Comparison of potential psychiatric drug interactions in six drug interaction database programs: A replication study after 2 years of updates

Exploring the level of agreement among different drug-drug interaction checkers: a comparative study on direct oral anticoagulants

BACKGROUND

Direct oral anticoagulants (DOACs) may be involved in drug-drug interactions (DDIs) potentially increasing the risk of adverse drug reactions. This study aimed to evaluate the level of agreement among interaction checkers (ICs) and DOACs' summary of product characteristics (SPCs), in listing DDIs and in attributing DDIs' severity.

RESEARCH DESIGN AND METHODS

The level of agreement among five ICs (i.e. INTERCheck WEB, Micromedex, Lexicomp, Epocrates, and drugs.com) in identifying potential DDIs and in attributing severity categories was evaluated using Gwet's AC1 on all five ICs and by comparing groups of four- and two-pair sets of ICs.

RESULTS

A total of 486 potentially interacting drugs with dabigatran, 556 for apixaban, 444 for edoxaban, and 561 for rivaroxaban were reported. The level of agreement among the ICs in identifying potential DDIs was poor (range: 0.12-0.16). Similarly, it was low in 4 and 2 sets analyses. The level of agreement among the ICs in classifying the severity of potential DDIs was poor (range: 0.32-0.34), also in 4 and 2 sets analyses.

CONCLUSIONS

The heterogeneity among different ICs and SPCs underscores the need to standardize DDI datasets and to conduct real-world studies to generate evidence regarding the frequency and clinical relevance of potential DOAC-related DDIs.

Drug-drug interactions between COVID-19 drug therapies and antidepressants

INTRODUCTION

Antidepressants are widely used for the pharmacological treatment of anxiety and mood disorders. Since the eruption of the SARS-COV-2 pandemic and the later development of targeted treatments against COVID-19, inevitably many patients receive antidepressants as well as targeted treatments against COVID-19 against COVID-19. Co-administration of antidepressants with COVID-19 therapeutics has the potential of drug-drug interactions, of varying severity and clinical significance.

AREAS COVERED

This is a curated narrative review of the current state of the art regarding drug-drug interactions between COVID-19 therapeutics and medications licensed for the pharmacotherapy of depression. A systematic search of electronic databases, using as keywords the international nonproprietaty names of currently approved COVID-19 therapeutics and antidepressants was performed, and additionally online interaction checker tools were consulted. Derived data were synthesized for each COVID-19 therapeutic and presented with up-to-date guidance.

EXPERT OPINION

Several COVID-19 therapeutics have potential for drug-drug interactions with antidepressants. Remdesivir and Nirmatrelvir-Ritonavir have the higher risk, whereas several monoclonal antibodies appear safer. The most serious drug-drug interactions (serotonin syndrome and QTc prolongation) require close monitoring; however, DDI toward reducing the efficacy of antidepressants may be difficult to recognize. As COVID-19 treatment protocols take precedence, psychiatrists should exert flexibility in antidepressant use and proactively monitor treatment progress.

Potential Drug-Drug Interactions among Patients with Schizophrenia Spectrum Disorders: Prevalence, Association with Risk Factors, and Replicate Analysis in 2021

Background and Objectives: Patients with schizophrenia are often exposed to polypharmacotherapy, which may lead to drug-drug interactions. The aim of the study was to investigate the prevalence of potential drug-drug interactions (pDDIs) in hospitalized patients with schizophrenia spectrum disorders and to identify factors associated with pDDIs and manifested symptoms and signs. Materials and Methods: This cross-sectional observational study included 311 inpatients admitted to a psychiatric hospital. The LexiComp drug interaction program was used to identify pDDIs in 2014. Factors associated with the prevalence of pDDIs and factors related to clinically observed symptoms and signs were assessed using multivariable regression. In addition, replicate analysis of pDDI was performed using 2021 program updates. Results: The prevalence of pDDIs was 88.7%. Our study showed that more than half of the patients received at least one drug combination that should be avoided. The most common pDDIs involved combinations of two antipsychotics or combinations of antipsychotics and benzodiazepines, which can lead to cardio-respiratory depression, sedation, arrhythmias, anticholinergic effects, and neuroleptic malignant syndrome. The number of prescribed drugs was a risk factor for pDDIs (OR 2.85; 95% CI 1.84-5.73). All groups of clinically observed symptoms and signs were associated with the number of drugs. In addition, symptoms and signs characteristic of the nervous system and psychiatric disorders were associated with antipsychotic dosage (IRR 1.33; 95% CI 1.12-1.58), which could contribute to the development of extrapyramidal syndrome, insomnia, anxiety, agitation, and bipolar mania. The 2021 version of the drug interaction program showed a shift in drug interactions toward a lower risk rating, implying less severe patient management and possibly less alert fatigue. Conclusions: Patients with schizophrenia spectrum disorders are at high risk of developing drug-drug interactions. Optimization of drug therapy, patient monitoring, and use of drug interaction programs could help to prevent pDDIs and subsequent adverse drug events.

