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

A Bayesian method to detect drug-drug interaction using external information for spontaneous reporting system

Due to the insufficiency of safety assessments of clinical trials for drugs, further assessments are required for post-marketed drugs. In addition to adverse drug reactions (ADRs) induced by one drug, drug-drug interaction (DDI)-induced ADR should also be investigated. The spontaneous reporting system (SRS) is a powerful tool for evaluating the safety of drugs continually. In this study, we propose a novel Bayesian method for detecting potential DDIs in a database collected by the SRS. By applying a power prior, the proposed method can borrow information from similar drugs for a drug assessed DDI to increase sensitivity of detection. The proposed method can also adjust the amount of the information borrowed by tuning the parameters in power prior. In the simulation study, we demonstrate the aforementioned increase in sensitivity. Depending on the scenarios, approximately 20 points of sensitivity of the proposed method increase from an existing method to a maximum. We also indicate the possibility of early detection of potential DDIs by the proposed method through analysis of the database shared by the Food and Drug Administration. In conclusion, the proposed method has a higher sensitivity and a novel criterion to detect potential DDIs early, provided similar drugs have similar observed-expected ratios to the drug under assessment.

Sodium-glucose cotransporter-2 inhibitors and abnormal serum potassium: a real-world, pharmacovigilance study

BACKGROUND

New trials indicated a potential of sodium-glucose cotransporter-2 inhibitors (SGLT2i) to reduce hyperkalemia, which might have important clinical implications, but real-world data are limited. Therefore, we examined the effect of SGLT2i on hyper- and hypokalemia occurrence using the FDA adverse event reporting system (FAERS).

METHODS

The FAERS database was retrospectively queried from 2004q1 to 2021q3. Disproportionality analyses were performed based on the reporting odds ratio (ROR) and 95% confidence interval (CI).

RESULTS

There were 84 601 adverse event reports for SGLT2i and 1 321 186 reports for other glucose-lowering medications. The hyperkalemia reporting incidence was significantly lower with SGLT2i than with other glucose-lowering medications (ROR, 0.83; 95% CI, 0.79-0.86). Reductions in hyperkalemia reports did not change across a series of sensitivity analyses. Compared with that with renin-angiotensin-aldosterone system inhibitors (RAASi) alone (ROR, 4.40; 95% CI, 4.31-4.49), the hyperkalemia reporting incidence was disproportionally lower among individuals using RAASi with SGLT2i (ROR, 3.25; 95% CI, 3.06-3.45). Compared with that with mineralocorticoid receptor antagonists (MRAs) alone, the hyperkalemia reporting incidence was also slightly lower among individuals using MRAs with SGLT-2i. The reporting incidence of hypokalemia was lower with SGLT2i than with other antihyperglycemic agents (ROR, 0.79; 95% CI, 0.75-0.83).

CONCLUSION

In a real-world setting, hyperkalemia and hypokalemia were robustly and consistently reported less frequently with SGLT2i than with other diabetes medications. There were disproportionally fewer hyperkalemia reports among those using SGLT-2is with RAASi or MRAs than among those using RAASi or MRAs alone.

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.

Safety signals of albumin-bound paclitaxel: Data mining of the Food and Drug Administration adverse event reporting system

BACKGROUND

With the extensive application of paclitaxel for injection (albumin-bound), its adverse reactions have also received increasing attention.

AIM

This study aims to provide a reference for the safe use of albumin-bound paclitaxel in clinical practice; adverse drug events signals of albumin-bound paclitaxel were reviewed and identified by data mining of the Food and Drug Administration (FDA) adverse event reporting system (FAERS).

METHODS

The reporting odds ratio method was used for the quantitative detection of signals from the data in the FDA public data program (OpenFDA) during 2004-2019 for the albumin-bound paclitaxel.

RESULTS

According to the OpenFDA, 1659 adverse events (AEs) were identified for albumin-bound paclitaxel. AEs were mostly observed in females rather than males, aged 45-64 years. AEs involved 17 system organ classes, mainly blood and lymphatic, gastrointestinal, hepatobiliary, respiratory, thoracic, and mediastinal systems, and general AEs. Safety signals were found in 20 unexpected adverse drug reactions which are not listed on drug labels, mainly including macular edema and lymphopenia.

CONCLUSION

Identifying and evaluating albumin-bound paclitaxel-associated AEs signals by mining FAERS may help evaluate the safety profiles of albumin-bound paclitaxel and reduce the risk of medical treatment. In the clinical application of albumin-bound paclitaxel in addition to the adverse reactions mentioned in the drug instructions, lymphocyte changes should be paid close attention to, and eye monitoring should be conducted regularly to avoid drug withdrawal or organ damage caused by adverse reactions.

