A statistical methodology for drug–drug interaction surveillance

Impact of Proton Pump Inhibitors on Osimertinib-Induced Cardiotoxicity in NSCLC Patients

There is a lack of comprehensive research investigating the relationship between proton pump inhibitors (PPIs) and osimertinib combination therapy concerning cardiotoxicity. We conducted a retrospective analysis of adverse event reports from the US Food and Drug Administration Adverse Event Reporting System (FAERS). In this analysis, we used patients with non-small cell lung cancer (NSCLC) who did not receive osimertinib or PPIs as a control group to assess the association between cardiotoxicity occurrence in patients receiving osimertinib with PPIs and those without PPIs. We employed disproportionality analysis along with both additive and multiplicative models. The reporting odds ratios (ROR) for cardiac events, including torsade de pointes/QT prolongation, cardiomyopathy, cardiac arrhythmias, cardiac failure, ischaemic heart disease, and embolic and thrombotic events, were significantly higher in patients using PPIs with osimertinib (14.11, 9.04-22.04; 4.67, 2.67-8.16; 4.43, 3.17-6.20; 3.67, 2.53-5.34; 2.24, 1.31-3.84; 1.92, 1.43-2.56, respectively) compared to osimertinib alone (4.87, 3.91-6.07; 2.50, 2.02-3.09; 1.59, 1.37-1.84; 2.00, 1.74-2.29; 0.65, 0.50-0.84; 1.01, 0.91-1.11). Our investigation unveiled an elevated risk of cardiotoxicity in NSCLC patients when osimertinib was combined with PPIs, compared to osimertinib monotherapy. Therefore, vigilant monitoring for cardiotoxicity is paramount in NSCLC patients undergoing these combined treatments.

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.

A theoretical model for detecting drug interaction with awareness of timing of exposure

Drug-drug interaction-induced (DDI-induced) adverse drug event (ADE) is a significant public health burden. Risk of ADE can be related to timing of exposure (TOE) such as initiating two drugs concurrently or adding one drug to an existing drug. Thus, real-world data based DDI detection shall be expanded to investigate precise adverse DDI with a special awareness on TOE. We developed a Sensitive and Timing-awarE Model (STEM), which was able to optimize the probability of detection and control false positive rate for mining all two-drug combinations under case-crossover design, in particular for DDIs with TOE-dependent risk. We analyzed a large-scale US administrative claims data and conducted performance evaluation analyses. We identified signals of DDIs by using STEM, in particular for DDIs with TOE-dependent risk. We also observed that STEM identified significantly more signals than the conditional logistic regression model-based (CLRM-based) methods and the Benjamini-Hochberg procedure. In the performance evaluation, we found that STEM demonstrated proper false positive control and achieved a higher probability of detection compared to CLRM-based methods and the Benjamini-Hochberg procedure. STEM has a high probability to identify signals of DDIs in high-throughput DDI mining while controlling false positive rate, in particular for detecting signals of DDI with TOE-dependent risk.

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.

Evaluating the impact of loperamide on irinotecan-induced adverse events: a disproportionality analysis of data from the World Health Organization pharmacovigilance database (VigiBase)

PURPOSE

SN-38, the active metabolite of irinotecan, may cause adverse events necessitating treatment discontinuation and management. Diarrhea, which is treated with loperamide, is one such event. However, loperamide may delay SN-38 elimination, causing more adverse events. Therefore, understanding the adverse events caused by symptomatic drugs is crucial for safe drug therapy. This study aimed to assess the association between loperamide and irinotecan-induced adverse events.

METHODS

We analyzed data up to December 2022 from VigiBase, the World Health Organization's adverse event database. The study used reporting odds ratios (RORs) to evaluate the associations between concomitant medications and irinotecan-induced adverse events. Fisher's exact probability test was used to analyze the adverse events. Logistic regression analysis was performed to identify associated adverse event signals.

RESULTS

Of the 32,520,983 cases analyzed, 57,454 involved the use of irinotecan. Among these, 1589 (2.8%) patients were co-treated with loperamide. Signals for neutropenia (ROR 1.37, 95% confidence interval (CI) 1.20-1.57, p < 0.001), anemia (ROR 1.81, 95% CI 1.43-2.30, p < 0.001), and alopecia (ROR 1.89, 95% CI 1.30-2.74, p < 0.01) were detected with concomitant loperamide. Multivariate logistic regression analysis confirmed that concomitant loperamide use was associated with signals for neutropenia, anemia, and alopecia.

