PCSK9 Inhibitors and Infection-Related Adverse Events: A Pharmacovigilance Study Using the World Health Organization VigiBase.

AIMS: Protein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) are novel lipid-lowering agents used in patients with cardiovascular disease. Despite reassuring safety data from pivotal trials, increasing evidence from real-world studies suggests that PCSK9i increase the risk of bacterial and viral infections. Therefore, this study aimed to identify signals of infection-related adverse events (AEs) associated with PCSK9i. METHODS: We performed an observational pharmacovigilance study using the World Health Organization's VigiBase, recorded up to December 2022. We included individual case safety reports (ICSRs) of PCSK9 inhibitors, alirocumab and evolocumab, and compared them with those of other drugs. Infection-related ICSRs were retrieved from the Medical Dictionary for Regulatory Activities System Organ Class 'infections and infestations.' RESULTS: Among 114,293 reports (258,099 drug-AE pairs) related to PCSK9 inhibitors, 54% included female patients, 41% included patients aged ≥65 years, and 82% included patients who received evolocumab. Additionally, beyond AEs recognized by regulatory authorities, organ infections such as influenza (reporting odds ratio [ROR] 2.89, 95% confidence interval [CI] 2.74-3.05), gastric infections (ROR 2.47, 95% CI 1.63-3.75), and kidney infections (ROR 1.36, 95% CI 1.06-1.73) were observed. Sensitivity analysis indicated a heightened risk of infection-related AEs associated with PCSK9i regardless of the specific drug type. CONCLUSIONS: In addition to the labelled respiratory infections, six infection-related symptoms in the gastrointestinal, urinary, and renal organs were identified. Our findings support the need for systematic surveillance of infections among PCSK9i users.

A real-world pharmacovigilance study on cardiovascular adverse events of tisagenlecleucel using machine learning approach.

Chimeric antigen receptor T-cell (CAR-T) therapies are a paradigm-shifting therapeutic in patients with hematological malignancies. However, some concerns remain that they may cause serious cardiovascular adverse events (AEs), for which data are scarce. In this study, gradient boosting machine algorithm-based model was fitted to identify safety signals of serious cardiovascular AEs reported for tisagenlecleucel in the World Health Organization Vigibase up until February 2024. Input dataset, comprised of positive and negative controls of tisagenlecleucel based on its labeling information and literature search, was used to train the model. Then, we implemented the model to calculate the predicted probability of serious cardiovascular AEs defined by preferred terms included in the important medical event list from European Medicine Agency. There were 467 distinct AEs from 3,280 safety cases reports for tisagenlecleucel, of which 363 (77.7%) were classified as positive controls, 66 (14.2%) as negative controls, and 37 (7.9%) as unknown AEs. The prediction model had area under the receiver operating characteristic curve of 0.76 in the test dataset application. Of the unknown AEs, six cardiovascular AEs were predicted as the safety signals: bradycardia (predicted probability 0.99), pleural effusion (0.98), pulseless electrical activity (0.89), cardiotoxicity (0.83), cardio-respiratory arrest (0.69), and acute myocardial infarction (0.58). Our findings underscore vigilant monitoring of acute cardiotoxicities with tisagenlecleucel therapy.

Cognitive dysfunction following finasteride use: a disproportionality analysis of the global pharmacovigilance database.

BACKGROUND: Finasteride is commonly prescribed for androgenic alopecia and benign prostatic hyperplasia. However, concerns regarding its safety have been growing as cases of cognitive dysfunction have been reported. METHODS: A disproportionality analysis was conducted on data collected between 1967 and 2022 to explore the potential association. Cases of cognitive dysfunction associated with finasteride use were identified, and the reporting odds ratio (rOR) was calculated with 95% confidence intervals to determine the strength of the association between the two variables. Sensitivity analyses were conducted to account for confounding by indication. RESULTS: Among the 54,766 cases of adverse events reported for finasteride use, 1,624 (2.97%) were associated with cognitive dysfunction. The study found a significant disproportionality for cognitive dysfunction related to finasteride use (rOR 5.43, 95% CI 5.17-5.71). Most cases were considered serious (65.83%), with no signs of recovery (58.37%). Sensitivity analyses showed that patients younger than 45 years (rOR 7.30, 95% CI 6.39-8.35) and those with alopecia (rOR 5.52, 95% CI 5.15-5.91) reported more cognitive dysfunctions than their counterparts. CONCLUSION: This study showed an increased reporting of cognitive dysfunction associated with finasteride use, especially among younger alopecia patients. Finasteride should be prescribed with caution, especially to younger alopecia patients.

