Potential Adverse Events Reported With the Janus Kinase Inhibitors Approved for the Treatment of Rheumatoid Arthritis Using Spontaneous Reports and Online Patient Reviews

The aim of this study was to analyze the potential adverse events (AEs) caused by Janus kinase (JAK) inhibitors, including tofacitinib, baricitinib, and upadacitinib, used to treat rheumatoid arthritis using spontaneous AE reports from the FDA (FAERS) and interpreting them in correlation with those from Korea (KAERS) and an online patient review (WebMD). Potential AEs were identified based on a disproportionality analysis using the proportional reporting ratio (PRR), reporting odds ratio (ROR), and the information component (IC). A total of 23,720 reports were analyzed from FAERS database, of which 91.5% were reports on tofacitinib. Potentially important medical AEs related to infections were reported frequently, as well as thromboembolism-related AEs. The AEs, such as malignancy, interstitial lung diseases, myocardial infarction, and gastrointestinal disorder, also reported. In an online patient review report, the ineffectiveness of the drug and gastrointestinal AEs were frequently reported. Infection with baricitinib and symptoms related to pain or edema due to upadacitinib were the main discomfort experienced by patients. In conclusion, the results of this study highlight the possible safety issues associated with JAK inhibitors. Routine clinical observations and further research using various real-world databases are needed.

Safety Profile of Ibrutinib: An Analysis of the WHO Pharmacovigilance Database

As ibrutinib has become a standard of care in B-cell malignancies in monotherapy or in combination with other agents, definition of its safety profile appears essential. The aim of this study was to further characterize the safety profile of ibrutinib through the identification of potential safety signals in a large-scale pharmacovigilance database. All serious individual case safety reports (ICSRs) in patients aged ≥18 years involving ibrutinib suspected in the occurrence of serious adverse drug reactions or drug interacting from November 13th, 2013 to December 31st, 2020 were extracted from VigiBase, the World Health Organization global safety database. Disproportionality reporting was assessed using the information component (IC) and the proportional reporting ratio (PRR), with all other anticancer drugs used as the reference group. To mitigate the confounding of age, two subgroups were considered: patients aged<75 years and ≥75 years. A signal of disproportionate reporting (SDR) was defined if both IC and PRR were significant. A total of 16,196 ICSRs were included. The median age of patients was 72.9 years, 42.6% of ICSRs concerned patients aged ≥75 years, and 64.2% male patients. More than half (56.2%) of ICSRs resulted in hospitalization or prolonged hospitalization. Among 713 SDRs, 36 potential safety signals emerged in ibrutinib-treated patients, mainly ischemic heart diseases, pericarditis, uveitis, retinal disorders and fractures. All potential safety signals having arisen in this analysis may support patient care and monitoring of ongoing clinical trials. However, owing to the mandatory limitations of this study, our results need further confirmation using population-based studies.

Adverse Drug Events in the Prevention and Treatment of COVID-19: A Data Mining Study on FDA Adverse Event Reporting System (FAERS) (Preprint)

BACKGROUND In the emergency situation of COVID-19, off-label therapies and newly developed vaccines may bring the patients adverse drug event (ADE) risks. Data mining based on spontaneous reporting systems (SRSs) is a promising and efficient way to detect potential ADEs so as to help health professionals and patients get rid of these risks. OBJECTIVE This pharmacovigilance study aimed to investigate the ADEs of “Hot Drugs” in COVID-19 prevention and treatment based on the data of the US Food and Drug Administration (FDA) adverse event reporting system (FAERS). METHODS FAERS ADE reports associated with COVID-19 from the 2nd quarter of 2020 to the 2nd quarter of 2021 were retrieved with “Hot Drugs” and frequent ADEs recognized. A combination of support, proportional reporting ratio (PRR) and Chi-square (2) test was applied to detect significant “Hot Drug” & ADE signals by Python programming language on Jupyter notebook. RESULTS 13,178 COVID-19 cases were retrieved with 18 “Hot Drugs” and 312 frequent ADEs on “Preferred Term” (PT) level. 18  312 = 5,616 “Drug & ADE” candidates were formed for further data mining. The algorithm finally produced 219 significant ADE signals associated with 17 “Hot Drugs”and 124 ADEs.Some unexpected ADE signals were observed for chloroquine, ritonavir, tocilizumab, Oxford/AstraZeneca COVID-19 Vaccine and Moderna COVID-19 Vaccine. CONCLUSIONS Data mining is a promising and efficient way to assist pharmacovigilance work and the result of this paper could help timely recognize ADEs in the prevention and treatment of COVID-19.