CONCLUSION

Our results suggest that loperamide increases the risk of irinotecan-induced adverse events and enhances irinotecan toxicity. The study methodology may be useful for predicting adverse event risk when choosing symptomatic therapy drugs during irinotecan use.

Concomitant use of sodium-glucose co-transporter 2 inhibitors and metformin and the risk of osteomyelitis reporting: a disproportionality analysis based on FAERS database

BACKGROUND

Recent clinical case reports have generated controversy concerning the adverse events (AEs) of amputation linked to sodium-glucose co-transporter 2 inhibitors (SGLT2i). We assessed the risk of osteomyelitis AE reporting linked to SGLT2i or SGLT2i-metformin co-medication.

RESEARCH DESIGN AND METHODS

Investigated the FDA Adverse Event Reporting System for osteomyelitis-related AEs associated with SGLT2i or SGLT2i-metformin co-medication from 2013q2 to 2023q1. Comprehensive disproportionality analysis and Bayesian confidence propagation methods were used to detect safe signals. The additive interaction model, multiplicative interaction model, and Ω shrinkage measure were employed to explore the latent interactions between SGLT2i and metformin. A Venn diagram was utilized to estimate the coincidence of related osteomyelitis and amputation.

RESULTS

Among 2,569 SGLT2i-associated osteomyelitis reports, we identified 2,509 related to canagliflozin (ROR 104.47; PRR 99.70, χ2 = 214840.90; EBGM05 = 84.38; IC025 = 4.78) and 103 related to the SGLT2i-metformin compound. Drug-drug interaction detection revealed a negative correlation RERI = -21.73, eβ3 = 0.699, Ω025=-1.370). The coincidence of osteomyelitis and amputation linked to SGLT2i (2,672 vs. 3,548) was 2,150(80%) by Venn diagram.

CONCLUSIONS

This study showed an increased risk of SGLT2i-associated osteomyelitis, focusing on canagliflozin, and presented a potential association between amputation and osteomyelitis, providing a reference for the clinical practice of diabetes with SGLT2i medication.

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.

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.

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.

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.

The evolving role of disproportionality analysis in pharmacovigilance

INTRODUCTION

From 2009 to 2015, the IMI PROTECT conducted rigorous studies addressing questions about optimal implementation and significance of disproportionality analyses, leading to the development of Good Signal Detection Practices. The ensuing period witnessed the independent exploration of research paths proposed by IMI PROTECT, accumulating valuable experience and insights that have yet to be seamlessly integrated.

AREAS COVERED

This state-of-the-art review integrates IMI PROTECT recommendations with recent acquisitions and evolving challenges. It deals with defining the object of study, disproportionality methods, subgrouping, masking, drug-drug interaction, duplication, expectedness, the debated use of disproportionality results as risk measures, integration with other types of data.

EXPERT OPINION

Despite the ongoing skepticism regarding the usefulness of disproportionality analyses and individual case safety reports, their ability to timely detect safety signals regarding rare and unpredictable adverse reactions remains unparalleled. Moreover, recent exploration into their potential for characterizing safety signals revealed valuable insights concerning potential risk factors and the patient's perspective. To fully realize their potential beyond hypothesis generation and achieve a comprehensive evidence synthesis with other kinds of data and studies, each with their unique limitations and contributions, we need to investigate methods for more transparently communicating disproportionality results and mapping and addressing pharmacovigilance biases.

Adverse events associated with eteplirsen: A disproportionality analysis using the 2016-2023 FAERS data

Background

Eteplirsen (Exondys 51) is an orphan drug approved for the treatment of Duchenne muscular dystrophy (DMD), having received accelerated approval from the U.S. Food and Drug Administration (FDA) in 2016. The primary aim of this study is to closely monitor adverse events (AEs) associated with eteplirsen and to identify emerging signals to better characterize their safety profile.

Methods

AEs due to eteplirsen usage reported from the third quarter (Q3) of 2016 to the fourth quarter (Q4) of 2023 were collected from the FDA Adverse Event Reporting System (FAERS). The role_code of AEs mainly includes primary suspect (PS), secondary suspect (SS), concomitant (C), and interaction (I). This study targeted reports with a role_cod of 'PS.' According to the FDA deduplication rule, the latest FDA_DT is selected when the CASEID is the same, and the higher PRIMARYID is selected when the CASEID and FDA_DT are the same. Disproportionality analyses, encompassing four algorithms for reporting odds ratio (ROR), proportional reporting ratio (PRR), Bayesian configuration promotion neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS), were utilized to quantify the signals of AEs associated with eteplirsen.

