Comparison of Signal Detection Algorithms Based on Frequency Statistical Model for Drug-Drug Interaction Using Spontaneous Reporting Systems

Safety concerns of maternal antiseizure medications exposure on perinatal and offspring outcomes: a disproportionality analysis based on FDA adverse event reporting system

BACKGROUND

Many women are exposed to antiseizure medications (ASMs) during pregnancy, raising concerns about pregnancy and offspring health risks. The current safety data remain insufficient, necessitating further investigation.

METHODS

Using data from the FDA Adverse Event Reporting System (2010-2023), this study employed both the Reporting Odds Ratio (ROR) and Bayesian Confidence Propagation Neural Network (BCPNN) for disproportionality analysis of pregnancy and offspring toxicity related to maternal ASM exposure. In addition, we performed signal adjustment by excluding polytherapy cases, and drug-drug interaction (DDI) signals of two ASMs were identified using Ω Shrinkage measures and Chi-square tests.

RESULTS

3,459 mothers were exposed to 23 ASMs, resulting in 10,910 adverse events. 59 malformation signals, 27 adverse perinatal outcome signals, and 35 dysplasia signals were identified. Among traditional ASMs, valproic acid (VPA) and carbamazepine (CBZ) exhibited the highest number of signals, while levetiracetam (LEV), lamotrigine (LTG), lacosamide, gabapentin, and topiramate (TPM) predominated among newer ASMs. Signals for cardiac malformations, adverse neurodevelopment, and adverse offspring growth outcomes were widespread, with the strongest signals for specific outcomes observed for zonisamide [ROR = 14.82, 95% CI: 5.43-40.41], gabapentin [ROR = 52.52, 95% CI: 15.68-175.95], and brivaracetam [ROR = 22.96, 95% CI: 8.42-62.61], respectively. Six DDI signals displayed ≥ 3, including LTG + LEV/VPA associated with malformation, CBZ + lacosamide/LTG, and VPA + clonazepam associated with fetal loss.

CONCLUSIONS

The potential risks associated with LEV and LTG surpass expectations, warranting further evaluation, particularly in combination therapy. In addition, ASMs with widespread signals, such as VPA, CBZ, TPM, and lacosamide, warrant heightened attention.

Development and Validation of an Approach to Assessing Clinical Relevance of Potential Drug-Drug Interactions Inducing Rare but Serious Adverse Events

Evaluating clinical relevance of potential drug-drug interactions is significant for managing safety risks. However, current approaches to the evaluation lack data on rare but serious adverse events. This study aims to develop an approach to assessing clinical relevance of potential drug-drug interactions that induce rare and serious adverse events, and test its performance. In the development, three key dimensions for evaluating clinical relevance were synthesized based on a literature review. A systematic five-step approach was proposed through designated dimensions and discussions within the research team. Subsequently, the approach was applied to patients with depression to validate its ability of demonstrating the dimensions, and exacting data on rare but serious adverse events. The test results showed varying signal intensities among different drug combinations in relation to adverse events including serotonin syndrome, long QT syndrome, and Torsade de Pointes. Advanced age was identified as a confounding factor for the QT prolongation signal. These findings operationalize Dimension one: Probabilities and risk factors for the occurrence of rare and serious adverse events. Besides, in the test, fatality occurred in 22.01% of the cases having drug-triggered QT prolongation. Advancing age was associated with the fatality (odds ratio = 1.03, 95% confidence interval = 1.01-1.07). The findings manifested Dimension two: Magnitude of adverse events and associated factors. Dimension three was achieved by findings of median time-to-onset of fatal serotonin syndrome and QT prolongation, which was one and 8 days, respectively. In summary, the proposed approach demonstrates effects in assessing the clinical relevance of potential drug-drug interactions.

Risk of ophthalmic adverse drug reactions in patients prescribed glucagon-like peptide 1 receptor agonists: a pharmacovigilance study based on the FDA adverse event reporting system database

OBJECTIVE

To investigate the association between glucagon-like peptide 1 receptor agonists (GLP-1 RAs) and ophthalmic adverse drug reactions (OADRs) using data from the FDA Adverse Event Reporting System (FAERS).

METHODS

This retrospective pharmacovigilance study analyzed post-marketing FAERS data from 2018 to 2023 to identify GLP-1 RA-related OADRs. This study employed the Weibull model for time-to-onset (TTO) analysis, Bayesian Information Component analysis for disproportionality comparing GLP-1 RAs with other drugs, and the Ω shrinkage method for co-medication analysis.

RESULTS

FAERS reported 5003 OADRs associated with GLP-1 RAs, including retinopathy and visual impairment. Disproportionality analysis identified significant signals for semaglutide, liraglutide, and exenatide, suggesting potential associations with OADRs. Co-medication analysis indicated that OADRs primarily resulted from GLP-1 RA use. TTO analysis categorized most OADRs as early failures, emphasizing the need for early monitoring.

CONCLUSION

This study emphasizes the importance of ophthalmic surveillance in patients using GLP-1 RAs, particularly semaglutide, dulaglutide, and exenatide. Enhanced monitoring and patient education are essential for timely detection and management of potential OADRs. Regulatory agencies should consider updating drug labels to include comprehensive warnings about OADRs associated with GLP-1 RA therapies.

Detection of Clinically Significant Drug-Drug Interactions in Fatal Torsades de Pointes: Disproportionality Analysis of the Food and Drug Administration Adverse Event Reporting System

BACKGROUND

Torsades de pointes (TdP) is a rare yet potentially fatal cardiac arrhythmia that is often drug-induced. Drug-drug interactions (DDIs) are a major risk factor for TdP development, but the specific drug combinations that increase this risk have not been extensively studied.

OBJECTIVE

This study aims to identify clinically significant, high-priority DDIs to provide a foundation to minimize the risk of TdP and effectively manage DDI risks in the future.

METHODS

We used the following 4 frequency statistical models to detect DDI signals using the Food and Drug Administration Adverse Event Reporting System (FAERS) database: Ω shrinkage measure, combination risk ratio, chi-square statistic, and additive model. The adverse event of interest was TdP, and the drugs targeted were all registered and classified as "suspect," "interacting," or "concomitant drugs" in FAERS. The DDI signals were identified and evaluated using the Lexicomp and Drugs.com databases, supplemented with real-world data from the literature.

