A comparison of potential psychiatric drug interactions from six drug interaction database programs

Assessment of potential drug-drug interactions in patients with hereditary angioedema from the ITACA cohort: simulations from a real-life dataset considering danazol versus berotralstat

Background

Danazol is regularly used as a prophylactic treatment in patients with Hereditary angioedema due to C1-inhibitor deficiency (HAE-C1INH). However, this drug is characterized by a risk of drug-drug interactions (DDIs). Berotralstat, the first oral kallikrein inhibitor, has been recently approved for the prevention of HAE attacks. Here, we sought to compare the risk of potential DDIs in real-life HAE patients hypothetically given Danazol or Berotralstat.

Methods

Our clinic's database was retrospectively reviewed to identify patients diagnosed with HAE who were treated with at least one concomitant medication. The DDIs were assessed using three freely available drug interaction checkers and scored based on their severity. The agreement between the three drug checkers was evaluated using weighted Cohen's kappa coefficient.

Results

75 HAE patients (64% female, mean age 56 ± 21 years) were considered. They were mainly treated with antihypertensives (37%), hypoglycemic (19%), and hypolipemic agents (17%). Significant discrepancies among the three-drug interaction checkers were found. The first checker identified 18 potential DDIs, all involving Danazol and a statin (simvastatin). The second checker identified, respectively, 66 and 14 DDIs for Danazol (20% severe, regarding Simvastatin and Rivaroxaban) and Berotralstat (0% severe). The third checker identified 49 and 43 DDIs for Danazol (22% severe, regarding Simvastatin) and Berotralstat (0%).

Conclusion

Berotralstat was consistently associated with a reduced risk of DDIs compared with Danazol. A rational assessment of DDIs would help select the best prophylactic treatment for HAE.

Alert prescribing of clozapine: a comparison of five drug-drug interaction sources

Introduction

Clozapine, an antipsychotic used in the treatment of schizophrenia, is known for its serious side effects. In order to promote patient safety, drug-drug interaction (DDI) databases can be consulted by clinicians. In this study, the degree of consensus between five sources on DDIs with clozapine is determined.

Methods

The summary of product characteristics of clozapine, Delphicare interaction database, Stockley's interaction checker, the Lexicomp interaction database, and the interaction database of Clinical Pharmacology are included. By comparing the original categories assigned to interactions with clozapine by the included DDI sources, a degree of consensus between sources is determined. Furthermore, based on the combined information, an evaluation on the severity of each potential interaction is made.

Results

One hundred eighty-three potential DDIs with clozapine are retrieved from the five included sources. A consensus between sources is found in 47.5% (n = 87) of DDIs.

Conclusion

This study shows major discrepancies between five different sources on DDIs with clozapine. The potential impact of the use of one specific database on patient safety and prescribing behavior could prove to be problematic.

Interactions between grapefruit juice and psychotropic medications: an update of the literature and an original case series

INTRODUCTION

There is a large body of preclinical data implicating that grapefruit juice (GJ) inhibits many CYP 450 isoforms. The potential of GJ-to-drug is of high relevance to clinical psychiatry, because a wide range of psychotropic medicines undergo CYP 450 metabolism and P-gp transport.

AREAS COVERED

Relevant data were identified by searching the electronic databases up to February 2024. This work constitutes a summary of preclinical and clinical data on GJ impact on CYP 450 metabolism, P-glycoprotein, and organic anion-transporting polypeptides (OATPs), with focus on studies that assessed GJ-to-psychotropic drug interactions. Additionally, an unpublished case series of nine patients is provided.

EXPERT OPINION

The impact of GJ on CYP 3A4 appears to be the critical mechanism for the majority of GJ-to-psychopharmacotherapy interactions described in human studies or case reports. However, there are studies and cases of patients clearly showing that this is not the only route explaining the GJ effect, and at times, this particular is of no relevance and that other CYP 450 isoforms as well as drug transporting proteins might be involved. The risk of GJ-to-psychotropic drugs needs to be further evaluated in a 'real-world' setting and apply not only measures of pharmacokinetics but also treatment effectiveness and safety.