Detecting early safety signals of infliximab using machine learning algorithms in the Korea adverse event reporting system.

There has been a growing attention on using machine learning (ML) in pharmacovigilance. This study aimed to investigate the utility of supervised ML algorithms on timely detection of safety signals in the Korea Adverse Event Reporting System (KAERS), using infliximab as a case drug, between 2009 and 2018. Input data set for ML training was constructed based on the drug label information and spontaneous reports in the KAERS. Gold standard dataset containing known AEs was randomly divided into the training and test sets. Two supervised ML algorithms (gradient boosting machine [GBM], random forest [RF]) were fitted with hyperparameters tuned on the training set by using a fivefold validation. Then, we stratified the KAERS data by calendar year to create 10 cumulative yearly datasets, in which ML algorithms were applied to detect five pre-specified AEs of infliximab identified during post-marketing surveillance. Four AEs were detected by both GBM and RF in the first year they appeared in the KAERS and earlier than they were updated in the drug label of infliximab. We further applied our models to data retrieved from the US Food and Drug Administration Adverse Event Reporting System repository and found that they outperformed existing disproportionality methods. Both GBM and RF demonstrated reliable performance in detecting early safety signals and showed promise for applying such approaches to pharmacovigilance.

Benzodiazepine-Related Cognitive Impairment or Dementia: A Signal Detection Study Using a Case/Non-Case Approach.

OBJECTIVE: The association between benzodiazepine use and the risk of cognitive impairment or dementia has been controversial. Our study aims to detect this association through a case/non-case method using the Korea Institute of Drug Safety & Risk Management-Korea adverse event reporting system database (KIDS-KD) between 2007 and 2016. METHODS: Cases were adverse event (AE)-pairs with suspected cognitive impairment or dementia. 10 non-cases were matched to each case on age and sex. Exposure was defined as use of benzodiazepines, including long-, intermediate-, and short-acting benzodiazepine. We conducted multivariable logistic regression analyses to estimate reporting odds ratios (ROR) and 95% confidence intervals (CI). RESULTS: Of the 1,086,584 AE-pairs, 887 cases were suspected AE-pairs of cognitive impairment or dementia, and 775,444 non-cases were selected. Benzodiazepine use was associated with increased AE-pairs of cognitive impairment or dementia when assessed using those with certain, probable, and/or possible in causality assessments (ROR=2.69, 95% CI=1.66-4.38). Higher ROR estimates were shown in female (2.33, 1.48-3.67) and in those with polypharmacy (2.20, 1.35-3.57). Dementia safety profiles were inconsistent across individual benzodiazepine components. CONCLUSION: These results suggest the potentially increased association between benzodiazepine use and cognitive impairment or dementia in female and those with polypharmacy. Inconsistent safety profiles of benzodiazepine components should be further investigated.

Machine Learning for Detection of Safety Signals From Spontaneous Reporting System Data: Example of Nivolumab and Docetaxel.

Introduction: Various methods have been implemented to detect adverse drug reaction (ADR) signals. However, the applicability of machine learning methods has not yet been fully evaluated. Objective: To evaluate the feasibility of machine learning algorithms in detecting ADR signals of nivolumab and docetaxel, new and old anticancer agents. Methods: We conducted a safety surveillance study of nivolumab and docetaxel using the Korea national spontaneous reporting database from 2009 to 2018. We constructed a novel input dataset for each study drug comprised of known ADRs that were listed in the drug labels and unknown ADRs. Given the known ADRs, we trained machine learning algorithms and evaluated predictive performance in generating safety signals of machine learning algorithms (gradient boosting machine [GBM] and random forest [RF]) compared with traditional disproportionality analysis methods (reporting odds ratio [ROR] and information component [IC]) by using the area under the curve (AUC). Each method then was implemented to detect new safety signals from the unknown ADR datasets. Results: Of all methods implemented, GBM achieved the best average predictive performance (AUC: 0.97 and 0.93 for nivolumab and docetaxel). The AUC achieved by each method was 0.95 and 0.92 (RF), 0.55 and 0.51 (ROR), and 0.49 and 0.48 (IC) for respective drug. GBM detected additional 24 and nine signals for nivolumab and 82 and 76 for docetaxel compared to ROR and IC, respectively, from the unknown ADR datasets. Conclusion: Machine learning algorithm based on GBM performed better and detected more new ADR signals than traditional disproportionality analysis methods.

