Potential Use of Data-Mining Algorithms for the Detection of ???Surprise??? Adverse Drug Reactions

Prescribed Drugs and Self-Directed Violence: A Descriptive Study in the Spanish Pharmacovigilance Database

Self-inflicted violence is a major and growing public health problem and its prediction and prevention is challenging for healthcare systems worldwide. Our aim was to identify prescribed drugs associated with self-directed violent behaviors in Spain. A descriptive, longitudinal and retrospective study of spontaneous reports of adverse drug reactions corresponding to self-directed violence was recorded in the Spanish Pharmacovigilance Database (FEDRA^®) from 1984 to 31 March 2021. A total of 710 cases were reported in the study period. The mean age was 45.52 years (range 1-94). There were no gender differences except in children, where most reports were of male children. The main therapeutic groups that were involved included drugs for the nervous system (64.5%) and anti-infectives for systemic use (13.2%). The most commonly reported drugs were varenicline, fluoxetine, lorazepam, escitalopram, venlafaxine, veralipride, pregabalin, roflumilast and bupropion. There were reports of montelukast, hydroxychloroquine, isotretinoin, methylphenidate, infliximab, natalizumab, ribavirin and efavirenz, which were less known to be involved in self-directed violence. This study shows that self-directed violence is a rare adverse drug reaction, and can be related to the use of some medicines. It is important for healthcare professionals to consider this risk in their clinical praxis, implementing person-centred approaches. Further studies are needed, considering comorbidities and potential interactions.

Automation in signal management in pharmacovigilance-an insight

Drugs are the imperial part of modern society, but along with their therapeutic effects, drugs can also cause adverse effects, which can be mild to morbid. Pharmacovigilance is the process of collection, detection, assessment, monitoring and prevention of adverse drug events in both clinical trials as well as in the post-marketing phase. The recent trends in increasing unknown adverse events, known as signals, have raised the need to develop an ideal system for monitoring and detecting the potential signals timely. The process of signal management comprises of techniques to identify individual case safety reports systematically. Automated signal detection is highly based upon the data mining of the spontaneous reporting system such as reports from health care professional, observational studies, medical literature or from social media. If a signal is not managed properly, it can become an identical risk associated with the drug which can be hazardous for the patient safety and may have fatal outcomes which may impact health care system adversely. Once a signal is detected quantitatively, it can be further processed by the signal management team for the qualitative analysis and further evaluations. The main components of automated signal detection are data extraction, data acquisition, data selection, and data analysis and data evaluation. This system must be developed in the correct format and context, which eventually emphasizes the quality of data collected and leads to the optimal decision-making based upon the scientific evaluation.

Novel adverse events of vortioxetine: A disproportionality analysis in USFDA adverse event reporting system database

BACKGROUND

Signal detection is one of the most advanced and emerging field in pharmacovigilance. It is a modern method of detecting new reaction (which can be desired or undesired) of a drug. It facilitates early adverse drug reaction detection which enables health professionals to identify adverse events that may not have been identified in pre-marketing clinical trials. Vortioxetine, the first mixed serotonergic antidepressant was initially approved by the US Food and Drug Administration (USFDA) on September 30, 2013 for the treatment of adults with Major Depressive Disorder (MDD). This study was to identify the signal strength for vortioxetine associated ADRs using data mining technique in USFDA Adverse Event Reporting System (AERS) database.

METHODOLOGY

Most commonly used three data mining algorithms, Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR) and Information Component (IC) were selected for the study and they were applied retrospectively in USFDA AERS database from 2015Q1 to 2016Q3. A value of ROR-1.96SE >1, PRR≥2, IC- 2SD>0 were considered as the positive signal.

RESULT

A study population of 61,22,000 were reported all over the world. Among which 3481 reactions were associated with vortioxetine which comprised of 632 unique events encompassed with 27 clinically relevant reactions. ROR, PRR and IC showed positive signal for weight loss, agitation, anger, ketoacidosis, insomnia and abnormal dreams.

CONCLUSION

The present study suggests that vortioxetine may result in these adverse events. Further pharmacoepidemiologic studies are necessary to confirm this conclusion and to improve the precision of the prevalence and/or the risk factors of this ADRs.

Adverse Drug Reactions in the Intensive Care Unit

Adverse drug reactions (ADRs) are undesirable effects of medications used in normal doses [1]. ADRs can occur during treatment in an intensive care unit (ICU) or result in ICU admissions. A meta-analysis of 4139 studies suggests the incidence of ADRs among hospitalized patients is 17% [2]. Because of underreporting and misdiagnosis, the incidence of ADRs may be much higher and has been reported to be as high as 36% [3]. Critically ill patients are at especially high risk because of medical complexity, numerous high-alert medications, complex and often challenging drug dosing and medication regimens, and opportunity for error related to the distractions of the ICU environment [4]. Table 1 summarizes the ADRs included in this chapter.

