Timing Matters: A Machine Learning Method for the Prioritization of Drug-Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure

INTRODUCTION

Current drug-drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases.

OBJECTIVE

This study aims to develop a method for detecting and prioritizing temporally plausible disproportionality signals of DDIs in SRS databases by incorporating co-exposure time in disproportionality analysis.

METHODS

The method was tested in the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS). The CRESCENDDI dataset of positive controls served as the primary source of true-positive DDIs. Disproportionality analysis was performed considering the time of co-exposure. Temporal plausibility was assessed using the flex point of cumulative reporting of disproportionality signals. Potential confounders were identified using a machine learning method (i.e. Lasso regression).

RESULTS

Disproportionality analysis was conducted on 122 triplets with more than three cases, resulting in the prioritization of 61 disproportionality signals (50.0%) involving 13 adverse events, with 61.5% of these included in the European Medicine Agency's (EMA's) Important Medical Event (IME) list. A total of 27 signals (44.3%) had at least ten cases reporting the triplet of interest, and most of them (n = 19; 70.4%) were temporally plausible. The retrieved confounders were mainly other concomitant drugs.

CONCLUSIONS

Our method was able to prioritize disproportionality signals with temporal plausibility. This finding suggests a potential for our method in pinpointing signals that are more likely to be furtherly validated.

Association between voriconazole-induced visual hallucination and dopamine in an analysis of the food and drug administration (FDA) adverse event reporting system database

Voriconazole is a second-generation azole used to treat serious fungal infections. Visual hallucinations constitute a representative adverse event caused by voriconazole. However, its mechanism of action remains unclear. In patients with schizophrenia or Parkinson's disease, the frequency of visual hallucinations is associated with brain dopamine levels. This study investigated the frequency of visual hallucinations in patients treated with voriconazole alone or in combination with dopaminergic medicines or dopamine antagonists, using data collected from the Food and Drug Administration Adverse event Reporting System (FAERS). The frequency of visual hallucinations with voriconazole alone and in combination with a dopaminergic medicine (levodopa) or dopamine antagonists (risperidone and chlorpromazine) was compared using data from the FAERS between 2004 and 2023, using the reporting odds ratio (ROR) with relevant 95% confidence intervals (CI). The reference group comprised patients who had been administered voriconazole without dopaminergic medication or dopamine antagonists. Of the patients, 22,839, 90,810, 109,757, 6,435, 20, 83, and 26, respectively were treated with voriconazole, levodopa, risperidone, chlorpromazine, voriconazole plus levodopa, voriconazole plus risperidone, and voriconazole plus chlorpromazine. The occurrence of visual hallucinations increased when used in combination with levodopa (ROR = 12.302, 95% CI = 3.587-42.183). No increase in incidence was associated with the concomitant use of dopamine antagonists (risperidone, ROR = 1.721, 95% CI = 0.421-7.030; chlorpromazine, ROR = none, 95% CI = none). Dopaminergic medicine may increase the risk of visual hallucinations in patients treated with voriconazole. Whether voriconazole positively modulates dopamine production warrants further investigation using a translational research approach.

Exploring the impact of co-exposure timing on drug-drug interactions in signal detection through spontaneous reporting system databases: a scoping review

INTRODUCTION

Drug-drug interactions (DDIs) are defined as the pharmacological effects produced by the concomitant administration of two or more drugs. To minimize false positive signals and ensure their validity when analyzing Spontaneous Reporting System (SRS) databases, it has been suggested to incorporate key pharmacological principles, such as temporal plausibility.

AREAS COVERED

The scoping review of the literature was completed using MEDLINE from inception to March 2023. Included studies had to provide detailed methods for identifying DDIs in SRS databases. Any methodological approach and adverse event were accepted. Descriptive analyzes were excluded as we focused on automatic signal detection methods. The result is an overview of all the available methods for DDI signal detection in SRS databases, with a specific focus on the evaluation of the co-exposure time of the interacting drugs. It is worth noting that only a limited number of studies (n = 3) have attempted to address the issue of overlapping drug administration times.

