Comparison of potential drug-drug interactions with metabolic syndrome medications detected by two databases

Drug-Drug Interactions Involving High-Alert Medications that Lead to Interaction-Associated Symptoms in Pediatric Intensive Care Patients: A Retrospective Study

BACKGROUND

Children treated in a pediatric intensive care unit (PICU) often receive several drugs together, among them drugs defined as high-alert medications (HAMs). Those drugs carry a high risk of causing patient harm, for example, due to a higher potential for interactions. HAMs should therefore be administered with caution, especially in a PICU.

OBJECTIVES

The objective of the current study was to identify drug-drug interactions involving HAMs that increase the risk of interaction-associated symptoms in pediatric intensive care.

METHODS

In a retrospective study, we analyzed the electronic documentation of patients hospitalized for at least 48 h in a general PICU who received at least two different drugs within a 24-h interval. We assessed potential drug-drug interactions involving HAM on the basis of the two drug information databases UpToDate and drugs.com. Furthermore, we analyzed whether symptoms were observed after the administration of drug pairs that could lead to interaction-associated symptoms. For drug pairs involving HAM administered on at least 2% of patient days, and symptoms observed at least ten times after a respective drug pair, we calculated odds ratios, 95% confidence intervals, and p-values by using a univariate binary logistic regression.

RESULTS

Among 315 analyzed patients, 81.3% (256/315) received drugs defined as high-alert medication for pediatric patients. Those high-alert medications were involved in 20,150 potential drug-drug interactions. In 14.0% (2830/20,150) of these, one or more symptoms were observed that could be a possible consequence of the interaction, resulting in 3203 observed symptoms affecting 56.3% (144/256) of patients receiving high-alert medication. The odds ratios for symptoms observed after a drug-drug interaction were increased for eight specific symptoms (each p ≤ 0.05), especially hemodynamic alterations and disturbances of electrolyte and fluid balance. The odds ratio was highest for decreased blood pressure observed after the administration of the drug pair fentanyl and furosemide (OR 5.06; 95% confidence interval 3.5-7.4; p < 0.001). Increased odds ratios for specific symptoms observed after drug-drug interactions resulted from eight combinations composed of eight different drugs: digoxin, fentanyl, midazolam, phenobarbital, potassium salts and vancomycin (high-alert medications), and the diuretics furosemide and hydrochlorothiazide (non-high-alert medications). The resulting drug pairs were: potassium salts-furosemide, fentanyl-furosemide, vancomycin-furosemide, digoxin-furosemide, digoxin-hydrochlorothiazide, fentanyl-phenobarbital, potassium salts-hydrochlorothiazide, and midazolam-hydrochlorothiazide.

CONCLUSIONS

In a cohort of PICU patients, this study identified eight specific drug pairs involving high-alert medications that may increase the risk of interaction-associated symptoms, mainly hemodynamic alterations and electrolyte/fluid balance disturbances. If the administration of those drug pairs is unavoidable, patients should be closely monitored.

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.

Potential drug-drug interactions of frequently prescribed medications in long COVID detected by two electronic databases

Infection by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) leads to a wide range of acute and chronic complications including long COVID, a well-known chronic sequela. Long COVID often necessitates long-term treatment, which may lead to an increased potential for drug-drug interactions (DDIs). The objective of this study was to assess potential DDIs among frequently prescribed medications in long COVID by using two electronic databases. Sixty frequently prescribed agents were selected from Thailand's National List of Essential Medicine 2022 for potential DDI analysis by Micromedex and Drugs.com. From these databases, 488 potential DDIs were identified. There were 271 and 434 DDI pairs based on Micromedex and Drugs.com, respectively. Among these DDIs, 77 pairs were labeled as contraindicated or major by both databases. The most common mechanisms for these serious interactions are cytochrome P450 (CYP) inhibition (45%), CYP induction (19%), and QT interval prolongation (7.8%). Based on Fleiss' kappa (0.073), there was only slight agreement of the DDI severity classifications between these two databases. In conclusion, a large number of potential DDIs were detected among frequently prescribed medications for long COVID. Health care providers should be aware of these DDIs, particularly those that are deemed as contraindicated or major. These DDIs are most likely to cause significant adverse events in patients with long COVID because polypharmacy is common.

The heterogeneity of metabolic syndrome presentation and challenges this causes in its pharmacological management: a narrative review focusing on principal risk modifiers

INTRODUCTION

Metabolic syndrome (MetS), i.e. the cluster of cardiometabolic risk factors comprising visceral obesity, impaired glucose metabolism, arterial hypertension and atherogenic dyslipidemia, is prevalent globally and exacts a heavy toll on health care expenditures.

