Leveraging the Variability of Pharmacovigilance Disproportionality Analyses to Improve Signal Detection Performances.
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ABSTRACT:
SUBMITTER: Khouri C
PROVIDER: S-EPMC8193489 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Esketamine was approved for the treatment of treatment-resistant depression in 2019. After the approval of esketamine, numerous concerns have been raised regarding its long-term safety and tolerability. A previous systematic pharmacovigilance study on esketamine-related adverse events (AEs) was published in 2020; however, it has not been updated 2 years later. The primary aim of this study was to detect and characterize neurological safety signals of esketamine to partially update the knowledge in this field using the FDA pharmacovigilance database. Reporting odds ratio (ROR) was calculated for esketamine-related neurological AEs from 2019 to 2021 with a signal considered when the lower limit of the 95% confidence interval (CI) of ROR (ROR025) exceeded one. Severe and non-severe cases were compared using an independent samples t-test or chi-squared (χ2) test, and a rating scale was used to prioritize the signals. The database contained 720 cases of esketamine-associated neurological AEs, with 21 signals detected, ranging from a ROR025 of 1.05 (disturbance in attention) to 204.00 (sedation). 16 latest neurological AEs emerged in the second year of marketing approval of esketamine, with eight signals detected. The associations between esketamine and nervous system disorders persisted when stratifying by sex, age, and reporter type, whereas the spectrum of neurological AEs differed in stratification regimens. Esketamine dosage, antidepressant polypharmacy, or co-prescription with benzodiazepines affected AEs severity (t = 2.41, p = 0.017; χ2 = 6.75, p = 0.009; and χ2 = 4.10, p = 0.043; respectively), while age and sex did not (p = 0.053 and p = 0.397, respectively). Three signals were categorized as moderate clinical priority [i.e., sedation, dizziness, and dysgeusia (priority points 7, 5, and 5, respectively)], showing the same early failure type profiles. Notably, seven detected disproportionality signals were not previously detected in clinical trials. Although the majority of results were in line with those obtained in the previous study, there were discrepancies in the spectrum of neurological AEs and the effects of several risk factors on AEs severity among the two studies that should be recognized and managed early in clinical treatments.
| S-EPMC9023790 | biostudies-literature
Project description:One of the main objectives in pharmacovigilance is the detection of adverse drug events (ADEs) through mining of healthcare databases, such as electronic health records or administrative claims data. Although different approaches have been shown to be of great value, research is still focusing on the enhancement of signal detection to gain efficiency in further assessment and follow-up. We applied similarity-based modeling techniques, using 2D and 3D molecular structure, ADE, target, and ATC (anatomical therapeutic chemical) similarity measures, to the candidate associations selected previously in a medication-wide association study for four ADE outcomes. Our results showed an improvement in the precision when we ranked the subset of ADE candidates using similarity scorings. This method is simple, useful to strengthen or prioritize signals generated from healthcare databases, and facilitates ADE detection through the identification of the most similar drugs for which ADE information is available.
| S-EPMC4211266 | biostudies-literature
Project description:BackgroundRucaparib has been approved for the maintenance treatment of adult patients with recurrent epithelial ovarian, fallopian tube, or primary peritoneal cancer. However, the long-term safety of rucaparib in large sample population was unknown. The presented study aimed to evaluate rucaparib-associated adverse events (AEs) according to the real-world pharmacovigilance database.MethodsDisproportionality analysis was conducted to assess the association between rucaparib and its AEs. Data were collected from the international pharmacovigilance database of US FDA Adverse Event Reporting System (FAERS) between January 2017 and June 2022. The characteristics of rucaparib-related AEs, and the onset time were further analyzed.ResultsA total of 9,296,694 AE reports were recorded in the FAERS during the study period, among which 7,087 reports were associated with rucaparib. About 135 rucaparib-related AE signals in 15 system organ class (SOCs) were identified. The most common AEs included anaemia, thrombocytopenia, nausea, vomiting, fatigue, blood creatinine increase, alanine aminotransferase increase, and aspartate aminotransferase increase, which were listed in the label for rucaparib. Of note, 21 new and unexpected significant AEs that off-label were also found in our study, such as preferred term (PTs) of intestinal obstruction, gastrooesophageal reflux disease, blood iron decreased, dehydration, and hypersomnia. The median onset time of rucaparib-related AEs was 12 days (interquartile range [IQR] 1-62 days), and had early failure types.ConclusionOur study demonstrated potential new AEs of rucaparib, and further studies were expected to confirm the results.