RESULTS

As of September 2023, this study included 4313 TdP cases, with 721 drugs and 4230 drug combinations that were reported for at least 3 cases. The Ω shrinkage measure model demonstrated the most conservative signal detection, whereas the chi-square statistic model exhibited the closest similarity in signal detection tendency to the Ω shrinkage measure model. The κ value was 0.972 (95% CI 0.942-1.002), and the Ppositive and Pnegative values were 0.987 and 0.985, respectively. We detected 2158 combinations using the 4 frequency statistical models, of which 241 combinations were indexed by Drugs.com or Lexicomp and 105 were indexed by both. The most commonly interacting drugs were amiodarone, citalopram, quetiapine, ondansetron, ciprofloxacin, methadone, escitalopram, sotalol, and voriconazole. The most common combinations were citalopram and quetiapine, amiodarone and ciprofloxacin, amiodarone and escitalopram, amiodarone and fluoxetine, ciprofloxacin and sotalol, and amiodarone and citalopram. Although 38 DDIs were indexed by Drugs.com and Lexicomp, they were not detected by any of the 4 models.

CONCLUSIONS

Clinical evidence on DDIs is limited, and not all combinations of heart rate-corrected QT interval (QTc)-prolonging drugs result in TdP, even when involving high-risk drugs or those with known risk of TdP. This study provides a comprehensive real-world overview of drug-induced TdP, delimiting both clinically significant DDIs and negative DDIs, providing valuable insights into the safety profiles of various drugs, and informing the optimization of clinical practice.

Safety of combined drug use in patients with cardiovascular and cerebrovascular diseases: an analysis based on the spontaneous reporting database of adverse drug reactions in Hubei Province

Objective

There is a lack of studies investigating the safety of combination regimens specifically for cardiovascular and cerebrovascular diseases. This study aimed to evaluate the safety of combination drugs for cardiovascular and cerebrovascular diseases using real-world data.

Methods

We analyzed adverse drug reaction data received by the Hubei Adverse Drug Reaction Center from the first quarter of 2014 to the fourth quarter of 2022. The safety of combined drugs for cardiovascular and cerebrovascular diseases in different people was assessed using the association rule method and Ω shrinkage measurement.

Results

A total of 53,038 reports were included in this study, revealing 9 signals of adverse reactions caused by combination drugs. The strongest signal found in this study was jaundice caused by the combination of amlodipine and atorvastatin (Ω 0.025:3.08, lift: 1116.69, conviction: 1.75). Additionally, the combination of aspirin with other drugs was associated with hemorrhaging in various organs. Female patients showed a cold signal when taking vitamin C and vitamin B6 together compared to male patients (Ω 0.025:0.89, lift: 7.15, conviction: 1.12). Patients under 60 years old had a palpitations signal when combining eritrea bei sha Tanzania and felodipine (Ω 0.025:0.41, lift: 14.65, conviction: 3.8), and an erythema signal when combining nifedipine (Ω 0.025:0.23, lift: 8.17, conviction: 1.077).

Conclusion

Among the 9 signals identified in this study, 4 were off-label adverse drug reactions that require further clinical research for exploration and confirmation, in order to provide more scientifically informed drug labeling. Five adverse events associated with aspirin-induced bleeding were identified. Notably, different adverse drug reactions were observed in different populations, suggesting the need for future studies to expedite the development of personalized medicine.

Real-world study of adverse events associated with triptan use in migraine treatment based on the U.S. Food and Drug Administration (FDA) adverse event reporting system (FAERS) database

BACKGROUND

Triptans selectively agoniste 5-Hydroxytryptamine(5-HT) receptors and are widely used in the treatment of migraine. Nevertheless, there is a dearth of comprehensive real-world clinical research on the safety of triptans. In light of the growing prevalence of migraine, it is imperative to gain a deeper understanding of the true extent of adverse events (AEs) associated with triptans in the clinical management of migraine.

METHODS

A database query of AEs reported to the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database for triptans was performed using the online platform Open Vigil 2.1. The query spanned the period from 1 January 2018 to 31 December 2023 and extracted all AEs for 'sumatriptan', 'zolmitriptan', 'rizatriptan', and 'naratriptan' from the 15-49 years old population and retrospective quantitative analyses. A proportional reporting ratio (PRR), reporting odds ratio (ROR), and Bayesian Confidence Propagation Neural Network (BCPNN) methodology were utilized to contrast AEs across the four triptans.

RESULTS

A total of 1.272 AEs reports for sumatriptan, 114 for zolmitriptan, 162 for rizatriptan, and 15 for naratriptan were identified. The ratio of females to males was approximately three times higher in all cases, with the highest number of reports originating from the Americas. A review of the FAERS database revealed that nervous system disorders were the primary SOC category for four drugs, with all four drugs exhibiting the AE indicative of reversible cerebral vasoconstriction syndrome, also classified as Nervous system disorders. The most frequently reported AE signal for sumatriptan was dyspnea, which is classified as respiratory, thoracic and mediastinal disorders. The most frequently reported AEs signals for the remaining three drugs were nausea, vomiting and terminal ileitis, all of which are classified as gastrointestinal disorders.

CONCLUSION

Analyses have demonstrated that AEs are present in a range of systems, including cardiac, nervous, gastrointestinal, and musculoskeletal disorders. It should be noted, however, that the incidence and signal intensity of these AEs vary depending on the specific drug in question. In clinical practice, the selection of an appropriate drug and the monitoring of AEs should be tailored to the individual patient's and specific characteristics.

Osteonecrosis of the jaw in patients with clear cell renal cell carcinoma treated with targeted agents: a case series and large-scale pharmacovigilance analysis

Objective

To optimize the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) for cancer patients, we characterized and evaluated ONJ related to TKIs and ICIs by analyzing a public database and reviewing the relevant literature. TKIs and ICIs are limited to drugs that treat renal cancer recommended by the National Comprehensive Cancer Network Clinical Practice Guidelines in Oncology for Kidney Cancer.

Methods

We described a case series of patients experiencing ONJ while on TKIs or ICIs. We also analyzed spontaneous reports submitted to the FAERS in an observational and retrospective manner between January 2004 and December 2022. Selecting ONJ' adverse events to TKIs and ICIs. Associations between TKIs, ICIs and ONJ were assessed using reporting odds ratios (ROR), drug interaction signals based on the Ω shrinkage measure.

