Signal detection methodologies to support effective safety management

Comparators in Pharmacovigilance: A Quasi-Quantification Bias Analysis

BACKGROUND AND OBJECTIVE

It is unclear which comparator is the most appropriate for bias reduction in disproportionality analyses based on spontaneous reports. We conducted a quasi-quantitative bias analysis using two well-studied drug-event combinations to assess how different comparators influence the directionality of bias in pharmacovigilance.

METHODS

We used the US Food and Drug Administration Adverse Event Reporting System focusing on two drug-event combinations with a propensity for stimulated reporting: rivaroxaban and hepatotoxicity, and canagliflozin and acute kidney injury. We assessed the directionality of three disproportionality analysis estimates (reporting odds ratio, proportional reporting ratio, information component) using one unrestricted comparator (full data) and two restricted comparators (active comparator, active comparator with class exclusion). Analyses were conducted within two calendar time periods, defined based on external events (approval of direct oral anticoagulants, Food and Drug Administration safety warning on acute kidney injury with sodium-glucose cotransporter 2 inhibitors) hypothesized to alter reporting rates.

RESULTS

There were no false-positive signals for rivaroxaban and hepatotoxicity irrespective of the comparator. Restricting to the initial post-approval period led to false-positive signals, with restricted comparators performing worse. There were false-positive signals for canagliflozin and acute kidney injury, with restricted comparators performing better. Restricting to the period before the Food and Drug Administration warning weakened the false-positive signal for canagliflozin and acute kidney injury across comparators.

CONCLUSIONS

We could not identify a consistent and predictable pattern to the directionality of disproportionality analysis estimates with specific comparators. Calendar time-based restrictions anchored on relevant external events had a considerable impact.

Considerations on the use of different comparators in pharmacovigilance: A methodological review

Pharmacovigilance studies based on spontaneous reporting systems use disproportionality analysis methods to identify drug-event combinations with higher-than-expected reporting. Enhanced reporting is deemed as a proxy for a detected signal and is used to generate drug safety hypotheses, which can then be tested in pharmacoepidemiologic studies or randomized controlled trials. Higher-than-expected reporting means that the reporting rate of a drug-event combination of interest is disproportionately higher than the rate in a specific comparator or reference set. Currently, it is unclear which comparator is the most appropriate for use in pharmacovigilance. Moreover, it is also unclear how the selection of a comparator may affect the directionality of the various reporting and other biases. This paper reviews commonly used comparators chosen for signal detection studies (active comparator, class-exclusion comparator, and full data reference set). We give an overview of the advantages and disadvantages of each method based on examples from the literature. We also touch upon the challenges related to the derivation of general recommendations for the selection of comparators when mining spontaneous reports for pharmacovigilance.

Myocarditis and Pericarditis Post-mRNA COVID-19 Vaccination: Insights from a Pharmacovigilance Perspective

Concerns remain regarding the rare cardiovascular adverse events, myocarditis and pericarditis (myo/pericarditis), particularly in younger individuals following mRNA COVID-19 vaccination. Our study aimed to comprehensively assess potential safety signals related to these cardiac events following the primary and booster doses, with a specific focus on younger populations, including children as young as 6 months of age. Using the Vaccine Adverse Events Reporting System (VAERS), the United States national passive surveillance system, we conducted a retrospective pharmacovigilance study analyzing spontaneous reports of myo/pericarditis. We employed both frequentist and Bayesian methods and conducted subgroup analyses by age, sex, and vaccine dose. We observed a higher reporting rate of myo/pericarditis following the primary vaccine series, particularly in males and mainly after the second dose. However, booster doses demonstrated a lower number of reported cases, with no significant signals detected after the fourth or fifth doses. In children and young adults, we observed notable age and sex differences in the reporting of myo/pericarditis cases. Males in the 12-17 and 18-24-year-old age groups had the highest number of cases, with significant signals for both males and females after the second dose. We also identified an increased reporting for a spectrum of cardiovascular symptoms such as chest pain and dyspnea, which increased with age, and were reported more frequently than myo/pericarditis. The present study identified signals of myo/pericarditis and related cardiovascular symptoms after mRNA COVID-19 vaccination, especially among children and adolescents. These findings underline the importance for continued vaccine surveillance and the need for further studies to confirm these results and to determine their clinical implications in public health decision-making, especially for younger populations.

