3D pharmacophoric similarity improves multi adverse drug event identification in pharmacovigilance.
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ABSTRACT:
SUBMITTER: Vilar S
PROVIDER: S-EPMC4351525 | biostudies-other | 2015 Mar
REPOSITORIES: biostudies-other
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Project description:BackgroundAlthough there have been increasing reports of intentional gabapentin misuse, epidemiological evidence for the phenomenon is limited. The purpose of this study was to determine whether there are pharmacovigilance abuse signals for gabapentin.MethodsUsing FDA Adverse Events Reporting System reports from January 1, 2005 to December 31, 2015, we calculated pharmacovigilance signal measures (i.e., reporting odds ratio, proportional reporting ratio, information component, and empirical Bayes geometric mean) for abuse-related adverse event (AR-AE)-gabapentin pairs. Loglinear modeling assessed the frequency of concurrent reporting of abuse-related and abuse-specific AEs (AS-AEs) associated with gabapentin. Findings were compared to a positive (pregabalin) and negative (duloxetine) control.ResultsFrom 2005-2015 there were 5,951,229 unique AE reports submitted to the FDA including 99,977 for gabapentin, 73,977 for duloxetine, and 97,813 for pregabalin. Significant drug-AR-AE pair signals involving gabapentin included: drug abuser, multiple drug overdose, and substance-induced psychotic disorder. Significant drug AR-AE signals involving gabapentin and pregabalin, but not duloxetine, were: ataxia, dependence, drug abuse, increased drug tolerance, and overdose. Compared to duloxetine, gabapentin had significantly greater odds of a co-report for an AS-AE with drug withdrawal syndrome (OR: 6.55), auditory hallucinations (OR: 4.57), delusions (OR: 2.36), euphoric mood (OR: 5.45), ataxia (OR: 2.85), drug abuser (OR: 3.01), aggression (OR: 1.98), psychotic disorder (OR: 1.96), and feeling abnormal (OR: 1.31).ConclusionsWe identified abuse-related signals for gabapentin and highlighted several CNS effects that may be associated with its abuse. Gabapentin prescribers should be aware of the drug's abuse liability and effects that may accompany its use.
| S-EPMC7762328 | biostudies-literature
Project description:AimsSeveral pharmacological treatments are recommended for Raynaud's phenomenon (RP) secondary to systemic sclerosis, but they only have modest efficacy. A way to efficiently identify new drugs is drug repurposing, which can be based on signature matching. The signature could be derived from chemical structures, pharmacological affinity or adverse event profiles. We propose to use the World Health Organization (WHO) pharmacovigilance database to generate repositioning hypotheses for treatments of RP through adverse event signature matching.MethodsWe first screened all drugs associated with at least 1 case of erythromelalgia, an adverse effect opposite to RP. In parallel, to define the adverse event signature of drugs recommended in secondary RP from the WHO pharmacovigilance database, we selected the 14 most representative adverse drug reactions (ADRs). Lastly, we performed a hierarchical cluster analysis to identify drugs with similar ADR signature to vasodilatory drugs used in RP.ResultsIn total, 179 drugs were associated with erythromelalgia; they were related to 860 334 adverse events representative of RP drugs in the WHO pharmacovigilance database. Hierarchical cluster analysis allowed identification of 6 clusters. The most stable cluster contained 7 drugs, among which 5 are recommended in secondary RP, or pertain to the same drug class: epoprostenol, nifedipine, nicardipine, lacidipine and israpidine. The 2 remaining drugs were alemtuzumab and fumaric acid.ConclusionOur ADR signature matching approach suggests that alemtuzumab and fumaric acid could be effective treatments of secondary RP. The latter is currently being investigated as a treatment of pulmonary hypertension in systemic sclerosis.
| S-EPMC7576623 | biostudies-literature
Project description:Objectives: As fall events and injuries have become a growing public health problem in older patients and the causes of falls are complex, there is an emerging need to identify the risk of drug-induced falls. Methods: To mine and analyze the risk signals of drug-induced falls in older patients to provide evidence for drug safety. The FDA Adverse Event Reporting System was used to collect drug-induced fall events among older patients. Disproportionality analyses of odds ratio (ROR) and proportional reported ratio were performed to detect the adverse effects signal. Results: A total of 208,849 reports (34,840 fall events and 1,898 drugs) were considered. The average age of the included patients was 76.95 ± 7.60 years, and there were more females (64.47%) than males. A total of 258 drugs with positive signals were detected to be associated with drug-induced fall incidence in older patients. The neurological drugs (104, 44.1%) with the largest number of positive detected signals mainly included antipsychotics, antidepressants, antiparkinsonian drugs, central nervous system drugs, anticonvulsants and hypnotic sedatives. Other systems mainly included the circulatory system (25, 10.6%), digestive system (15, 6.4%), and motor system (12, 5.1%). Conclusion: Many drugs were associated with a high risk of falls in older patients. The drug is one of the critical and preventable factors for fall control, and the risk level of drug-induced falls should be considered to optimize drug therapy in clinical practice.