EXPERT OPINION

Current guidelines for signal validation focus on factors like the number of reports and temporal association, but they lack guidance on addressing overlapping drug administration times, highlighting a need for further research and method development.

Musculoskeletal adverse events induced by immune checkpoint inhibitors: a large-scale pharmacovigilance study

Background: The musculoskeletal toxicity of immune checkpoint inhibitors (ICIs) is receiving increasing attention with clinical experience. Nevertheless, the absence of a systematic investigation into the musculoskeletal toxicity profile of ICIs currently results in the under-recognition of associated adverse events. Further and more comprehensive investigations are warranted to delineate the musculoskeletal toxicity profile of ICIs and characterize these adverse events. Material and methods: The present study employed the FDA Adverse Event Reporting System database to collect adverse events between January 2010 and March 2021. We utilized both the reporting odds ratio and the Bayesian confidence propagation neural network algorithms to identify suspected musculoskeletal adverse events induced by ICIs. Subsequently, the clinical characteristics and comorbidities of the major musculoskeletal adverse events were analyzed. The risk of causing these events with combination therapy versus monotherapy was compared using logistic regression model and Ω shrinkage measure model. Results: The musculoskeletal toxicity induced by ICIs primarily involves muscle tissue, including neuromuscular junctions, fascia, tendons, and tendon sheaths, as well as joints, spine, and bones, including cartilage. The toxicity profile of PD-1/PD-L1 and CTLA-4 inhibitors varies, wherein the PD-1 inhibitor pembrolizumab exhibits a heightened overall risk of inducing musculoskeletal adverse events. The major ICIs-induce musculoskeletal adverse events, encompassing conditions such as myositis, neuromyopathy (including myasthenia gravis, Lambert-Eaton myasthenic syndrome, Guillain-Barré syndrome, and Chronic inflammatory demyelinating polyradiculoneuropathy), arthritis, fractures, myelitis, spinal stenosis, Sjogren's syndrome, fasciitis, tenosynovitis, rhabdomyolysis, rheumatoid myalgia, and chondrocalcinosis. Our study provides clinical characteristics and comorbidities of the major ICIs-induced musculoskeletal adverse events. Furthermore, the combination therapy of nivolumab and ipilimumab does not result in a statistically significant escalation of the risk associated with the major musculoskeletal adverse events. Conclusion: Immune checkpoint inhibitors administration triggers a range of musculoskeletal adverse events, warranting the optimization of their management during clinical practice.

Exploring the impact of design criteria for reference sets on performance evaluation of signal detection algorithms: The case of drug-drug interactions

PURPOSE

To evaluate the impact of multiple design criteria for reference sets that are used to quantitatively assess the performance of pharmacovigilance signal detection algorithms (SDAs) for drug-drug interactions (DDIs).

METHODS

Starting from a large and diversified reference set for two-way DDIs, we generated custom-made reference sets of various sizes considering multiple design criteria (e.g., adverse event background prevalence). We assessed differences observed in the performance metrics of three SDAs when applied to FDA Adverse Event Reporting System (FAERS) data.

RESULTS

For some design criteria, the impact on the performance metrics was neglectable for the different SDAs (e.g., theoretical evidence associated with positive controls), while others (e.g., restriction to designated medical events, event background prevalence) seemed to have opposing and effects of different sizes on the Area Under the Curve (AUC) and positive predictive value (PPV) estimates.

CONCLUSIONS

The relative composition of reference sets can significantly impact the evaluation metrics, potentially altering the conclusions regarding which methodologies are perceived to perform best. We therefore need to carefully consider the selection of controls to avoid misinterpretation of signals triggered by confounding factors rather than true associations as well as adding biases to our evaluation by "favoring" some algorithms while penalizing others.