Results

From the FAERS database, a total of 13,205,369 reports were amassed throughout the study duration. Following the eradication of duplicates, the number of reports with eteplirsen designated as the PS amounted to 1480 encompassed 25 organ systems. Among these, "general disorders and administration site conditions," "injury, poisoning, and procedural complications," "respiratory, thoracic, and mediastinal disorders," "infections and infestations," "vascular disorders," and "product issues" met at least one of the four computational criteria. Additionally, 55 Preferred Terms (PTs) aligned with the prescribed algorithms. The median time to AEs in these patients was 903 days with an interquartile range (IQR) of 269-1575 days. Moreover, 70.04 % of AEs manifested one year or more after the initiation of treatment.

Conclusion

As an orphan drug granted accelerated approval, our study has confirmed well-known adverse drug reactions and identified potential safety issues associated with eteplirsen treatment. This has contributed to a deeper understanding of the complex interrelations between adverse reactions and the use of eteplirsen. The findings underscore the critical importance of ongoing monitoring and sustained observation to promptly detect and effectively manage AEs, thereby enhancing the overall safety and well-being of patients treated with eteplirsen for DMD.

Identifying Drug-Drug Interactions in Spontaneous Reports Utilizing Signal Detection and Biological Plausibility Aspects

Translational approaches can benefit post-marketing drug safety surveillance through the growing availability of systems pharmacology data. Here, we propose a novel Bayesian framework for identifying drug-drug interaction (DDI) signals and differentiating between individual drug and drug combination signals. This framework is coupled with a systems pharmacology approach for automated biological plausibility assessment. Integrating statistical and biological evidence, our method achieves a 16.5% improvement (AUC: from 0.620 to 0.722) with drug-target-adverse event associations, 16.0% (AUC: from 0.580 to 0.673) with drug enzyme, and 15.0% (AUC: from 0.568 to 0.653) with drug transporter information. Applying this approach to detect potential DDI signals of QT prolongation and rhabdomyolysis within the FDA Adverse Event Reporting System (FAERS), we emphasize the significance of systems pharmacology in enhancing statistical signal detection in pharmacovigilance. Our study showcases the promise of data-driven biological plausibility assessment in the context of challenging post-marketing DDI surveillance.

The International Working Group on New Developments in Pharmacovigilance: Advancing Methods and Communication in Pharmacovigilance

PURPOSE

In 2019, the International Working Group (IWG), focusing on New Developments in Pharmacovigilance, was established. This group is coordinated by the Drug Safety Research Unit in the United Kingdom, and the mission of the IWG is to progress pharmacovigilance methodologies and promote the safe and effective use of medicines and vaccines, thereby further protecting patients. Novel therapeutics are continuously being developed to alleviate medical conditions, but with advancing technologies, innovative pharmacovigilance methodologies need to be developed to effectively monitor the use and safety of these products. With reduced timelines proposed for premarketing clinical trials and increased application of real-world evidence supporting regulatory approvals, products may be used in real-world clinical practice in shorter timeframes than before. Therefore, the need for effective methods of monitoring medicines and collecting safety data in real-time is of paramount importance to public health.

METHODS

The IWG aims to advance existing methodologies used in the detection, monitoring, and analysis of safety data in pharmacovigilance and to communicate best practice proposals to support decision making in health care. The IWG will identify areas requiring review of current processes or methodologic research and will communicate the output of the IWG through peer-reviewed publications, reports, and presentation of findings at relevant conferences and scientific meetings.

FINDINGS

The IWG is currently reviewing two areas in pharmacovigilance; case-level causality assessment and the strengths and limitations of data sources. The IWG is advancing these areas by producing two scoping reviews which will be easily accessible to regulatory agencies, industry, academia, and interested persons or organizations.

IMPLICATIONS

The scoping reviews comply with the IWGs mission to progress pharmacovigilance methodologies and promote the safe and effective use of medicines and vaccines. The present article shares details of the objectives of the IWG and provides an overview on the status of IWG activities.