An Assessment of Different Decision Support Software from the Perspective of Potential Drug-Drug Interactions in Patients with Chronic Kidney Diseases

Chronic kidney disease (CKD) is a multifaceted disorder influenced by various factors. Drug-drug interactions (DDIs) present a notable risk factor for hospitalization among patients with CKD. This study aimed to assess the frequency and attributes of potential DDIs (pDDIs) in patients with CKD and to ascertain the concordance among different Clinical Decision Support Software (CDSS). A cross-sectional study was conducted in a nephrology outpatient clinic at a university hospital. The pDDIs were identified and evaluated using Lexicomp® and Medscape®. The patients' characteristics, comorbidities, and medicines used were recorded. The concordance of different CDSS were evaluated using the Kendall W coefficient. An evaluation of 1121 prescribed medications for 137 patients was carried out. The mean age of the patients was 64.80 ± 14.59 years, and 41.60% of them were male. The average year with CKD was 6.48 ± 5.66. The mean number of comorbidities was 2.28 ± 1.14. The most common comorbidities were hypertension, diabetes, and coronary artery disease. According to Medscape, 679 pDDIs were identified; 1 of them was contraindicated (0.14%), 28 (4.12%) were serious-use alternative, and 650 (9.72%) were interventions that required closely monitoring. According to Lexicomp, there were 604 drug-drug interactions. Of these interactions, 9 (1.49%) were in the X category, 60 (9.93%) were in the D category, and 535 (88.57%) were in the C category. Two different CDSS systems exhibited statistically significant concordance with poor agreement (W = 0.073, p < 0.001). Different CDSS systems are commonly used in clinical practice to detect pDDIs. However, various factors such as the operating principles of these programs and patient characteristics can lead to incorrect guidance in clinical decision making. Therefore, instead of solely relying on programs with lower reliability and consistency scores, multidisciplinary healthcare teams, including clinical pharmacists, should take an active role in identifying and preventing pDDIs.

Implications of Online Self-Diagnosis in Psychiatry

Online self-diagnosis of psychiatric disorders by the general public is increasing. The reasons for the increase include the expansion of Internet technologies and the use of social media, the rapid growth of direct-to-consumer e-commerce in healthcare, and the increased emphasis on patient involvement in decision making. The publicity given to artificial intelligence (AI) has also contributed to the increased use of online screening tools by the general public. This paper aims to review factors contributing to the expansion of online self-diagnosis by the general public, and discuss both the risks and benefits of online self-diagnosis of psychiatric disorders. A narrative review was performed with examples obtained from the scientific literature and commercial articles written for the general public. Online self-diagnosis of psychiatric disorders is growing rapidly. Some people with a positive result on a screening tool will seek professional help. However, there are many potential risks for patients who self-diagnose, including an incorrect or dangerous diagnosis, increased patient anxiety about the diagnosis, obtaining unfiltered advice on social media, using the self-diagnosis to self-treat, including online purchase of medications without a prescription, and technical issues including the loss of privacy. Physicians need to be aware of the increase in self-diagnosis by the general public and the potential risks, both medical and technical. Psychiatrists must recognize that the general public is often unaware of the challenging medical and technical issues involved in the diagnosis of a mental disorder, and be ready to treat patients who have already obtained an online self-diagnosis.

Assessment of Potential Drug-drug Interactions (pDDIs) and Their Risk Factors Among Hospitalized Cardiac Patients in a Tertiary-care Center of Central India: A Retrospective Record-based Study