Improving drug safety with a systems pharmacology approach

Systems pharmacology is used to mechanistically analyze drug-adverse drug reaction (ADRs) pairs and is a promising solution to the complex problem of understanding mechanisms of toxicity. In this research, we have explored the feasibility of retrospectively mapping population-level adverse events from the FDA Adverse Event Reporting System (FAERS) to chemical and biological databases to identify drug safety signals and the underlying molecular mechanisms. We used an analytic platform - Molecular Analysis of Side Effects (MASE™). For this purpose, we selected the adverse event of severe and potentially fatal cutaneous reactions (SCARs) that are associated with acetaminophen (APAP). SCARs encompass the continuum between Stevens-Johnson Syndrome (SJS) and Toxic Epidermal Necrolysis (TEN). We found a statistically significant association between APAP and TEN, the most severe form of SCARs. We also explored the influence of APAP on other classes of drugs commonly associated with SCARs. We found that APAP significantly reduced the risk of SCARs commonly associated with carbamazepine (CBZ). We used molecular docking simulations to propose a mechanism for APAP's reduction in CBZ-induced SCARs which is competitive inhibition of the binding of CBZ to HLA-B*15:02. We conclude that systems pharmacology can complement established surveillance methodologies by providing a means to undertake an independent investigation and review of the mechanisms by which drugs cause adverse events.

Comparison of statistical signal detection methods within and across spontaneous reporting databases

BACKGROUND

Most pharmacovigilance departments maintain a system to identify adverse drug reactions (ADRs) through analysis of spontaneous reports. The signal detection algorithms (SDAs) and the nature of the reporting databases vary between operators and it is unclear whether any algorithm can be expected to provide good performance in a wide range of environments.

OBJECTIVE

The objective of this study was to compare the performance of commonly used algorithms across spontaneous reporting databases operated by pharmaceutical companies and national and international pharmacovigilance organisations.

METHODS

220 products were chosen and a reference set of ADRs was compiled. Within four company, one national and two international databases, 15 SDAs based on five disproportionality methods were tested. Signals of disproportionate reporting (SDRs) were calculated at monthly intervals and classified by comparison with the reference set. These results were summarised as sensitivity and precision for each algorithm in each database.

RESULTS

Different algorithms performed differently between databases but no method dominated all others. Performance was strongly dependent on the thresholds used to define a statistical signal. However, the different disproportionality statistics did not influence the achievable performance. The relative performance of two algorithms was similar in different databases. Over the lifetime of a product there is a reduction in precision for any method.

CONCLUSIONS

In designing signal detection systems, careful consideration should be given to the criteria that are used to define an SDR. The choice of disproportionality statistic does not appreciably affect the achievable range of signal detection performance and so this can primarily be based on ease of implementation, interpretation and minimisation of computing resources. The changes in sensitivity and precision obtainable by replacing one algorithm with another are predictable. However, the absolute performance of a method is specific to the database and is best assessed directly on that database. New methods may be required to gain appreciable improvements.

Evaluating the completeness and accuracy of MedWatch data

The Food and Drug Administration's MedWatch system--a voluntary surveillance program--received 600,000 adverse event reports on marketed drugs and devices in 2011. The Food and Drug Administration credits the MedWatch system with improving awareness, and expediting early detection, of drug and device risks and in illuminating the adoption of medical treatments. Reporting bias has been acknowledged as a limitation of the MedWatch system. No systematic assessment of the accuracy and completeness of adverse event reporting has been conducted, yet inaccurate adverse event reporting may lead drug safety professionals to draw incorrect conclusions, manufacturers may be wrongly forced to suspend and withdraw medications and interventions, health professionals may mistakenly alter their clinical practices, and patients may be denied safe and effective treatments. In 2011, the Tufts Center for the Study of Drug Development gathered and analyzed 10.2 million adverse event reports filed with the MedWatch system. Patient information was generally complete and accurate. Suspect product information, on the other hand, showed high levels of incomplete and inaccurate data. Start and end dates of suspect product use had 37% and 23% completion rates, respectively. Dosage level was completed only 31% of the time, and product lot numbers had only a 9% completion rate. More than 25% of the names of reported suspect products were inaccurate, and 31% of suspect product start dates were inaccurate. Higher levels of completion and accuracy were associated with reports filed closer to the date when the adverse event was observed. Implications of the results and suggested improvements are discussed.