The REporting of A Disproportionality Analysis for DrUg Safety Signal Detection Using Individual Case Safety Reports in PharmacoVigilance (READUS-PV): Explanation and Elaboration

In pharmacovigilance, disproportionality analyses based on individual case safety reports are widely used to detect safety signals. Unfortunately, publishing disproportionality analyses lacks specific guidelines, often leading to incomplete and ambiguous reporting, and carries the risk of incorrect conclusions when data are not placed in the correct context. The REporting of A Disproportionality analysis for drUg Safety signal detection using individual case safety reports in PharmacoVigilance (READUS-PV) statement was developed to address this issue by promoting transparent and comprehensive reporting of disproportionality studies. While the statement paper explains in greater detail the procedure followed to develop these guidelines, with this explanation paper we present the 14 items retained for READUS-PV guidelines, together with an in-depth explanation of their rationale and bullet points to illustrate their practical implementation. Our primary objective is to foster the adoption of the READUS-PV guidelines among authors, editors, peer reviewers, and readers of disproportionality analyses. Enhancing transparency, completeness, and accuracy of reporting, as well as proper interpretation of their results, READUS-PV guidelines will ultimately facilitate evidence-based decision making in pharmacovigilance.

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 Comparison of Signals of Designated Medical Events and Non-designated Medical Events: Results from a Scoping Review

INTRODUCTION AND OBJECTIVE

The European Medicines Agency (EMA) maintains a list of designated medical events (DMEs), events that are inherently serious and are prioritized for signal detection, irrespective of statistical criteria. We have analysed the results of our previously published scoping review to determine whether DME signals differ from those of other adverse events in terms of time to communication and characteristics of supporting reports of suspected adverse drug reactions.

METHODS

For all signals, we obtained the launch year of medicinal products from textbooks or regulatory agencies, extracted the year of the first report in VigiBase and calculated the interval between the first report and communication (time to communication, TTC). We further retrieved the average completeness (via vigiGrade) of the reports in each case series in the years before the communication. We categorised as DME signals those concerning an event in the EMA's list. We described the two groups of signals using medians and interquartile ranges (IQR) and compared them using the Brunner-Munzel test, calculating 95% confidence intervals (95% CI) and P values.

RESULTS

Of 4520 signals, 919 concerned DMEs and 3601 concerned non-DMEs. Signals of DMEs were supported by a median of 15 reports (IQR 6-38 reports) with a completeness score of 0.52 (IQR 0.43-0.62) and signals of non-DMEs by 20 reports (IQR 6-84 reports) with a completeness score of 0.46 (IQR 0.38-0.56). The probability that a random DME signal was supported by fewer reports than non-DME signals was 0.56 (95% CI 0.54-0.58, P < 0.001) and that of one having lower average completeness was 0.39 (95% CI 0.36-0.41, P < 0.001). The median TTCs of DME and non-DME signals did not differ (10 years), but the TTC was as low as 2 years when signals (irrespective of classification) were supported by reports whose average completeness was > 0.80.

CONCLUSIONS

Signals of designated medical events were supported by fewer reports and higher completeness scores than signals of other adverse events. Although statistically significant, the differences in effect sizes between the two groups were small. This suggests that listing certain adverse events as DMEs is not having the expected effect of encouraging a focus on reports of the types of suspected adverse reactions that deserve special attention. Further enhancing the completeness of the reports of suspected adverse drug reactions supporting signals of designated medical events might shorten their time to communication and reduce the number of reports required to support them.

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.

Exploring signals of myopathy associated with statin and contraindicated comedications in the realworld

BACKGROUND

Using statins in combination with other drugs was reported to increase the risk of myopathy. However, there was a sparse number of studies on the incidence of adverse events (AEs) associated with the concomitant use of statin and contraindicated drugs in the real world.

OBJECTIVES

This study aimed to identify the risk of concomitant use of statins with contraindicated drugs by exploring signals related to statin-drug interactions.

METHODS

We performed a disproportionality analysis for drugs and AEs by applying the case/non-case study using the KIDS-KAERS database (KIDS-KD), 2016-2020. A case was defined as an individual case safety reports (ICSRs) including "rhabdomyolysis/myopathy." A non-case was defined as an ICSR, including all other AEs. We applied Ω shrinkage measure model, chi-square statics model, additive model, multiplicative model, and combination risk ratio model to detect signals of myopathy due to statin with concomitant drugs including antiviral agents, immunosuppressants, and antifungals.

RESULTS

Among 1 011 234 ICSRs, 2708 were cases, with 861 cases of statin monotherapy and 1248 of concomitant uses of statin. The adjusted reporting odds ratios were 3.27 (95% confidence interval [CI]: 3.11-3.43), 8.70 (95% CI: 8.04-9.40), and 1.83 (95% CI: 1.73-1.94), respectively. Several combinations of signals were detected through an additive model or multiplicative model.

CONCLUSION

Signals of an increased risk of myopathy associated with the use of statins with concomitant drugs, including contraindicated drugs, were confirmed in a real-world setting.

