Target Adverse Event Profiles for Predictive Safety in the Postmarket Setting.
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ABSTRACT:
SUBMITTER: Schotland P
PROVIDER: S-EPMC8246740 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Clinical trials can fail to detect rare adverse events (AEs). We assessed the ability of pharmacological target adverse-event (TAE) profiles to predict AEs on US Food and Drug Administration (FDA) drug labels at least 4 years after approval. TAE profiles were generated by aggregating AEs from the FDA adverse event reporting system (FAERS) reports and the FDA drug labels for drugs that hit a common target. A genetic algorithm (GA) was used to choose the adverse event (AE) case count (N), disproportionality score in FAERS (proportional reporting ratio (PRR)), and percent of comparator drug labels with an AE to maximize F-measure. With FAERS data alone, precision, recall, and specificity were 0.57, 0.78, and 0.61, respectively. After including FDA drug label data, precision, recall, and specificity improved to 0.67, 0.81, and 0.71, respectively. Eighteen of 23 (78%) postmarket label changes were identified correctly. TAE analysis shows promise as a method to predict AEs at the time of drug approval.
| S-EPMC6310867 | biostudies-other
Project description:BackgroundAdalimumab has been approved by the U.S. Food and Drug Administration (FDA) for the treatment of adult rheumatoid arthritis (RA), and subsequently approved for pediatric treatment of various autoimmune diseases in children of different ages. Due to genetic differences between children and adults in terms of physiology and immunity, there is a need to explore the safety of adalimumab in children in the real world. The aim of this study is to identify potential adverse event (AE) signals associated with the use of adalimumab in pediatric patients (<18 years old) using data from the FDA Adverse Event Reporting System (FAERS).MethodsAEs associated with adalimumab in pediatric patients reported in the FAERS database from the first quarter (Q1) of 2017 to the third quarter (Q3) of 2022 were systematically gathered. Reporting odds ratio (ROR), the proportional reporting ratio (PRR), the information component (IC), and the empirical Bayes geometric mean (EBGM) were used to assess the relationship between adalimumab and AEs in children.ResultsOut of 8,363,304 reports collected from the FAERS database during the study period, 3819 reports on children on adalimumab were identified. Adalimumab-related AEs reports were concentrated on 10 toxicity areas and a total of 202 positive signals were detected, of which injection site papule (ROR = 261.97) and intestinal fistula (ROR = 122.09) had the strongest signals. Unexpected significant AEs, including intestinal obstruction, immunodeficiency, abdominal abscess, and Takayasu's arteritis might also occur. In comparison with patients of all ages in the same time window, the median onset time of children was shorter (99 vs. 149 days). Most of the AE cases occurred in children within the first 1 (1.71%), 2 (8.12%), and 3 months (8.39%) and had early failure types after adalimumab initiation. Methotrexate, folic acid, prednisone, azathioprine, and mesalamine were the top five drugs used concomitantly for adalimumab-associated AEs.ConclusionsWhen adalimumab is used in children, especially in the first 3 months of treatment, in addition to the AEs recorded in the drug package insert, close attention should be paid to the new potential AEs off-label to ensure the safety of adalimumab in children.
| S-EPMC11357133 | biostudies-literature
Project description:BACKGROUND:To describe and analyze the patterns of adverse events associated with dipeptidyl peptidase-4 inhibitors (DPP-4is) (sitagliptin, saxagliptin, linagliptin, vildagliptin, and alogliptin) from the FDA Adverse Event Reporting System (FAERS) and to highlight areas of safety concerns. METHODS:Adverse events spontaneously submitted to the FAERS between 2004 Q1 to 2019 Q2 were included. The online tool OpenVigil 2.1 was used to query the database. The research relied on definitions of preferred terms (PTs) specified by the Medical Dictionary for Regulatory Activities (MedDRA) and the standardized MedDRA Queries (SMQ). The reporting odds ratio (ROR), with 95% confidence intervals (CIs) was calculated for disproportionality analysis. RESULTS:Over 16?years, a total of 9706 adverse event reports were identified. Alogliptin was excluded from further analysis due to insufficient sample size. Compared with the non-insulin antidiabetic drugs, the four DPP-4is were all disproportionately associated with four SMQs: "gastrointestinal nonspecific inflammation and dysfunctional conditions," "hypersensitivity," "severe cutaneous adverse reactions," and "noninfectious diarrhoea". As for PT level analyses, DPP-4is are associated with higher reporting of the gastrointestinal tract, pancreas, malignancies, infection, musculoskeletal disorders, general disorders, hypersensitivity, and skin AEs. CONCLUSIONS:Data mining of the FAERS is useful for examining DPP-4 inhibitors-associated adverse events. The findings of the present study are compatible with clinical experience, and it provides valuable information to decision-makers and healthcare providers in clinical practice.
