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

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.

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.

Indirect and Direct 65+ Patient Reporting of Non-Steroidal Anti-Inflammatory Drug-Induced Adverse Drug Reactions as a Source of Information on Polypharmacy and Polypharmacy-Related Risk

Background and Objectives: Non-steroidal anti-inflammatory drugs (NSAIDs), which have anti-inflammatory and analgesic properties, are commonly used in the treatment of various, particularly frequent, as well as chronic, conditions in older patients. Due to common polypragmasia in these patients and a high risk of adverse drug reactions (ADRs) and drug interactions, pain management poses a therapeutic challenge. This study describes the importance of ADR reports in the identification of polypharmacy and the ensuing interactions. Materials and Methods: Both healthcare professionals (HPs) and non-healthcare professionals (non-HPs) reports collected in the EudraVigilance database of NSAIDs, including most commonly co-reported medications and reported reactions, were analysed and differences between HPs and non-HPs reports were identified. Results: In the analysed period and group, non-HPs reported more reactions but indicated fewer drugs as suspect or concomitant. The outcomes of our analysis indicate more HP engagement and more detailed reports of serious ADRs when compared to non-serious individual case safety reports (ICSRs) by non-HPs, which appeared more detailed. Such reactions as kidney failure and increased risk of bleeding are known adverse reactions to NSAIDs and common symptoms of their interactions, which were described in the available literature. They were much more frequently reported by HPs than by non-HPs. Non-HPs more frequently reported reactions that may have been considered less significant by HPs. Conclusions: The differences between healthcare professionals' (HPs) and non-healthcare professionals' (non-HPs) reports may result from the fact that the reports from patients and their caregivers require a professional medical diagnosis based on symptoms described by the patient or additional diagnostic tests. This means that when appropriately classified, medically verified, and statistically analysed, the data may provide new evidence for the risks of medication use or drug interactions.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Drug-drug interaction of Nirmatrelvir/ritonavir and tacrolimus: A potential risk disproportionality analysis of nephrotoxicity from COVID-19 reports in FAERS

BACKGROUND

Nirmatrelvir/ritonavir is a new oral antiviral agent for COVID-19, and tacrolimus is a widely used immunosuppressant. Drug-drug interaction between Nirmatrelvir/ritonavir and tacrolimus is expected. However, information regarding the drug-drug interaction in a real-world setting is limited. We aim to evaluate drug-drug interaction between tacrolimus and Nirmatrelvir/ritonavir and perform a disproportionality analysis to assess the potential risk of nephrotoxicity due to their combination for COVID-19 treatment based on the FAERS database.

RESEARCH DESIGN AND METHODS

Disproportionality analysis was performed using the reporting odds ratio (ROR) method, and subset analysis was conducted based on the background of COVID-19 drugs combined with tacrolimus more than 10 times.

RESULTS

In disproportionality analysis, combination of Nirmatrelvir/ritonavir and tacrolimus was significantly associated with acute kidney injury (41.13%), serum creatinine increased (14.18%), renal failure (2.84%), and renal impairment (2.84%). These positive signals of acute kidney injury and serum creatinine increased still strongly retained in subset analysis. No similar positive signals were detected in Nirmatrelvir/ritonavir-single group. Only in Cilgavimab/Tixagevimab-tacrolimus group and Remdesivir-tacrolimus group, acute kidney injury was recognized as weakly positive signals and disappeared in subset analysis.

CONCLUSIONS

The study results show significant drug-drug interaction between Nirmatrelvir/ritonavir and tacrolimus and confirm that their combination for COVID-19 treatment greatly increases risk of acute kidney injury.

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.

Artificial intelligence-driven prediction of multiple drug interactions

When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug-drug, drug-food (excipients) and drug-microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug-drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.

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.

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.

Combination of vancomycin plus piperacillin and risk of acute kidney injury: a worldwide pharmacovigilance database analysis

BACKGROUND

Excess of acute kidney injury (AKI) secondary to the association of vancomycin plus piperacillin is debated.

OBJECTIVES

To detect a signal for an increased risk of AKI with the vancomycin and piperacillin combination compared with other vancomycin-based regimens.

METHODS

Using VigiBase, the WHO global database of individual case safety reports (ICSR) from 1997 to 2019, we conducted a disproportionality analysis comparing the reporting of AKI cases between different vancomycin-based regimens (vancomycin plus piperacillin, cefepime or meropenem). To take into account a possible notoriety bias, we secondarily restricted the study period to before 2014, the date of the first publication of AKI in patients receiving vancomycin plus piperacillin. Results are expressed using the reporting OR (ROR) and its 95% CI.