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.

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.

Comprehensive Review of Drug-Drug Interaction Prediction Based on Machine Learning: Current Status, Challenges, and Opportunities

Detecting drug-drug interactions (DDIs) is an essential step in drug development and drug administration. Given the shortcomings of current experimental methods, the machine learning (ML) approach has become a reliable alternative, attracting extensive attention from the academic and industrial fields. With the rapid development of computational science and the growing popularity of cross-disciplinary research, a large number of DDI prediction studies based on ML methods have been published in recent years. To give an insight into the current situation and future direction of DDI prediction research, we systemically review these studies from three aspects: (1) the classic DDI databases, mainly including databases of drugs, side effects, and DDI information; (2) commonly used drug attributes, which focus on chemical, biological, and phenotypic attributes for representing drugs; (3) popular ML approaches, such as shallow learning-based, deep learning-based, recommender system-based, and knowledge graph-based methods for DDI detection. For each section, related studies are described, summarized, and compared, respectively. In the end, we conclude the research status of DDI prediction based on ML methods and point out the existing issues, future challenges, potential opportunities, and subsequent research direction.

Comparative study on the occurrence of adverse effects in the concomitant use of azathioprine and aldehyde oxidase inhibitors

OBJECTIVES

Aldehyde oxidase (AO) is a molybdenum-containing redox enzyme similar to xanthine oxidase that is involved in the thiopurine metabolism. This study investigated the effects of drug-drug interactions (DDIs) between azathioprine (AZA) and AO inhibitors on hematologic and hepatic disorders using the U.S. Food and Drug Administration Adverse Event Reporting System and the Japanese Adverse Drug Event Report database.

METHODS

The presence of DDI was assessed using the interaction signal scores (ISSs) calculated via the reporting odds ratios and 95% confidence intervals. The study used reports of 'azathioprine' as a suspect drug for adverse effects. AO inhibitors were selected based on previous in vitro reports.

RESULTS

Some drugs tested positive for ISSs in each database and type of adverse effect (hematologic or hepatic disorder) analysis. Among these drugs, chlorpromazine, clozapine, hydralazine, and quetiapine could inhibit AZA metabolism via AO, given the previously reported clinical blood concentration and inhibitory effects of each drug.

CONCLUSION

Concomitant use of AO inhibitors increased the signals for AZA-induced adverse effects. To date, no studies have evaluated the clinical importance of AO as a drug-metabolizing enzyme, and further in vitro and clinical research is needed to clarify the contribution of AO to the pharmacokinetics of thiopurines.

Levetiracetam Interaction with Direct Oral Anticoagulants: A Pharmacovigilance Study

BACKGROUND

Levetiracetam is widely used in post-stroke epilepsy. However, it is suspected to possess P-glycoprotein (P-gp) induction properties, and therefore, a potentially significant interaction with direct oral anticoagulants (DOACs). We aimed to search for ischemic stroke signals with levetiracetam and the DOACs.

METHODS

In this retrospective pharmacovigilance study, we used the FAERS database to identify ischemic stroke events associated with DOACs and concomitant use of levetiracetam. We evaluated disproportionate reporting by the adjusted reporting odds ratio (adjROR) and the lower bound of the shrinkage 95% confidence interval. When shrinkage is positive, an increased risk of a specific adverse event occurrence is emphasized over the sum of the individual risks when these same drugs are used separately.

RESULTS

We identified 1841 (1.5%), 3731 (5.3%), 338 (4.9%), and 1723 (1.3%) ischemic stroke reports with apixaban, dabigatran, edoxaban, and rivaroxaban, respectively. The adjROR of the interaction effect was 3.57 (95% CI 2.81-4.58) between DOACs and levetiracetam. The shrinkage analysis detected an interaction between each of the DOACs and levetiracetam. The logistic model and shrinkage analysis failed to detect an interaction when queried for hemorrhagic stroke. A significant signal in the classical enzyme inducer, carbamazepine, strengthened our results (adjROR; 8.47, 95% CI 5.37-13.36).

CONCLUSIONS

Our study shows a strong signal for the levetiracetam interaction with the DOACs. Our findings suggest implementation of a drug monitoring strategy.

