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.

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.

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.

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.

Beyond the Label: Real-World Side Effects Experienced With FDA-Approved Drugs for Non-Melanoma Skin Cancers.

With immunotherapy historically focused on cutaneous melanoma, there has been a new wave of systemic medications available for treating non-melanoma skin cancers including basal cell carcinoma (BCC), squamous cell carcinoma (SCC), and Merkel cell carcinoma (MCC). The immune checkpoint inhibitors approved by the FDA target programmed cell death protein 1 (PD-1) and the Hedgehog (Hh) signaling pathway. These medications have expanded treatment options; however, side effects are an important consideration. We used the FDA Adverse Events Reporting System (FAERS) to characterize the most prevalent, real-world side effects experienced by patients on these agents. Muscle spasms (23.45%), alopecia (16.06%), ageusia (12.02%), taste disorder (11.91%), and fatigue (11.67%) were the five most common side effects reported with medications used for BCC treatment. Logistic regression analysis showed males on vismodegib for BCC having greater odds of experiencing muscle spasms (aOR 1.33, P<0.001) and ageusia (aOR 1.34,  P<0.001) versus females, who were more likely to exhibit alopecia (aOR 1.82, P<0.001) and nausea (aOR 1.96, P<0.001). With SCC treatment, the 5 most reported adverse events were fatigue (5.58%), rash (3.59%), asthenia (3.59%), pruritus (3.19%), and pyrexia (2.79%). Patients taking cemiplimab-rwlc for BCC compared to SCC were more likely to experience disease progression (aOR 10.98, P=0.02). With medication labels providing an excessively daunting list of side effects, we characterize practical side effects seen in patients receiving systemic treatments for non-melanoma skin cancers.  J Drugs Dermatol. 2024;23(5):301-305. doi:10.36849/JDD.7968.

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.

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.

Developing an AI-based prediction model for anaphylactic shock from injection drugs using Japanese real-world data and chemical structure-based analysis.

BACKGROUND: This study aims to develop an AI-based prediction model for injection drugs that cause anaphylactic shock using Japanese Real-World Data (JADER database) and chemical structure-based analysis. METHODS: Data sourced from the JADER database included adverse drug reaction reports from April 2004 to December 2020. Only drugs with an adverse reaction named "anaphylactic shock" were selected for analysis. For model building, various models were constructed to predict anaphylactic shock-inducing drugs, such as logistic regression, LASSO, XGBoost, RF, SVM, and NNW. These models used chemical properties and structural similarities as feature variables. Dimension reduction was applied using principal component analysis. The dataset was split into training (80%) and validation (20%) sets. Six different models were trained and optimized through fivefold cross-validation. RESULTS: From April 2004 to December 2020, 947 drugs with the adverse reaction name "anaphylactic shock" were extracted from the JADER database. 320 drugs were excluded due to analytical challenges, and another 400 were removed due to their administration route. 227 drugs were finalized as target medicines. For model validation, the performance of each model was evaluated based on metrics like AUCs of ROC curve, sensitivity, and specificity. Additionally, two ensemble models, constructed from the six models were assessed using bootstrap sampling. Interestingly, it was identified that mepivacaine structural similarity had the highest importance in the final model. CONCLUSIONS: The study successfully developed an AI-based prediction model for anaphylactic shock inducing-injection drugs. The model would offer potential for drug safety evaluation and anaphylactic shock risk assessment.

Evaluation of time-to-onset and outcome of cardiac adverse events related to pembrolizumab using post-marketing surveillance in Japanese patients.

BACKGROUND: Pembrolizumab has been widely used in patients since its release, but information on cardiac Adverse Events (AEs) related to pembrolizumab remains lacking, particularly in Japanese populations. OBJECTIVES: This study aims to evaluate time to onset, incidence rates, and outcomes for pembrolizumab-induced cardiac AEs in patients with cancer using the Japanese Adverse Drug Event Report database. METHODS: We analysed data for the period from April 2004 to March 2022. Data on cardiac AEs were extracted and relative risks of AEs were estimated using the reporting odds ratio. RESULTS: We analysed 2,021,907 reports and identified 15,306 reports of AEs caused by pembrolizumab. Of these, 399 cardiac AEs were associated with pembrolizumab. Signals were detected for six cardiac AEs: myocarditis, immune-mediated myocarditis, pericardial effusion, cardiac tamponade, pericarditis, and pericarditis malignant. A histogram of median times to onset showed occurrence from 33 (21-97) days for immune-mediated myocarditis to 138 (67-168) days for pericarditis malignant, but some cases occurred even more than 1 year after the start of administration. Among these, myocarditis was the most frequently reported (27.1%), with fatal cases also reported. CONCLUSION: This study focused on cardiac AEs caused by pembrolizumab as post-marketing AEs. Patients should be monitored not only at the time of administration, but also over time for signs of these AEs, especially myocarditis, as some patients may have serious outcomes.

