Development of an adverse drug event network to predict drug toxicity.
Ontology highlight
ABSTRACT:
SUBMITTER: Wu Q
PROVIDER: S-EPMC8320634 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Severe cutaneous adverse reactions (SCARs), such as Stevens-Johnson syndrome/toxic epidermal necrolysis and drug-induced hypersensitivity syndrome, are rare and occasionally fatal. However, it is difficult to detect SCARs at the drug development stage, necessitating a new approach for prediction. Therefore, in this study, using the chemical structure information of SCAR-causative drugs from the Japanese Adverse Drug Event Report (JADER) database, we tried to develop a predictive classification model of SCAR through deep learning. In the JADER database from 2004 to 2017, we defined 185 SCAR-positive drugs and 195 SCAR-negative drugs using proportional reporting ratios as the signal detection method, and the total number of reports. These SCAR-positive and SCAR-negative drugs were randomly divided into the training dataset for model construction and the test dataset for evaluation. The model performance was evaluated in the independent test dataset inside the applicability domain (AD), which is the chemical space for reliable prediction results. Using the deep learning model with molecular descriptors as the drug structure information, the area under the curve was 0.76 for the 148 drugs of the test dataset inside the AD. The method developed in the present study allows for utilizing the JADER database for SCAR classification, with potential to improve screening efficiency in the development of new drugs. This method may also help to noninvasively identify the causative drug, and help assess the causality between drugs and SCARs in postmarketing surveillance.
| S-EPMC7993315 | biostudies-literature
Project description:The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs.The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs.We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs.The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions.To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.
| S-EPMC5741828 | biostudies-literature
Project description:Combined estrogen-progestin preparations (CEPs) are associated with thromboembolic (TE) side effects. The aim of this study was to evaluate the incidence of TE using the Japanese Adverse Drug Event Report (JADER) database. Adverse events recorded from April 2004 to November 2014 in the JADER database were obtained from the Pharmaceuticals and Medical Devices Agency (PMDA) website (www.pmda.go.jp). We calculated the reporting odds ratios (RORs) of suspected CEPs, analyzed the time-to-onset profile, and assessed the hazard type using Weibull shape parameter (WSP). Furthermore, we used the applied association rule mining technique to discover undetected relationships such as the possible risk factors. The total number of reported cases in the JADER contained was 338,224. The RORs (95% confidential interval, CI) of drospirenone combined with ethinyl estradiol (EE, Dro-EE), norethisterone with EE (Ne-EE), levonorgestrel with EE (Lev-EE), desogestrel with EE (Des-EE), and norgestrel with EE (Nor-EE) were 56.2 (44.3-71.4), 29.1 (23.5-35.9), 42.9 (32.3-57.0), 44.7 (32.7-61.1), and 38.6 (26.3-56.7), respectively. The medians (25%-75%) of the time-to-onset of Dro-EE, Ne-EE, Lev-EE, Des-EE, and Nor-EE were 150.0 (75.3-314.0), 128.0 (27.0-279.0), 204.0 (44.0-660.0), 142.0 (41.3-344.0), and 16.5 (8.8-32.0) days, respectively. The 95% CIs of the WSP-β for Ne-EE, Lev-EE, and Nor-EE were lower and excluded 1. Association rule mining indicated that patients with anemia had a potential risk of developing a TE when using CEPs. Our results suggest that it is important to monitor patients administered CEP for TE. Careful observation is recommended, especially for those using Nor-EE, and this information may be useful for efficient therapeutic planning.