Drug interactions with apixaban: A systematic review of the literature and an analysis of VigiBase, the World Health Organization database of spontaneous safety reports

Apixaban, a direct oral anticoagulant, has emerged over the past few years because it is considered to have a low risk of drug‐drug interactions compared to vitamin K antagonists. To better characterize these interactions, we systematically reviewed studies evaluating the drug‐drug interactions involving apixaban and analyzed the drug‐drug interactions resulting in an adverse drug reaction reported in case reports and VigiBase. We systematically searched Medline, Embase, and Google Scholar up to 20 August 2018 for articles that investigated the occurrence of an adverse drug reaction due to a potential drug interacting with apixaban. Data from VigiBase came from case reports retrieved up to the 2 January 2018, where identification of potential interactions is performed in terms of two drugs, one adverse drug reaction triplet and potential signal detection using Omega, a three‐way measure of disproportionality. We identified 15 studies and 10 case reports. Studies showed significant variations in the area under the curve for apixaban and case reports highlighted an increased risk of hemorrhage or thromboembolic events due to a drug‐drug interaction. From VigiBase, a total of 1617 two drugs and one adverse drug reaction triplet were analyzed. The most reported triplet were apixaban—aspirin—gastrointestinal hemorrhage. Sixty‐seven percent of the drug‐drug interactions reported in VigiBase were not described or understood. In the remaining 34%, the majority were pharmacodynamic drug‐drug interactions. These data suggest that apixaban has significant potential for drug‐drug interactions, either with CYP3A/P‐gp modulators or with drugs that may impair hemostasis. The most described adverse drug reactions were adverse drug reactions related to hemorrhage or thrombosis, mostly through pharmacodynamic interactions. Pharmacokinetic drug‐drug interactions seem to be poorly detected.

Drug-Drug Interactions Leading to Adverse Drug Reactions with Rivaroxaban: A Systematic Review of the Literature and Analysis of VigiBase

Rivaroxaban has become an alternative to vitamin K antagonists, which are considered to be at higher risk of drug-drug interactions (DDI) and more difficult to use. However, DDI do occur. We systematically reviewed studies that evaluated them and analysed DDI and subsequent adverse drug reactions (ADR) reported in spontaneous reports and VigiBase. We systematically searched articles that explored DDI with rivaroxaban up to 20 August 2018 via Medline, Embase and Google Scholar. Data from VigiBase came from spontaneous reports recovered up to 2 January 2018, where Omega was used to detect signals and identify potential interactions in terms of triplets with two drugs and one ADR. We identified 31 studies and 28 case reports. Studies showed significant variation in the pharmacokinetic for rivaroxaban, and an increased risk of haemorrhage or thromboembolic events due to DDI was highlighted in case reports. From VigiBase, a total of 21,261 triplets were analysed and the most reported was rivaroxaban–aspirin–gastrointestinal haemorrhage. In VigiBase, only 34.8% of the DDI reported were described or understood, and most were pharmacodynamic DDI. These data suggest that rivaroxaban should be considered to have significant potential for DDI, especially with CYP3A/P-gp modulators or with drugs that impair haemostasis.

Artificial Intelligence, Real-World Automation and the Safety of Medicines

Despite huge technological advances in the capabilities to capture, store, link and analyse data electronically, there has been some but limited impact on routine pharmacovigilance. We discuss emerging research in the use of artificial intelligence, machine learning and automation across the pharmacovigilance lifecycle including pre-licensure. Reasons are provided on why adoption is challenging and we also provide a perspective on changes needed to accelerate adoption, and thereby improve patient safety. Last, we make clear that while technologies could be superimposed on existing pharmacovigilance processes for incremental improvements, these great societal advances in data and technology also provide us with a timely opportunity to reconsider everything we do in pharmacovigilance operations to maximise the benefit of these advances.