Prevalence and predictors of clinically significant statin-drug interactions among Yemeni patients taking statins for primary and secondary prevention of cardiovascular disease

BACKGROUND

Statins are extensively used in clinical practice for the primary and secondary prevention of cardiovascular diseases. Statins are usually taken in combination with other medications. This may increase the risk of statin-drug interactions. The study aimed to evaluate the prevalence, patterns, and predictors of clinically significant statin-drug interactions among patients on statin therapy.

MATERIAL AND METHODS

A cross-sectional study was conducted at the cardiology, endocrine, and internal medicine outpatient clinics in four tertiary care hospitals in Sana'a, Yemen. Lexicomp Drug Interaction database was used to analyze the prescriptions for potential statin-drug interactions. Binary and multivariable logistic regression were utilized for analysis.

RESULTS

Of the total number of patients (634), 114 individuals (18%) had a total of 122 statin-drug interactions. According to Lexicomp risk classification, 102 (83.6%) DDIs were class C (monitor therapy), 19 (15.6%) were class D (therapy modification), and only one (0.8%) class X (avoid combination). Simvastatin use was significantly associated with the presence of category D and X DDIs (15.9% vs. 1.6%, p < .001). Polypharmacy (OR = 2.571, p < .001) and having ≥3 comorbidities (OR = 2.512, p < .001) were the only variables associated with the presence of statin-drug interactions (C, D, and/or X).

CONCLUSION

Patients with polypharmacy and those with three or more comorbidities had a higher risk for statin-drug interactions. Therefore, routine screening by physicians and pharmacists for potential interactions should occur before prescribing or dispensing any medication to avoid clinically significant statin-drug interactions.

Peripheral Neuropathy During Concomitant Administration of Proteasome Inhibitors and Factor Xa Inhibitors: Identifying the Likelihood of Drug-Drug Interactions

Backgrounds: Proteasome inhibitors (PI) cause toxic peripheral neuropathy (PN), which is one of the dose-limiting adverse events of these treatments. Recent preclinical studies find that factor Xa inhibitor (FXaI), rivaroxaban, promotes PN in animals receiving oxaliplatin. Cancer patients can receive combined therapy of PI and FXaI. This study aimed to identify and characterize the interaction signals for the concomitant use of PI and FXaI resulting in PN.

Methods: Reports from the United States FDA Adverse Event Reporting System (FAERS) were extracted from the first quarter of 2004 to the first quarter of 2020 for analysis. The Standardized Medical Dictionary for Regulatory Activities (MedDRA) query was used to identify PN cases. We conducted an initial disproportionality investigation to detect PN adverse event signals associated with the combined use of PI and FXaI by estimating a reporting odds ratio (ROR) with a 95% confidence interval (CI). The adjusted RORs were then analyzed by logistic regression analysis (adjusting for age, gender, and reporting year), and additive/multiplicative models were performed to further confirm the findings. Additionally, subset data analysis was performed on the basis of a single drug of PI and FXaI.

Results: A total of 159,317 adverse event reports (including 2,822 PN reports) were included. The combined use of PI and FXaI was associated with a higher reporting of PN (RORadj = 7.890, 95%CI, 5.321–11.698). The result remained significant based on additive/multiplicative methods. The observed association was consistent in the analysis restricted to all specific PI agents (bortezomib and ixazomib) and FXaI (rivaroxaban), except apixaban.

Conclusion: Analysis of FAERS data identified reporting associations of PN in the combined use of PI and FXaI, suggesting the need for more robust preclinical and clinical studies to elucidate the relationship.

Drug-Drug Interaction of the Sodium Glucose Co-Transporter 2 Inhibitors with Statins and Myopathy: A Disproportionality Analysis Using Adverse Events Reporting Data

INTRODUCTION

An increased risk of myopathy due to a potential interaction between sodium glucose co-transporter-2 inhibitors (SGLT-2i) and HMG-CoA reductase inhibitors (statins) has been suggested by case reports.

OBJECTIVE

We aimed to assess if the reporting of myopathy is disproportionally higher among people using both SGLT-2i and statins compared to using either SGLT-2i or statins alone.

METHODS

We conducted a disproportionality analysis using data from the US Food and Drug Administration Adverse Event Reporting System (FAERS). We included reports with at least one antihyperglycemic agent. We compared the proportion of myopathy cases to non-cases between those not using SGLT-2i or statins, using SGLT-2i only, statins only, or both. We calculated the reporting odds ratio and 95% confidence interval. We further stratified by individual SGLT-2i and selected statins (rosuvastatin or atorvastatin).