A novel efficient drug repurposing framework through drug-disease association data integration using convolutional neural networks

Drug repurposing is an exciting field of research toward recognizing a new FDA-approved drug target for the treatment of a specific disease. It has received extensive attention regarding the tedious, time-consuming, and highly expensive procedure with a high risk of failure of new drug discovery. Data-driven approaches are an important class of methods that have been introduced for identifying a candidate drug against a target disease. In the present study, a model is proposed illustrating the integration of drug-disease association data for drug repurposing using a deep neural network. The model, so-called IDDI-DNN, primarily constructs similarity matrices for drug-related properties (three matrices), disease-related properties (two matrices), and drug-disease associations (one matrix). Then, these matrices are integrated into a unique matrix through a two-step procedure benefiting from the similarity network fusion method. The model uses a constructed matrix for the prediction of novel and unknown drug-disease associations through a convolutional neural network. The proposed model was evaluated comparatively using two different datasets including the gold standard dataset and DNdataset. Comparing the results of evaluations indicates that IDDI-DNN outperforms other state-of-the-art methods concerning prediction accuracy.

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.

Artificial Intelligence and Data Mining for the Pharmacovigilance of Drug-Drug Interactions

Despite increasing mechanistic understanding, undetected and underrecognized drug-drug interactions (DDIs) persist. This elusiveness relates to an interwoven complexity of increasing polypharmacy, multiplex mechanistic pathways, and human biological individuality. This persistent elusiveness motivates development of artificial intelligence (AI)-based approaches to enhancing DDI detection and prediction capabilities. The literature is vast and roughly divided into "prediction" and "detection." The former relatively emphasizes biological and chemical knowledge bases, drug development, new drugs, and beneficial interactions, whereas the latter utilizes more traditional sources such as spontaneous reports, claims data, and electronic health records to detect novel adverse DDIs with authorized drugs. However, it is not a bright line, either nominally or in practice, and both are in scope for pharmacovigilance supporting signal detection but also signal refinement and evaluation, by providing data-based mechanistic arguments for/against DDI signals. The wide array of intricate and elegant methods has expanded the pharmacovigilance tool kit. How much they add to real prospective pharmacovigilance, reduce the public health impact of DDIs, and at what cost in terms of false alarms amplified by automation bias and its sequelae are open questions. (Clin Ther. 2023;45:XXX-XXX) © 2023 Elsevier HS Journals, Inc.

Hydroxychloroquine safety in Covid-19 vs non-Covid-19 patients: analysis of differences and potential interactions

BACKGROUND

The use of hydroxychloroquine (HCQ) in the first COVID-19 epidemic wave raised safety concerns.

RESEARCH DESIGN AND METHODS

Adverse reactions (ADR) suspected to be induced by HCQ and submitted to the Spanish Pharmacovigilance Database were studied. A disproportionality analysis was performed to determine adverse effects reported in non-Covid and Covid patients. To explore potential drug-drug interactions, Omega (Ω) statistics was calculated.

RESULTS

More severe cases were reported when used in COVID-19. Main differences in frequency were observed in hepatobiliary, skin, gastrointestinal, eye, nervous system and heart ADRs. During the COVID-19 pandemic, high disproportionality in reports was found for Torsade de Pointes/QT prolongation with a ROR (-ROR) of 132.8 (76.7); severe hepatotoxicity, 18.7 (14.7); dyslipidaemias, 12.1 (6.1); shock, 9.5 (6.9) and ischemic colitis, 8.9 (2.6). Myopathies, hemolytic disorders and suicidal behavior increased their disproportionality during the pandemic. Disproportionality was observed for neoplasms, hematopoietic cytopaenias and interstitial lung disease in the pre-COVID-19 period. Potential interactions were showed between HCQ and azithromycin, ceftriaxone, lopinavir and tocilizumab.

CONCLUSIONS

The use of HCQ during the Covid-19 pandemic changed its ADRs reporting profile. Of particular concern during the pandemic were arrhythmias, hepatotoxicity, severe skin reactions and suicide, but not ocular disorders. Some signals identified would require more detailed analyses.

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.

TP-DDI: A Two-Pathway Deep Neural Network for Drug-Drug Interaction Prediction

Adverse drug-drug interactions (DDIs) can severely damage the body. Thus, it is essential to accurately predict DDIs. DDIs are complex processes in which many factors can cause interactions. Rather than merely considering one or two of the factors, we design a two-pathway drug-drug interaction framework named TP-DDI that uses multimodal data for DDI prediction. TP-DDI effectively explores the combined effect of a topological structure-based pathway and a biomedical object similarity-based pathway to obtain multimodal drug representations. For the topology-based pathway, we focus on drug chemistry structures through the self-attention mechanism, which can capture hidden critical relationships, especially between pairs of atoms at remote topological distances. For the similarity-based pathway, our model can emphasize useful biomedical objects according to the channel weights. Finally, the fusion of multimodal data provides a holistic view of DDIs by learning the complementary features. On a real-world dataset, experiments show that TP-DDI can achieve better performance than the state-of-the-art models. Moreover, we can find the most critical substructures with certain interpretability in the newly predicted DDIs.

