association between vaccine administration and adverse events, we investigated the global burden of vaccine-associated MS and potential related vaccines from 1967 to 2022. Reports on vaccine-associated MS between 1967 and 2022 were obtained from the World Health Organization International Pharmacovigilance Database (total number of reports = 120 715 116). We evaluated global reports, reporting odds ratio (ROR), and information components (IC) to investigate associations between 19 vaccines and vaccine-associated MS across 156 countries and territories. We identified 8288 reports of vaccine-associated MS among 132 980 cases of all-cause MS. The cumulative number of reports on vaccine-associated MS gradually increased over time, with a substantial increase after 2020, owing to COVID-19 mRNA vaccine-associated MS. Vaccine-associated MS develops more frequently in males and adolescents. Nine vaccines were significantly associated with higher MS reporting, and the highest disproportional associations were observed for hepatitis B vaccines (ROR 19.82; IC025 4.18), followed by encephalitis (ROR 7.42; IC025 2.59), hepatitis A (ROR 4.46; IC025 1.95), and papillomavirus vaccines (ROR 4.45; IC025 2.01). Additionally, MS showed a significantly disproportionate signal for COVID-19 mRNA vaccines (ROR 1.55; IC025 0.52). Fatal clinical outcomes were reported in only 0.3% (21/8288) of all cases of vaccine-associated MS. Although various vaccines are potentially associated with increased risk of MS, we should be cautious about the increased risk of MS following vaccination, particularly hepatitis B and COVID-19 mRNA vaccines, and should consider the risk factors associated with vaccine-associated MS.

The OMOP common data model in Australian primary care data: Building a quality research ready harmonised dataset.

BACKGROUND: The use of routinely collected health data for secondary research purposes is increasingly recognised as a methodology that advances medical research, improves patient outcomes, and guides policy. This secondary data, as found in electronic medical records (EMRs), can be optimised through conversion into a uniform data structure to enable analysis alongside other comparable health metric datasets. This can be achieved with the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM), which employs a standardised vocabulary to facilitate systematic analysis across various observational databases. The concept behind the OMOP-CDM is the conversion of data into a common format through the harmonisation of terminologies, vocabularies, and coding schemes within a unique repository. The OMOP model enhances research capacity through the development of shared analytic and prediction techniques; pharmacovigilance for the active surveillance of drug safety; and 'validation' analyses across multiple institutions across Australia, the United States, Europe, and the Asia Pacific. In this research, we aim to investigate the use of the open-source OMOP-CDM in the PATRON primary care data repository. METHODS: We used standard structured query language (SQL) to construct, extract, transform, and load scripts to convert the data to the OMOP-CDM. The process of mapping distinct free-text terms extracted from various EMRs presented a substantial challenge, as many terms could not be automatically matched to standard vocabularies through direct text comparison. This resulted in a number of terms that required manual assignment. To address this issue, we implemented a strategy where our clinical mappers were instructed to focus only on terms that appeared with sufficient frequency. We established a specific threshold value for each domain, ensuring that more than 95% of all records were linked to an approved vocabulary like SNOMED once appropriate mapping was completed. To assess the data quality of the resultant OMOP dataset we utilised the OHDSI Data Quality Dashboard (DQD) to evaluate the plausibility, conformity, and comprehensiveness of the data in the PATRON repository according to the Kahn framework. RESULTS: Across three primary care EMR systems we converted data on 2.03 million active patients to version 5.4 of the OMOP common data model. The DQD assessment involved a total of 3,570 individual evaluations. Each evaluation compared the outcome against a predefined threshold. A 'FAIL' occurred when the percentage of non-compliant rows exceeded the specified threshold value. In this assessment of the primary care OMOP database described here, we achieved an overall pass rate of 97%. CONCLUSION: The OMOP CDM's widespread international use, support, and training provides a well-established pathway for data standardisation in collaborative research. Its compatibility allows the sharing of analysis packages across local and international research groups, which facilitates rapid and reproducible data comparisons. A suite of open-source tools, including the OHDSI Data Quality Dashboard (Version 1.4.1), supports the model. Its simplicity and standards-based approach facilitates adoption and integration into existing data processes.