RESULTS

We included 688,388 reports with at least one antihyperglycemic agent recorded, of which 9.80% had at least one SGLT-2i agent. Among all included reports, there were a total of 2202 myopathy cases with the majority, 61.26%, occurring among those using statins alone and only 2.72% of myopathy cases were among those using both SGLT-2i and statins together. Reporting of myopathy was not disproportionally higher among those reporting the use of SGLT-2i with statins (reporting odds ratio 2.95, 95% confidence interval 2.27-3.85) compared to statins alone (reporting odds ratio 6.41, 95% confidence interval 5.86-7.02).

CONCLUSIONS

Reports of myopathy were not disproportionally higher among those using SGLT-2i with statins compared to SGLT-2i or statins alone at the class level. Further observational studies may be needed to better assess this interaction at the agent level.

Musculoskeletal Adverse Events Associated with PCSK9 Inhibitors: Disproportionality Analysis of the FDA Adverse Event Reporting System

Background. Some studies suggest that potential safety issues about PCSK9 inhibitors have not been sufficiently explored in clinical trials, including musculoskeletal adverse events (MAEs). Objective. To examine the association between use of PCSK9 inhibitors with and without concurrent statins and risk of MAEs. Patients and Methods. FDA Adverse Event Reporting System (FAERS) dataset of PCSK9 inhibitors and statins from October 2015 to June 2021 was queried. The reporting odds ratio (ROR) with relevant 95% confidence interval (95% CI) was calculated as the index of disproportionality. Outcome of MAEs of different PCSK9 inhibitors regimens was also investigated. Results. 3,185 cases of PCSK9 inhibitor-associated MAEs were recorded. PCSK9 inhibitor class alone demonstrated a strong link to MAEs (ROR 5.92; 95% CI 5.70-6.15), and evolocumab was associated with more reports of MAEs than alirocumab. Concomitant use with statins leaded to an increased occurrence of MAEs (ROR 32.15 (25.55-40.46)), and the risk differed among different statins. The PCSK9 inhibitors were safer than statins in terms of hospitalization rate and death rate (15.64% vs. 36.83%; 0.72% vs. 3.53%). Conclusions. This pharmacovigilance investigation suggests that PCSK9 inhibitors are associated with MAEs. The risk significantly increases when combined with statins. Increased laboratory and clinical monitoring are required to timely diagnose and manage MAEs.

Verification of the "Upward Variation in the Reporting Odds Ratio Scores" to Detect the Signals of Drug-Drug Interactions

The reporting odds ratio (ROR) is easy to calculate, and there have been several examples of its use because of its potential to speed up the detection of drug-drug interaction signals by using the "upward variation of ROR score". However, since the validity of the detection method is unknown, this study followed previous studies to investigate the detection trend. The statistics models (the Ω shrinkage measure and the "upward variation of ROR score") were compared using the verification dataset created from the Japanese Adverse Drug Event Report database (JADER). The drugs registered as "suspect drugs" in the verification dataset were considered as the drugs to be investigated, and the target adverse event in this study was Stevens-Johnson syndrome (SJS), as in previous studies. Of 3924 pairs that reported SJS, the number of positive signals detected by the Ω shrinkage measure and the "upward variation of ROR score" (Model 1, the Susuta Model, and Model 2) was 712, 2112, 1758, and 637, respectively. Furthermore, 1239 positive signals were detected when the Haldane-Anscombe 1/2 correction was applied to Model 2, the statistical model that showed the most conservative detection trend. This result indicated the instability of the positive signal detected in Model 2. The ROR scores based on the frequency-based statistics are easily inflated; thus, the use of the "upward variation of ROR scores" to search for drug-drug interaction signals increases the likelihood of false-positive signal detection. Consequently, the active use of the "upward variation of ROR scores" is not recommended, despite the existence of the Ω shrinkage measure, which shows a conservative detection trend.

DPP-4 Inhibitors in Combination with Lipid-Lowering Agents and Risk of Serious Muscular Injury: A Nested Case-Control Study in a Nationwide Cohort of Patients with Type 2 Diabetes Mellitus

INTRODUCTION

After a safety warning was issued for a risk of muscular injury associated with dipeptidyl peptidase-4 (DPP-4) inhibitor use, especially when co-prescribed with statins, spontaneous reporting analyses provided conflicting results.

OBJECTIVE

The aim of this study was to investigate the association between DPP-4 inhibitor use and the risk of muscular injury in individuals with type 2 diabetes mellitus using statins or fibrates.