| S-EPMC5521832 | biostudies-other
Project description:BACKGROUND:A summary measure that reflects the global toxicity burden of a treatment is essential for comparing therapies. Current toxicity summaries are ad hoc and do not distinguish among the severities and types of toxicities. Here a clinically feasible method for estimating the toxicity burden, based on a prospective evaluation of the toxicity profile of a randomized clinical trial of 746 prostate cancer patients conducted by SWOG, is proposed. METHODS:For 308 patients who experienced severe toxicities, 2 physicians randomly selected from 14 physicians evaluated each toxicity profile and assigned a visual analogue scale score (0-10) based on their impression of the global burden of toxicities. With mixed-effects models, severity scores and a 10-point toxicity burden score (TBS) were derived from 27 predictors accounting for severe (grade 3) and life-threatening (grade 4) toxicities for each organ class of the Common Terminology Criteria for Adverse Events. RESULTS:For most organ classes, grade 3 toxicities had a TBS of 4.14 (95% confidence interval [CI], 3.65-4.63), but infections, cardiovascular events, and pulmonary events had a higher TBS with differences of 0.87 (95% CI, 0.53-1.21), 0.88 (95% CI, 0.51-1.25), and 0.73 (95% CI, 0.22-1.24), respectively. Moreover, most grade 4 events had a higher TBS than grade 3 events, except for hemorrhaging, pain, metabolic events, and musculoskeletal events. The intrarater and interrater correlations were 0.91 and 0.59, respectively. CONCLUSIONS:The burden of toxicity grades differs with toxicity types. A TBS provides a toxicity burden summary that incorporates physicians' perspectives and differentiates between severe and life-threatening toxicities and organ classes. Cancer 2018;124:858-64. © 2017 American Cancer Society.
| S-EPMC5801103 | biostudies-literature
Project description:Adverse events are a common and for the most part unavoidable consequence of therapeutic intervention. Nevertheless, available tomes of such data now provide us with an invaluable opportunity to study the relationship between human phenotype and drug-induced protein perturbations within a patient system. Deciphering the molecular basis of such adverse responses is not only paramount to the development of safer drugs but also presents a unique opportunity to dissect disease systems in search of novel response biomarkers, drug targets, and efficacious combination therapies. Inspired by the potential applications of this approach, we first examined adverse event circumstances reported in FAERS and then performed a molecular level interrogation of cancer patient adverse events to investigate the prevalence of drug-drug interactions in the context of patient responses. We discuss avoidable and/or preventable cases and how molecular analytics can help optimize therapeutic use of co-medications. While up to one out of three adverse events in this dataset might be explicable by iatrogenic, patient, and product/device related factors, almost half of the patients in FAERS received multiple drugs and one in four may have experienced effects attributable to drug interactions.
| S-EPMC6473808 | biostudies-other
Project description:The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) is an important source for detecting adverse drug event (ADE) signals. In this article, we propose a three-component mixture model (3CMM) for FAERS signal detection. In 3CMM, a drug-ADE pair is assumed to have either a zero relative risk (RR), or a background RR (mean RR = 1), or an increased RR (mean RR >1). By clearly defining the second component (mean RR = 1) as the null distribution, 3CMM estimates local false discovery rates (FDRs) for ADE signals under the empirical Bayes framework. Compared with existing approaches, the local FDR's top signals have noninferior or better sensitivities to detect true signals in both FAERS analysis and simulation studies. Additionally, we identify that the top signals of different approaches have different patterns, and they are complementary to each other.
| S-EPMC6118321 | biostudies-literature
Project description:AimAdverse drug events (ADEs) are harmful and unintended consequences of medications. Their reporting is essential for drug safety monitoring and research, but it has not been standardized internationally. Our aim was to synthesize information about the type and variety of data collected within ADE reporting systems.MethodsWe developed a systematic search strategy, applied it to four electronic databases, and completed an electronic grey literature search. Two authors reviewed titles and abstracts, and all eligible full-texts. We extracted data using a standardized form, and discussed disagreements until reaching consensus. We synthesized data by collapsing data elements, eliminating duplicate fields and identifying relationships between reporting concepts and data fields using visual analysis software.ResultsWe identified 108 ADE reporting systems containing 1782 unique data fields. We mapped them to 33 reporting concepts describing patient information, the ADE, concomitant and suspect drugs, and the reporter. While reporting concepts were fairly consistent, we found variability in data fields and corresponding response options. Few systems clarified the terminology used, and many used multiple drug and disease dictionaries such as the Medical Dictionary for Regulatory Activities (MedDRA).ConclusionWe found substantial variability in the data fields used to report ADEs, limiting the comparability of ADE data collected using different reporting systems, and undermining efforts to aggregate data across cohorts. The development of a common standardized data set that can be evaluated with regard to data quality, comparability and reporting rates is likely to optimize ADE data and drug safety surveillance.