Background: Patients with cardiovascular disorders (CVD) possess multiple comorbidities and are prone to be prescribed multiple drugs, thus predisposing them to various drug-drug interactions (DDIs). Objective: This study was carried out to assess the potential-DDIs (pDDIs) among the drugs prescribed to hospitalized patients with CVD and associated factors. Method: It was a retrospective study conducted with the help of the medical records department. Medical records of all the patients admitted to the cardiology department of our tertiary care center from January 1st, 2019, to December 31st, 2019, were included for analysis using Lexicomp, an up-to-date drug interaction screening tool. The pDDIs were divided into classes A, B, C, D, and X, and those belonging to classes D or X were considered clinically significant. Multiple logistic regression was used to analyze the association between the factors associated with and the occurrence of clinically significant pDDIs, with a P-value < .05 considered statistically significant. Results: Almost all the records reflected (335/338) at least 1 pDDI. A total of 4966 pDDIs were detected, of which the majority belonged to category C (75.3%), and 5.1% of pDDIs were clinically significant. The patients who were prescribed more than 10 drugs per day (OR = 2.46; 95% CI: 1.27-4.82; P = .008), prescribed parenteral formulation (OR = 1.84; 95% CI: 1.57-2.21; P < .0001), or had a diagnosis of acute coronary syndrome (OR = 2.33; 95% CI:1.1-5.12; P = .03) were associated with clinically significant pDDIs. Other factors, that is, female sex, use of fixed-dose combinations, and the triad of diabetes mellitus, hypertension, and dyslipidemia, were positively associated with clinically significant pDDIs. Conclusion: Even though every patient had at least 1 pDDI, the prevalence of clinically significant pDDIs was relatively less. Use of >10 drugs/day, parenteral formulation, patients with acute coronary syndrome were significantly associated with clinically significant pDDIs.

Drug-drug Interaction between Psychotropic Medications and Medications Used in COVID-19: Comparison of Online Databases

OBJECTIVE

COVID-19 has gravely affected patients with psychiatric conditions. Potential interactions may occur between psychotropic medications and medications used in treatment of COVID-19. This study aimed to compare the online databases in terms of the quality of drug-drug interaction related information available on them.

METHODS

216 drug interactions which included fifty-four psychotropic medication interactions with four COVID-19 drugs across six databases were analyzed by four authors independently. The overall grading of the databases was done on Likert scale independently by the authors using the parameters of ease of understanding for consumers and professionals, level of completeness, discussion on level of evidence and the number of available drugs, congruity with other databases and the mean score was tabulated.

RESULTS

Drugbank and Lexicomp had maximum discrepancy. The safety profile of Hydroxychloroquine was the best (eighteen moderate/severe psychotropic medication reactions) while Ritonavir has worst profile with thirty-nine medications. Drugbank had the highest SCOPE score (1.00) for completeness and covid19druginteractions.com had least (0.81). Overall, Liverpool© Drug Interaction Group and Lexicomp scored the highest (23/30 each) and were the best interaction checker software closely followed by Drugs.com (22/30). Medscape and WebMD were the poorest interaction checker databases.

CONCLUSION

There is significant variability in the available online databases. Liverpool© Drug Interaction Group and Lexicomp were the most reliable sources for healthcare workers whereas for patients, Drugs.com was the easiest to understand (as it segregates the needs of general consumers and professionals distinctly to explain the interaction).

Interaction alerts: A comparison of classifications and recommendations for clinical management between Janusmed and three other knowledge resources

Classifications of drug interaction alerts differ between knowledge resources, but agreement regarding recommendations for clinical management is less explored. Starting from the medication lists of 274 older patients with ≥2 drugs, all unique drug pairs that triggered a clinically significant interaction alert in Janusmed were included: 100 Category C (manageable by, e.g. dose adjustment) and nine Category D interactions (should be avoided). Out of 109 C/D alerts in Janusmed, 89 (82%), 75 (69%) and 45 (41%) drug pairs triggered an alert of similar clinical significance in Lexicomp, Micromedex® and Stockley's Drug Interactions/Checker (Stockley), respectively. Eight (7%), 20 (18%) and 10 (9%) drug pairs did not trigger any alert in these resources. For 81 (74%), 81 (74%) and 94 (86%) drug pairs, Lexicomp, Micromedex and Stockley provided at least one recommendation for clinical management similar to those provided by Janusmed. For 16 (15%), 9 (8%) and 21 (19%) drug pairs, these resources provided recommendation(s) entirely in agreement with Janusmed. Although many drug pairs elicit alerts of similar significance, and partly concordant recommendations, a non-negligible proportion do not. The findings encourage medical/pharmaceutical reflection by prescribing clinicians and dispensing pharmacists; recommendations provided by knowledge resources vary considerably and cannot be considered definite.

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.