Life-threatening dermatologic adverse events in oncology

The incidences of life-threatening toxicities such as Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are inconsistently reported. The potential association of anticancer agents with SJS or TEN has not been systematically investigated. We searched the literature (Ovid: 1950 to June 2013 and PubMed: 1948 to June 2013) using terms for SJS/TEN and anticancer therapies. Primary case reports, case series, and clinical trials were included. In addition, MedWatch, the Food and Drug Administration Adverse Event Reporting System (FAERS), was searched (1968 to August 2012) for SJS/TEN reports associated with anticancer therapies. Proportional reporting ratios (PRR>2, N>3), empirical Bayes geometric mean (EBGM>2, N>3), and lower 95% confidence interval (EBGM0.05>2) were used as thresholds to constitute a signal of association between SJS/TEN and anticancer drugs. There were 46 SJS and 37 TEN cases associated with 18 and 22 anticancer drugs in the literature, respectively. Among cases in the FAERS, significant signals were associated with SJS for bendamustine and with TEN for bendamustine, busulfan, chlorambucil, fludarabine, lomustine, and procarbazine. Several drugs reported in the published literature to be associated with SJS/TEN were not found to have significant signals in FAERS. Proactive pharmacovigilance to detect and define safety signals serves to aid oncology practitioners in the recognition of possible, yet uncommon, serious, and/or life-threatening skin reactions.

A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions

Improving the prediction of chemical toxicity is a goal common to both environmental health research and pharmaceutical drug development. To improve safety detection assays, it is critical to have a reference set of molecules with well-defined toxicity annotations for training and validation purposes. Here, we describe a collaboration between safety researchers at Pfizer and the research team at the Comparative Toxicogenomics Database (CTD) to text mine and manually review a collection of 88 629 articles relating over 1 200 pharmaceutical drugs to their potential involvement in cardiovascular, neurological, renal and hepatic toxicity. In 1 year, CTD biocurators curated 2 54 173 toxicogenomic interactions (1 52 173 chemical–disease, 58 572 chemical–gene, 5 345 gene–disease and 38 083 phenotype interactions). All chemical–gene–disease interactions are fully integrated with public CTD, and phenotype interactions can be downloaded. We describe Pfizer’s text-mining process to collate the articles, and CTD’s curation strategy, performance metrics, enhanced data content and new module to curate phenotype information. As well, we show how data integration can connect phenotypes to diseases. This curation can be leveraged for information about toxic endpoints important to drug safety and help develop testable hypotheses for drug–disease events. The availability of these detailed, contextualized, high-quality annotations curated from seven decades’ worth of the scientific literature should help facilitate new mechanistic screening assays for pharmaceutical compound survival. This unique partnership demonstrates the importance of resource sharing and collaboration between public and private entities and underscores the complementary needs of the environmental health science and pharmaceutical communities.

Database URL: http://ctdbase.org/

Postmarketing safety surveillance : where does signal detection using electronic healthcare records fit into the big picture?

The safety profile of a drug evolves over its lifetime on the market; there are bound to be changes in the circumstances of a drug's clinical use which may give rise to previously unobserved adverse effects, hence necessitating surveillance postmarketing. Postmarketing surveillance has traditionally been carried out by systematic manual review of spontaneous reports of adverse drug reactions. Vast improvements in computing capabilities have provided opportunities to automate signal detection, and several worldwide initiatives are exploring new approaches to facilitate earlier detection, primarily through mining of routinely-collected data from electronic healthcare records (EHR). This paper provides an overview of ongoing initiatives exploring data from EHR for signal detection vis-à-vis established spontaneous reporting systems (SRS). We describe the role SRS has played in regulatory decision making with respect to safety issues, and evaluate the potential added value of EHR-based signal detection systems to the current practice of drug surveillance. Safety signal detection is both an iterative and dynamic process. It is in the best interest of public health to integrate and understand evidence from all possibly relevant information sources on drug safety. Proper evaluation and communication of potential signals identified remains an imperative and should accompany any signal detection activity.

Trends in reporting methadone-associated cardiac arrhythmia, 1997-2011: an analysis of registry data

BACKGROUND

Long-acting opioids are a leading cause of accidental death in the United States, and methadone is associated with greater mortality rates. Whether this increase is related to the proarrhythmic properties of methadone is unclear.

OBJECTIVE

To describe methadone-associated arrhythmia events reported in the U.S. Food and Drug Administration Adverse Event Reporting System (FAERS).

DESIGN

Description of national adverse event registry data before and after publication of a 2002 report describing an association between methadone and arrhythmia.

