Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

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.

Screening for severe drug-drug interactions in patients with multiple sclerosis: A comparison of three drug interaction databases

Background: Patients with multiple sclerosis (MS) often undergo complex treatment regimens, resulting in an increased risk of polypharmacy and potential drug-drug interactions (pDDIs). Drug interaction databases are useful for identifying pDDIs to support safer medication use. Objective: To compare three different screening tools regarding the detection and classification of pDDIs in a cohort of MS patients. Furthermore, we aimed at ascertaining sociodemographic and clinical factors that are associated with the occurrence of severe pDDIs. Methods: The databases Stockley's, Drugs.com and MediQ were used to identify pDDIs by screening the medication schedules of 627 patients. We determined the overlap of the identified pDDIs and the level of agreement in pDDI severity ratings between the three databases. Logistic regression analyses were conducted to determine patient risk factors of having a severe pDDI. Results: The most different pDDIs were identified using MediQ (n = 1,161), followed by Drugs.com (n = 923) and Stockley's (n = 706). The proportion of pDDIs classified as severe was much higher for Stockley's (37.4%) than for Drugs.com (14.4%) and MediQ (0.9%). Overall, 1,684 different pDDIs were identified by at least one database, of which 318 pDDIs (18.9%) were detected with all three databases. Only 55 pDDIs (3.3%) have been reported with the same severity level across all databases. A total of 336 pDDIs were classified as severe (271 pDDIs by one database, 59 by two databases and 6 by three databases). Stockley's and Drugs.com revealed 47 and 23 severe pDDIs, respectively, that were not included in the other databases. At least one severe pDDI was found for 35.2% of the patients. The most common severe pDDI was the combination of acetylsalicylic acid with enoxaparin, and citalopram was the drug most frequently involved in different severe pDDIs. The strongest predictors of having a severe pDDI were a greater number of drugs taken, an older age, living alone, a higher number of comorbidities and a lower educational level. Conclusions: The information on pDDIs are heterogeneous between the databases examined. More than one resource should be used in clinical practice to evaluate pDDIs. Regular medication reviews and exchange of information between treating physicians can help avoid severe pDDIs.

Drug Combinations for Mood Disorders and Physical Comorbidities That Need Attention: A Cross-Sectional National Database Survey

INTRODUCTION

This study investigated combined prescriptions of drugs for mood disorders and physical comorbidities that need special attention in the light of frequent physical comorbidities in patients with mood disorders.

METHODS

We used the claims sampling data of 581,990 outpatients in January 2015 from the National Database of Health Insurance Claims and Specific Health Checkups of Japan. Fisher's exact test was performed to compare the prescription rates of non-steroidal anti-inflammatory drugs (NSAIDs), loop/thiazide diuretics, angiotensin-converting enzyme inhibitors, and/or angiotensin II receptor blockers between lithium users and age- and sex-matched non-lithium users; NSAIDs, antiplatelet drugs, and/or anticoagulants between selective serotonin reuptake inhibitor (SSRI)/serotonin-noradrenaline reuptake inhibitor (SNRI) users and non-users; warfarin between mirtazapine users and non-users; and the proportions of patients in the two groups with a diagnosis of somatic conditions for which these medications were indicated and actually received them. A Bonferroni corrected p-value of<0.05/3 was considered statistically significant.

RESULTS

Prescriptions of the above-mentioned medications were less frequent in lithium and mirtazapine users and comparable in SSRI/SNRI users, compared to non-users (18.3 vs. 31.9%, p=7.6×10^-10; 0.78 vs. 1.65%, p=0.01; 23.1 vs. 24.1%, p=0.044). In a subgroup of patients with somatic diseases for which these medications were indicated, the prescription rates were comparable in lithium and mirtazapine users and higher in SSRI/SNRI users compared to non-users (28.0 vs. 29.4%, p=0.73; 4.7 vs. 7.4%, p=0.28; 35.6 vs. 33.4%, p=0.0026).

DISCUSSION

Pharmacotherapy with drugs for mood disorders and physical comorbidities that require attention was commonly observed in clinical practice.

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.

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

OBJECTIVE

Drug interaction database programs are a fundamental clinical tool. In 2018, we compared the category of potential drug-drug interaction (DDI) provided by six drug interaction database programs for 100 drug interaction pairs including psychiatric drugs, and found the category often differed. This study replicated the comparison in 2020 after 2 years of updates to all six drug interaction database programs.