Important Considerations for Signal Detection and Evaluation

Safety clinicians have a wealth of resources describing how to perform signal detection. Nevertheless, there are some nuances concerning approaches taken by regulatory authorities and statistical considerations that should be appreciated. New approaches, such as the FDA Medical Queries, illustrate the value of considering medical concepts over individual adverse events. One area which would benefit from further clarity is how safety signals may be evaluated for evidence of a causal relationship to the drug of interest. Just as such safety signals can take many forms, the types of tools and methods required to interrogate these signals are equally as diverse. An understanding of the complexity of this process can aid the safety reviewer in successfully characterizing the emerging safety profile of a drug during the pre-marketing phase of development.

Pharmacovigilance in low- and middle-income countries: A review with particular focus on Africa

Low- and middle-income countries (LMIC) face unique challenges with regard to the establishment of robust pharmacovigilance systems capable of generating data to inform healthcare policy and practice. These include the limited integration and reliability of pharmacovigilance systems across LMIC despite recent efforts to harmonize pharmacovigilance rules and regulations in several regional economic communities. There are particular challenges relating to the need to translate reporting tools into numerous local languages and the low numbers of healthcare providers relative to number of patients, with very short consultation times. Additional factors frequent in LMIC include high uptake of herbal and traditional medication, mostly by self-medication; disruptive political conflicts jeopardizing fragile systems; and little or no access to drug utilization data, which makes it difficult to reliably estimate the true risks of medicines use. Pharmacovigilance activities are hindered by the scarcity of well-trained personnel with little or no budgetary support from national governments; high turnover of pharmacovigilance staff whose training involves a substantial amount of resources; and little awareness of pharmacovigilance among healthcare workers, decision makers and consumers. Furthermore, little collaboration between public health programmes and national medicines regulatory authorities coupled with limited investment in pharmacovigilance activities, especially during mass drug administration for neglected tropical diseases and mass vaccinations, produces major challenges in establishing a culture where pharmacovigilance is systematically embedded. Very low spontaneous reporting rates with poor quality reports hinders robust signal detection analyses. This review summarises the specific challenges and areas of progress in pharmacovigilance in LMIC with special focus on the situation in Africa.

The potential antidepressant effect of antidiabetic agents: New insights from a pharmacovigilance study based on data from the reporting system databases FAERS and VigiBase

Background: Growing evidence supports a bidirectional association between diabetes and depression; promising but limited and conflicting data from human studies support the intriguing possibility that antidiabetic agents may be used to relieve effectively depressive symptoms in diabetic patients. We investigated the potential antidepressant effects of antidiabetic drugs in a high-scale population data from the two most important pharmacovigilance databases, i.e., the FDA Adverse Event Reporting System (FAERS) and the VigiBase. Material and methods: From the two primary cohorts of patients treated with antidepressants retrieved from FDA Adverse Event Reporting System and VigiBase we identified cases (depressed patients experiencing therapy failure) and non-cases (depressed patients experiencing any other adverse event). We then calculated the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Empirical Bayes Geometric Mean (EBGM), and Empirical Bayes Regression-Adjusted Mean (ERAM) for cases versus non-cases in relation with the concurrent exposure to at least one of the following antidiabetic agent: A10BA Biguanides; A10BB Sulfonylureas; A10BG Thiazolidinediones; A10BH DPP4-inhibitors; A10BJ GLP-1 analogues; A10BK SGLT2 inhibitors (i.e., those agents for which preliminary evidence from literature supports our pharmacological hypothesis). Results: For GLP-1 analogues, all the disproportionality scores showed values <1, i.e., statistically significant, in both analyses [from the FAERS: ROR confidence interval of 0.546 (0.450-0.662); PRR (p-value) of 0.596 (0.000); EBGM (CI) of 0.488 (0.407-0.582); ERAM (CI) of 0.480 (0.398-0.569) and VigiBase: ROR (CI) of 0.717 (0.559-0.921); PRR (p-value) of 0.745 (0.033); EBGM (CI) of 0.586 (0.464-0.733); ERAM of (CI): 0.515 (0.403-0.639)]. Alongside GLP-1 analogues, DPP-4 Inhibitors and Sulfonylureas showed the greatest potential protective effect. With regard to specific antidiabetic agents, liraglutide and gliclazide were associated with a statistically significant decrease in all disproportionality scores, in both analyses. Conclusion: The findings of this study provide encouraging results, albeit preliminary, supporting the need for further clinical research for investigating repurposing of antidiabetic drugs for neuropsychiatric disorders.

