Detecting high-quality signals of adverse drug-drug interactions from spontaneous reporting data

Comparison of the safety profiles for pirfenidone and nintedanib: a disproportionality analysis of the US food and drug administration adverse event reporting system

Background

Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive interstitial lung disease of unknown etiology. Pirfenidone (PFD) and nintedanib (NDN) were both conditionally recommended in the clinical practice guideline published in 2015. Safety and tolerability are related to the risk of treatment discontinuation. Therefore, this study evaluated and compared the adverse events (AEs) of PFD and NDN in a large real-world population by analyzing data from the FDA Adverse Event Reporting System (FAERS) to provide a reference for their rational and safe use.

Methods

The AEs of PFD and NDN were extracted from the FAERS database. The pharmacovigilance online analysis tool OpenVigil 2.1 was used to retrieve data from the FAERS database from the first quarter of 2012 to the second quarter of 2022. The reporting odds ratio (ROR) and proportional reporting ratio were used to detect the risk signals.

Results

The database included 26,728 and 11,720 reports for PFD and NDN, respectively. The most frequent AEs of PFD and NDN were gastrointestinal disorders. The RORs for these drugs were 5.874 and 5.899, respectively. "Cardiac disorders" was the most statistically significant system order class for NDN with an ROR of 9.382 (95% confidence interval = 8.308-10.594). Furthermore, the numbers of designated medical events of PFD and NDN were 552 and 656, respectively. Notably, liver injury was reported more frequently for NDN (11.096%) than for PFD (6.076%).

Conclusion

This study revealed differences in the reporting of AEs between PFD and NDN. The findings provide reference for physicians in clinical practice. Attention should be paid to the risks of cardiac disorders and liver injury associated with NDN.

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.

Withdrawn 2.0-update on withdrawn drugs with pharmacovigilance data

One challenge in the development of novel drugs is their interaction with potential off-targets, which can cause unintended side-effects, that can lead to the subsequent withdrawal of approved drugs. At the same time, these off-targets may also present a chance for the repositioning of withdrawn drugs for new indications, which are potentially rare or more severe than the original indication and where certain adverse reactions may be avoidable or tolerable. To enable further insights into this topic, we updated our database Withdrawn by adding pharmacovigilance data from the FDA Adverse Event Reporting System (FAERS), as well as mechanism of action and human disease pathway prediction features for drugs that are or were temporarily withdrawn or discontinued in at least one country. As withdrawal data are still spread over dozens of national websites, we are continuously updating our lists of discontinued or withdrawn drugs and related (off-)targets. Furthermore, new systematic entry points for browsing the data, such as an ATC tree, were added, increasing the accessibility of the database in a user-friendly way. Withdrawn 2.0 is publicly available without the need for registration or login at https://bioinformatics.charite.de/withdrawn_3/index.php.

MCFF-MTDDI: multi-channel feature fusion for multi-typed drug-drug interaction prediction

Adverse drug-drug interactions (DDIs) have become an increasingly serious problem in the medical and health system. Recently, the effective application of deep learning and biomedical knowledge graphs (KGs) have improved the DDI prediction performance of computational models. However, the problems of feature redundancy and KG noise also arise, bringing new challenges for researchers. To overcome these challenges, we proposed a Multi-Channel Feature Fusion model for multi-typed DDI prediction (MCFF-MTDDI). Specifically, we first extracted drug chemical structure features, drug pairs' extra label features, and KG features of drugs. Then, these different features were effectively fused by a multi-channel feature fusion module. Finally, multi-typed DDIs were predicted through the fully connected neural network. To our knowledge, we are the first to integrate the extra label information into KG-based multi-typed DDI prediction; besides, we innovatively proposed a novel KG feature learning method and a State Encoder to obtain target drug pairs' KG-based features which contained more abundant and more key drug-related KG information with less noise; furthermore, a Gated Recurrent Unit-based multi-channel feature fusion module was proposed in an innovative way to yield more comprehensive feature information about drug pairs, effectively alleviating the problem of feature redundancy. We experimented with four datasets in the multi-class and the multi-label prediction tasks to comprehensively evaluate the performance of MCFF-MTDDI for predicting interactions of known-known drugs, known-new drugs and new-new drugs. In addition, we further conducted ablation studies and case studies. All the results fully demonstrated the effectiveness of MCFF-MTDDI.

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

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

On the road to explainable AI in drug-drug interactions prediction: a systematic review

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A systematic review on applications of explainable AI in drug-drug interaction prediction.

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Review is conducted on a comprehensive set of 94 papers from five prestigious databases.

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Discussions on the promises and challenges of explainable AI algorithms for drug-drug interaction prediction.

Over the past decade, polypharmacy instances have been common in multi-diseases treatment. However, unwanted drug-drug interactions (DDIs) that might cause unexpected adverse drug events (ADEs) in multiple regimens therapy remain a significant issue. Since artificial intelligence (AI) is ubiquitous today, many AI prediction models have been developed to predict DDIs to support clinicians in pharmacotherapy-related decisions. However, even though DDI prediction models have great potential for assisting physicians in polypharmacy decisions, there are still concerns regarding the reliability of AI models due to their black-box nature. Building AI models with explainable mechanisms can augment their transparency to address the above issue. Explainable AI (XAI) promotes safety and clarity by showing how decisions are made in AI models, especially in critical tasks like DDI predictions. In this review, a comprehensive overview of AI-based DDI prediction, including the publicly available source for AI-DDIs studies, the methods used in data manipulation and feature preprocessing, the XAI mechanisms to promote trust of AI, especially for critical tasks as DDIs prediction, the modeling methods, is provided. Limitations and the future directions of XAI in DDIs are also discussed.