Characteristics and trends of spontaneous reporting of therapeutic ineffectiveness in South Korea from 2000 to 2016.

Therapeutic ineffectiveness involves drug-related therapeutic failure, inefficacy or resistance and has not been sufficiently studied. Objective of our study was to evaluate reporting trends in therapeutic ineffectiveness by year and describe factors affecting therapeutic ineffectiveness using the Korea Adverse Event Reporting System. Proportion of therapeutic ineffectiveness reports was based on total submitted reports between 2000 and 2016. Utilizing 2016 alone, we compared the characteristics of therapeutic ineffectiveness with age group and gender matching by random extraction. We conducted a logistic regression analysis to estimate reporting odds ratios (ROR) and its 95% confidence intervals (CI) for reports by type of reporters, e.g., doctors, pharmacists, or consumers. We presented most frequent reports by the anatomical main groups and therapeutic subgroups according to the Anatomical Therapeutic Chemical (ATC) classification system. For the 17-years, the proportion of therapeutic ineffectiveness adverse drug reactions reporting ranged from 0.0% to 3.7% between 2000 and 2016. Of 228,939 reports, 2,797 (1.2%) were submitted in 2016. Consumers accounted for 6.92% of reports and doctors accounted for 45.49%, in which, consumers were more likely to report therapeutic ineffectiveness than doctors (adjusted ROR 3.98; 95% CI, 2.92 to 5.41). According to the ATC classification system, "nervous system" was the most frequently reported anatomical group (18.7%) and "parathyroid hormones and analogues" was reported most frequently in the pharmacological subgroup (23.7%). Teriparatide, a drug used to treat osteoporosis, had the most reports (11.0%). Therapeutic ineffectiveness reports may be used as a scientific tool for the reevaluation of respective drugs in order to confirm of its therapeutic effects.

Discrepancies in the Evidence and Recommendation Levels of Pregnancy Information in Prescription Drug Labeling in the United States, United Kingdom, Japan, and Korea.

OBJECTIVE: To compare the evidence-of-risk and recommendation levels of new drugs' label information in pregnancy among US, UK, Japan, and Korea. Despite the importance of drug labels in guiding decisions, officially approved prescribing information in drug labels is often inconsistent across countries and contains misleading content. Women are unintentionally exposed to drugs during early stages of unplanned or unrecognized pregnancies when treated for medical conditions. METHODS: We selected 81 drugs approved in all 4 countries mentioned above from 2008 to 2016. Evidence level was classified into five categories ("Definite," "Probable," "Possible," "Unlikely," and "Unclassified"), and recommendation level was classified into four categories ("Contraindicated," "Cautious," "Compatible," and "Unclassified"). We calculated kappa coefficient with 95% confidence intervals (CIs) to estimate the agreement of each category. RESULTS: For evidence level, "Unclassified" was the highest in Japan (33.3%), while representing a much smaller proportion in US (2.5%), UK (6.2%), and Korea (6.2%; p < 0.01). For recommendation level, "Contraindicated" was the lowest in US (9.9%), while it was greater in UK (50.6%), Japan (50.6%), and Korea (42.0%; p < 0.01). Korea-UK presented substantial agreement for both evidence-of-risk (kappa = 0.80, 95% CI: 0.67-0.94) and recommendation level (kappa = 0.64, 95% CI: 0.46-0.82), while Korea-Japan and Korea-US were in fair or moderate agreement. CONCLUSIONS: Label information in pregnancy was discrepant among the four countries. Discrepancies in labeling information among countries may cause confusion to patients and healthcare professionals alike. To better assist healthcare professionals and patients in the use of prescription drugs, global harmonization of safety information is needed to minimize confusion with potential risk.