A Review of the Food and Drug Administration Adverse Event Reporting System for Tramadol-Related Hypoglycemia

Background: Hypoglycemia is a rare adverse effect of tramadol that is described in the medical literature and package insert. Objective: The purpose of this study was to review reports of tramadol and hypoglycemia in the Food and Drug Administration Adverse Event Reporting System (FAERS) database to determine a potential association. Methods: Disproportionality analysis with Bayesian correction was used to compare tramadol and hypoglycemia with other medications in FAERS. The results were considered significant if the fifth percentile of the Empirical Bayesian geometric mean distribution (EB05) >2. Logistic regression odds ratios was used to determine if age, diabetes medications, and renal insufficiency masked the disproportionality of hypoglycemia, with the fifth percentile of the logistic regression odds ratio (LR05) >2 indicating a potential signal. The Interaction Signal Score (INTSS) was computed to determine the influence of predisposing risk factors on the signal. Results: A total of 605 cases of tramadol-associated hypoglycemia were reported, but our results were not significant (EB05: 1.590). Tramadol-associated hypoglycemia was significant in patients who did not take diabetes medications (EB05: 2.256; LR05: 2.2104). Renal insufficiency was not found to increase the risk of tramadol-associated hypoglycemia (INTSS: 0.865). There was a significant signal for tramadol-associated hypoglycemia in patients aged 0 to 1 year (LR05: 3.0240) and 2 to 4 years (LR05: 2.6853). Conclusion and Relevance: Results of our analysis suggest a potential signal between hypoglycemia and tramadol use in patients not taking diabetes medications. Our results do not support a predisposition for tramadol-associated hypoglycemia in patients with renal insufficiency, increasing age, and/or diabetes as noted in the tramadol package insert.

Postpartum Bleeding in Pregnant Women Receiving SSRIs/SNRIs: New Insights From a Descriptive Observational Study and an Analysis of Data from the FAERS Database

PURPOSE

To date, the available data on the relationship between the use of selective serotonin reuptake inhibitors (SSRIs) or the serotonin and norepinephrine reuptake inhibitor (SNRI) venlafaxine and postpartum hemorrhage (PPH) are conflicting and have not been extensively investigated, especially in terms of plasma drug concentrations. We performed data mining of antidepressant-induced PPH reported to the US Food and Drug Administration's Adverse Event Reporting System database, to assess the strength of the potential association between antidepressant pharmacotherapy and PPH in pregnant women. Concurrently, we carried out a descriptive observational population (pregnant women) analysis of the correlation between the plasma concentrations of SSRIs/SNRIs used during pregnancy and the extent of bleeding at delivery.