| S-EPMC9746618 | biostudies-literature
Project description:IntroductionThe US FDA is interested in a tool that would enable pharmacovigilance safety evaluators to automate the identification of adverse drug events (ADEs) mentioned in FDA prescribing information. The MITRE Corporation (MITRE) and the FDA organized a shared task-Adverse Drug Event Evaluation (ADE Eval)-to determine whether the performance of algorithms currently used for natural language processing (NLP) might be good enough for real-world use.ObjectiveADE Eval was conducted to evaluate a range of NLP techniques for identifying ADEs mentioned in publicly available FDA-approved drug labels (package inserts). It was designed specifically to reflect pharmacovigilance practices within the FDA and model possible pharmacovigilance use cases.MethodsPharmacovigilance-specific annotation guidelines and annotated corpora were created. Two metrics modeled the experiences of FDA safety evaluators: one measured the ability of an algorithm to identify correct Medical Dictionary for Regulatory Activities (MedDRA®) terms for the text from the annotated corpora, and the other assessed the quality of evidence extracted from the corpora to support the selected MedDRA® term by measuring the portion of annotated text an algorithm correctly identified. A third metric assessed the cost of correcting system output for subsequent training (averaged, weighted F1-measure for mention finding).ResultsIn total, 13 teams submitted 23 runs: the top MedDRA® coding F1-measure was 0.79, the top quality score was 0.96, and the top mention-finding F1-measure was 0.89.ConclusionWhile NLP techniques do not perform at levels that would allow them to be used without intervention, it is now worthwhile exploring making NLP outputs available in human pharmacovigilance workflows.
| S-EPMC7813736 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) is associated with significant increases in short- and long-term morbidity and mortality. Drug-induced AKI is a major concern in the present healthcare system. Our spontaneous reporting system (SRS) analysis assessed links between AKI, along with patients' age, as healthcare-associated risks and administered anti-infectives. We also generated anti-infective-related AKI-onset profiles.MethodWe calculated reporting odds ratios (RORs) for reports of anti-infective-related AKI (per Medical Dictionary for Regulatory Activities) in the Japanese Adverse Drug Event Report database and evaluated the effect of anti-infective combination therapy. The background factors of cases with anti-infective monotherapy and combination therapy (≥ 2 anti-infectives) were matched using propensity score. We evaluated time-to-onset data and hazard types using the Weibull parameter.ResultsAmong 534,688 reports (submission period: April 2004-June 2018), there were 21,727 AKI events. The reported number of AKI associated with glycopeptide antibacterials, fluoroquinolones, third-generation cephalosporins, triazole derivatives, and carbapenems were 596, 494, 341, 315, and 313, respectively. Crude RORs of anti-infective-related AKI increased among older patients and were higher in anti-infective combination therapies [anti-infectives, ≥ 2; ROR, 1.94 (1.80-2.09)] than in monotherapies [ROR, 1.29 (1.22-1.36)]. After propensity score matching, the adjusted RORs of anti-infective monotherapy and combination therapy (≥ 2 anti-infectives) were 0.67 (0.58-0.77) and 1.49 (1.29-1.71), respectively. Moreover, 48.1% of AKI occurred within 5 days (median, 5.0 days) of anti-infective therapy initiation.ConclusionRORs derived from our new SRS analysis indicate potential AKI risks and number of administered anti-infectives.
| S-EPMC8404262 | biostudies-literature
Project description:BackgroundAdverse drug events (ADEs) detection and assessment is at the center of pharmacovigilance. Data mining of systems, such as FDA's Adverse Event Reporting System (AERS) and more recently, Electronic Health Records (EHRs), can aid in the automatic detection and analysis of ADEs. Although different data mining approaches have been shown to be valuable, it is still crucial to improve the quality of the generated signals.ObjectiveTo leverage structural similarity by developing molecular fingerprint-based models (MFBMs) to strengthen ADE signals generated from EHR data.MethodsA reference standard of drugs known to be causally associated with the adverse event pancreatitis was used to create a MFBM. Electronic Health Records (EHRs) from the New York Presbyterian Hospital were mined to generate structured data. Disproportionality Analysis (DPA) was applied to the data, and 278 possible signals related to the ADE pancreatitis were detected. Candidate drugs associated with these signals were then assessed using the MFBM to find the most promising candidates based on structural similarity.ResultsThe use of MFBM as a means to strengthen or prioritize signals generated from the EHR significantly improved the detection accuracy of ADEs related to pancreatitis. MFBM also highlights the etiology of the ADE by identifying structurally similar drugs, which could follow a similar mechanism of action.ConclusionThe method proposed in this paper provides evidence of being a promising adjunct to existing automated ADE detection and analysis approaches.