Navigating duplication in pharmacovigilance databases: a scoping review.

OBJECTIVES: Pharmacovigilance databases play a critical role in monitoring drug safety. The duplication of reports in pharmacovigilance databases, however, undermines their data integrity. This scoping review sought to provide a comprehensive understanding of duplication in pharmacovigilance databases worldwide. DESIGN: A scoping review. DATA SOURCES: Reviewers comprehensively searched the literature in PubMed, Web of Science, Wiley Online Library, EBSCOhost, Google Scholar and other relevant websites. ELIGIBILITY CRITERIA: Peer-reviewed publications and grey literature, without language restriction, describing duplication and/or methods relevant to duplication in pharmacovigilance databases from inception to 1 September 2023. DATA EXTRACTION AND SYNTHESIS: We used the Joanna Briggs Institute guidelines for scoping reviews and conformed with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. Two reviewers independently screened titles, abstracts and full texts. One reviewer extracted the data and performed descriptive analysis, which the second reviewer assessed. Disagreements were resolved by discussion and consensus or in consultation with a third reviewer. RESULTS: We screened 22 745 unique titles and 156 were eligible for full-text review. Of the 156 titles, 58 (47 peer-reviewed; 11 grey literature) fulfilled the inclusion criteria for the scoping review. Included titles addressed the extent (5 papers), prevention strategies (15 papers), causes (32 papers), detection methods (25 papers), management strategies (24 papers) and implications (14 papers) of duplication in pharmacovigilance databases. The papers overlapped, discussing more than one field. Advances in artificial intelligence, particularly natural language processing, hold promise in enhancing the efficiency and precision of deduplication of large and complex pharmacovigilance databases. CONCLUSION: Duplication in pharmacovigilance databases compromises risk assessment and decision-making, potentially threatening patient safety. Therefore, efficient duplicate prevention, detection and management are essential for more reliable pharmacovigilance data. To minimise duplication, consistent use of worldwide unique identifiers as the key case identifiers is recommended alongside recent advances in artificial intelligence.

Post-marketing safety concerns with palbociclib: a disproportionality analysis of the FDA adverse event reporting system.

OBJECTIVES: To explore the association between palbociclib and related adverse events (AEs) in the real world through U.S. Food and Drug Administration Adverse Event Reporting System (FAERS) database. METHODS: The signal strength of palbociclib-related AEs was done by disproportionality analysis. Clinical priority of palbociclib-related AEs was scored and ranked by assessing five different features. Outcome analysis, time to onset analysis, dose-report /AEs number analysis, and stratification analysis were all performed. RESULTS: There were 61,821 'primary suspected (PS)' reports of palbociclib and 195,616 AEs associated with palbociclib. The four algorithms simultaneously detected 18 positive signals at the SOC level, and 65 positive signals at the PT level. Bone marrow failure, neuropathy, peripheral, pleural effusion, myelosuppression, pulmonary edema, and pulmonary thrombosis were also found to have positive signals. Gender (female vs male, χ2 = 5.287, p = 0.022) and age showed significant differences in serious and non-serious reports. Palbociclib-related AEs had a median onset time of 79 days (interquartile range [IQR] 20-264 days). CONCLUSIONS: The study identified potential Palbociclib-related AEs and offered warnings for special AEs, providing further data for palbociclib safety studies in breast cancer patients. Nonetheless, prospective clinical trials are needed to validate these results and explain their relationship.

Detection and analysis of signals of adverse events of memantine based on the US food and drug administration adverse event reporting system.