Propensity score-adjusted three-component mixture model for drug-drug interaction data mining in FDA Adverse Event Reporting System

With increasing trend of polypharmacy, drug-drug interaction (DDI)-induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score-adjusted three-component mixture model (PS-3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug-drug-ADE combinations in FDA Adverse Event Reporting System (FAERS), which is a preeminent SRS database. In simulation studies, PS-3CMM performs better in detecting true DDIs comparing to the existing approach. It is more sensitive in selecting the DDI signals that have nonpositive individual drug relative ADE risk (NPIRR). The application of PS-3CMM is illustrated in analyzing the FAERS database. Compared to the existing approaches, PS-3CMM prioritizes DDI signals differently. PS-3CMM gives high priorities to DDI signals that have NPIRR. Both simulation studies and FAERS data analysis conclude that our new PS-3CMM is a new method that is complement to the existing DDI signal detection methods.

Myopathy with DPP-4 inhibitors and statins in the real world: investigating the likelihood of drug-drug interactions through the FDA adverse event reporting system

AIMS

To investigate the occurrence of myopathy with oral glucose-lowering drugs (OGLDs) and statins, with a focus on dipeptidyl peptidase-4 inhibitors (DPP4-is).

METHODS

The FDA adverse event reporting system (FAERS) was queried (2004-2016) to compare the proportion of adverse events with OGLDs, alone and in combination with statins, using the reporting odds ratio (ROR) with relevant 95% confidence interval (95%Cl), adjusted for sex, age and concomitant presence of other OGLDs/lipid-lowering drugs. Drug-drug interaction is claimed whenever the frequency of an event is enhanced by combination treatment. Consistency/robustness of findings was tested by applying additive/multiplicative models and accounting for competition bias (i.e., adverse events previously known to be associated with OGLDs were removed).

RESULTS

Over a 13-year period, we retrieved 142,888 cases of myopathy. The use of DPP4-is alone was not associated with higher reporting of myopathy (no. of cases = 4898; adjusted ROR = 1.00; 95%CI = 0.96-1.04), with the notable exclusion of vildagliptin (262; 1.64; 1.42-1.88). No increased occurrence emerged when used in combination with statins, with consistent findings from additive/multiplicative models for all DPP4-is. Likewise, no increased reporting was found for other OGLDs (28,964; 0.64; 0.62-0.67); data on the interaction with statins were consistent/robust across analyses only for sulfonylureas (the interaction is likely and biologically plausible) and sodium glucose cotransporter-2 inhibitors.

CONCLUSIONS

Real-world FAERS data do not raise concern for muscular toxicity with DPP4-is in combination with statins, making a drug interaction very unlikely.

Pharmacovigilance of sodium-glucose co-transporter-2 inhibitors: What a clinician should know on disproportionality analysis of spontaneous reporting systems

Sodium-glucose co-transporter-2 inhibitors (SGLT2-Is) have consistently demonstrated a clinically significant reduction of cardiovascular mortality. However, their safety in clinical practice is still incompletely characterized, and post-marketing monitoring is required considering the expected increase in clinical use. Different analyses of international spontaneous reporting systems, known as disproportionality analyses (DAs), have highlighted the occurrence of ketoacidosis, amputations, acute renal failure and skin toxicity. In this viewpoint, we critically appraise these pharmacovigilance data on SGLT2-Is, with the aim of supporting clinicians in proper interpretation of these studies, and discussing their risk-benefit profile. To this aim, we offer a broad perspective on basic technical aspects subtending DAs of spontaneous reporting databases (describing peculiarities of the Food and Drug Administration Adverse Event Reporting System), their common and evolving uses, key pitfalls in presenting study results (in terms of "risk" or "association") and relevant strategies to account for major confounders. This will also facilitate reviewers and editors in proper evaluation of DAs, and prompt pharmacovigilance experts in converging towards a set of minimum requirements in standardization of design, performance and reporting of DAs. A consensus on quality assessment of DAs will finally establish their transferability to clinical practice. It is anticipated that DAs cannot be used per se as a standalone approach to assess a drug-related risk and cannot replace clinical judgment in the individual patient.

