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

Discovering Severe Adverse Reactions From Pharmacokinetic Drug-Drug Interactions Through Literature Analysis and Electronic Health Record Verification

While drug-drug interactions (DDIs) and their pharmacokinetic (PK) mechanisms are well-studied prior to drug approval, severe adverse drug reactions (SADRs) caused by DDIs often remain underrecognized due to limitations in pre-marketing clinical trials. To address this gap, our study utilized a literature database, applied natural language processing (NLP) techniques, and conducted multi-source electronic health record (EHR) validation to uncover underrecognized DDI-SADR signals that warrant further investigation. PubMed abstracts related to DDIs from January 1962 to December 2023 were retrieved. We utilized PubTator Central for Named Entity Recognition (NER) to identify drugs and SADRs and employed SciFive for Relation Extraction (RE) to extract DDI-SADR signals. The extracted signals were cross-referenced with the DrugBank database and validated using logistic regression, considering risk factors including patient demographics, drug usage, and comorbidities, based on EHRs from Vanderbilt University Medical Center (VUMC) and the All of Us research program. From 160,321 abstracts, we identified 111 DDI-SADR signals. Seventeen were statistically significant (13 by one EHR and 4 by both EHR databases), with 9 being previously not recorded in the DrugBank. These included methadone-ciprofloxacin-respiratory depression, oxycodone-fluvoxamine-clonus, tramadol-fluconazole-hallucination, simvastatin-fluconazole-rhabdomyolysis, ibrutinib-amiodarone-atrial fibrillation, fentanyl-diltiazem-delirium, clarithromycin-voriconazole-acute kidney injury, colchicine-cyclosporine-rhabdomyolysis, and methadone-voriconazole-arrhythmia (odds ratios (ORs) ranged from 1.9 to 35.83, with P-values ranging from < 0.001 to 0.017). Utilizing NLP to extract DDI-SADRs from Biomedical Literature and validating these findings through multiple-source EHRs represents a pioneering approach in pharmacovigilance. This method uncovers clinically relevant SADRs resulting from DDIs that were not evident in pre-marketing trials or the existing DDI knowledge base.

Safety Profile of Lipid Emulsion in Clinical Practice: A Pharmacovigilance Study Using the FDA Adverse Event Reporting System

INTRODUCTION

Lipid emulsion preparations, known for their clinical utility, are associated with various adverse events related to lipid metabolism. In this study, we analyzed the safety profile of lipid emulsions in clinical practice, using a real-world database.

METHODS

The US Food and Drug Administration Adverse Event Reporting System database was used to retrieve adverse events associated with lipid emulsion use. The risk of adverse events was evaluated based on the reported odds ratio and time-to-onset analysis.

RESULTS

A total of 4,430 relevant adverse event reports were identified. Hepatic dysfunction tended to occur in the early stages after administration, regardless of the lipid emulsion type. The incidence of hepatic dysfunction varies depending on the triglyceride content of the administered lipid emulsion. Infection tended to occur in the early stages of lipid emulsion administration; however, the incidence did not significantly differ depending on triglyceride content.

CONCLUSION

Our study revealed adverse lipid emulsion events, indicating the need for comprehensive safety management, particularly in the early stages, for clinical use. Particularly, patients receiving parenteral nutrition, irrespective of lipid emulsion administration, necessitate thorough monitoring of liver function and triglyceride levels and reassessment of infusion rates.

Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges

Polypharmacy is commonly employed in clinical settings. The potential risks of drug-drug interactions (DDIs) can compromise efficacy and pose serious health hazards. Integrating pharmacokinetics (PK) and pharmacodynamics (PD) models into DDIs research provides a reliable method for evaluating and optimizing drug regimens. With advancements in our comprehension of both individual drug mechanisms and DDIs, conventional models have begun to evolve towards more detailed and precise directions, especially in terms of the simulation and analysis of physiological mechanisms. Selecting appropriate models is crucial for an accurate assessment of DDIs. This review details the theoretical frameworks and quantitative benchmarks of PK and PD modeling in DDI evaluation, highlighting the establishment of PK/PD modeling against a backdrop of complex DDIs and physiological conditions, and further showcases the potential of quantitative systems pharmacology (QSP) in this field. Furthermore, it explores the current advancements and challenges in DDI evaluation based on models, emphasizing the role of emerging in vitro detection systems, high-throughput screening technologies, and advanced computational resources in improving prediction accuracy.