BACKGROUND: Despite its widespread use, the adverse effects (AEs) of memantine have not been well documented, and there is a need to find new ways to analyze the AEs of memantine. RESEARCH DESIGN AND METHODS: AEs in which the primary suspected drug was memantine were retrieved from the FAERS database. The proportional report ratio (PRR), reporting odds ratio (ROR), Bayesian confidence propagation neural network (BCPNN), and empirical Bayesian geometric mean (EBGM) were used to detect potential positive signals between memantine and AEs. SAS, MySQL, EXCEL, and R language software were used for data processing and statistical analysis. RESULTS: This study gathered a total of 5808 reports of AEs associated with memantine. Of these reports, a greater proportion of female patients (51.17%) than male patients (36.33%) had AEs. The AEs reported by FAERS were mainly in psychiatric category (n = 2157, IC025 = 2.69), various neurologic disorders (n = 1608, IC025 = 2.04), systemic disorders and various site reactions (n = 842, IC025 = 1.29). Unexpected ocular adverse events have been reported, ophthalmic vein thrombosis (n = 4, IC025 = 3.47) and scleral discolouration (n = 7, IC025 = 3.1), which may worsen glaucoma. CONCLUSIONS: This study observed conceivable new AEs signals and may supply important assist for scientific monitoring and threat identification of memantine.

A real­world pharmacovigilance study of FDA adverse event reporting system events for daratumumab.

BACKGROUND: Daratumumab, a first-in-class humanized IgG1κ monoclonal antibody that targets the CD38 epitope, has been approved for treatment of multiple myeloma by FDA. The current study was to evaluate daratumumab-related adverse events (AEs) through data mining of the US Food and Drug Administration Adverse Event Reporting System (FAERS). RESEARCH DESIGN AND METHODS: Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN) and the multi-item gamma Poisson shrinker (MGPS) algorithms were employed to quantify the signals of daratumumab-associated AEs. RESULTS: Out of 10,378,816 reports collected from the FAERS database, 8727 reports of daratumumab-associated AEs were identified. A total of 183 significant disproportionality preferred terms (PTs) were retained. Unexpected significant AEs such as meningitis aseptic, leukoencephalopathy, tumor lysis syndrome, disseminated intravascular coagulation, hyperviscosity syndrome, sudden hearing loss, ileus and diverticular perforation were also detected. The median onset time of daratumumab-related AEs was 11 days (interquartile range [IQR] 0-76 days), and most of the cases occurred within 30 days. CONCLUSION: Our study found potential new and unexpected AEs signals for daratumumab, suggesting prospective clinical studies are needed to confirm these results and illustrate their relationship.

A simulation-based comparison of drug-drug interaction signal detection methods.

Several statistical methods have been proposed to detect adverse drug reactions induced by taking two drugs together. These suspected adverse drug reactions can be discovered through post-market drug safety surveillance, which mainly relies on spontaneous reporting system database. Most previous studies have applied statistical models to real world data, but it is not clear which method outperforms the others. We aimed to assess the performance of various detection methods by implementing simulations under various conditions. We reviewed proposed approaches to detect signals indicating drug-drug interactions (DDIs) including the Ω shrinkage measure, the chi-square statistic, the proportional reporting ratio, the concomitant signal score, the additive model and the multiplicative model. Under various scenarios, we conducted a simulation study to examine the performances of the methods. We also applied the methods to Korea Adverse Event Reporting System (KAERS) data. Of the six methods considered in the simulation study, the Ω shrinkage measure and the chi-square statistic with threshold = 2 had higher sensitivity for detecting the true signals than the other methods in most scenarios while controlling the false positive rate below 0.05. When applied to the KAERS data, the two methods detected one known DDI for QT prolongation and one unknown (suspected) DDI for hyperkalemia. The performance of various signal detection methods for DDI may vary. It is recommended to use several methods together, rather than just one, to make a reasonable decision.

A real-world pharmacovigilance study of amivantamab-related cardiovascular adverse events based on the FDA adverse event reporting system (FAERS) database.