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 drug-drug interactions when managing status epilepticus patients in intensive care: A cohort study

AIMS

The risk for drug-drug interactions (DDIs) associated with antiseizure drugs (ASDs) used to manage status epilepticus (SE) patients in the intensive care unit (ICU) has been poorly investigated. We aimed to quantify and describe those potential DDIs and determine SE patient risk profiles.

METHODS

We conducted an observational bi-centric cohort study including all SE patients admitted to the ICU in the period 2016-2020.

RESULTS

Overall, 431 SE patients were included and 5504 potential DDIs were identified including 1772 DDIs (33%) between ASDs, 2610 DDIs (47%) between ASDs and previous usual treatments (PUTs), and 1067 DDIs (20%) between ASDs and ICU treatments (ICUTs). DDIs were moderate (n = 4871), major (n = 562) or severe (n = 16). All patients exhibited potential DDIs, which were major-to-severe DDIs in 47% of the cases. DDIs were pharmacokinetic (n = 1972, 36%), mostly involving cytochrome P450 modulators, and pharmacodynamic (n = 3477, 64%), mainly leading to increased sedation. ASD/PUT DDIs were the most frequent and severe. Age, PUT and ASD drug numbers and length of ICU stay were significantly associated with increased DDI number. We identified four SE patient profiles with different DDI risks and outcomes including (1) epileptic or brain trauma patients, (2) withdrawal syndrome patients, (3) older patients with comorbidities and (4) self-poisoned patients with psychiatric disorders and/or past epilepsy.

CONCLUSION

SE patients are subject to potential DDIs between ASDs, ASD/PUT and ASD/ICUT. Major-to-severe DDIs mostly occur between ASDs and PUTs. Physicians should pay attention to SE patient characteristics and history to limit DDI numbers and prevent their consequences.

Drug-Related Problems and Polypharmacy in Nursing Home Residents: A Cross-Sectional Study

At present, 19.2% of the Spanish population is aged 65 or older. Polypharmacy is a frequent condition among the elderly, especially in those living in nursing homes, which is associated with adverse outcomes, such as adverse drug events or drug-drug interactions. This study aimed to assess the pattern of polypharmacy in a nursing home in Leon, one of Spain's most ageing regions, and its relationship with different drug-related problems. A descriptive, observational, and cross-sectional study design was used; 222 residents were involved in this study. Data on drug use were collected from medical charts. Information was screened with the software CheckTheMeds, BOT PLUS and Drug-Reax. Residents were on a median of 7 medicines. Polypharmacy and inappropriate medications were present in 78.8% and 96.8% of residents, respectively. Drug-related problems were present in almost all the populations evaluated. Drug-drug interactions were very common in participants (81.1%), being severe/moderate in 24.7%. A high prevalence of polypharmacy and drug-related problems in the nursing home population assessed has been observed. A significantly higher risk of suffering drug-drug interactions was revealed for increasing polypharmacy and anticholinergic risk. A regular evaluation of drug prescribing in nursing home residents is necessary to minimize drug-related problems risk.

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.

Assessing Consistency of Drug-Drug Interaction-Related Information Across Various Drug Information Resources

Background

Information related to drug-drug interactions (DDIs) varies significantly from one drug information (DI) resource to another. These variations pose challenges for healthcare professionals in making the right decisions regarding using some of the drug combinations in needy patients. The objective of this study was to review eight different DI resources for scope, completeness, and consistency of information related to DDIs.

Methodology

A total of eight DI resources, namely, Micromedex^®, Portable Electronic Physician Information Database^©, UpToDate^®, Medscape.com drug interaction checker, Drugs.com drug interaction checker, Stockley’s Drug Interactions (ninth edition, 2010), Drug Interactions Analysis & Management: Facts and Comparisons 2014 (ninth edition, 2014), and the drug interaction appendix of the British National Formulary-76, were compared. Each DI resource was scored for scope by calculating the percentage of interactions that had an entry in each resource. A completeness score was calculated for each resource describing severity, clinical effects, mechanism, and DDI management. The consistency of the information was assessed using Fleiss Kappa (k) score estimated using ReCal3 0.1 (alpha) web service and Statistical Package for the Social Sciences version 24.