METHODS

We conducted a nested case-control study amongst a cohort of individuals with type 2 diabetes using statins or fibrates, identified from a nationwide French health insurance database (2009-2014). Cases of serious muscular injury were defined as subjects hospitalized for rhabdomyolysis or myopathy, or for whom testing for myoglobin or creatine phosphokinase followed by a change in statin or fibrate prescription (dose decrease, treatment switch, or stop) was identified. Up to ten controls were matched to each case according to sex, age, and type of lipid-lowering agent. Associations between DPP-4 inhibitor use and serious muscular injury were estimated using a multivariate conditional logistic regression model, providing odds ratios (ORs) adjusted for alcoholism, chronic renal failure, hypothyroidism, and number of concomitant drugs.

RESULTS

Within the 35,117 individuals with type 2 diabetes mellitus constituting the source cohort, 437 statin-user cases were identified who were matched to 4358 statin-user controls. Similarly, 54 fibrate-user cases were identified who were matched to 540 fibrate-user controls. The adjusted OR for DPP-4 inhibitor use and serious muscular injury was estimated at 1.0 (95% confidence interval [CI] 0.7-1.2) in statin users and 0.8 (95% CI 0.4-1.9) in fibrate users.

CONCLUSION

In this study, DPP-4 inhibitor use was not associated with an increased risk of serious muscular injury among patients with type 2 diabetes mellitus using statins or fibrates.

Influenza Vaccination and Myo-Pericarditis in Patients Receiving Immune Checkpoint Inhibitors: Investigating the Likelihood of Interaction through the Vaccine Adverse Event Reporting System and VigiBase

Background: Evidence on whether the influenza vaccine could exacerbate immune-related adverse events, including myopericarditis (MP), in patients treated with immune checkpoint inhibitors (ICIs), is still conflicting. We explored this issue through a global real-world approach. Methods: We queried the Vaccine Adverse Event Reporting System (VAERS) and VigiBase to retrieve cases of MP in which the influenza vaccine and ICIs were recorded as suspect and were concomitantly reported. For the included cases, causality assessment and Drug Interaction Probability Scale (DIPS) algorithms were applied. Results: There were 191 and 399 reports of MP with the influenza vaccine that were retrieved (VAERS and VigiBase, respectively). No case of MP reporting the concomitant use of ICIs and the influenza vaccine was found in VAERS, while three cases of myocarditis were retrieved in VigiBase. All of the cases were unclassifiable for a causality assessment because of the lack of data concerning latency. According to the DIPS, one report was categorized as possible and two as doubtful. Conclusion: The paucity of cases coupled with the doubtful causality assessment make the potential interaction between influenza vaccines and ICIs in cancer patients negligible from clinical and epidemiological standpoints. These findings support the cardiovascular safety of the influenza vaccination, which remains strongly recommended in cancer patients, especially in the current COVID-19 era.

Serotonin syndrome by drug interactions with linezolid: clues from pharmacovigilance-pharmacokinetic/pharmacodynamic analysis

Purpose

To characterize the post-marketing reporting of serotonin syndrome (SS) due to drug-drug interactions (DDIs) with linezolid and investigate the relationship with pharmacokinetic/pharmacodynamic (PK/PD) properties of serotonergic agents.

Methods

We queried the worldwide FDA Adverse Event Reporting System to extract SS records due to DDIs where linezolid was reported as suspect. For each serotonergic agent concomitantly reported, proportion of SS reports and mean number of DDIs were calculated and three different “SS reporting zones” were created. Relevant PK (peak concentration, area under plasma concentration curve, volume of distribution (V_D), and lipophilicity) and PD (values of binding affinity (Ki) and IC₅₀ for serotonin reuptake transporter (SERT) and 5-HT_2A) parameters were extracted for each serotonergic agent, and relevant PK/PD indexes were calculated to assess correlation with mean number of DDIs (PV index).

Results

Six hundred sixty-nine reports of SS mentioning linezolid were found, being linezolid-citalopram (N = 69; 10.3%) the most frequently DDI reported. Citalopram and methadone showed respectively the highest proportion of SS reports (0.28%) and the lowest mean number of DDIs (1.41). Citalopram, escitalopram, and methadone emerged as red (i.e., alert)-zone medications: they exhibited high lipophilicity and large V_D (proxies of excellent central nervous system penetration) coupled with high potency. Among PK/PD indexes, a significant correlation with PV index was found for V_D/Ki SERT ratio (p = 0.05).

Discussion

Our integrated approach suggests that linezolid is more likely to cause SS when co-administered with citalopram, escitalopram, and methadone, as inferred from their pharmacological properties. Proper management of SS should be tailored on a case-by-case basis.

Electronic supplementary material

The online version of this article (10.1007/s00228-020-02990-1) contains supplementary material, which is available to authorized users.