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.

Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records

We sought to craft a drug safety signalling pipeline associating latent information in clinical free text with exposures to single drugs and drug pairs. Data arose from 12 secondary and tertiary public hospitals in two Danish regions, comprising approximately half the Danish population. Notes were operationalised with a fastText embedding, based on which we trained 10,720 neural-network models (one for each distinct single-drug/drug-pair exposure) predicting the risk of exposure given an embedding vector. We included 2,905,251 admissions between May 2008 and June 2016, with 13,740,564 distinct drug prescriptions; the median number of prescriptions was 5 (IQR: 3-9) and in 1,184,340 (41%) admissions patients used ≥5 drugs concomitantly. 10,788,259 clinical notes were included, with 179,441,739 tokens retained after pruning. Of 345 single-drug signals reviewed, 28 (8.1%) represented possibly undescribed relationships; 186 (54%) signals were clinically meaningful. 16 (14%) of the 115 drug-pair signals were possible interactions and 2 (1.7%) were known. In conclusion, we built a language-agnostic pipeline for mining associations between free-text information and medication exposure without manual curation, predicting not the likely outcome of a range of exposures, but the likely exposures for outcomes of interest. Our approach may help overcome limitations of text mining methods relying on curated data in English and can help leverage non-English free text for pharmacovigilance.

A Comprehensive Review of Computational Methods For Drug-Drug Interaction Detection

The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions.

Artificial Intelligence and Machine Learning for Safe Medicines

Authors' views on the role of artificial intelligence and machine learning in pharmacovigilance. (MP4  139807 kb).

A reference set of clinically relevant adverse drug-drug interactions

The accurate and timely detection of adverse drug-drug interactions (DDIs) during the postmarketing phase is an important yet complex task with potentially major clinical implications. The development of data mining methodologies that scan healthcare databases for drug safety signals requires appropriate reference sets for performance evaluation. Methodologies for establishing DDI reference sets are limited in the literature, while there is no publicly available resource simultaneously focusing on clinical relevance of DDIs and individual behaviour of interacting drugs. By automatically extracting and aggregating information from multiple clinical resources, we provide a scalable approach for generating a reference set for DDIs that could support research in postmarketing safety surveillance. CRESCENDDI contains 10,286 positive and 4,544 negative controls, covering 454 drugs and 179 adverse events mapped to RxNorm and MedDRA concepts, respectively. It also includes single drug information for the included drugs (i.e., adverse drug reactions, indications, and negative drug-event associations). We demonstrate usability of the resource by scanning a spontaneous reporting system database for signals of DDIs using traditional signal detection algorithms.

Measurement(s)	Adverse Event
Technology Type(s)	digital curation
Sample Characteristic - Organism	Homo sapiens

Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16681933

A pharmacovigilance study of pharmacokinetic drug interactions using a translational informatics discovery approach

BACKGROUND

While the pharmacokinetic (PK) mechanisms for many drug interactions (DDIs) have been established, pharmacovigilance studies related to these PK DDIs are limited. Using a large surveillance database, a translational informatics approach can systematically screen adverse drug events (ADEs) for many DDIs with known PK mechanisms.

METHODS

We collected a set of substrates and inhibitors related to the cytochrome P450 (CYP) isoforms, as recommended by the United States Food and Drug Administration (FDA) and Drug Interactions Flockhart table™. The FDA's Adverse Events Reporting System (FAERS) was used to obtain ADE reports from 2004 to 2018. The substrate and inhibitor information were used to form PK DDI pairs for each of the CYP isoforms and Medical Dictionary for Regulatory Activities (MedDRA) preferred terms used for ADEs in FAERS. A shrinkage observed-to-expected ratio (Ω) analysis was performed to screen for potential PK DDI and ADE associations.

RESULTS

We identified 149 CYP substrates and 62 CYP inhibitors from the FDA and Flockhart tables. Using FAERS data, only those DDI-ADE associations were considered that met the disproportionality threshold of Ω > 0 for a CYP substrate when paired with at least two inhibitors. In total, 590 ADEs were associated with 2085 PK DDI pairs and 38 individual substrates, with ADEs overlapping across different CYP substrates. More importantly, we were able to find clinical and experimental evidence for the paclitaxel-clopidogrel interaction associated with peripheral neuropathy in our study.