Results

29 patients with ONJ events while on TKIs and ICIs were included in our case series. 240 were related to ONJ AEs. Specifically, 32.1% ICSRs were linked to sunitinib, 16.7% to lenvatinib, 12.9% to pazopanib, 12.5% to nivolumab, 10.0% to axitinib, 5.4% to sorafenib, 5.0% to pembrolizumab, 4.2% to cabozantinib, and 1.3% to ipilimumab. More ICSRs were generally seen in male and reported in Europe. The median age was 63 years. Renal cancer and lung cancer was the most common indication for TKIs and ICIs, respectively. Excluding missing data, the prevalence of mortality was highest for sunitinib-related ONJ ICSRs (18.5%), followed by sorafenib-related ONJ ICSRs (15.4%). With the criteria of ROR, sunitinib and lenvatinib were significantly associated with ONJ AEs. With the criteria of Ω, nivolumab + cabozantinib was significantly associated with ONJ AEs.

Conclusion

TKIs and ICIs have been reported to have significant ONJ side effects. Patients and physicians need to recognize and monitor these potentially fatal adverse events.

Adverse reactions of immune checkpoint inhibitors combined with Proton pump inhibitors: a pharmacovigilance analysis of drug-drug interactions

BACKGROUND

Combining immune checkpoint and proton pump inhibitors is widely used in cancer treatment. However, the drug-drug interactions of these substances are currently unknown. This study aimed to explore drug-drug interactions associated with concomitant immune checkpoint and proton pump inhibitors.

METHODS

Data were obtained from the US Food and Drug Administration Adverse Event Reporting System from 2014 to 2023. Disproportionality analysis was used for data mining by calculating the reporting odds ratios (RORs) with 95% confidence intervals (95%Cls). The adjusted RORs (RORadj) were then analysed using logistic regression analysis, considering age, sex, and reporting year. Drug-drug interactions occur when a combination treatment enhances the frequency of an event. Further confirmation of the robustness of the findings was achieved using additive and multiplicative models, which are the two statistical methodologies for signal detection of DDIs using spontaneous reporting system.

RESULTS

The total number of reports on immune checkpoint combined with proton pump inhibitors was 4,276. Median patient age was 66 years (interquartile range [IQR]: 60-74 years). Significant interaction signals were observed for congenital, familial and genetic disorders (RORadj = 2.66, 95%CI, 1.38-5.14, additive models = 0.7322, multiplicative models = 3.5142), hepatobiliary disorders (RORcrude = 6.64, 95%CI, 5.82-7.58, RORadj = 7.10, 95%CI, 6.16-8.18, additive models = 2.0525, multiplicative models = 1.1622), metabolism and nutrition disorders (RORcrude = 3.27, 95%CI, 2.90-3.69, RORadj = 2.66, 95%CI, 2.30-3.08, additive models = 0.6194), and skin and subcutaneous tissue disorders (RORcrude = 1.41, 95%CI, 1.26-1.58, RORadj = 1.53, 95%CI, 1.34-1.75, additive models = 0.6927, multiplicative models = 5.3599). Subset data analysis showed that programmed death-1 combined with proton pump inhibitors was associated with congenital, familial, and genetic disorders; hepatobiliary disorders; and skin and subcutaneous tissue disorders. Programmed death ligand-1 combined with proton pump inhibitors was associated with adverse reactions of metabolism and nutrition disorders. Cytotoxic T-lymphocyte antigen-4 combined with proton pump inhibitors was associated with congenital, familial, and genetic disorders, and skin and subcutaneous tissue disorders.

CONCLUSIONS

Based on real-world data, four Standardized MedDRA Query System Organ Class toxicities were identified as drug-drug interactions associated with combining immune checkpoint and proton pump inhibitors. Clinicians should be cautious when administering these drugs concomitantly. Preclinical trials and robust clinical studies are required to explore the mechanisms and relationships underlying interactions, thus improving understanding of drug-drug interactions associated with this combination therapy.

Rhabdomyolysis associated with concomitant use of colchicine and statins in the real world: identifying the likelihood of drug-drug interactions through the FDA adverse event reporting system

Background

Currently, there remains substantial controversy in research regarding whether the concomitant use of colchicine and statins increases the occurrence of rhabdomyolysis, warranting further substantiation.

Objective

This study aimed to identify the likelihood drug-drug interactions (DDIs) for the co-administration of colchicine and statins resulting in rhabdomyolysis.

Methods

A disproportionality analysis was conducted by using data sourced from the US Food and Drug Administration Adverse Event Reporting System (FAERS) to detect rhabdomyolysis signals associated with the combined use of colchicine and statins. The association between (colchicine/statins/colchicine and statins) and rhabdomyolysis were evaluated using information component (IC). DDI signals were calculated based on the Ω shrinkage measure and Bayesian confidence propagation neural network (BCPNN) method. Furthermore, stratification was performed based on colchicine and individual statins agents.

Results

In total, 11,119 reports of rhabdomyolysis were identified in the FAERS database, 255 (2.29%) involved both colchicine and statins. Our analysis showed potential DDI signals of rhabdomyolysis (Ω025 = 1.17) among individuals concurrent use of colchicine and statins. Moreover, further drug-specific analysis suggests DDI signals in the colchicine-atorvastatin pair (Ω025 = 1.12), and colchicine-rosuvastatin pair (Ω025 = 1.05), along with a higher proportion of rhabdomyolysis (IC025 = 5.20) and (IC025 = 4.26), respectively.

Conclusion

The findings suggest that concomitant use of colchicine and statins may increase the risk of rhabdomyolysis, particularly when combined with atorvastatin or rosuvastatin. Therefore, healthcare professionals should pay special attention to life-threatening AE such as rhabdomyolysis, when co-prescribing colchicine statins.

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.