| S-EPMC10416473 | biostudies-literature
Project description:INTRODUCTION:Adverse drug reactions related to drug-drug interactions cause harm to patients. There is a body of research on signal detection for drug interactions in collections of individual case reports, but limited use in regular pharmacovigilance. OBJECTIVE:The aim of this study was to evaluate the feasibility of signal detection of drug-drug interactions in collections of individual case reports of suspected adverse drug reactions. METHODS:This study was conducted in VigiBase, the WHO global database of individual case safety reports. The data lock point was 31 August 2016, which provided 13.6 million reports for analysis after deduplication. Statistical signal detection was performed using a previously developed predictive model for possible drug interactions. The model accounts for an interaction disproportionality measure, expressed suspicion of an interaction by the reporter, potential for interaction through cytochrome P450 activity of drugs, and reported information indicative of unexpected therapeutic response or altered therapeutic effect. Triage filters focused the preliminary signal assessment on combinations relating to serious adverse events with case series of no more than 30 reports from at least two countries, with at least one report during the previous 2 years. Additional filters sought to eliminate already known drug interactions through text mining of standard literature sources. Preliminary signal assessment was performed by a multidisciplinary group of pharmacovigilance professionals from Uppsala Monitoring Centre and collaborating organizations, whereas in-depth signal assessment was performed by experienced pharmacovigilance assessors. RESULTS:We performed preliminary signal assessment for 407 unique drug pairs. Of these, 157 drug pairs were considered already known to interact, whereas 232 were closed after preliminary assessment for other reasons. Ten drug pairs were subjected to in-depth signal assessment and an additional eight were decided to be kept under review awaiting additional reports. The triage filters had a major impact in focusing our preliminary signal assessment on just 14% of the statistical signals generated by the predictive model for drug interactions. In-depth assessment led to three signals communicated with the broader pharmacovigilance community, six closed signals and one to be kept under review. CONCLUSION:This study shows that signals of adverse drug interactions can be detected through broad statistical screening of individual case reports. It further shows that signal assessment related to possible drug interactions requires more detailed information on the temporal relationship between different drugs and the adverse event. Future research may consider whether interaction signal detection should be performed not for individual adverse event terms but for pairs of drugs across a spectrum of adverse events.
| S-EPMC7395907 | biostudies-literature
Project description:BackgroundAdverse effects of drugs are often identified after market introduction. Post-marketing pharmacovigilance aims to detect them as early as possible and relies on spontaneous reporting systems collecting suspicious cases. Signal detection tools have been developed to mine these large databases and counts of reports are analysed with disproportionality methods. To address disproportionality method biases, recent methods apply to individual observations taking into account all exposures for the same patient. In particular, the logistic lasso provides an efficient variable selection framework, yet the choice of the regularization parameter is a challenging issue and the lasso variable selection may give inconsistent results.MethodsWe propose a new signal detection methodology based on the adaptive lasso. We derived two new adaptive weights from (i) a lasso regression using the Bayesian Information Criterion (BIC), and (ii) the class-imbalanced subsampling lasso (CISL), an extension of stability selection. The BIC is used in the adaptive lasso stage for variable selection. We performed an extensive simulation study and an application to real data, where we compared our methods to the existing adaptive lasso, and recent detection approaches based on lasso regression or propensity scores in high dimension. For both studies, we evaluate the methods in terms of false discoveries and sensitivity.ResultsIn the simulations and the application, both proposed adaptive weights show equivalent or better performances than the other competitors, with an advantage for the CISL-based adaptive weights. CISL and lasso regression using BIC are solid alternatives.ConclusionOur proposed adaptive lasso is an appealing methodology for signal detection in pharmacovigilance. Although we cannot rely on test theory, our approaches show a low and stable False Discovery Rate in all simulation settings. All methods evaluated in this work are implemented in the adapt4pv R package.
| S-EPMC8638444 | biostudies-literature
Project description:Since the 1960s, several drugs have been linked to the onset or aggravation of pulmonary arterial hypertension (PAH): dasatinib, some amphetamine-like appetite suppressants (aminorex, fenfluramine, dexfenfluramine, benfluorex) and recreational drugs (methamphetamine). Moreover, in numerous cases, the implication of other drugs with PAH have been suggested, but the precise identification of iatrogenic aetiologies of PAH is challenging given the scarcity of this disease and the potential long latency period between drug intake and PAH onset. In this context, we used the World Health Organization's pharmacovigilance database, VigiBase, to generate new hypotheses about drug associated PAH.MethodsWe used VigiBase, the largest pharmacovigilance database worldwide to generate disproportionality signals through the Bayesian neural network method. All disproportionality signals were further independently reviewed by experts in pulmonary arterial hypertension, pharmacovigilance and vascular pharmacology and their plausibility ranked according to World Health Organization causality categories.ResultsWe included 2184 idiopathic PAH cases, yielding a total of 93 disproportionality signals. Among them, 25 signals were considered very likely, 15 probable, 28 possible and 25 unlikely. Notably, we identified 4 new protein kinases inhibitors (lapatinib, lorlatinib, ponatinib and ruxolitinib), 1 angiogenesis inhibitor (bevacizumab), and several chemotherapeutics (etoposide, trastuzumab), antimetabolites (cytarabine, fludarabine, fluorouracil, gemcitabine) and immunosuppressants (leflunomide, thalidomide, ciclosporin).ConclusionSuch signals represent plausible adverse drug reactions considering the knowledge of iatrogenic PAH, the drugs' biological and pharmacological activity and the characteristics of the reported case. Although confirmatory studies need to be performed, the signals identified may help clinicians envisage an iatrogenic aetiology when faced with a patient who develops PAH.