Comparison of Online Patient’s Reviews and National Pharmacovigilance Data for Tramadol-Related Adverse Events: Comparative Observational Study (Preprint)

BACKGROUND Tramadol is known to cause fewer adverse events (AE) than other opioids. However, recent research has raised concerns about various safety issues. OBJECTIVE We aimed to explore these new AE related to tramadol using social media and conventional pharmacovigilance data. METHODS This study used two datasets, one from patients’ drug reviews on WebMD and one from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). We analyzed 2,062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities (MedDRA). To analyze AE from FAERS, a disproportionality analysis was performed with three measures: the proportional reporting ratio (PRR), the reporting odds ratio (ROR), and the information component (IC). RESULTS From the 869 AE reported, we identified 125 new signals related to tramadol use not listed on the drug label that satisfied all three signal detection criteria. In addition, 20 serious AE were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients’ symptom descriptions, tramadol-induced pain might also be an unexpected AE. CONCLUSIONS This study detected several novel signals related to tramadol use, suggesting newly identified possible AE. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data. CLINICALTRIAL N/A

Social Media analytics for Pharmacovigilance of Anti-Epileptic Drugs (Preprint)

UNSTRUCTURED Epilepsy is a common neurological disorder worldwide and Anti-Epileptic Drugs (AEDs) therapy is the cornerstone of its treatment. It has a laudable aim of achieving seizure freedom and minimal, if any, Adverse Drug Reactions (ADRs). Too often, AEDs treatment is a long-lasting journey, in which ADRs have a crucial role in its administration. Therefore, from pharmacovigilance perspective, the detection of the ADRs of AEDs is a task of utmost importance. Typically, it is accomplished by applying data mining algorithms to a relevant data from spontaneous reporting systems. Despite their wide adoption for pharmacovigilance, the passiveness and high under-reporting ratio associated with them have encouraged considering other data source such as electronic health databases and pharmaceutical databases. Social media is the most recent alternative data source with many promising potentials to overcome the shortcomings of the traditional ones. Although, in the literature, some attempts have investigated the validity and utility of social media for ADRs detection of different groups of drugs, none of them was dedicated to the ADRs of AEDs. Hence, this paper presents a novel investigation of the validity and utility of social media as an alternative data source for the ADRs detection of AEDs. To this end, a dataset of consumers' reviews from two online health communities have been collected. The dataset is preprocessed, the unigram, bigram, and trigram are generated, and the ADRs of each AED are extracted with the aid of consumer health vocabulary and ADRs lexicon. Three widely used measures, namely proportional reporting ratio, reporting odd ratio, and information component are used to measure the association between each ADR and AED. The results, lists of signaled ADRs for each AED, are validated against Side Effect Resource (SIDER), a widely used ADRs database, in terms of precision of the ADRs detection. The validation results, 73%-74%, indicate the validity of the online health communities for the detection of AEDs ADRs. Furthermore, the lists of signaled AEDs ADRs are analyzed to answer questions regarding the common ADRs for all AEDs and the mutual similarities between AEDs in terms of their signaled ADRs. The consistency of the drawn answers with the existing pharmaceutical knowledge suggests the utility of the online health communities' data for knowledge discovery tasks of AEDs.