Clinical drug interactions between linezolid and other antibiotics: For adverse drug event monitoring

Detailed data on safety associated with drug-drug interactions (DDIs) between Linezolid (LZD) and other antibiotics are limited. The aim of this study was to investigate the safety signals related to these DDIs and to provide a reference for clinically related adverse drug event monitoring. Adverse event (AE) information from 1 January 2004 to 16 June 2022 of the target antibiotics including LZD using alone or in combination with LZD was extracted from the OpenVigil FDA data platform for safety signal analysis. The combined risk ratio model, reporting ratio method, Ω shrinkage measure model, and chi-square statistics model were used to analyze the safety signals related to DDIs. Meanwhile, we evaluated the correlation and the influence of sex and age between the drug(s) and the target AE detected. There were 18991 AEs related to LZD. There were 2293, 1726, 4449, 821, 2431, 1053, and 463 AE reports when LZD was combined with amikacin, voriconazole, meropenem, clarithromycin, levofloxacin, piperacillin-tazobactam, and azithromycin, respectively. Except for azithromycin, there were positive safety signals related to DDIs between LZD and these antibiotics. These DDIs might influence the incidence of 13, 16, 7, 7, 6, and 15 types of AEs, respectively, and is associated with higher reporting rates of AEs compared with use alone. Moreover, sex and age might influence the occurrence of AEs. We found that the combinations of LZD and other antibiotics are related to multiple AEs, such as hepatotoxicity, drug resistance and electrocardiogram QT prolonged, but further research is still required to investigate their underlying mechanisms. This study can provide a new reference for the safety monitoring of LZD combined with other antibiotics in clinical practice.

Possible adverse events of imidazole antifungal drugs during treatment of vulvovaginal candidiasis: analysis of the FDA Adverse Event Reporting System

Azole antifungal drugs are commonly used to treat vulvovaginal candidiasis (VVC). The nephrotoxicity and developmental toxicity of azole drugs have not been systematically analyzed in the real world. We used the FDA Adverse Event Reporting System (FAERS) to investigate the adverse events (AEs) associated with imidazole therapy for VVC. FAERS data (from quarter 1 2004 to quarter 3 2022) were retrieved using OpenVigil 2.1, and AEs were retrieved and standardized according to the Medical Dictionary for Regulatory Activities (MedDRA). In the top 10 System Organ Class (SOC), all four drugs have been found to have kidney and urinary system diseases and pregnancy. We found significant signals, including clotrimazole [bladder transitional cell carcinoma, (report odds ratio, ROR = 291.66)], [fetal death, (ROR = 10.28)], ketoconazole[nephrogenic anemia (ROR = 22.1)], [premature rupture of membranes (ROR = 22.91 46.45, 11, 3)], Miconazole[hematuria (ROR = 19.03)], [neonatal sepsis (ROR = 123.71)], [spontaneous abortion (ROR = 5.98)], Econazole [acute kidney injury (ROR = 4.41)], [spontaneous abortion (ROR = 19.62)]. We also discovered new adverse reactions that were not reported. Therefore, when using imidazole drugs for treatment, it is necessary to closely monitor the patient's renal function, pay attention to the developmental toxicity of the fetus during pregnancy, and be aware of potential adverse reactions that may occur.

Detection Algorithms for Simple Two-Group Comparisons Using Spontaneous Reporting Systems

Medical science has often used adult males as the standard to establish pathological conditions, their transitions, diagnostic methods, and treatment methods. However, it has recently become clear that sex differences exist in how risk factors contribute to the same disease, and these differences also exist in the efficacy of the same drug. Furthermore, the elderly and children have lower metabolic functions than adult males, and the results of clinical trials on adult males cannot be directly applied to these patients. Spontaneous reporting systems have become an important source of information for safety assessment, thereby reflecting drugs' actual use in specific populations and clinical settings. However, spontaneous reporting systems only register drug-related adverse events (AEs); thus, they cannot accurately capture the total number of patients using these drugs. Therefore, although various algorithms have been developed to exploit disproportionality and search for AE signals, there is no systematic literature on how to detect AE signals specific to the elderly and children or sex-specific signals. This review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.

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.

A simulation-based comparison of drug-drug interaction signal detection methods

Several statistical methods have been proposed to detect adverse drug reactions induced by taking two drugs together. These suspected adverse drug reactions can be discovered through post-market drug safety surveillance, which mainly relies on spontaneous reporting system database. Most previous studies have applied statistical models to real world data, but it is not clear which method outperforms the others. We aimed to assess the performance of various detection methods by implementing simulations under various conditions. We reviewed proposed approaches to detect signals indicating drug-drug interactions (DDIs) including the Ω shrinkage measure, the chi-square statistic, the proportional reporting ratio, the concomitant signal score, the additive model and the multiplicative model. Under various scenarios, we conducted a simulation study to examine the performances of the methods. We also applied the methods to Korea Adverse Event Reporting System (KAERS) data. Of the six methods considered in the simulation study, the Ω shrinkage measure and the chi-square statistic with threshold = 2 had higher sensitivity for detecting the true signals than the other methods in most scenarios while controlling the false positive rate below 0.05. When applied to the KAERS data, the two methods detected one known DDI for QT prolongation and one unknown (suspected) DDI for hyperkalemia. The performance of various signal detection methods for DDI may vary. It is recommended to use several methods together, rather than just one, to make a reasonable decision.

Exploration of the potential association between GLP-1 receptor agonists and suicidal or self-injurious behaviors: a pharmacovigilance study based on the FDA Adverse Event Reporting System database

BACKGROUND

Establishing whether there is a potential relationship between glucagon-like peptide 1 receptor agonists (GLP-1RAs) and suicidal or self-injurious behaviors (SSIBs) is crucial for public safety. This study investigated the potential association between GLP-1RAs and SSIBs by exploring the FDA Adverse Event Reporting System (FAERS) database.

METHODS

A disproportionality analysis was conducted using post-marketing data from the FAERS repository (2018 Q1 to 2022 Q4). SSIB cases associated with GLP-1RAs were identified and analyzed through disproportionality analysis using the information component. The parametric distribution with a goodness-of-fit test was employed to analyze the time-to-onset, and the Ω shrinkage was used to evaluate the potential effect of co-medication on the occurrence of SSIBs.

RESULTS

In total, 204 cases of SSIBs associated with GLP-1RAs, including semaglutide, liraglutide, dulaglutide, exenatide, and albiglutide, were identified in the FAERS database. Time-of-onset analysis revealed no consistent mechanism for the latency of SSIBs in patients receiving GLP-1RAs. The disproportionality analysis did not indicate an association between GLP-1RAs and SSIBs. Co-medication analysis revealed 81 cases with antidepressants, antipsychotics, and benzodiazepines, which may be proxies of mental health comorbidities.