SETTING

FAERS, November 1997 and June 2011.

PATIENTS

Adults with QTc prolongation or torsade de pointes and ventricular arrhythmia or cardiac arrest.

MEASUREMENTS

FAERS reports before and after the 2002 report.

RESULTS

1646 cases of ventricular arrhythmia or cardiac arrest and 379 cases of QTc prolongation or torsade de pointes were associated with methadone. Monthly reports of QTc prolongation or torsade de pointes increased from a mean of 0.3 (95% CI, 0.1 to 0.5) before the 2002 publication to a mean of 3.5 (CI, 2.5 to 4.8) after it. After 2000, methadone was the second-most common primary suspect in cases of QTc prolongation or torsade de pointes after dofetilide (a known proarrhythmic drug) and was associated with disproportionate reporting similar to that of antiarrhythmic agents known to promote torsade de pointes. Antiretroviral drugs for HIV were the most common coadministered drugs.

LIMITATION

Reports to FAERs are voluntary and selective, and incidence rates cannot be determined from spontaneously reported data.

CONCLUSION

Since 2002, reports to FAERS of methadone-associated arrhythmia have increased substantially and are disproportionately represented relative to other events with the drug. Coadministration of methadone with antiretrovirals in patients with HIV may pose particular risk.

PRIMARY FUNDING SOURCE

Colorado Clinical and Translational Sciences Institute, National Institutes of Health, and Agency for Healthcare Research and Quality.

Pharmacovigilance analysis of adverse event reports for aliskiren hemifumarate, a first-in-class direct renin inhibitor

Background

The purpose of this study was to examine the postmarketing safety profile of aliskiren hemifumarate, a first-in-class direct renin inhibitor.

Methods

The US Adverse Event Reporting System (AERS) was utilized to conduct a retrospective pharmacovigilance analysis by applying the Multi-item Gamma Poisson Shrinker data mining algorithm to calculate empiric Bayes geometric mean (EBGM) values of aliskiren-related adverse event reports. Reports received from January 2007 through December 2008 are included in this analysis.

Results

In total, 1592 reports for aliskiren are identified in the AERS. Aliskiren was associated with reports of angioedema (EBGM 3.9, 95% confidence interval [CI] 3.2–4.7) and renal dysfunction (EBGM 3.4, 95% CI 2.6–4.5). Reports of hyperkalemia, dry cough, and diarrhea were also linked to aliskiren (EBGM 7.4, 95% CI 3.4–13.0, EBGM 11.0, 95% CI 7.8–14.2, EBGM 4.3, 95% CI 3.2–5.8, respectively).

Conclusion

Angioedema and renal dysfunction are potential adverse events associated with exposure to aliskiren. Patients with signs and symptoms of angioedema should stop aliskiren and seek urgent medical help. Aliskiren should not be used by patients with a risk of renal impairment. Additional studies are warranted to quantify further the risk of these events in patients with hypertension.

Prospective data mining of six products in the US FDA Adverse Event Reporting System: disposition of events identified and impact on product safety profiles

BACKGROUND

The use of data mining has increased among regulators and pharmaceutical companies. The incremental value of data mining as an adjunct to traditional pharmacovigilance methods has yet to be demonstrated. Specifically, the utility in identifying new safety signals and the resources required to do so have not been elucidated.

OBJECTIVES

To analyse the number and types of disproportionately reported product-event combinations (DRPECs), as well as the final disposition of each, in order to understand the potential utility and resource implications of routinely conducting data mining in the US FDA Adverse Event Reporting System (AERS).

METHODS

We generated DRPECs from AERS for six of Wyeth's products, prospectively tracked their dispositions and evaluated the appropriate DRPECs in the company's safety database. We chose EB05 (the lower bound of the 90% confidence interval around the Empirical Bayes Geometric Mean) > or =2 as the appropriate metric, employing stratification based on age, sex and year of report.

RESULTS

A total of 861 DRPECs were identified - the average number of DRPECs was 144 per product. The proportion of unique preferred terms (PTs) in AERS for each drug with an EB05 > or =2 was similar across the six products (5.1-8.5%). Overall, 64.0% (551) of the DRPECs were closed after the initial screening (44.8% labelled, 14.3% indication related, 4.9% non-interpretable). An additional 9.9% (85) had been reviewed within the prior year and were not further reviewed. The remaining 26.1% (225) required full case review. After review of all pertinent reports and additional data, it was determined which of the DRPECs necessitated a formal review by the company's ongoing Safety Review Team (SRT) process. In total, 3.6% (31/861) of the DRPECs, yielding 16 medical concepts, were reviewed by the SRT, leading to seven labelling changes. These labelling changes involved 1.9% of all DRPECs generated. Four of the six compounds reviewed as part of this pilot had an identified labelling change. The workload required for this pilot, which was driven primarily by those DRPECs requiring review, was extensive, averaging 184 hours per product.