| S-EPMC7493367 | biostudies-literature
Project description:ObjectivesPaclitaxel is a highly effective antitumor agent with notable adverse events, including hypersensitivity reactions, peripheral neuropathy, arthralgia, myalgias, and neutropenia. Solvent-based paclitaxel causes severe allergic, hypersensitivity, and anaphylactic reactions. Nanoparticle albumin-bound paclitaxel was recently developed and provides an advantage over solvent-based paclitaxel in avoiding solvent/surfactant-related adverse events. The aim of this study was to assess the adverse event profiles of solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel formulations using data from the spontaneous adverse event reporting system of the US Food and Drug Administration Adverse Event Reporting System database.MethodsThis study relied on Medical Dictionary for Regulatory Activities preferred terms and standardized queries, and calculated the reporting ratio and reporting odds ratios of paclitaxel formulations.ResultsOf 8,867,135 reports recorded in the US Food and Drug Administration Adverse Event Reporting System database from January 2004 to December 2016, 3469 and 4447 adverse events corresponded to solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel, respectively. Reporting odds ratios (95% confidence interval) for anaphylactic reaction (standardized MedDRA query code: 20000021) associated with the use of solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel were 1.69 (1.56-1.84) and 0.75 (0.68-0.83), respectively. Reporting odds ratio signal for anaphylactic reaction was not detected for nanoparticle albumin-bound paclitaxel. Reporting odds ratios (95% confidence interval) for acute renal failure (standardized MedDRA query code: 20000003) associated with the use of solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel were 0.75 (0.58-0.98) and 1.60 (1.37-1.89), respectively.ConclusionThis is the first study to evaluate the adverse event profile of nanoparticle albumin-bound paclitaxel using US Food and Drug Administration Adverse Event Reporting System data. Considering that the US Food and Drug Administration Adverse Event Reporting System database does not allow to infer causality or risk ranking, the different reporting frequencies of anaphylactic reaction and acute renal failure between solvent-based paclitaxel and nanoparticle albumin-bound paclitaxel must be further investigated via analytical observational research.
| S-EPMC6413422 | biostudies-literature
Project description:Direct oral anticoagulants (DOACs) are used in anticoagulant therapy. The purpose of this study was to evaluate the association of DOAC-induced gastrointestinal (GI) and nervous system hemorrhage using the FDA's Adverse Event Reporting System (FAERS) database and the Japanese Adverse Drug Event Report (JADER) database. We identified and analyzed the reports of hemorrhagic reactions between 2004 and 2016 from the FAERS and JADER databases, and calculated the adjusted reported odds ratio (ROR) using the multiple logistic regression method. Additionally, we used the time-to-onset analysis. In the FAERS database, the adjusted ROR of apixaban, rivaroxaban, and dabigatran for GI hemorrhage was 6.79 (5.84-7.91), 19.58 (18.85-20.34), and 14.51 (13.58-15.51), respectively. In the JADER database, the adjusted ROR of apixaban, rivaroxaban, edoxaban, and dabigatran for GI hemorrhage was 11.80 (9.50-14.64), 11.03 (9.18-13.26), 10.17 (6.95-14.88), and 9.85 (7.23-13.42), respectively. We found that the association of GI hemorrhage with DOACs was affected by sex (female). Additionally, 30% of GI hemorrhage was observed after 30 days. Hemorrhagic reactions of both GI and nervous systems were observed in both the spontaneous reporting system databases. We recommend that female patients who experience symptoms related to GI hemorrhage should be closely monitored and advised to adhere to an appropriate care plan. Additionally, our results show that patients should be closely monitored for hemorrhage even after a month.
| S-EPMC6775265 | biostudies-literature
Project description:Immune checkpoint inhibition represents an important therapeutic option for advanced melanoma patients. Results from clinical studies have shown that treatment with the PD-1 inhibitors Pembrolizumab and Nivolumab provides improved response and survival rates. Moreover, combining Nivolumab with the CTLA-4 inhibitor Ipilimumab is superior to the respective monotherapies. However, use of these immunotherapies frequently associated with, sometimes life-threatening, immune-related adverse events. Thus, more evidence-based studies are required to characterize the underlying mechanisms, towards more effective clinical management and treatment monitoring. Our study examines two sets of public adverse event data coming from FAERS and VigiBase, each with more than two thousand melanoma patients treated with Pembrolizumab. Standard disproportionality metrics are utilized to characterize the safety of Pembrolizumab and its reaction profile is compared to those of the widely used Ipilimumab and Nivolumab based on melanoma cases that report only one of them. Our results confirm known toxicological considerations for their related and distinct side-effect profiles and highlight specific immune-related adverse reactions. Our retrospective computational analysis includes more patients than examined in other studies and relies on evidence coming from public pharmacovigilance data that contain safety reports from clinical and controlled studies as well as reports of suspected adverse events coming from real-world post-marketing setting. Despite these informative insights, more prospective studies are necessary to fully characterize the efficacy of these agents.