METHODS

A disproportionality analysis of individual case study reports of PPH associated with SSRIs or venlafaxine in pregnant women was performed. Reporting odds ratio was used as a measure of disproportionality analysis. Pregnant women treated with an SSRI or SNRI (venlafaxine) for depressive or anxiety disorder and who consented to plasma drug concentration monitoring at the time of delivery were recruited. Plasma drug concentration assay was performed according to validated LC-MS/MS. Based on plasma drug concentrations, patients were classified into 1 of 2 groups, in therapeutic range or below therapeutic range for the drug administered, in accordance with the Arbeitsgemeinschaft für Neuropsychopharmakologie und Pharmakopsychiatrie guideline, and correlations with blood loss were identified, with PPH defined as a blood loss of >500 mL.

FINDINGS

Only 43 Individual Case Safety Reports (ICSRs) reported at least one SSRIs or venlafaxine as suspect drug in 14 years (database analyses). Forty-three women were enrolled in the study population (observational study). In 24 patients (55.8%) the plasma drug concentration was below the therapeutic threshold. Unexpectedly, the mean blood loss in the below-range group was significantly higher than that in the in-range group. PPH occurred in 30% of women: in 9.3% and in 20.7% of patients in the in-range and below-range groups, respectively.

IMPLICATIONS

Although preliminary, these data indicate a rather good tolerability profile of SSRIs/SNRIs regarding postpartum bleeding. Moreover, they suggest that keeping the plasma levels of SSRIs/SNRIs low as a precautionary measure does not reduce postpartum bleeding, which was higher in the below-range group. The findings from this study suggest that the use of therapeutic drug monitoring in pregnancy, a period in which multiple variables affect drug metabolism, may allow for better treatment customization, with subsequent advantages in terms of tolerability and efficacy of treatment.

Signal detection for bleeding associated with the use of direct oral anticoagulants

PURPOSE

The potential link between serious or life-threatening bleeding and the use of direct oral anticoagulants (DOACs) was evaluated.

METHODS

Qualitative and quantitative reviews of case reports of bleeding events involving dabigatran, rivaroxaban, apixaban, edoxaban, and warfarin through March 31, 2017, were performed. A disproportionality analysis was conducted for each DOAC using an empirical Bayesian approach based on the relative reporting rate. Subanalyses were performed to assess (1) bleeding events (including mortality and life-threatening events) associated with DOACs among all adverse-event reports and (2) warfarin-related bleeding events. These analyses were conducted based on clinical definitions from the Medical Dictionary for Regulatory Activities.

RESULTS

During the Food and Drug Administration Adverse Event Reporting System (FAERS) review period, 35 adverse-event terms (in any system organ class) with a disproportionality score (EB₀₅) of >7.5 for DOACs were identified; this accounted for 40,109 adverse-event reports. Adverse events with the highest disproportionality scores included atrial thrombosis, increased factor X level, dysfunctional uterine bleeding, high-frequency ablation, pericardial hemorrhage, and internal hemorrhage. Adverse events with the highest EB₀₅ (>5) included internal hemorrhage, hemorrhage, and exsanguination; events with the greatest number of patient experiences included hemorrhage (6,881 events), internal hemorrhage (2,569 events), and hematoma (1,995 events). Warfarin-related events (including death or life-threatening events) were also assessed. A total of 8,729 adverse events were associated with warfarin use. The most common of these included hemorrhage (6,225 events), hematoma (2,199 events), and internal hemorrhage (270 events).

CONCLUSION

The disproportionality analysis of the FAERS database suggests a quantitative signal between DOAC use and life-threatening or serious bleeding.

Tool to assess causality of direct and indirect adverse events associated with therapeutic interventions

OBJECTIVE

To develop and test a tool to assess the causality of direct and indirect adverse events associated with therapeutic interventions. The intervention was one or more drugs and/or natural health products, a device, or practice (professional delivering the intervention).