Drug interactions between common dermatological medications and the oral anti-COVID-19 agents nirmatrelvir-ritonavir and molnupiravir

Introduction: The oral antiviral agents nirmatrelvir-ritonavir (NMV/r) and molnupiravir are used to treat mild-to-moderate COVID-19 infection in outpatients. However, the use of NMV/r is complicated by significant drug-drug interactions (DDIs) with frequently prescribed medications. Healthcare professionals should be aware of the possible risk of DDIs, given the emergence of COVID-19 variants and the widespread use of oral COVID-19 treatments. We reviewed available data on DDIs between NMV/r, molnupiravir and common dermatological medications; summarised the potential side effects; and suggest strategies for safe COVID-19 treatment. Method: A systematic review using PubMed was conducted on data published from inception to 18 July 2022 to find clinical outcomes of DDIs between NMV/r, molnupiravir and dermatological medications. We also searched the Lexicomp, Micromedex, Liverpool COVID-19 Drug Interactions database and the National Institutes of Health COVID-19 Treatment Guidelines for interactions between NMV/r and molnupiravir, and commonly used dermatological medications. Results: NMV/r containing the cytochrome P-450 (CYP) 3A4 inhibitor ritonavir has DDIs with other medications similarly dependent on CYP3A4 metabolism. Dermatological medications that have DDIs with NMV/r include rifampicin, clofazimine, clarithromycin, erythromycin, clindamycin, itraconazole, ketoconazole, fluconazole, bilastine, rupatadine, dutasteride, ciclosporin, cyclophosphamide, tofacitinib, upadacitinib, colchicine and systemic glucocorticoids. With no potential DDI identified yet in in vitro studies, molnupiravir may be an alternative COVID-19 therapy in patients taking medications that have complicated interactions with NMV/r, which cannot be stopped or dose adjusted. Conclusion: NMV/r has significant DDIs with many common dermatological medications, which may require temporary discontinuation, dosage adjustment or substitution with other anti-COVID-19 agents such as molnupiravir. Keywords: COVID-19, dermatology, drug interactions, molnupiravir, nirmatrelvir-ritonavir, pharmacology

Antiretroviral drug-drug interactions: A comparison of online drug interaction databases

WHAT IS KNOWN AND OBJECTIVE

Antiretrovirals have a high drug interaction potential, which can lead to increased toxicity and/or decreased efficacy. Multiple databases are available to assess drug-drug interactions. The aim of our study was to compare interaction identification for commonly used ARVs and concomitant medications between six different online drug-drug interaction databases.

COMMENT

This was a cross-sectional review using each of the following six databases: LexiComp®, Clinical Pharmacology®, Micromedex®, Epocrates®, University of Liverpool, and University of Toronto. Sixteen antiretroviral drugs and 100 of the DrugStats Database "Top 200 of 2019" list of medications were included. Each of the six databases identified a different number of actual or potential interactions. The number of interactions ranged from 211 to 283.

WHAT IS NEW AND CONCLUSIONS

A variety of databases exist with inconsistent identification of actual or potential drug-drug interactions amongst them. It may be beneficial to cross-reference multiple databases prior to making decisions regarding patient care.