CONCLUSION

In this study, we utilized a translational informatics approach to discover potentially novel CYP-related substrate-inhibitor and ADE associations using FAERS. Future clinical, population-based and experimental studies are needed to confirm our findings.

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.

A Feasibility Study of Drug-Drug Interaction Signal Detection in Regular Pharmacovigilance

Introduction

Adverse drug reactions related to drug–drug interactions cause harm to patients. There is a body of research on signal detection for drug interactions in collections of individual case reports, but limited use in regular pharmacovigilance.

Objective

The aim of this study was to evaluate the feasibility of signal detection of drug–drug interactions in collections of individual case reports of suspected adverse drug reactions.

Methods

This study was conducted in VigiBase, the WHO global database of individual case safety reports. The data lock point was 31 August 2016, which provided 13.6 million reports for analysis after deduplication. Statistical signal detection was performed using a previously developed predictive model for possible drug interactions. The model accounts for an interaction disproportionality measure, expressed suspicion of an interaction by the reporter, potential for interaction through cytochrome P450 activity of drugs, and reported information indicative of unexpected therapeutic response or altered therapeutic effect. Triage filters focused the preliminary signal assessment on combinations relating to serious adverse events with case series of no more than 30 reports from at least two countries, with at least one report during the previous 2 years. Additional filters sought to eliminate already known drug interactions through text mining of standard literature sources. Preliminary signal assessment was performed by a multidisciplinary group of pharmacovigilance professionals from Uppsala Monitoring Centre and collaborating organizations, whereas in-depth signal assessment was performed by experienced pharmacovigilance assessors.

Results

We performed preliminary signal assessment for 407 unique drug pairs. Of these, 157 drug pairs were considered already known to interact, whereas 232 were closed after preliminary assessment for other reasons. Ten drug pairs were subjected to in-depth signal assessment and an additional eight were decided to be kept under review awaiting additional reports. The triage filters had a major impact in focusing our preliminary signal assessment on just 14% of the statistical signals generated by the predictive model for drug interactions. In-depth assessment led to three signals communicated with the broader pharmacovigilance community, six closed signals and one to be kept under review.

Conclusion

This study shows that signals of adverse drug interactions can be detected through broad statistical screening of individual case reports. It further shows that signal assessment related to possible drug interactions requires more detailed information on the temporal relationship between different drugs and the adverse event. Future research may consider whether interaction signal detection should be performed not for individual adverse event terms but for pairs of drugs across a spectrum of adverse events.

Electronic supplementary material

The online version of this article (10.1007/s40264-020-00939-y) contains supplementary material, which is available to authorized users.

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.

The Impact of Marijuana on Antidepressant Treatment in Adolescents: Clinical and Pharmacologic Considerations

The neuropharmacology of marijuana, including its effects on selective serotonin reuptake inhibitor (SSRI)/antidepressant metabolism and the subsequent response and tolerability in youth, has received limited attention. We sought to (1) review clinically relevant pharmacokinetic (PK) and pharmacodynamic (PD) interactions between cannabinoids and selected SSRIs, (2) use PK models to examine the impact of cannabinoids on SSRI exposure (area under curve (AUC)) and maximum concentration (C_MAX) in adolescents, and (3) examine the frequency of adverse events reported when SSRIs and cannabinoids are used concomitantly. Cannabinoid metabolism, interactions with SSRIs, impact on relevant PK/PD pathways and known drug–drug interactions were reviewed. Then, the impact of tetrahydrocannabinol (THC) and cannabidiol (CBD) on exposure (AUC₂₄) and C_MAX for escitalopram and sertraline was modeled using pediatric PK data. Using data from the Food and Drug Administration Adverse Events Reporting System (FAERS), the relationship between CBD and CYP2C19-metabolized SSRIs and side effects was examined. Cannabis and CBD inhibit cytochrome activity, alter serotonergic transmission, and modulate SSRI response. In PK models, CBD and/or THC increases sertraline and es/citalopram concentrations in adolescents, and coadministration of CBD and CYP2C19-metabolized SSRIs increases the risk of cough, diarrhea, dizziness, and fatigue. Given the significant SSRI–cannabinoid interactions, clinicians should discuss THC and CBD use in youth prescribed SSRIs and be aware of the impact of initiating, stopping, or decreasing cannabinoid use as this may significantly affect es/citalopram and sertraline exposure.

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.