CONCLUSIONS

We found no signal of disproportionate reporting of an association between GLP-1RA use and SSIBs. Clinicians need to maintain heightened vigilance on patients premedicated with neuropsychotropic drugs. This contributes to the greater acceptance of GLP-1RAs in patients with type 2 diabetes mellitus or obesity.

Drug-drug interaction assessment based on a large-scale spontaneous reporting system for hepato- and renal-toxicity, and thrombocytopenia with concomitant low-dose methotrexate and analgesics use

BACKGROUND

Methotrexate (MTX) is the cornerstone of rheumatoid arthritis (RA) treatment and is highly effective with low-dose intermittent administration. MTX is occasionally used in combination with non-steroidal anti-inflammatory drugs (NSAIDs) and acetaminophen (APAP)/paracetamol for pain or inflammation control. With MTX treatment, the side effects, such as hepatotoxicity, renal failure, and myelosuppression should be considered. These are also seen with analgesics treatment.

METHODS

We used a large spontaneously reported adverse event database (FAERS [JAPIC AERS]) to analyze whether the reporting of adverse events increased upon MTX and analgesic therapy in patients with RA.

RESULTS

After identifying RA cases, the crude reporting odds ratios (cRORs) for hepatotoxicity, renal failure, and thrombocytopenia associated with the use of MTX, APAP, or NSAIDs were calculated by disproportionality analysis, which revealed significantly higher cRORs for these events. No analgesics showed consistent positive signals for drug-drug interaction (DDI) with concomitant low-dose MTX analyzed using four algorithms for DDI interaction (the Ω shrinkage measure, additive or multiplicative, and combination risk ratio models). However, in renal failure and thrombocytopenia, loxoprofen (Ω025 = 0.08) and piroxicam (Ω025 = 0.46), and ibuprofen (Ω025 = 0.74) and ketorolac (Ω025 = 3.52), respectively, showed positive signals in the Ω shrinkage measure model, and no consistency was found among adverse events or NSAIDs.

CONCLUSIONS

Studies using spontaneous reporting systems have limitations such as reporting bias or lack of patient background; however, the results of our comprehensive analysis support the results of previous clinical or epidemiological studies. This study also demonstrated the usefulness of FAERS for DDI assessment.

Disproportional signal of pericarditis with biological diseasemodifying antirheumatic drugs (bDMARDs) in patients with ankylosing spondylitis: a disproportionality analysis in the FAERS database

Objective: This study aimed to investigate the potential association between biological disease-modifying antirheumatic drugs (bDMARDs) and pericarditis and uncover relevant clinical characteristics in ankylosing spondylitis (AS). Methods: Reports of pericarditis recorded in the FDA Adverse Event Reporting System (FAERS) (January 2004-December 2022) were identified through the preferred term "pericarditis." Demographic and clinical characteristics were described, and disproportionality signals were assessed through the reporting odds ratio (ROR) and information component (IC). A significant signal was detected if the lower bound of IC (IC025) was more than zero. Results: We found 1,874 reports of pericarditis with bDMARDs (11.3% of cases with fatal outcomes). Adalimumab (IC025 3.24), infliximab (IC025 4.90), golimumab (IC025 5.40), certolizumab (IC025 5.43), etanercept (IC025 3.24), secukinumab (IC025 3.97), and ustekinumab (IC025 7.61) exhibit significant disproportionality signals compared to other medications in the FAERS database. After excluding pre-existing diseases and co-treated drugs that may increase the susceptibility of pericarditis, the disproportionality signal associated with infliximab, certolizumab, etanercept, secukinumab, and ustekinumab remained strong. Pericarditis cases associated with all bDMARDs were predominantly recorded in women aged 25-65 years. Conclusion: More reports of pericarditis were detected with AS patients on bDMARDs than with other drugs in the overall database. Further studies are warranted to investigate the underlying mechanisms and identify patient-related susceptibility factors, thus supporting timely diagnosis and safe(r) prescribing of bDMARDs.

Multivariate generalized mixed-effects models for screening multiple adverse drug reactions in spontaneous reporting systems

Introduction: For assessing drug safety using spontaneous reporting system databases, quantitative measurements, such as proportional reporting rate (PRR) and reporting odds ratio (ROR), are widely employed to assess the relationship between a drug and a suspected adverse drug reaction (ADR). The databases contain numerous ADRs, and the quantitative measurements need to be calculated by performing the analysis multiple times for each ADR. We proposed a novel, simple, and easy-to-implement method to estimate the PRR and ROR of multiple ADRs in a single analysis using a generalized mixed-effects model for signal detection. Methods: The proposed method simultaneously analyzed the association between any drug and numerous ADRs, as well as estimated the PRR and ROR for a specific combination of drugs and suspected ADRs. Furthermore, the proposed method was applied to detect drug-drug interactions associated with the concurrent use of two or more drugs. Results and discussion: In our simulation studies, the false-positive rate and sensitivity of the proposed method were similar to those of the traditional PRR and ROR. The proposed method detected known ADRs when applied to the Food and Drug Administration Adverse Event Reporting System database. As an important advantage, the proposed method allowed the simultaneous evaluation of several ADRs using multiple drugs.

Metformin-Cimetidine Drug Interaction and Risk of Lactic Acidosis in Renal Failure: A Pharmacovigilance-Pharmacokinetic Appraisal

OBJECTIVE

This study aimed to evaluate lactic acidosis (LA) risk when using metformin combined with histamine H2 receptor inhibitors (H2RI) in patients with renal failure (RF).

RESEARCH DESIGN AND METHODS

This study analyzed FDA Adverse Event Reporting System data (2012Q4 to 2022Q4) to characterize reports of LA associated with metformin alone or combined with H2RI. Using a disproportionality approach, LA risk signal in the overall population and in patients with RF was assessed. A physiologically based pharmacokinetic (PBPK) model was developed to predict metformin and cimetidine pharmacokinetic changes following conventional doses of the combinations in patients with various degrees of RF. To explore its correlation with LA risk, a peak plasma metformin concentration of 3 mg/L was considered the threshold.

RESULTS

Following the 2016 U.S. Food and Drug Administration metformin approval for mild-to-moderate RF, the percentage of patients with RF reporting LA associated with metformin combined with H2RI increased. Disproportionality analysis showed reported LA risk signal associated with metformin and cimetidine in the overall population within the study timeframe only. Furthermore, with PBPK simulations, for metformin (1,000 mg b.i.d.) with cimetidine (300 mg q.i.d. or 400 mg b.i.d.) in stage 1 of chronic kidney disease, metformin (1,000 mg b.i.d.) with cimetidine (300 mg q.i.d. or 400 mg b.i.d. or 800 mg q.d.) in stage 2, and most combinations in stage 3, the peak plasma metformin concentrations exceeded the 3 mg/L threshold.