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.

Discovering clinical drug-drug interactions with known pharmacokinetics mechanisms using spontaneous reporting systems and electronic health records

OBJECTIVE

Although the mechanisms behind pharmacokinetic (PK) drug-drug interactions (DDIs) are well-documented, bridging the gap between this knowledge and clinical evidence of DDIs, especially for serious adverse drug reactions (SADRs), remains challenging. While leveraging the FDA Adverse Event Reporting System (FAERS) database along with disproportionality analysis tends to detect a vast number of DDI signals, this abundance complicates further investigation, such as validation through clinical trials. Our study proposed a framework to efficiently prioritize these signals and assessed their reliability using multi-source Electronic Health Records (EHR) to identify top candidates for further investigation.

METHODS

We analyzed FAERS data spanning from January 2004 to March 2023, employing four established disproportionality methods: Proportional Reporting Ratio (PRR), Reporting Odds Ratio (ROR), Multi-item Gamma Poisson Shrinker (MGPS), and Bayesian Confidence Propagating Neural Network (BCPNN). Building upon these models, we developed four ranking models to prioritize DDI-SADR signals and cross-referenced signals with DrugBank. To validate the top-ranked signals, we employed longitudinal EHRs from Vanderbilt University Medical Center and the All of Us research program. The performance of each model was assessed by counting how many of the top-ranked signals were confirmed by EHRs and calculating the average ranking of these confirmed signals.

RESULTS

Out of 189 DDI-SADR signals identified by all four disproportionality methods, only two were documented in the DrugBank database. By prioritizing the top 20 signals as determined by each of the four disproportionality methods and our four ranking models, 58 unique DDI-SADR signals were selected for EHR validations. Of these, five signals were confirmed. The ranking model, which integrated the MGPS and BCPNN, demonstrated superior performance by assigning the highest priority to those five EHR-confirmed signals.

CONCLUSION

The fusion of disproportionality analysis with ranking models, validated through multi-source EHRs, presents a groundbreaking approach to pharmacovigilance. Our study's confirmation of five significant DDI-SADRs, previously unrecorded in the DrugBank database, highlights the essential role of advanced data analysis techniques in identifying ADRs.

Translational aspects of cytochrome P450-mediated drug-drug interactions: A case study with clopidogrel

Multimorbidity, polypharmacotherapy and drug interactions are increasingly common in the ageing population. Many drug-drug interactions (DDIs) are caused by perpetrator drugs inhibiting or inducing cytochrome P450 (CYP) enzymes, resulting in alterations of the plasma concentrations of a victim drug. DDIs can have a major negative health impact, and in the past, unrecognized DDIs have resulted in drug withdrawals from the market. Signals to investigate DDIs may emerge from a variety of sources. Nowadays, standard methods are widely available to identify and characterize the mechanisms of CYP-mediated DDIs in vitro. Clinical pharmacokinetic studies, in turn, provide experimental data on pharmacokinetic outcomes of DDIs. Physiologically based pharmacokinetic (PBPK) modelling utilizing both in vitro and in vivo data is a powerful tool to predict different DDI scenarios. Finally, epidemiological studies can provide estimates on the health outcomes of DDIs. Thus, to fully characterize the mechanisms, clinical effects and implications of CYP-mediated DDIs, translational research approaches are required. This minireview provides an overview of translational approaches to study CYP-mediated DDIs, going beyond regulatory DDI guidelines, and an illustrative case study of how the DDI potential of clopidogrel was unveiled by combining these different methods.