Amivantamab is the first dual-specificity antibody targeting EGFR and MET, which is approved for the treatment of locally advanced or metastatic non-small cell lung cancer (NSCLC) with EGFR exon 20 insertion mutations. Cardiovascular toxicities related to amivantamab have not been reported in the CHRYSALIS study. However, the occurrence of cardiovascular events in the real world is unknown. To comprehensively investigate the clinical characteristics, onset times, and outcomes of cardiovascular toxicities associated with amivantamab. The Food and Drug Administration Adverse Event Reporting System (FAERS) database from 1st quarter of 2019 to the 2nd quarter of 2023 was retrospectively queried to extract reports of cardiovascular adverse events (AEs) associated with amivantamab. To perform disproportionality analysis, the reporting odds ratios (RORs) and information components (ICs) were calculated with statistical shrinkage trans-formation formulas and a lower limit of the 95% confidence interval (CI) for ROR (ROR025) > 1 or IC (IC025) > 0 with at least 3 reports was considered statistically significant. A total of 20,270,918 eligible records were identified, among which 98 records were related to cardiovascular events associated with amivantamab. 4 categories of cardiovascular events exhibited positive signals: venous thrombotic diseases, abnormal blood pressure, arrhythmia, and pericardial effusion. Venous thrombotic diseases and abnormal blood pressure were the two most common signals. The median time to onset (TTO) for cardiovascular AEs was 33 days. The cumulative incidence within 90 days was 100% for cardiac failure, 75% for stroke, 63.16% for arrhythmia, 50% for sudden death, and 44.18% for venous thrombotic diseases. Death accounted for 16.3% of all cardiovascular AEs associated with amivantamab. The mortality rates for Major Adverse Cardiovascular Events (MACE) were up to 60%. This pharmacovigilance study systematically explored the cardiovascular adverse events of amivantamab and provided new safety signals based on past safety information. Early and intensified monitoring is crucial, and attention should be directed towards high-risk signals.

Rising cases of drug-induced pulmonary fibrosis: Analysis of the Food and Drug Administration Adverse Event Reporting System (FAERS) database, 2000-2022.

PURPOSE: Pulmonary fibrosis (PF) is a severe, progressive disease, which may be caused by exposure to certain medications. METHODS: We queried the U.S. FDA Adverse Event Reporting System (FAERS) from 2000 to 2022, using the search terms "pulmonary fibrosis" and "idiopathic pulmonary fibrosis" and excluded reports with patients under the age of 18 years, and patients with unknown sex or age. Reports were sorted by generic drug names, counted, and plotted over time using a best-fit trendline based on an exponential function. RESULTS: From 2000 to 2022, there were 24 095 935 adverse drug events reported in FAERS, of which 17 520 (0.07%) were reported as PF. After excluding reports containing patients with unknown age (5255, 30%), sex (122, 0.7%), and age below 18 years old (155, 0.9%), our study included 11 988 reports. The mean age of the study sample was 66.5 ± 13.1 years, and 6248 patients (52.1%) were male. Plotting the 11 988 reports by year revealed an exponential best fit line (R2 = 0.88) with a positive slope over time. The top five drug classes associated with PF were disease modifying antirheumatic drugs (DMARDs, 39.4%), antineoplastic agents (26.4%), cardiovascular agents (12.6%), corticosteroids (4.6%), and immunosuppressive agents (4.0%). CONCLUSION: A 23-year analysis of the FAERS database revealed exponentially increasing adverse event reports of PF. Significant annual increases in reporting of PF suspected with DMARDs and antineoplastic agents were identified. Our study highlights important trends, which should be used to guide PF research related to drugs of potential importance.

Postmarket safety communications on drugs approved in Japan: A 25-year analysis.

Drug safety communications (DSCs) are essential tools for communicating important postmarket serious drug safety information to healthcare professionals and patients. Previous studies characterized DSCs issued by the U.S. Food and Drug Administration (FDA) and the European Medicines Agency (EMA); however, knowledge about the activities of the Pharmaceuticals and Medical Devices Agency (PMDA)/the Ministry of Health, Labor and Welfare (MHLW) is limited. This study characterized DSCs by the PMDA/MHLW in comparison with previously reported DSCs by the FDA and the EMA. We retrospectively analyzed 37 DSCs of 41 adverse drug reactions (ADRs) for 33 drugs in Japan from 1997 to 2022. Most DSCs were related to non-oncology drugs (30/37, 81.1%), and the median (interquartile range) time from approval to DSC issuance was 19 (10-51) months. Notably, the regulatory review reports and the latest labels before DSC issuance did not describe 16/28 (57.1%) and 12/37 (32.4%) of the ADRs related to DSCs, respectively. Most DSCs resulted in label revisions (36/37, 97.3%) and seven drugs were eventually withdrawn. Some DSC characteristics are similar among the PMDA/MHLW, the FDA, and the EMA; however, the number, contents, and range of new safety issues addressed by DSCs differ among the three jurisdictions. Our study emphasized the importance of continuous efforts to gather postmarket drug safety information because substantial ADRs that led to DSCs were recognized after approval and were associated with critical label revisions and withdrawals. Future studies are required to address global challenges for regulatory harmonization of safety-related regulatory actions.