Evaluating risk detection methods to uncover ontogenic-mediated adverse drug effect mechanisms in children

Background Identifying adverse drugs effects (ADEs) in children, overall and within pediatric age groups, is essential for preventing disability and death from marketed drugs. At the same time, however, detection is challenging due to dynamic biological processes during growth and maturation, called ontogeny, that alter pharmacokinetics and pharmacodynamics. As a result, methodologies in pediatric drug safety have been limited to event surveillance and have not focused on investigating adverse event mechanisms. There is an opportunity to identify drug event patterns within observational databases for evaluating ontogenic-mediated adverse event mechanisms. The first step of which is to establish statistical models that can identify temporal trends of adverse effects across childhood. Results Using simulation, we evaluated a population stratification method (the proportional reporting ratio or PRR) and a population modeling method (the generalized additive model or GAM) to identify and quantify ADE risk at varying reporting rates and dynamics. We found that GAMs showed improved performance over the PRR in detecting dynamic drug event reporting across child development stages. Moreover, GAMs exhibited normally distributed and robust ADE risk estimation at all development stages by sharing information across child development stages. Conclusions Our study underscores the opportunity for using population modeling techniques, which leverage drug event reporting across development stages, as biologically-inspired detection methods for evaluating ontogenic mechanisms.

A VigiBase descriptive study of fluoroquinolone induced disabling and potentially permanent musculoskeletal and connective tissue disorders

Recent drug safety concerns described fluoroquinolone (FQ)-induced serious musculoskeletal reactions. The objective of this study was to characterize reports with FQ-associated disabling musculoskeletal disorders, from VigiBase. The analysis included all FQ-induced musculoskeletal and connective tissue disorders adverse drug reaction (ADR) reports (up to July-2019), (disabling/incapacitating, or recovered/resolved with sequelae or fatal). We described aspects like reporter, suspected FQs, ADRs, associated corticosteroid therapy. We also looked into the disproportionality data in terms of proportional reporting ratio (PRR) and information component (IC) values. A total of 5355 reports with 13,563 ADRs and 5558 FQs were reported. The majority of reports were for patients aged 18–64 (62.67%), and the female gender prevailed (61.76%). Consumers reported almost half (45.99%), with a peak in reporting rates in 2017. Top reported ADRs were arthralgia (16.34%), tendonitis (11.04%), pain in extremity (9.98%), tendon pain (7.63%), and myalgia (7.17%). Top suspected FQs were levofloxacin (50.04%), ciprofloxacin (38.41%), moxifloxacin (5.16%), ofloxacin (3.17%) and norfloxacin (1.01%). For these, FQs-ADR association was supported by the disproportionality analysis. Corticosteroids were associated with about 7% of tendon related reports. The results augment the existing data on FQs safety concerns, specifically their potential effect on the musculoskeletal system.

Relative frequency of drug-induced sleep disorders for 32 antidepressants in a large set of Internet user reviews

Study Objectives It is known that antidepressant drugs can induce sleep disorders in patients, but little data exist about high or low-risk molecules. The aim was to study the frequency of antidepressant drugs-induced sleep disorders (DISD) by molecule. Methods 77,391 patient comments for 32 antidepressant drugs were collected from drug review websites and screened for DISD. Association between drugs and nightmare disorder, restless legs syndrome, sleep paralysis, sleep terrors, sleep-related hallucinations or sleep walking was expressed as relative proportion [proportional reporting ratio (PRR)]. A detailed analysis of the dreams content was also carried out. Results Amitriptyline, doxepin, fluvoxamine, mirtazapine, nortriptyline, trazodone, venlafaxine and vilazodone were associated with a greater frequency of DISD compared to other antidepressants. Vilazodone heavily increased the probability of developing 5 of the 6 studied DISD (PRR 3.3 to 19.3) and mirtazapine increased the probability for developing 4 DISD (PRR 2.4 to 6.4). Bupropion and citalopram were associated with lower probabilities for 5 DISD (PRR 0.2 to 0.7). Sentiment analysis showed that patients described disturbing dreams for vilazodone or mirtazapine and strange but less negative dreams for bupropion, citalopram or duloxetine. Conclusions Relative frequencies of sleep disorders were obtained for a vast panel of antidepressant drugs through an original analysis of user’s drug reviews on drug rating websites. Our results could guide clinicians in appropriate choice of antidepressant drug for high DISD-risk patients in need of such treatment. These results may however be cautiously taken, considering the uncertain reliability and generalisability of web-based data.