CONCLUSION

The number of DRPECs identified for each drug approximately correlated with the number of unique PTs in the database. Over one-half of DRPECs were either labelled as per the company's reference safety information (RSI) or were under review after identification by traditional pharmacovigilance activities, suggesting that for marketed products these methods do identify adverse events detected by traditional pharmacovigilance methods. Approximately three-quarters of the 861 DRPECs identified were closed without case review after triage. Of the approximately one-quarter of DRPECs that required formal case review, seven resulted in an addition to the RSI for the relevant products. While this pilot does not allow us to comment on the utility of routine data mining for all products, it is significant that several new safety concepts were identified through this prospective exercise.

Venous thromboembolism in myelodysplastic syndrome patients receiving lenalidomide: results from postmarketing surveillance and data mining techniques

BACKGROUND AND OBJECTIVES

Multiple myeloma treatment with lenalidomide-based regimens is associated with risk of venous thromboembolism (VTE), particularly during concomitant use with erythropoiesis-stimulating agents (ESAs). The risk of VTE in myelodysplastic syndrome (MDS) patients treated with lenalidomide is not well characterized and the background risk in untreated patients is not known. This study set out to determine the reporting rate of VTE in MDS patients on lenalidomide in the two years of postmarketing experience in the US, and to investigate whether there is a disproportional signal of VTE in MDS patients on lenalidomide by screening the US FDA Adverse Event Reporting System (AERS) safety database.

METHODS

The MDS population exposed to lenalidomide was obtained from RevAssist, the company's proprietary restrictive distribution programme. VTE reports were identified from the company's postmarketing surveillance safety database. The FDA AERS database was used for disproportionality analysis, and signal scores computed using three algorithms: multi-item gamma Poisson shrinker (MGPS), proportional reporting ratio (PRR), and reporting odds ratios (ROR).

RESULTS

A total of 7764 MDS patients were prescribed lenalidomide during the first two years of commercial use in the US. VTE representing deep vein thrombosis and pulmonary embolism was reported in 41 patients, a reporting rate of 0.53%. The computed signal scores did not exceed the statistical threshold for identification of a significant disproportional signal for VTE in MDS reports involving use of lenalidomide without concomitant use of ESAs. However, a disproportional signal of VTE was detected in MDS reports where lenalidomide was concurrently used with ESAs.

CONCLUSION

The VTE reporting rate for MDS patients receiving lenalidomide during the first two years of postmarketing exposure was low (0.53%). Disproportionality analysis demonstrated a statistically meaningful association of VTE with lenalidomide concomitantly used with ESAs in MDS patients, but the association was not statistically significant when lenalidomide was used in the absence of ESAs.

Exposure to antibacterial agents with QT liability in 14 European countries: trends over an 8-year period

AIMS

(i) To classify antibacterial agents with QT liability on the basis of the available evidence, and (ii) to assess trends in their consumption over an 8-year period (1998-2005) in 14 European countries.

METHODS

Current published evidence on QT liability of antibiotics was retrieved through MEDLINE search and joined to official warnings from regulatory agencies. Each drug was classified according to an already proposed algorithm based on the strength of evidence: from group A (any evidence) to group E (clinical reports of torsades de pointes and warnings on QT liability). Consumption data were provided by the European Surveillance of Antibacterial Consumption (ESAC) project and were expressed as defined daily doses per 1000 inhabitants per day (DID).

RESULTS

Among 21 detected compounds, nine [six fluoroquinolones (FQs) and three macrolides (MACs)] belonged to group E. Use of group E drugs ranged from 1.3 (Sweden) to 4.1 DID (Italy) in 1998 and from 1.2 (Sweden) to 6.5 DID (Italy) in 2005. Significant exposure was observed in Italy and Spain (6.5 and 3.8 DID, respectively, in 2005). Only Denmark, Sweden and UK showed a slight decrease in use. Exposure to clarithromycin increased in 10 out of 14 countries, with a marked increment in Italy (3 DID in 2005).

CONCLUSIONS

Notwithstanding regulatory measures, in 2005 there was still significant exposure to antibacterials with strong evidence of QT liability and, in most countries, it was even increased. This warrants further investigation of appropriateness of use and suggests closer monitoring of group E drugs. Physicians should be aware when prescribing them to susceptible patients.