Results

The scope score was the highest (100%) for UpToDate^® and Portable Electronic Physician Information Database^©, whereas the completeness score was the highest (100%) for Drug Interaction Analysis & Management: Facts and comparisons 2014. The inter-source reliability scores among the eight different DI sources were poor (k < 0.20, p < 0.05) for documentation of information related to severity, clinical effects, mechanism, and management of DDIs.

Conclusions

Variations in the information cause uncertainty among healthcare professionals concerning interacting drug pairs in clinical practice. This may also increase the possibility of adverse drug outcomes when interacting drug pairs are used in at-risk patients. We recommend comprehensive preventive and management strategies for DDIs depending on a uniform scale of severity and clinical effects across various DI resources.

Drug-drug interactions between COVID-19 treatments and antipsychotics drugs: integrated evidence from 4 databases and a systematic review

RATIONALE

Management of anxiety, delirium, and agitation cannot be neglected in coronavirus disease (COVID-19). Antipsychotics are usually used for the pharmacological management of delirium, and confusion and behavioral disturbances. The concurrent use of treatments for COVID-19 and antipsychotics should consider eventual drug-drug interactions OBJECTIVE: To systematically review evidence-based available on drug-drug interactions between COVID-19 treatments and antipsychotics.

EVIDENCE REVIEW

Three databases were consulted: Lexicomp® Drug Interactions, Micromedex® Solutions Drugs Interactions, and Liverpool© Drug Interaction Group for COVID-19 therapies. To acquire more information on QT prolongation and Torsade de Pointes (TdP), the CredibleMeds® QTDrugs List was searched. The authors made a recommendation agreed to by consensus. Additionally, a systematic review of drug-drug interactions between antipsychotics and COVID-19 treatment was conducted.

RESULTS

The main interactions between COVID-19 drugs and antipsychotics are the risk of QT-prolongation and TdP, and cytochromes P450 interactions. Remdesivir, baricinitib, and anakinra can be used concomitantly with antipsychotics without risk of drug-drug interaction (except for hematological risk with clozapine and baricinitib). Favipiravir only needs caution with chlorpromazine and quetiapine. Tocilizumab is rather safe to use in combination with antipsychotics. The most demanding COVID-19 treatments for coadministration with antipsychotics are chloroquine, hydroxychloroquine, azithromycin, and lopinavir/ritonavir because of the risk of QT prolongation and TdP and cytochromes interactions. The systematic review provides highly probable drug interaction between lopinavir/ritonavir plus quetiapine and ritonavir/indinavir plus risperidone.

CONCLUSIONS

Clinicians prescribing antipsychotics should be aware of the likely risk of drug-drug interaction with COVID-19 medication and may benefit from taking into account present recommendations of use to preserve patient safety.

Survey of psychiatrist use of digital technology in clinical practice

Background

Psychiatrists were surveyed to obtain an overview of how they currently use technology in clinical practice, with a focus on psychiatrists who treat patients with bipolar disorder.

Methods

Data were obtained using an online-only survey containing 46 questions, completed by a convenience sample of 209 psychiatrists in 19 countries. Descriptive statistics, and analyses of linear associations and to remove country heterogeneity were calculated.

Results

Virtually all psychiatrists seek information online with many benefits, but some experience information overload. 75.2% of psychiatrists use an EMR/EHR at work, and 64.6% communicate with patients using a new technology, primarily email (48.8%). 66.0% do not ask patients if they use the Internet in relation to bipolar disorder. 67.3% of psychiatrists feel it is too early to tell if patient online information seeking about bipolar disorder is improving the quality of care. 66.3% of psychiatrists think technology-based treatments will improve the quality of care for some or many patients. However, 60.0% of psychiatrists do not recommend technology-based treatments to patients, and those who recommend select a variety of treatments. Psychiatrists use technology more frequently when the patients live in urban rather than rural or suburban areas. Only 23.9% of psychiatrists have any formal training in technology.