| S-EPMC9795981 | biostudies-literature
Project description:Signal-detection algorithms (SDAs) are recognized as vital tools in pharmacovigilance. However, their performance characteristics are generally unknown. By leveraging a unique gold standard recently made public by the Observational Medical Outcomes Partnership (OMOP) and by conducting a unique systematic evaluation, we provide new insights into the diagnostic potential and characteristics of SDAs that are routinely applied to the US Food and Drug Administration (FDA) Adverse Event Reporting System (AERS). We find that SDAs can attain reasonable predictive accuracy in signaling adverse events. Two performance classes emerge, indicating that the class of approaches that address confounding and masking effects benefits safety surveillance. Our study shows that not all events are equally detectable, suggesting that specific events might be monitored more effectively using other data sources. We provide performance guidelines for several operating scenarios to inform the trade-off between sensitivity and specificity for specific use cases. We also propose an approach and demonstrate its application in identifying optimal signaling thresholds, given specific misclassification tolerances.
| S-EPMC3857139 | biostudies-literature
Project description:With the creation of accurate, chromosome-scale genomes, the next challenge facing the genomics community is the accurate idenfication of transcriptional units, distinguishing them from aberrant transcriptional noise. This has proven to be a challenge as annotation by traditional means, such as short read RNA-seq followed by transcriptome assembly, which is prone to the generation of in-silico artifacts. To address this issue, we took advantage of epigenomic data in the form of ChIP-seq to unbiasedly annotate plant genomes and identify potential annotation issues, as well as identify novel genes. Histone modifications appear in the genome in a reproducible and predictable manner, making them an ideal resource to use in annotation. Trimethylation of histone 3 lysine 4 (H3K4me3), as well as acetylation of histone 3 lysine 56 are well documented to coincide with initiation of transcription by polymerase II (Pol II) at promoter sequences. These initiation marks, paired with marks deposited across the gene body during transcriptional elongation, such as histone 3 lysine 36 tri-methylation (H3K36me3) and histone 3 lysine 4 mono-methylation (H3K4me1), offer a framework to begin identifying complete transcriptional units. We leveraged these data on a genome-wide scale, allowing for identification of annotations discordant with empirical data. In total, 13,159 potential annotation issues were found in Zea mays across three different tissues, which were corroborated using complementary RNA-based approaches. Upon correction and validation, genes were extended by an average of 2,128 base pairs, and the length of discovered novel genes was 1,962 base pairs. Application of this method to five additional plant genomes revealed a variety of novel gene annotations, including 13,836 in Asparagus officianalis, 2,724 in Setaria viridis, 2,446 in Sorghum bicolor, 8,631 in Glycine max, and 2,585 in Phaseolous vulgaris.
2021-04-26 | GSE160944 | GEO
Project description:The elaborate bipectinate antennae of male moths are thought to increase their sensitivity to female sex pheromones, and so should be favoured by selection. Yet simple filamentous antennae are the most common structure among moths. The stereotypic arrangements of scales on the surface of antennae may resolve this paradox. We use computational fluid dynamics techniques to model how scales on the filamentous antennae of moths affect the passage of different particles in the airflow across the flagellum in both small and large moths. We found that the scales provide an effective solution to improve the efficacy of filamentous antennae, by increasing the concentration of nanoparticles, which resemble pheromones, around the antennae. The smaller moths have a greater increase in antennal efficiency than larger moths. The scales also divert microparticles, which resemble dust, away from the antennal surface, thereby reducing contamination. The positive correlations between antennal scale angles and sensilla number across Heliozelidae moths are consistent with the predictions of our model.
| S-EPMC5879631 | biostudies-literature
Project description:This case-non-case study aims to detect signals not currently listed on cephalosporin drug labels. From 2009 to 2018, adverse event (AE) reports concerning antibacterial drugs (anatomical therapeutic chemical (ATC) code J01) in the Korea Adverse Events Reporting System (KAERS) database were examined. For signal detection, three indices of disproportionality, proportional reporting ratio (PRR), reporting odds ratio (ROR), and information component (IC), were calculated. The list of signals was compared with ADRs on the drug labels from the United States, United Kingdom, Japan, and South Korea. A total of 163,800 cephalosporin-AE combinations and 72,265 all other J01-AE combinations were analyzed. This study detected 472 signals and 114 new signals that are not included on the drug labels. Cefatrizine-corneal edema (PRR, 440.64; ROR, 481.67; IC, 3.84) and cefatrizine-corneal ulceration (PRR, 346.22; ROR, 399.70; IC, 4.40) had the highest PRR, ROR, and IC among all signals. Additionally, six serious AEs that were not listed on drug labels such as cefaclor-induced stupor (ten cases) and cefaclor-induced respiratory depression (four cases) were found. Detecting signals using a national pharmacovigilance database is useful for identifying unknown ADRs. This study identified signals of cephalosporins that warrant further investigation.
| S-EPMC8147424 | biostudies-literature