METHODS

Through the assessment of causality of adverse events, we can learn about factors contributing to the harm and consider what modification may prevent its reoccurrence. Existing scales (WHO-UMC, Naranjo and Horn) were adapted to develop a tool (algorithm and table) to evaluate cases of serious harmful events reported through a national surveillance study. We also incorporated a novel approach that assesses indirect harm (caused by the delay in diagnosis/treatment) and the health provider delivering the intervention (practice). The tool was tested, revised and then implemented to assess all reported cases of serious events resulting from use of complementary therapies. The use of complementary therapies was the trigger to report the event. Each case was evaluated by two assessors, out of a panel of five, representing different health care professionals.

RESULTS

The tool was used in assessment of eight serious adverse events. Each event was independently evaluated by two assessors. The algorithm facilitated assessment of a serious direct or indirect harm. Assessors agreed in the final score on seven of eight cases (weighted kappa coefficient of 0.75).

CONCLUSION

A tool to support the assessment of causality of adverse events was developed and tested. We propose a novel method to assess direct and indirect harms related to product(s), device(s), practice or a combination of the previous. Further research will probably help evaluate this approach across different settings and interventions.

A Survey on Pharmacovigilance Activities in ASEAN and Selected Non-ASEAN Countries, and the Use of Quantitative Signal Detection Algorithms

INTRODUCTION

Most Countries have pharmacovigilance (PV) systems in place to monitor the safe use of health products. The process involves the detection and assessment of safety issues from various sources of information, communicating the risk to stakeholders and taking other relevant risk minimization measures.

OBJECTIVES

This study aimed to assess the PV status in Association of Southeast Asian Nation (ASEAN) countries, sources for postmarket safety monitoring, methods used for signal detection and the need for a quantitative signal detection algorithm (QSDA). Comparisons were conducted with centres outside ASEAN.

METHODS

A questionnaire was sent to all PV centres in ASEAN countries, as well as seven other countries, from November 2015 to June 2016. The questionnaire was designed to collect information on the status of PV, with a focus on the use of a QSDA.

RESULTS

Data were collected from nine ASEAN countries and seven other countries. PV activities were conducted in all these countries, which were at different stages of development. In terms of adverse drug reaction (ADR) reports, the average number received per year ranged from 3 to 50,000 reports for ASEAN countries and from 7000 to 1,103,200 for non-ASEAN countries. Thirty-three percent of ASEAN countries utilized statistical methods to help detect signals from ADR reports compared with 100% in the other non-ASEAN countries. Eighty percent agreed that the development of a QSDA would help in drug signal detection. The main limitation identified was the lack of knowledge and/or lack of resources.

CONCLUSION

Spontaneous ADR reports from healthcare professionals remains the most frequently used source for safety monitoring. The traditional method of case-by-case review of ADR reports prevailed for signal detection in ASEAN countries. As the reports continue to grow, the development of a QSDA would be useful in helping detect safety signals.

Harnessing scientific literature reports for pharmacovigilance. Prototype software analytical tool development and usability testing

Objectives

We seek to develop a prototype software analytical tool to augment FDA regulatory reviewers’ capacity to harness scientific literature reports in PubMed/MEDLINE for pharmacovigilance and adverse drug event (ADE) safety signal detection. We also aim to gather feedback through usability testing to assess design, performance, and user satisfaction with the tool.

Methods

A prototype, open source, web-based, software analytical tool generated statistical disproportionality data mining signal scores and dynamic visual analytics for ADE safety signal detection and management. We leveraged Medical Subject Heading (MeSH) indexing terms assigned to published citations in PubMed/MEDLINE to generate candidate drug-adverse event pairs for quantitative data mining. Six FDA regulatory reviewers participated in usability testing by employing the tool as part of their ongoing real-life pharmacovigilance activities to provide subjective feedback on its practical impact, added value, and fitness for use.

Results

All usability test participants cited the tool’s ease of learning, ease of use, and generation of quantitative ADE safety signals, some of which corresponded to known established adverse drug reactions. Potential concerns included the comparability of the tool’s automated literature search relative to a manual ‘all fields’ PubMed search, missing drugs and adverse event terms, interpretation of signal scores, and integration with existing computer-based analytical tools.