| S-EPMC3404072 | biostudies-literature
Project description:Background: Cardiovascular safety concerns for major cardiovascular events (MACE) were raised during the clinical trials of romosozumab. We aimed to evaluate the cardiovascular safety profile of romosozumab in a large pharmacovigilance database. Methods: All cases reported between January 2019 and December 2020 where romosozumab was reported were extracted from the Food and Drug Administration Adverse Event Reporting System (FAERS). The outcome of interest was MACE (myocardial infarction (MI), stroke, or cardiovascular death). A disproportionality analysis was conducted by estimating the reporting odds ratios (RORs) and 95% confidence intervals. Disproportionality analyses were stratified by sex and reporting region (US, Japan, other). Results: Of the 1995 eligible cases with romosozumab, the majority (N = 1188; 59.5%) originated from Japan. Overall, 206 suspected MACE reports were identified, of which the majority (n = 164; 13.8%) were from Japan, and 41 (5.2%) were from the United States (US). Among Japanese reports, patients were older and more frequently male than reports from the US. Similarly, cases with a reported MACE were older and had higher reports of cardioprotective drugs than those without cardiovascular events. Elevated reports for MACE (ROR 4.07, 95% CI: 2.39-6.93) was identified overall, which was primarily driven by the significant disproportionality measures in the Japanese reports. Conclusions: The current pharmacovigilance study identified a potential signal for elevated MACE, particularly in Japan. The results support the current safety warnings from the Food and Drug Administration (FDA) and the European Medicines Agency (EMA) to avoid use in high-risk patients.
| S-EPMC8070537 | biostudies-literature
Project description:Our objective was to develop a model for measuring re-identification risk that more closely mimics the behaviour of an adversary by accounting for repeated attempts at matching and verification of matches, and apply it to evaluate the risk of re-identification for Canada's post-marketing adverse drug event database (ADE).Re-identification is only demonstrably plausible for deaths in ADE. A matching experiment between ADE records and virtual obituaries constructed from Statistics Canada vital statistics was simulated. A new re-identification risk is considered, it assumes that after gathering all the potential matches for a patient record (all records in the obituaries that are potential matches for an ADE record), an adversary tries to verify these potential matches. Two adversary scenarios were considered: (a) a mildly motivated adversary who will stop after one verification attempt, and (b) a highly motivated adversary who will attempt to verify all the potential matches and is only limited by practical or financial considerations.The mean percentage of records in ADE that had a high probability of being re-identified was computed.Under scenario (a), the risk of re-identification from disclosing the province, age at death, gender, and exact date of the report is quite high, but the removal of province brings down the risk significantly. By only generalizing the date of reporting to month and year and including all other variables, the risk is always low. All ADE records have a high risk of re-identification under scenario (b), but the plausibility of that scenario is limited because of the financial and practical deterrent even for highly motivated adversaries.It is possible to disclose Canada's adverse drug event database while ensuring that plausible re-identification risks are acceptably low. Our new re-identification risk model is suitable for such risk assessments.
| S-EPMC4137558 | biostudies-literature
Project description:BackgroundMedical articles covering adverse drug events (ADEs) are systematically reported by pharmaceutical companies for drug safety information purposes. Although policies governing reporting to regulatory bodies vary among countries and regions, all medical article reporting may be categorized as precision or recall based. Recall-based reporting, which is implemented in Japan, requires the reporting of any possible ADE. Therefore, recall-based reporting can introduce numerous false negatives or substantial amounts of noise, a problem that is difficult to address using limited manual labor.ObjectiveOur aim was to develop an automated system that could identify ADE-related medical articles, support recall-based reporting, and alleviate manual labor in Japanese pharmaceutical companies.MethodsUsing medical articles as input, our system based on natural language processing applies document-level classification to extract articles containing ADEs (replacing manual labor in the first screening) and sentence-level classification to extract sentences within those articles that imply ADEs (thus supporting experts in the second screening). We used 509 Japanese medical articles annotated by a medical engineer to evaluate the performance of the proposed system.ResultsDocument-level classification yielded an F1 of 0.903. Sentence-level classification yielded an F1 of 0.413. These were averages of fivefold cross-validations.ConclusionsA simple automated system may alleviate the manual labor involved in screening drug safety-related medical articles in pharmaceutical companies. After improving the accuracy of the sentence-level classification by considering a wider context, we intend to apply this system toward real-world postmarketing surveillance.
| S-EPMC7732716 | 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