CONCLUSIONS

Metformin combined with cimetidine at conventional doses may cause LA in patients with mild-to-moderate RF.

Adverse reactions of immune checkpoint inhibitors combined with angiogenesis inhibitors: A pharmacovigilance analysis of drug-drug interactions

The combination of immune checkpoint inhibitors (ICIs) and angiogenesis inhibitors (AGIs) is widely used in cancer treatment; however, drug-drug reactions (DDIs) remain unknown. We aimed to identify interaction signals for the concomitant use of ICIs and AGIs. Data were obtained from the US FDA Adverse Event Reporting System (FAERS) from January 1, 2015, to December 31, 2023. Disproportionality analysis was used for data mining by calculating the reporting odds ratio (ROR) and 95% confidence interval (95% CI). Adjusted RORs were analysed using logistic regression analysis, considering age, sex and reporting year. Further confirmation was assessed via additive and multiplicative models. We identified 75,936 reports on ICIs combined with AGIs. Significant interaction signals were observed for hepatobiliary disorders (RORcrude: 5.25, 95% CI: 5.07-5.44, RORadj: 5.01, 95% CI: 4.82-5.22, additive models: 0.2323), investigations (RORcrude: 1.66, 95% CI: 1.62-1.70, RORadj: 1.63, 95% CI: 1.58-1.67, additive models: 0.2187, multiplicative models: 1.1265), renal and urinary disorders (RORcrude: 1.87, 95% CI: 1.80-1.95, RORadj: 1.72, 95% CI: 1.64-1.79, additive models: 0.3239, multiplicative models: 1.1799) and vascular disorders (RORcrude: 1.94, 95% CI: 1.87-2.02, RORadj: 1.87, 95% CI: 1.80-1.95, additive models: 0.5823, multiplicative models: 1.5676). Subset data analysis showed positive interaction signals for PDL-1/CTLA-4 inhibitors + AGI in hepatobiliary disorders, PD-1 inhibitors + AGI in investigations, or PD-1/PDL-1 inhibitors + AGI in renal and urinary/ vascular disorders. Based on FAERS data, four systemic disorders were identified as having DDIs related to the combined use of ICIs and AGIs. Pre-clinical trials are required to explore the mechanisms underlying these interactions.

The role of antipsychotics and other drugs on the development and progression of neuroleptic malignant syndrome

Neuroleptic malignant syndrome (NMS) is a rare but serious and sometimes fatal complication in patients taking antipsychotic drugs, and its underlying mechanism still remains unclear. The pharmacotherapy for psychotic disorders is complicated and often involves a combination of two or more drugs, including drugs other than antipsychotics. In the present study, we used the Japanese Adverse Drug Event Report (JADER) database to broadly investigate the drugs associated with NMS, following their related pathways, as well as the drug-drug interactions (DDIs) in NMS. All analyses were performed using data from the JADER database from April 2004 to May 2022. Single-drug signals were evaluated using the reporting odds ratio (ROR) and proportional reporting ratio (PRR), and drug pathways were investigated using the Kyoto Encyclopedia of Genes and Genomes (KEGG). DDIs were evaluated using the Ω shrinkage measure and Chi-square statistics models. All drugs associated with 20 or more NMS cases in the JADER database exhibited signals for NMS, including non-antipsychotics. Pathways associated with the drugs included the dopaminergic or serotonergic synapses related to antipsychotics. DDIs leading to NMS were confirmed for several drug combinations exhibiting single-drug signals. This study confirmed the significant association of various drugs, including non-psychotics, with NMS and suggested that various pathways related to these drugs may be involved in the progression of NMS. In addition, several combinations of these drugs were found to interact (DDI), increasing the risk of NMS, which suggests that appropriate caution should be taken when administering these drugs.

Prediction of Adverse Effects of Drug-Drug Interactions on Cardiovascular System Based on the Analysis of Structure-Activity Relationships

Co-administration of drugs often leads to drug-drug interactions, which could be accompanied by various adverse drug reactions that pose a threat to life and health of the patient. The effect caused by adverse drug reactions on cardiovascular system is one of the most significant manifestations of drug-drug interaction. Clinical assessment of adverse drug reactions resulting from drug-drug interaction between all drug pairs used in therapeutic practice is not possible. The purpose of this work was to build models using structure-activity analysis to predict adverse effects of drugs on cardiovascular system, mediated by pairwise interactions between the drug pairs when they are taken together. Data on the adverse effects resulting from drug-drug interaction were obtained from the DrugBank database. The data on drug pairs that do not cause such effects, which are necessary for building accurate structure-activity models, were obtained from the TwoSides database, which contains the results of analysis of the spontaneous reports. Two types of descriptors were used to describe a pair of drug structures: PoSMNA descriptors and probabilistic estimates of the prediction of biological activities obtained using the PASS program. Structure-activity relationships were established using the Random Forest method. Prediction accuracy was calculated by means of five-fold cross-validation. The highest accuracy values were obtained using PASS probabilistic estimates as descriptors. The area under the ROC curve was 0.94 for bradycardia, 0.96 for tachycardia, 0.90 for arrhythmia, 0.90 for ECG QT prolongation, 0.91 for hypertension, 0.89 for hypotension.