A Rule-Based Inference Framework to Explore and Explain the Biological Related Mechanisms of Potential Drug-Drug Interactions

As more drugs are developed and the incidence of polypharmacy increases, it is becoming critically important to anticipate potential DDIs before they occur in the clinic, along with those for which effects might go unobserved. However, traditional methods for DDI identification are unable to coalesce interaction mechanisms out of vast lists of potential or known DDIs, much less study them accurately. Computational methods have great promise but have realized only limited clinical utility. This work develops a rule-based inference framework to predict DDI mechanisms and support determination of their clinical relevance. Given a drug pair, our framework interrogates and describes DDI mechanisms based on a knowledge graph that integrates extensive available biomedical resources through semantic web technologies and backward chaining inference, effectively identifying facts within the graph that prove and explain the mechanisms of the drugs' interaction. The framework was evaluated through a case study combining a chemotherapy agent, irinotecan, and a widely used antibiotic, levofloxacin. The mutual interactions identified indicate that our framework can effectively explore and explain the mechanisms of potential DDIs. This approach has the potential to improve drug discovery and design and to support rapid and cost-effective identification of DDIs along with their putative mechanisms, a key step in determining clinical relevance and supporting clinical decision-making.

A comparison of four drug-drug interaction databases for patients undergoing haematopoietic stem cell transplantation

WHAT IS KNOWN AND OBJECTIVE

Patients who have undergone haematopoietic stem cell transplantation are prone to drug-drug interactions due to polypharmacy. Drug-drug interaction databases are essential tools for identifying interactions in this patient group. However, drug-drug interaction checkers, which help manage interactions, may have disagreements about assessing the existence or severance of the interactions. The study aimed to determine differences among popular drug-drug interaction databases from several angles for patients who underwent haematopoietic stem cell transplantation.

METHODS

The 21-day treatment sheets of one hundred patients who underwent haematopoietic stem cell transplantation were examined in two subscription-based (Uptodate and Micromedex) and two open-access databases (Drugs.com and Epocrates) in terms of several categories two years in a row. Statistical analysis was utilized to understand the compatibility of databases in terms of severity scores, evidence levels, given references, and word counts in interaction reports. Fleiss' and Cohen's kappa statistics were used to analyse the databases' agreement levels.

RESULTS AND DISCUSSION

A total of 1393 and 1382 different drug-drug interactions were detected in subsequent versions of the databases, namely the 2021 and 2022 versions. The Fleiss kappa overall agreement among databases was slight. Uptodate and Micromedex showed fair agreement, and other database pairs showed slight agreement in severity ratings.

CONCLUSION

There was a poor agreement among databases for interactions seen in bone marrow transplantation patients. Therefore, it would be safer to use more than one database in daily practice. Further work needs to be done to understand the agreement level of databases for different types of interactions.

Treatment pearls: Management of physical healthcare needs in patients with mental illness

ABSTRACT

Clinicians caring for persons with mental illness should be aware of increased mortality, physical problems, and health disparities in this population. This article provides a brief overview of physical health problems in the context of mental illness as well as those related to psychotropic medications, and discusses strategies to manage treatment effectively.

Potential Psychotropic and COVID-19 Drug Interactions: A Comparison of Integrated Evidence From Six Database Programs

Background

Drug interactions are a significant issue in mental illnesses and coronavirus disease 2019 (COVID-19) infections. Inconsistency in drug interaction resources makes prescribing challenging for healthcare professionals. To assess the scope, completeness, and consistency of drug-drug interactions (DDIs) between psychotropic and COVID-19 medications in six specific drug information (DI) databases.

Methodology

For the comparison, six DI resources were used: Portable Electronic Physician Information Database, Micromedex®, Medscape.com, UpToDate®, Drugs.com drug interaction checker, and WebMD.com drug interaction checker. Using the Statistical Package for the Social Sciences (SPSS) software version 27 (IBM Corp., Armonk, NY), the gathered data were examined for scope, completeness, and consistency.

Results

Scope scores were higher for PEPID© than all the other resources (p < 0.001) for each comparison. PEPID© had better overall completeness scores (median 5, Interquartile range [IQR] 5 to 5; p<0.05 for each comparison), except for Drugs.com (p < 0.05 for each comparison), and were more remarkable for Micromedex® (median 5, IQR 5 to 5). The Fleiss kappa scores among the six different DI sources were poor (k < 0.20, p < 0.05) for the category of information related to clinical effects and level of documentation, moderate agreement (k = 0.4 - 0.6, p < 0.05) for the severity and course of action of DDIs, and fair agreement (k = 0.4 - 0.6, p < 0.05) for mechanism.

Conclusion

A comprehensive, accurate information among DI resources is essential for healthcare professionals that will significantly impact patient care in the clinical practice. Banking on high-quality resources will help healthcare professionals to make an informed decision while prescribing to avoid inappropriate combinations that can adversely affect patient outcomes.