Conclusions

Digital technology is routinely used by psychiatrists in clinical practice. There is near unanimous agreement about the benefits of psychiatrist online information-seeking, but research on information overload is needed. There is less agreement about the appropriate use of other clinical technologies, especially those involving patients. It is too early to tell if technology-based treatments or patient Internet activities will improve the quality of care. The digital divide remains between use of technology for psychiatrists with patients living in urban and rural or suburban areas. Psychiatrists need more formal training in technology to understand risks, benefits and limitations of clinical products.

Evaluation of drug information resources for interactions between therapeutic drugs and drugs of abuse

Objective

The study evaluated point-of-care resources for scope, completeness, and consistency of information describing interactions between therapeutic drugs and drugs of abuse (DoA).

Methods

A cross-sectional evaluation study was conducted focusing on seven resources: Clinical Pharmacology, Facts & Comparisons eAnswers, Lexicomp Online, Micromedex, Drug Interactions Analysis and Management, Drug Interaction Facts, and Stockley's Drug Interactions. A sample of clinically relevant interactions was developed through review of tertiary literature and resources, and input was solicited from subject matter experts. Entries from each resource for each interaction were evaluated for scope (i.e., whether there was an entry for the interaction); completeness (i.e., whether there was information addressing mechanism; clinical effects, severity, course of action, and level of certainty, described as a median rating on a 5-point scale); and consistency (i.e., whether the information in the resource was similar to the majority) among resources with an entry.

Results

Following review by subject matter experts, the final sample contained 159 interactions. Scope scores ranged from 0.6% (Drug Interactions Analysis and Management) to 43.4% (Lexicomp Online). Completeness scores ranged from 2 (interquartile range [IQR] 0 to 3, Stockley's Drug Interactions) to 5 (IQR 5 to 5, Drug Interaction Facts, Micromedex, Facts & Comparisons eAnswers). Consistency scores ranged from 30.8% (Stockley's Drug Interactions) to 87.1% (Clinical Pharmacology) for severity and from 15.4% (Facts & Comparisons eAnswers) to 71.4% (Drug Interaction Facts) for course of action.

Conclusions

Although coverage of drug-DoA interactions was low and content was often inconsistent among resources, the provided information was generally complete.

Assessment of drug-drug interaction in an elderly human immunodeficiency virus population: Comparison of 3 expert databases

AIMS

Polypharmacy increase the risk of drug-drug interactions (DDIs) in the elderly population living with human immunodeficiency virus (HIV). Several expert databases can be used to evaluate DDIs. The aim of the study was to describe actual DDIs between antiretroviral drugs and comedications in an elderly population and to compare grading of the DDIs in 3 databases.

METHODS

All treatments of HIV-infected subjects aged 65 years and older were collected in 6 French HIV centres. Summary of Product Characteristic (SPC), French DDI Thesaurus (THES), and Liverpool HIV DDI website (LIV) were used to define each DDI and specific grade. DDIs were classified in yellow flag interaction (undefined grade in SPC and THES or potential weak interaction in LIV), amber flag interaction (to be considered/precaution of use in SPC and THES and potential interaction in LIV) and red flag interaction (not recommended/contraindication in SPC and THES and do not administer/contraindication in LIV).

RESULTS

Among 239 subjects included, 60 (25.1%) had at least 1 DDI for a total of 126 DDIs: 23/126 red flag DDIs were identified in 17 patients. All these 23 DDIs were identified in LIV. THES and SPC missed 6 and 1 red flag DDIs, respectively. Seven of 23 red flag DDIs were identified in the 3 databases concomitantly.

CONCLUSION

Polypharmacy is frequent in this elderly HIV population leading to DDI in a quarter of the subjects. The discrepancies between databases can be explained by differences in analysis methods. A consensus between databases would be helpful for clinicians.