RESULTS

From 1997 to 2019, 53 701 ICSR concerning vancomycin have been registered in the database, including 6016 reports of AKI (11.2%), among which 925 (15.4%) were reported with vancomycin/piperacillin, 339 (5.6%) with vancomycin/cefepime and 197 (3.7%) with vancomycin/meropenem. ROR (95% CI) for AKI was 2.6 (2.4-2.8) for vancomycin/piperacillin, 2.5 (2.2-2.9) for vancomycin/cefepime and 0.5 (0.4-0.6) for vancomycin/meropenem versus other vancomycin-containing regimens. After restriction of the study period to 1997-2013, the ROR for AKI remains significant only for vancomycin/piperacillin [ROR (95% CI) = 2.1 (1.8-2.4)].

CONCLUSIONS

We found a disproportionality in reports of AKI in patients receiving vancomycin plus piperacillin compared with vancomycin in other regimens. This suggests a drug-drug interaction between these two antibiotics resulting in an increased risk of AKI.

Adverse drug reactions in SARS-CoV-2 hospitalised patients: a case-series with a focus on drug-drug interactions

Due to the need of early and emergency effective treatments for COVID-19, less attention may have been paid to their safety during the global emergency. In addition, characteristics of drug-drug interaction (DDI)-related adverse drug reactions (ADRs) in COVID-19 patients have not yet been studied in depth. The aim of the present case-series study is to describe clinical and pharmacological characteristics of SARS-CoV-2 hospitalised patients, focusing on ADRs, particularly those related to DDIs. We evaluated all reports of COVID-19 medication-related ADRs collected within the COVID-19 Units of Careggi University Hospital, Florence (Italy), between January 1st and 31st May 2020. Information regarding COVID-19 medications, patients' demographic and clinical characteristics, concomitant drugs, ADRs description and outcome, were collected. Each case was evaluated for the causality assessment and to identify the presence of DDIs. During the study period, 23 Caucasian patients (56.5% males, mean age 76.1 years) experienced one or more ADRs. The majority of them were exposed to polypharmacy and 17.4% presented comorbidities. ADRs were referred to cardiovascular, psychiatric and gastrointestinal disorders. The most frequently reported preferred term was QT prolongation (mean QT interval 496.1 ms). ADRs improved or resolved completely in 60.8% of cases. For all patients, a case-by-case evaluation revealed the presence of one or more DDIs, especially those related to pharmacokinetic interactions. Despite the small number of patients, our evidence underline the clinical burden of DDIs in SARS-CoV-2 hospitalised patients and the risk of unexpected and uncommon psychiatric ADRs.

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.

Identifying and Characterizing Serious Adverse Drug Reactions Associated With Drug-Drug Interactions in a Spontaneous Reporting Database

Background: Drug-drug interactions (DDIs) are an important cause of adverse drug reactions (ADRs). In literature most of studies focus only on potential DDIs, while detailed data on serious ADRs associated with DDIs are limited. Our aim is to identify and characterize serious ADRs caused by DDIs using a spontaneous reporting database.

Methods: All serious ADR reports, not related to vaccines and with a “definite”, “probable” or “possible” causality assessment, inserted into the National Pharmacovigilance database from Veneto Region (January 1, 2015 to May 31, 2020) were analyzed. A list of drug pairs was created by selecting the reports containing at least two suspected or concomitant drugs. We verified which drug pairs potentially interacted according to the online version of DRUGDEX^® system. For each potential DDI we controlled whether the ADR description in the report corresponded to the interaction effect as described in Micromedex. A detailed characterization of all serious reports containing an occurring DDI was performed.

Results: In the study period a total of 31,604 reports of suspected ADRs from the Veneto Region were identified, of which 2,195 serious reports (6.9% of all ADR reports) containing at least two suspected or concomitant drugs were analyzed. We identified 1,208 ADR reports with at least one potential DDI (55.0% of 2,195) and 381 reports (17.4% of 2,195 reports) with an occurring ADR associated with a DDI. The median age of patients and the number of contraindicated or major DDIs were significantly higher in reports with an occurring DDI. Warfarin was the most frequently reported interacting drug and the most common ADRs were gastrointestinal or cerebral hemorrhagic events. The proton pump inhibitors/warfarin, followed by platelet aggregation inhibitors/warfarin were the drug-drug combinations most frequently involved in ADRs caused by DDIs. The highest proportion of fatal reports was observed with platelet aggregation inhibitors/warfarin and antidepressants/warfarin.

Conclusion: Our findings showed that about one-third of patients exposed to a potential DDI actually experienced a serious ADR. Furthermore, our study confirms that a spontaneous reporting database could be a valuable resource for identifying and characterizing ADRs caused by DDIs and the drugs leading to serious ADRs and deaths.