Association of Pulmonary Sepsis and Immune Checkpoint Inhibitors: A Pharmacovigilance Study

Background: Although some sepsis cases were reported with immune checkpoint inhibitors (ICIs) in clinical trials, the link between pulmonary sepsis and ICIs remains mostly unknown. We aim to investigate the association between pulmonary sepsis and ICIs, and to describe the clinical features. Methods: A disproportionality analysis was performed using FAERS data and compared rates of pulmonary sepsis in cancer patients receiving ICIs vs. other drug regimens (such as chemotherapy and targeted therapy). Associations between ICIs and sepsis were assessed using reporting odds ratios (ROR) and information component (IC). We also detected drug interaction signals based on the Ω shrinkage measure. Age and gender distribution were compared between pulmonary sepsis and all adverse events associated with ICIs. Results: We identified 120 reports of pulmonary sepsis associated with ICIs between Q1, 2011 to Q3, 2021. A total of 82 of 120 (68.3%) patients on ICIs suffered from pulmonary sepsis and progressed to death. In addition, there is no significant difference in age and gender in the occurrence of pulmonary sepsis in cancer patients on ICIs. Overall ICIs, nivolumab, and atezolizumab still have a significant signal of pulmonary sepsis (ROR025 > 1, IC025 > 0, p < 0.001) compared with targeted therapy (such as tyrosine kinase inhibitors) or chemotherapy. Co-administration of ICIs and glucocorticoids or proton pump inhibitors synergistically increased the risk of pulmonary sepsis (Ω025 > 0). Conclusions: Our study suggested ICIs, especially nivolumab and atezolizumab, tended to increase the risk of pulmonary sepsis more than other anticancer regimens. Clinicians should be vigilant in the prevention and management of pulmonary sepsis during ICIs therapy.

Do antibody-drug conjugates increase the risk of sepsis in cancer patients? A pharmacovigilance study

Introduction: Antibody-drug conjugates (ADCs) produce unparalleled efficacy in refractory neoplasms but can also lead to serious toxicities. Although ADC-related sepsis has been reported, the clinical features are not well characterized in real-world studies. Objective: The aim of this study was to identify the association between ADCs and sepsis using FAERS data and uncover the clinical characteristics of ADC-related sepsis. Methods: We performed disproportionality analysis using FAERS data and compared rates of sepsis in cancer patients receiving ADCs vs. other regimens. Associations between ADCs and sepsis were assessed using reporting odds ratios (RORs) and information component (IC). For each treatment group, we detected drug interaction signals, and conducted subgroup analyses (age, gender, and regimens) and sensitivity analyses. Results: A total of 24,618 cases were reported with ADCs between Q1, 2004 and Q3, 2021. Sepsis, septic shock, multiple organ dysfunction syndrome, and other sepsis-related toxicities were significantly associated with ADCs than other drugs in this database. Sepsis and multiple organ dysfunction syndrome have the highest safety concerns with ADCs compared with other anticancer monotherapies. Gemtuzumab ozogamicin and inotuzumab ozogamicin showed increased safety risks than other ADCs. For the top nine ADC-related sepsis, males showed higher sepsis safety concern than females (p <0.001); however, age did not exert influence on the risk of sepsis. We identified that 973 of 2,441 (39.9%) cases had acute myeloid leukemia (AML), and 766 of 2613 (29.3%) cases on ADCs died during therapy. Time-to-onset analysis indicated ADC-related sepsis is prone to occur within a month after administration. Co-administration of ADCs with colony-stimulating factors, proton pump inhibitors, H2-receptor antagonists, or CYP3A4/5 inhibitors showed to synergistically increase the risk of sepsis-related toxicities. Conclusion: Antibody-drug conjugates may increase the risk of sepsis in cancer patients, leading to high mortality. Further studies are warranted to characterize the underlying mechanisms and design preventive measures for ADC-related sepsis.

Detecting drug-drug interactions between therapies for COVID-19 and concomitant medications through the FDA adverse event reporting system

Background: COVID-19 patients with underlying medical conditions are vulnerable to drug-drug interactions (DDI) due to the use of multiple medications. We conducted a discovery-driven data analysis to identify potential DDIs and associated adverse events (AEs) in COVID-19 patients from the FDA Adverse Event Reporting System (FAERS), a source of post-market drug safety.

Materials and Methods: We investigated 18,589 COVID-19 AEs reported in the FAERS database between 2020 and 2021. We applied multivariate logistic regression to account for potential confounding factors, including age, gender, and the number of unique drug exposures. The significance of the DDIs was determined using both additive and multiplicative measures of interaction. We compared our findings with the Liverpool database and conducted a Monte Carlo simulation to validate the identified DDIs.

Results: Out of 11,337 COVID-19 drug-Co-medication-AE combinations investigated, our methods identified 424 signals statistically significant, covering 176 drug-drug pairs, composed of 13 COVID-19 drugs and 60 co-medications. Out of the 176 drug-drug pairs, 20 were found to exist in the Liverpool database. The empirical p-value obtained based on 1,000 Monte Carlo simulations was less than 0.001. Remdesivir was discovered to interact with the largest number of concomitant drugs (41). Hydroxychloroquine was detected to be associated with most AEs (39). Furthermore, we identified 323 gender- and 254 age-specific DDI signals.

Conclusion: The results, particularly those not found in the Liverpool database, suggest a subsequent need for further pharmacoepidemiology and/or pharmacology studies.

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.

ALDH2, ADCY3 and BCMO1 polymorphisms and lifestyle-induced traits are jointly associated with CAD risk in Chinese Han people

BACKGROUNDS

To investigate associations of genetic and environmental factors with coronary artery disease (CAD), we collected medical reports, lifestyle details, and blood samples of 2113 individuals, and then used the polymerase chain reaction (PCR)-ligase detection reaction (LDR) to genotype the targeted 102 SNPs.

METHODS

We adopted elastic net algorithm to build an association model that considered simultaneously genetic and lifestyle/clinical factors associated with CAD in Chinese Han population.

RESULTS

In this study, we developed an all covariates-based model to explain the risk of CAD, which incorporated 8 lifestyle/clinical factors and a gene-score variable calculated from 3 significant SNPs (rs671, rs6751537 and rs11641677), attaining an area under the curve (AUC) value of 0.71. It was found that, in terms of genetic variants, the AA genotype of rs671 in the additive (adjusted odds ratio (OR) = 2.51, p = 0.008) and recessive (adjusted OR = 2.12, p = 0.021) models, the GG genotype of rs6751537 in the additive (adjusted OR = 3.36, p = 0.001) and recessive (adjusted OR = 3.47, p = 0.001) models were associated with increased risk of CAD, while GG genotype of rs11641677 in additive model (adjusted OR = 0.39, p = 0.044) was associated with decreased risk of CAD. In terms of lifestyle/clinical factors, the history of hypertension (unadjusted OR = 2.37, p < 0.001) and dyslipidemia (unadjusted OR = 1.82, p = 0.007), age (unadjusted OR = 1.07, p < 0.001) and waist circumference (unadjusted OR = 1.02, p = 0.05) would significantly increase the risk of CAD, while height (unadjusted OR = 0.97, p = 0.006) and regular intake of chicken (unadjusted OR = 0.78, p = 0.008) reduced the risk of CAD. A significantinteraction was foundbetween rs671 and dyslipidemia (the relative excess risk due to interaction (RERI) = 3.36, p = 0.05).