Prevalence and sort of pharmacokinetic drug-drug interactions in hospitalized psychiatric patients

Psychiatric patients are high-risk patients for the development of pharmacokinetic drug-drug interactions (DDIs), leading to highly variable (victim) drug serum concentrations. Avoiding and targeting high-risk drug combinations could reduce preventable adverse drug reactions (ADRs). Pharmacokinetic cytochrome P450 (CYP)-mediated DDIs are often predictable and, therefore, preventable. The retrospective, longitudinal analysis used informations from a large pharmacovigilance study (Optimization of pharmacological treatment in hospitalized psychiatric patients study, study number 01VSF16009, 01/2017), conducted in 10 psychiatric hospitals in Germany. Medication data were examined for the co-prescription of clinically relevant CYP inhibitors or inducers and substrates of these enzymes (victim drugs). In total, data from 27,396 patient cases (45.6% female) with a mean (mean ± standard deviation (SD)) age of 47.3 ± 18.3 years were available for analysis. CYP inhibitors or inducers were at least once prescribed in 14.4% (n = 3946) of the cases. The most frequently prescribed CYP inhibitors were melperone (n = 2504, 28.1%) and duloxetine (n = 1324, 14.9%). Overall, 51.0% of the cases taking melperone were combined with a victim drug (n = 1288). Carbamazepine was the most frequently prescribed CYP inducer (n = 733, 88.8%). Combinations with victim drugs were detected for 58% (n = 427) of cases on medication with carbamazepine. Finally, a DDI was detected in 43.6% of the cases in which a CYP inhibitor or inducer was prescribed. The frequency of CYP-mediated DDI is considerably high in the psychiatric setting. Physicians should be aware of the CYP inhibitory and inducing potential of psychotropic and internistic drugs (especially, melperone).

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

Background Patients with bipolar disorder frequently experience polypharmacy, putting them at risk for clinically significant drug-drug interactions (DDI). Online drug interaction database programs are used to alert physicians, but there are no internationally recognized standards to define DDI. This study compared the category of potential DDI returned by 6 commercial drug interaction database programs for drug interaction pairs involving drugs commonly prescribed for bipolar disorder.

Methods The category of potential DDI provided by 6 drug interaction database programs (3 subscription, 3 open access) was obtained for 125 drug interaction pairs. The pairs involved 103 drugs (38 psychiatric, 65 nonpsychiatric); 88 pairs included a psychiatric and nonpsychiatric drug; 37 pairs included 2 psychiatric drugs. Every pair contained at least 1 mood stabilizer or antidepressant. The category provided by 6 drug interaction database programs was compared using percent agreement and Fleiss kappa statistic of interrater reliability.

Results For the 125 drug pairs, the overall percent agreement among the 6 drug interaction database programs was 60%; the Fleiss kappa agreement was slight. For drug interaction pairs with any category rating of severe (contraindicated), the kappa agreement was moderate. For drug interaction pairs with any category rating of major, the kappa agreement was slight.

Conclusion There is poor agreement among drug interaction database programs for the category of potential DDI involving psychiatric drugs. Drug interaction database programs provide valuable information, but the lack of consistency should be recognized as a limitation. When assistance is needed, physicians should check more than 1 drug interaction database program.

Interactions of cobicistat and ritonavir in patients with HIV and its clinical consequences

INTRODUCTION

The prescription of antiretroviral treatment (ART) that contains pharmacokinetic enhancers such as ritonavir and cobicistat is frequent. The objective of this stdy was to analyze the potential interactions of ART that include these molecules in their formulation with the patient's home medication, as well as the clinical management of those potentially serious.

METHODS

Prospective study conducted in the pharmacy care clinic of a third level hospital between January and December of 2018. Those HIV+patients with an ART containing cobicistat or ritonavir were included in the study. Potential interactions between ART and concomitant medication were analysed in three databases (Micromedex®, Drugs.com and Liverpool), the interventions carried out were detailed, and adverse drug reactions analysed.

RESULTS

968 patients were included with a total of 2,148 prescriptions (274 different medications). A total of 86 interventions were performed regarding potential interactions in patients. The most frequent were substitutions of corticoid treatments, treatment suspensions and closer monitoring of treatments. A total of possible adverse drug reactions were analysed. The degree of agreement in the severity classification of the interactions for cobicistat and ritonavir was good among the three databases. It was remarkable Micromedex® as the most complete because it has more registered medications.

CONCLUSION

The interactions between ART with pharmacokinetic enhancers in its composition and concomitant medication is frequent and requires a significant variety of interventions. The check of interactions in different databases is recommended since they can cause adverse drug reactions.