Conclusions

Usability testing demonstrated that this novel tool can automate the detection of ADE safety signals from published literature reports. Various mitigation strategies are described to foster improvements in design, productivity, and end user satisfaction.

Sensitivity of Quantitative Signal Detection in Regards to Pharmacological Neuroenhancement

Pharmacological neuroenhancement (PNE) is a form of abuse and has not yet been addressed by methods of pharmacovigilance. In the present study, we tested if quantitative signal detection may be sensitive in regards to PNE. We evaluated the risk of drug abuse and dependence (DAAD) related to substances that are known to be used for PNE and divided this group into agents with (methylphenidate) and without a known abuse potential outside the field of PNE (atomoxetine, modafinil, acetylcholine esterase inhibitors, and memantine). Reporting odds ratios (RORs) were calculated using a case/non-case approach based on global and country-specific drug safety data from the Uppsala Monitoring Centre (UMC). Both control substances (diazepam and lorazepam) and methylphenidate were statistically associated with DAAD in all datasets (except methylphenidate in Italy). Modafinil was associated with DAAD in the total dataset (ROR, 2.7 (95% confidence interval (CI), 2.2-3.3)), Germany (ROR, 4.6 (95% CI, 1.8-11.5)), and the USA (ROR, 2.0 (95% CI, 1.6-2.5)). Atomoxetine was associated with DAAD in the total dataset (ROR, 1.3 (95% CI, 1.2-1.5)) and in the UK (ROR, 3.3 (95% CI, 1.8-6.1)). Apart from memantine, which was associated with DAAD in Germany (ROR, 1.8 (95% CI, 1.0-3.2)), no other antidementia drug was associated with DAAD. Quantitative signal detection is suitable to detect agents with a risk for DAAD. Its sensitivity regarding PNE is limited, although atomoxetine and modafinil, which do not have a known abuse potential outside PNE, and no antidementia drugs, whose use in PNE is presumably low, were associated with DAAD in our analysis.

Generalized enrichment analysis improves the detection of adverse drug events from the biomedical literature

Background

Identification of associations between marketed drugs and adverse events from the biomedical literature assists drug safety monitoring efforts. Assessing the significance of such literature-derived associations and determining the granularity at which they should be captured remains a challenge. Here, we assess how defining a selection of adverse event terms from MeSH, based on information content, can improve the detection of adverse events for drugs and drug classes.

Results

We analyze a set of 105,354 candidate drug adverse event pairs extracted from article indexes in MEDLINE. First, we harmonize extracted adverse event terms by aggregating them into higher-level MeSH terms based on the terms’ information content. Then, we determine statistical enrichment of adverse events associated with drug and drug classes using a conditional hypergeometric test that adjusts for dependencies among associated terms. We compare our results with methods based on disproportionality analysis (proportional reporting ratio, PRR) and quantify the improvement in signal detection with our generalized enrichment analysis (GEA) approach using a gold standard of drug-adverse event associations spanning 174 drugs and four events. For single drugs, the best GEA method (Precision: .92/Recall: .71/F1-measure: .80) outperforms the best PRR based method (.69/.69/.69) on all four adverse event outcomes in our gold standard. For drug classes, our GEA performs similarly (.85/.69/.74) when increasing the level of abstraction for adverse event terms. Finally, on examining the 1609 individual drugs in our MEDLINE set, which map to chemical substances in ATC, we find signals for 1379 drugs (10,122 unique adverse event associations) on applying GEA with p < 0.005.

Conclusions

We present an approach based on generalized enrichment analysis that can be used to detect associations between drugs, drug classes and adverse events at a given level of granularity, at the same time correcting for known dependencies among events. Our study demonstrates the use of GEA, and the importance of choosing appropriate abstraction levels to complement current drug safety methods. We provide an R package for exploration of alternative abstraction levels of adverse event terms based on information content.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1080-z) contains supplementary material, which is available to authorized users.