| S-EPMC7226447 | biostudies-literature
Project description: Not available
| S-EPMC10978795 | biostudies-literature
Project description:Identification of adverse drug reactions (ADRs) during the post-marketing phase is one of the most important goals of drug safety surveillance. Spontaneous reporting systems (SRS) data, which are the mainstay of traditional drug safety surveillance, are used for hypothesis generation and to validate the newer approaches. The publicly available US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) data requires substantial curation before they can be used appropriately, and applying different strategies for data cleaning and normalization can have material impact on analysis results. We provide a curated and standardized version of FAERS removing duplicate case records, applying standardized vocabularies with drug names mapped to RxNorm concepts and outcomes mapped to SNOMED-CT concepts, and pre-computed summary statistics about drug-outcome relationships for general consumption. This publicly available resource, along with the source code, will accelerate drug safety research by reducing the amount of time spent performing data management on the source FAERS reports, improving the quality of the underlying data, and enabling standardized analyses using common vocabularies.
| S-EPMC4872271 | biostudies-literature
Project description:ObjectiveTo develop and evaluate a text mining system for extracting key clinical features from vaccine adverse event reporting system (VAERS) narratives to aid in the automated review of adverse event reports.DesignBased upon clinical significance to VAERS reviewing physicians, we defined the primary (diagnosis and cause of death) and secondary features (eg, symptoms) for extraction. We built a novel vaccine adverse event text mining (VaeTM) system based on a semantic text mining strategy. The performance of VaeTM was evaluated using a total of 300 VAERS reports in three sequential evaluations of 100 reports each. Moreover, we evaluated the VaeTM contribution to case classification; an information retrieval-based approach was used for the identification of anaphylaxis cases in a set of reports and was compared with two other methods: a dedicated text classifier and an online tool.MeasurementsThe performance metrics of VaeTM were text mining metrics: recall, precision and F-measure. We also conducted a qualitative difference analysis and calculated sensitivity and specificity for classification of anaphylaxis cases based on the above three approaches.ResultsVaeTM performed best in extracting diagnosis, second level diagnosis, drug, vaccine, and lot number features (lenient F-measure in the third evaluation: 0.897, 0.817, 0.858, 0.874, and 0.914, respectively). In terms of case classification, high sensitivity was achieved (83.1%); this was equal and better compared to the text classifier (83.1%) and the online tool (40.7%), respectively.ConclusionOur VaeTM implementation of a semantic text mining strategy shows promise in providing accurate and efficient extraction of key features from VAERS narratives.
| S-EPMC3534466 | biostudies-literature
Project description:ObjectiveAutomated analysis of vaccine postmarketing surveillance narrative reports is important to understand the progression of rare but severe vaccine adverse events (AEs). This study implemented and evaluated state-of-the-art deep learning algorithms for named entity recognition to extract nervous system disorder-related events from vaccine safety reports.Materials and methodsWe collected Guillain-Barré syndrome (GBS) related influenza vaccine safety reports from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016. VAERS reports were selected and manually annotated with major entities related to nervous system disorders, including, investigation, nervous_AE, other_AE, procedure, social_circumstance, and temporal_expression. A variety of conventional machine learning and deep learning algorithms were then evaluated for the extraction of the above entities. We further pretrained domain-specific BERT (Bidirectional Encoder Representations from Transformers) using VAERS reports (VAERS BERT) and compared its performance with existing models.Results and conclusionsNinety-one VAERS reports were annotated, resulting in 2512 entities. The corpus was made publicly available to promote community efforts on vaccine AEs identification. Deep learning-based methods (eg, bi-long short-term memory and BERT models) outperformed conventional machine learning-based methods (ie, conditional random fields with extensive features). The BioBERT large model achieved the highest exact match F-1 scores on nervous_AE, procedure, social_circumstance, and temporal_expression; while VAERS BERT large models achieved the highest exact match F-1 scores on investigation and other_AE. An ensemble of these 2 models achieved the highest exact match microaveraged F-1 score at 0.6802 and the second highest lenient match microaveraged F-1 score at 0.8078 among peer models.
| S-EPMC8279785 | biostudies-literature