Integration of botanicals in contemporary medicine: road blocks, checkpoints and go-ahead signals

The use of botanicals for maintaining good health and preventing diseases is undisputed. The claimed health benefits of natural health products and herbal medicines are based on traditional claims, positive results obtained in preclinical studies and early phase clinical trials that are not backed by safety and efficacy evidences approved by regulatory agencies. Although, the popularity of botanicals is growing, health care practitioners of modern medicine seldom recommend their use because of ill equipped database of their safety and potency. This review discusses problems that preclude botanicals from integrating into the mainstream contemporary therapeutics and cues that provide impetus for their realisation.

Data mining of the public version of the FDA Adverse Event Reporting System

The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, formerly AERS) is a database that contains information on adverse event and medication error reports submitted to the FDA. Besides those from manufacturers, reports can be submitted from health care professionals and the public. The original system was started in 1969, but since the last major revision in 1997, reporting has markedly increased. Data mining algorithms have been developed for the quantitative detection of signals from such a large database, where a signal means a statistical association between a drug and an adverse event or a drug-associated adverse event, including the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM). A survey of our previous reports suggested that the ROR provided the highest number of signals, and the EBGM the lowest. Additionally, an analysis of warfarin-, aspirin- and clopidogrel-associated adverse events suggested that all EBGM-based signals were included in the PRR-based signals, and also in the IC- or ROR-based ones, and that the PRR- and IC-based signals were in the ROR-based ones. In this article, the latest information on this area is summarized for future pharmacoepidemiological studies and/or pharmacovigilance analyses.

Characterisation of non-warfarin-associated bleeding events reported to the Norwegian spontaneous reporting system

OBJECTIVE

The aim of the study was to analyse non-warfarin-associated bleeding adverse drug events reported to the Norwegian spontaneous reporting system, with characterisation of the bleeding locations, outcome and drug interactions. In addition, concordance in assessments between reporters and evaluators, trend shifts in reporting, and detection of potentially new adverse drug interaction signals were studied.

METHODS

Data on bleeding events reported between 1 January 2003 and 31 December 2005 were retrieved from the Norwegian spontaneous reporting system database.

RESULTS

Of 327 case reports of non-warfarin-associated bleeding events, 270 reports (82.6 %) were characterised as serious and 69 (21.1 %) had a fatal outcome. One hundred and eighty-seven bleeds (57.5 %) were gastrointestinal, 57 (17.4 %) were cerebral, and 81 (24.8 %) were from other bleeding sites. The bleeding sites differed with respect to the patient's age, drug use, diagnoses and outcomes. Of drugs associated with bleeding, nonsteroidal anti-inflammatory drugs (NSAIDs)/COX-2 inhibitors (145 reports) and acetylsalicylic acid (128 reports) were most frequently used. Only fibrinolytics were associated with increased mortality. There was a 67.4 % correlation between reporters and evaluators in assessment of drugs associated with bleeding (P < 0.001), with considerable variation in concordance between drug groups.

CONCLUSION

Non-warfarin-associated bleeding events are associated with substantial mortality. Old age, cerebral bleeds, number of drugs used, and use of fibrinolytics are all independently associated with increased mortality. The recognition of the bleeding risk of commonly used drugs such as acetylsalicylic acid and heparins may be insufficient among prescribers.

Pharmacovigilance of herbal medicine

Pharmacovigilance is essential for developing reliable information on the safety of herbal medicines as used in Europe and the US. The existing systems were developed for synthetic medicines and require some modification to address the specific differences of medicinal herbs. Traditional medicine from many different cultures is used in Europe and the US which adds to the complexities and difficulties of even basic questions such as herb naming systems and chemical variability. Allied to this also is the perception that a 'natural' or herbal product must be safe simply because it is not synthetic which means that the safety element of monitoring for such medicines can be overlooked because of the tag associated with such products. Cooperation between orthodox physicians and traditional practitioners is needed to bring together the full case details. Independent scientific assistance on toxicological investigation, botanical verification can be invaluable for full evaluation of any case report. Systematic pharmacovigilance is essential to build up reliable information on the safety of herbal medicines for the development of appropriate guidelines for safe effective use.