Dipeptidyl Peptidase-4 Inhibitor-Associated Pancreatic Carcinoma: A Review of the FAERS Database

BACKGROUND

To date, there is limited literature regarding the association between dipeptidyl peptidase-4 (DPP-4) inhibitors and pancreatic carcinoma.

OBJECTIVE

To describe the comparative incidence of DPP-4 inhibitors and pancreatic carcinoma as reportedly available in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. The goal was to provide health care practitioners a general understanding of the drug-disease occurrence.

METHODS

This is a case/noncase study utilizing Empirica Signal software to query FAERS from November 1968 to December 31, 2013. The software was used to calculate a disproportionality statistic--namely, the empirical Bayesian geometric mean (EBGM)--for reports of DPP-4 inhibitors-associated pancreatic carcinoma. The FDA considers an EBGM significant if the fifth percentile of the distribution is at least 2, defined as an EB05 ≥ 2. With use of a disproportionality analysis, DPP-4 inhibitors were compared with all agents listed in FAERS.

RESULTS

A total of 156 patients experienced pancreatic carcinoma while receiving DPP-4 inhibitor therapy. An EB05 of 10.3 was determined for sitagliptin, 7.1 for saxagliptin, 4.9 for linagliptin, and 1.4 for alogliptin, compared with all other agents included in FAERS. Although an EB05 > 2 was achieved in 2 other antihyperglycemic agents, the findings were not consistent within their medication classes.

CONCLUSION

There appears to be a statistical association between DPP-4 inhibitor use and pancreatic carcinoma. Causality cannot be inferred from the data provided. Additional clinical studies are needed to further explore this statistical association.

Exploring adverse drug events at the class level

Background

While the association between a drug and an adverse event (ADE) is generally detected at the level of individual drugs, ADEs are often discussed at the class level, i.e., at the level of pharmacologic classes (e.g., in drug labels). We propose two approaches, one visual and one computational, to exploring the contribution of individual drugs to the class signal.

Methods

Having established a dataset of ADEs from MEDLINE, we aggregate drugs into ATC classes and ADEs into high-level MeSH terms. We compute statistical associations between drugs and ADEs at the drug level and at the class level. Finally, we visualize the signals at increasing levels of resolution using heat maps. We also automate the exploration of drug-ADE associations at the class level using clustering techniques.

Results

Using our visual approach, we were able to uncover known associations, e.g., between fluoroquinolones and tendon injuries, and between statins and rhabdomyolysis. Using our computational approach, we systematically analyzed 488 associations between a drug class and an ADE.

Conclusions

The findings gained from our exploratory techniques should be of interest to the curators of ADE repositories and drug safety professionals. Our approach can be applied to different drug-ADE datasets, using different drug classification systems and different signal detection algorithms.

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-015-0017-1) contains supplementary material, which is available to authorized users.

Hepatotoxicity associated with agomelatine and other antidepressants: Disproportionality analysis using pooled pharmacovigilance data from the Uppsala Monitoring Centre

Since its marketing approval, the attention to the hepatic side-effect profile of the antidepressant agomelatine (AGM) has gradually increased. Several cases of severe hepatotoxic adverse drug reactions (ADR) have been reported and the European Medicines Agency has released a safety warning regarding AGM-associated hepatotoxicity. However, there are insufficient data for an adequate safety assessment of AGM-related hepatotoxicity. Therefore, we performed a quantitative signal detection analysis using pharmacovigilance data from the Uppsala Monitoring Centre from the WHO that records ADR data from worldwide sources; we calculated reporting odds ratios (ROR) as measures for disproportionality within a case/non-case approach for AGM and several other antidepressants. AGM was statistically associated with an increased risk of hepatotoxicity (ROR 6.4 [95%CI 5.7-7.2]) as well as both positive controls: amineptine (ROR 38.4 [95%CI 33.8-43.6]) and nefazodone (ROR 3.2 [95%CI 3.0-3.5]). Following amineptine, AGM was associated with the second highest ROR, followed by tianeptine (ROR 4.4 [95%CI 3.6-5.3]), mianserin (ROR 3.6 [95%CI 3.3-3.9]), and nefazodone. These results support the hypothesis that AGM is associated with relevant hepatotoxicity. However, the used data and applied method do not allow a quantitative evaluation of hepatotoxicity or assessment of substance-specific differences regarding the extent of hepatotoxicity.