METHODS

The 100 drug pairs included 94 different drugs: 67 pairs with a psychiatric and non-psychiatric drug, and 33 pairs with two psychiatric drugs. The assigned category of potential DDI for the drug pairs was compared using percent agreement and Fleiss kappa statistic of interrater reliability.

RESULTS

Despite 67 updates involving 46 of the 100 drug pairs, differences remained. The overall percent agreement among the six drug interaction database programs for the category of potential DDI was 67%. The interrater agreement results did not change. The Fleiss kappa overall interrater agreement was fair. The kappa agreement for a drug pair with any severe category rating was substantial, and the kappa agreement for a drug pair with any major category rating was fair.

CONCLUSIONS

Physicians should be aware of the inconsistency among drug interaction database programs in the category of potential DDI for drug pairs including psychiatric drugs. Additionally, the category of potential DDI for a drug pair may change over time. This study highlights the importance of ongoing international efforts to standardize methods used to define and classify potential DDI.

Drug-drug Interactions between COVID-19 Treatments and Antidepressants, Mood Stabilizers/Anticonvulsants, and Benzodiazepines: Integrated Evidence from 3 Databases

INTRODUCTION

The SARS-CoV-2 pandemic with psychiatric comorbidities leads to a scenario in which the use of psychotropic drugs may be required. This requires the support of evidence-based medicine to take into account possible interactions between antidepressants, mood stabilizers, benzodiazepines, and coronavirus infection treatments.

METHODS

Three databases were consulted: (a) Lexicomp Drug Interactions, (b) Micromedex Solutions Drugs Interactions, (c)Liverpool Drug Interaction Group for COVID-19 therapies. The CredibleMeds QTDrugs List was also queried. Hydroxychloroquine, chloroquine, azithromycin, lopinavir-ritonavir, remdesivir, favipiravir, tocilizumab, baricitinib, anakinra, and dexamethasone - drugs used for SARS-CoV-2 - were analyzed, and consensus recommendations are made.

RESULTS

The potential interactions of agomelatine, desvenlafaxine, duloxetine, milnacipran, and vortioxetine with COVID-19 treatments shall be considered less risky. Antidepressant interactions with hydroxychloroquine, chloroquine, and azithromycin enhance the risk of QT prolongation, and ECG monitoring is advised for most antidepressants. Antidepressants with lopinavir/ritonavir involve multiple CYP enzyme interactions (except with milnacipran). Gabapentin, oxcarbazepine, pregabalin, topiramate, and zonisamide are safe treatment options that have no significant interactions with COVID-19 treatments. Lithium is contraindicated with hydroxychloroquine, chloroquine, and azithromycin. Precaution should be taken in using valproic acid with lopinavir-ritonavir. The use of benzodiazepines does not present a risk of drug interaction with COVID-19 treatments, except lopinavir/ritonavir.

CONCLUSIONS

Clinicians prescribing antidepressants, mood stabilizers/anticonvulsants, and benzodiazepines, should be aware of the probable risk of drug-drug interaction with COVID-19 medications and may benefit from heeding these recommendations for use to ensure patient safety.

Patients' Use and Perceptions of a Drug-Drug Interaction Database: A Survey of Janusmed Interactions

Janusmed interactions is a drug-drug interactions (DDI) database available online for healthcare professionals (HCP) at all levels of the healthcare system including pharmacies. The database is aimed at HCP but is also open to the public for free, for those individuals who register for a personal account. The aim of this study was to investigate why and how patients use the database Janusmed interactions, how they perceive content and usability, and how they would react if they found an interaction. A web-based questionnaire was sent by email to all users who had registered for Janusmed interactions as a “patient” (n = 3219). A total of 406 patients completed the survey (response rate 12.6%). The study shows that there is an interest among patients to use a DDI database to check their own or a relative’s medication. The respondents found the database easy to use and perceive they understand the information aimed at HCP. Most patients stated they would talk to their HCP if they found an interaction and not adjust their treatment by themselves. However, the respondents in this study are actively searching for information and seem to have high health literacy. Thus, the findings are not generalizable for the general population.