| S-EPMC4917803 | biostudies-literature
Project description:Drugs are designed for therapy, but medication-related adverse events are common, and risk/benefit analysis is critical for determining clinical use. Rosiglitazone, an efficacious antidiabetic drug, is associated with increased myocardial infarctions (MIs), thus limiting its usage. Because diabetic patients are often prescribed multiple drugs, we searched for usage of a second drug ("drug B") in the Food and Drug Administration's Adverse Event Reporting System (FAERS) that could mitigate the risk of rosiglitazone ("drug A")-associated MI. In FAERS, rosiglitazone usage is associated with increased occurrence of MI, but its combination with exenatide significantly reduces rosiglitazone-associated MI. Clinical data from the Mount Sinai Data Warehouse support the observations from FAERS. Analysis for confounding factors using logistic regression showed that they were not responsible for the observed effect. Using cell biological networks, we predicted that the mitigating effect of exenatide on rosiglitazone-associated MI could occur through clotting regulation. Data we obtained from the db/db mouse model agreed with the network prediction. To determine whether polypharmacology could generally be a basis for adverse event mitigation, we analyzed the FAERS database for other drug combinations wherein drug B reduced serious adverse events reported with drug A usage such as anaphylactic shock and suicidality. This analysis revealed 19,133 combinations that could be further studied. We conclude that this type of crowdsourced approach of using databases like FAERS can help to identify drugs that could potentially be repurposed for mitigation of serious adverse events.
| S-EPMC3963511 | biostudies-literature
Project description:IntroductionSignal detection yields confirmed signals in only 2.1%, which imposes a heavy burden on the pharmacovigilance system in the European Union.ObjectivesWe aimed to develop a network theoretical metric to increase the confirmed signal ratio of individual case safety report (ICSR) networks.MethodsICSRs of five cardiovascular adverse events were requested from EudraVigilance. We developed Vigilace™, a web-based application to build network representation of ICSRs. Three network-based signal scores, which we termed NEWS (normalized edge weight for signals) scores, were calculated by normalizing the weight of each edge in the report-based weighted network by the weight of the same edge in topological weighted networks. Depending on the third node in topological network edges, we defined full-, adverse event-, and drug-type NEWS scores. Area under the receiver operating characteristic curves (AUROC) were analyzed to compare the reporting odds ratio (ROR) and NEWS scores.ResultsOverall, 72,475 ICSRs were accessed from EudraVigilance. Drug-type NEWS (NEWSD) score performed better (DeLong test, p-value <0.05) compared with the ROR in case of four adverse events: acute myocardial infarction (AUROC: 0.856 vs. 0.720), arrhythmia (0.657 vs. 0.614), pulmonary hypertension (0.861 vs. 0.720), and QT prolongation (0.830 vs. 0.749). Postural orthostatic tachycardia syndrome was excluded due to the lack of reference data.ConclusionThis is the first demonstration that report-based weighting normalized by topological weighting of co-reported drugs, which we termed as NEWSD score, can perform better compared with the ROR. An application was developed for ICSR network analysis that facilitates the calculation of this score.
| S-EPMC9561003 | biostudies-literature
Project description:Increasing evidence has shown that sex differences exist in Adverse Drug Events (ADEs). Identifying those sex differences in ADEs could reduce the experience of ADEs for patients and could be conducive to the development of personalized medicine. In this study, we analyzed a normalized US Food and Drug Administration Adverse Event Reporting System (FAERS). Chi-squared test was conducted to discover which treatment regimens or drugs had sex differences in adverse events. Moreover, reporting odds ratio (ROR) and P value were calculated to quantify the signals of sex differences for specific drug-event combinations. Logistic regression was applied to remove the confounding effect from the baseline sex difference of the events. We detected among 668 drugs of the most frequent 20 treatment regimens in the United States, 307 drugs have sex differences in ADEs. In addition, we identified 736 unique drug-event combinations with significant sex differences. After removing the confounding effect from the baseline sex difference of the events, there are 266 combinations remained. Drug labels or previous studies verified some of them while others warrant further investigation.
| S-EPMC4840306 | biostudies-literature