Pulmonary adverse drug event data in hypertension with implications on COVID-19 morbidity

Hypertension is a recognized comorbidity for COVID-19. The association of antihypertensive medications with outcomes in patients with hypertension is not fully described. However, angiotensin-converting enzyme 2 (ACE2), responsible for host entry of the novel coronavirus (SARS-CoV-2) leading to COVID-19, is postulated to be upregulated in patients taking angiotensin-converting enzyme inhibitors (ACEIs) and angiotensin II receptor blockers (ARBs). Here, we evaluated the occurrence of pulmonary adverse drug events (ADEs) in patients with hypertension receiving ACEIs/ARBs to determine if disparities exist between individual drugs within the respective classes using data from the FDA Spontaneous Reporting Systems. For this purpose, we proposed the proportional reporting ratio to provide a statistical summary for the commonality of an ADE for a specific drug as compared to the entire database for drugs in the same or other classes. In addition, a statistical procedure, multiple logistic regression analysis, was employed to correct hidden confounders when causative covariates are underreported or untrusted to correct analyses of drug-ADE combinations. To date, analyses have been focused on drug classes rather than individual drugs which may have different ADE profiles depending on the underlying diseases present. A retrospective analysis of thirteen pulmonary ADEs showed significant differences associated with quinapril and trandolapril, compared to other ACEIs and ARBs. Specifically, quinapril and trandolapril were found to have a statistically significantly higher incidence of pulmonary ADEs compared with other ACEIs as well as ARBs (P < 0.0001) for group comparison (i.e., ACEIs vs. ARBs vs. quinapril vs. trandolapril) and (P ≤ 0.0007) for pairwise comparison (i.e., ACEIs vs. quinapril, ACEIs vs. trandolapril, ARBs vs. quinapril, or ARBs vs. trandolapril). This study suggests that specific members of the ACEI antihypertensive class (quinapril and trandolapril) have a significantly higher cluster of pulmonary ADEs.

Systematic analysis of safety profile for darunavir and its boosted agents using data mining in the FDA Adverse Event Reporting System database

This current investigation was aimed to generate signals for adverse events (AEs) of darunavir-containing agents by data mining using the US Food and Drug Administration Adverse Event Reporting System (FAERS). All AE reports for darunavir, darunavir/ritonavir, or darunavir/cobicistat between July 2006 and December 2019 were identified. The reporting Odds Ratio (ROR), proportional reporting ratio (PRR), and Bayesian confidence propagation neural network (BCPNN) were used to detect the risk signals. A suspicious signal was generated only if the results of the three algorithms were all positive. A total of 10,756 reports were identified commonly observed in hepatobiliary, endocrine, cardiovascular, musculoskeletal, gastrointestinal, metabolic, and nutrition system. 40 suspicious signals were generated, and therein 20 signals were not included in the label. Severe high signals (i.e. progressive extraocular muscle paralysis, acute pancreatitis, exfoliative dermatitis, acquired lipodystrophy and mitochondrial toxicity) were identified. In pregnant women, umbilical cord abnormality, fetal growth restriction, low birth weight, stillbirth, premature rupture of membranes, premature birth and spontaneous abortion showed positive signals. Darunavir and its boosted agents induced AEs in various organs/tissues, and were shown to be possibly associated with multiple adverse pregnant conditions. This study highlighted some novel and severe AEs of darunavir which need to be monitored prospectively.