Tigecycline-related pancreatitis: a review of spontaneous adverse event reports

STUDY OBJECTIVE

To provide clinicians with an understanding of the comparative occurrence of tigecycline and pancreatitis, and to provide any clinically relevant characteristics that may be useful in identifying the patients at risk.

DESIGN

Retrospective cohort study.

DATA SOURCE

Spontaneous reports in the United States Food and Drug Administration (FDA) Adverse Event Reporting System (AERS) database generated between January 1997 and December 2010.

PATIENTS

Sixty-two patients who experienced pancreatitis while receiving tigecycline therapy.

MEASUREMENTS AND MAIN RESULTS

Disproportionality analysis with bayesian correction methodology was used to compare tigecycline with all other agents listed in the AERS. Disproportionality analysis uses an adverse event relative reporting ratio (RRR) to compare the occurrence of a specific adverse event with an index drug of interest to the occurrence of the same adverse event with similar agents or with all other FDA-approved prescription drugs. The value was considered meaningful if the 5th percentile of the distribution of the RRR (RRR(05)) was 2 or greater. Our review identified 62 potential cases of tigecyline-related pancreatitis. An RRR(05) score of 10.4, 10.38, and 2.87 was determined for tigecycline-related pancreatitis compared with all other agents, systemic antibiotics, and select tetracyclines listed in the AERS, respectively. In addition, a sex-based RRR(05) score was higher in women versus men (14.432 vs 3.125) when tigecycline was compared with all agents in the AERS.

CONCLUSION

Our analysis suggests a quantitative signal between tigecycline use and pancreatitis; however, given the limitations of our study, a cause-and-effect relationship cannot be inferred. Thus, additional rigorous scientific analyses are warranted to explore these findings.

A shrinkage-based comparative assessment of observed-to-expected disproportionality measures

PURPOSE

Disproportionality analysis is an important tool for interpreting spontaneous adverse event reports in pharmacovigilance. There exist a variety of disproportionality measures (DPMs) for use in safety signaling; however, it is not always clear which method is best suited for a particular need. A framework for comparing the various DPMs is necessary to fully understand the consequences of quantitative signal detection implementation decisions. Here, the mathematical relationship between these measures is explored through a comparison of the underlying equations and a shrinkage approach is adopted to further clarify these relationships.

METHODS

Many DPMs take the form of a ratio of the number of observed (O) cases and the number of expected (E) cases (i.e., O/E). Because O is unchanged by the method selected, the method-specific E (E(DPM) ) is the fundamental difference between the values produced by these DPMs. Clarification of the relationship between these DPMs is pursued through the use of a shrinkage parameter (s).

RESULTS

It is demonstrated that any arbitrary DPM, which can be defined as O/E(DPM) , can also be expressed as a function of the reporting odds ratio (ROR) and s. This common equation allows for a straightforward comparison of the varying methods and the ability to clearly characterize the approaches according to their relative signal detection performance irrespective of the specific dataset to which the methods are applied. A novel DPM, the independent reporting ratio (IRR), provides an example of how the described framework can improve our understanding of disproportionality analyses and lead to the development of new methods.

CONCLUSIONS

Explicitly defining DPMs as RORs with applied shrinkage provides a convenient method for understanding their relative signal detection performance and insight into the relative contributions to DPM shrinkage.