CONCLUSION

In this study, we constructed an association model and identified a set of SNPs and lifestyle/clinical risk factors of CAD in Chinese Han population. By considering both genetic and non-genetic risk factors, the built model may provide implications for CAD pathogenesis and clues for screening tool development in Chinese Han population.

Detection algorithms and attentive points of safety signal using spontaneous reporting systems as a clinical data source

Continuous evaluation of drug safety is needed following approval to determine adverse events (AEs) in patient populations with diverse backgrounds. Spontaneous reporting systems are an important source of information for the detection of AEs not identified in clinical trials and for safety assessments that reflect the real-world use of drugs in specific populations and clinical settings. The use of spontaneous reporting systems is expected to detect drug-related AEs early after the launch of a new drug. Spontaneous reporting systems do not contain data on the total number of patients that use a drug; therefore, signal detection by disproportionality analysis, focusing on differences in the ratio of AE reports, is frequently used. In recent years, new analyses have been devised, including signal detection methods focused on the difference in the time to onset of an AE, methods that consider the patient background and those that identify drug-drug interactions. However, unlike commonly used statistics, the results of these analyses are open to misinterpretation if the method and the characteristics of the spontaneous reporting system cannot be evaluated properly. Therefore, this review describes signal detection using data mining, considering traditional methods and the latest knowledge, and their limitations.

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.

Improved Detection Criteria for Detecting Drug-Drug Interaction Signals Using the Proportional Reporting Ratio

There is a current demand for “safety signal” screening, not only for single drugs but also for drug-drug interactions. The detection of drug-drug interaction signals using the proportional reporting ratio (PRR) has been reported, such as through using the combination risk ratio (CRR). However, the CRR does not consider the overlap between the lower limit of the 95% confidence interval of the PRR of concomitant-use drugs and the upper limit of the 95% confidence interval of the PRR of single drugs. In this study, we proposed the concomitant signal score (CSS), with the improved detection criteria, to overcome the issues associated with the CRR. “Hypothetical” true data were generated through a combination of signals detected using three detection algorithms. The signal detection accuracy of the analytical model under investigation was verified using machine learning indicators. The CSS presented improved signal detection when the number of reports was ≥3, with respect to the following metrics: accuracy (CRR: 0.752 → CSS: 0.817), Youden’s index (CRR: 0.555 → CSS: 0.661), and F-measure (CRR: 0.780 → CSS: 0.820). The proposed model significantly improved the accuracy of signal detection for drug-drug interactions using the PRR.

Subset Analysis for Screening Drug-Drug Interaction Signal Using Pharmacovigilance Database

Many patients require multi-drug combinations, and adverse event profiles reflect not only the effects of individual drugs but also drug-drug interactions. Although there are several algorithms for detecting drug-drug interaction signals, a simple analysis model is required for early detection of adverse events. Recently, there have been reports of detecting signals of drug-drug interactions using subset analysis, but appropriate detection criterion may not have been used. In this study, we presented and verified an appropriate criterion. The data source used was the Japanese Adverse Drug Event Report (JADER) database; "hypothetical" true data were generated through a combination of signals detected by three detection algorithms. The accuracy of the signal detection of the analytic model under investigation was verified using indicators used in machine learning. The newly proposed subset analysis confirmed that the signal detection was improved, compared with signal detection in the previous subset analysis, on the basis of the indicators of Accuracy (0.584 to 0.809), Precision (= Positive predictive value; PPV) (0.302 to 0.596), Specificity (0.583 to 0.878), Youden's index (0.170 to 0.465), F-measure (0.399 to 0.592), and Negative predictive value (NPV) (0.821 to 0.874). The previous subset analysis detected many false drug-drug interaction signals. Although the newly proposed subset analysis provides slightly lower detection accuracy for drug-drug interaction signals compared to signals compared to the Ω shrinkage measure model, the criteria used in the newly subset analysis significantly reduced the amount of falsely detected signals found in the previous subset analysis.

Antiepileptic combination therapy with Stevens-Johnson syndrome and toxic epidermal necrolysis: Analysis of a Japanese pharmacovigilance database

OBJECTIVE

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are immune-mediated diseases characterized by an extensive loss of the epidermal skin layer, often resulting in death. SJS and TEN are often triggered by certain drugs, including antiepileptic drugs (AEDs). Epilepsy is very difficult to treat and often involves the combination of two or more AEDs. In this study, we quantified not only the risk of SJS or TEN associated with single-AED therapy but also the risk related to concomitant AED treatment using reporting-derived signals.

METHODS

An analysis of the Japanese Adverse Drug Event Report (JADER) database was performed from the first quarter of 2004 to the fourth quarter of 2018. The single-AED signals were evaluated using the proportional reporting ratio (PRR), and the combination therapy signals were evaluated using Ω shrinkage measure and combination risk ratio (CRR).

RESULTS

SJS signals were associated with 11 AEDs, and TEN signals were related to 12 AEDs. Moreover, the following AED combinations were associated with SJS signals: carbamazepine-lorazepam (Ω₀₂₅ : 0.33, CRR: 2.18) and fosphenytoin-lorazepam (Ω₀₂₅ : 0.99, CRR: 39.20). The TEN signals were related to the following combinations: clobazam-gabapentin (Ω₀₂₅ : 0.35, CRR: 3.14), phenytoin-gabapentin (Ω₀₂₅ : 0.03, CRR: 2.18), valproic acid-gabapentin (Ω₀₂₅ : 0.15, CRR: 2.25), clobazam-clonazepam (Ω₀₂₅ : 0.03, CRR: 2.93), clobazam-valproic acid (Ω₀₂₅ : 0.29, CRR: 1.55), fosphenytoin-lamotrigine (Ω₀₂₅ : 0.05, CRR: 7.37), and lacosamide-levetiracetam (Ω₀₂₅ : 0.74, CRR: 1.85).

SIGNIFICANCE

This study identified two AED combinations that increased the SJS signals and seven combinations that increased the TEN signals. Although AED monotherapies require attention for SJS and TEN, some AED combinations require extra caution.