AREAS COVERED

The pathophenotypes of individual traits of the MetS in adults are discussed first, with strong emphasis on nonalcoholic fatty liver disease (NAFLD) and sex differences. Next, I discuss recent studies on phenotypic and outcome heterogeneity of the MetS, highlighting the role of NAFLD, sex, reproductive status, and depressive disorders. In the second half of the article, the therapeutic implications of the variable MetS types and features are analyzed, focusing on the most recent developments, and guidelines.

EXPERT OPINION

I have identified physiological, pathological, social and medical sources of phenotypical heterogeneity in the MetS and its constitutive traits. Improved understanding of these variables may be utilized in the setting of future precision medicine approaches in the field of metabolic disorders and target organ damage.

Retrospective cohort observation on psychotropic drug-drug interaction and identification utility from 3 databases: Drugs.com®, Lexicomp®, and Epocrates®

BACKGROUND

Pharmacotherapy is necessary for many people with psychiatric disorders and polypharmacy is common. The psychotropic drug-drug interaction (DDI) should be concerned and efficiently monitored by a proper instrument.

OBJECTIVES

This study aimed to investigate the prevalence and associated factors of psychotropic DDI and to compare the identification utility from three databases: Drugs.com®, Lexicomp®, and Epocrates®.

METHODS

This was a retrospective cohort design. We collected demographic and clinical data of all patients hospitalised in the psychiatric inpatient unit in 2020. Psychotropic DDI profiles were examined through three databases. Descriptive statistics were used to report comprehensiveness of each database and prevalence of psychotropic DDI. The Fleiss' kappa index would be analysed to indicate agreement strength of DDI severity classification among three databases.

RESULTS

From 149 total admissions, the psychotropic DDIs were found in 148 admissions (99.3%). Thorough the study, there were 182 of both psychotropic and other agents prescribed under 1,357 prescriptions. In total, 2,825 psychotropic DDIs were identified by using Drugs.com® 2,500 times, Epocrates® 2,269 times, and Lexicomp® 2,265 times. Interactions with clonazepam was the three most frequent agents when co-administrated with quetiapine (n = 56), risperidone (n = 36), and valproic acid and derivatives (n = 36). Serious DDIs were comparatively lower in incidence and there was no evidence of its association with reported clinical adverse consequences. The study revealed slight and fair agreement regarding severity classification among the three databases was found. DDI events detected by Drugs.com® were greatest in number, but Lexicomp® provided the broadest list of medications prescribed in our study.

CONCLUSION

Among three databases, interactions detected by Drugs.com® were greatest in number, whereas Lexicomp® provided the broadest list of medications. Development of such databases, based on both theoretical and clinical conceptions, should be focused to balance safety of patients and weariness of healthcare providers.

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.

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.

Prevalence of potential drug‐drug interactions with disease specific treatments in patients with pulmonary arterial hypertension or chronic thromboembolic pulmonary hypertension – a registry study

Polypharmacy increases the risk of drug–drug interactions that may disturb treatment effects. The aim of this study was to investigate the frequency of codispensing of potentially interacting or contraindicated drugs related to PH‐specific treatment in the Swedish pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) population. All prescribed drugs, on an individual level, dispensed 2016–2017 at pharmacies to patients with PAH or CTEPH were obtained from The National Board of Health and Welfare's pharmaceutical registry. Potential drug–drug interactions were investigated using the Drug Interaction tool in the IBM Micromedex® database. There were 4785 different dispensed drugs from 572 patients (mean age 61 ± 16 years, 61% female, mean number of drugs per patient 8.4 ± 4.2) resulting in 1842 different drug combinations involving a PH‐specific treatment. Of these drug combinations, 67 (3.5%) had a potential drug–drug interaction considered clinically relevant and it affected 232 patients (41%). The PH‐specific drugs with the highest number of potential drug–drug interactions was bosentan (n = 23, affected patients = 171) while the most commonly codispensed, potentially interacting drug combination was sildenafil/furosemide (119 patients affected). Other common codispensed and potentially interacting drugs were anticoagulants (n = 11, affected patients = 100) and antibiotic treatment (n = 12, affected patients = 26). In conclusion, codispensing of PH‐specific therapy and potentially interacting drugs was common, but codispensing of potentially contraindicated drugs was rare.

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.

Evaluation of potential drug-drug interactions in cancer patients at a tertiary care hospital in Pakistan

BACKGROUND

Despite harboring a high burden of cancer patients who are at high risk of potential drug-drug interactions (pDDIs), there is scarcity of published information about pDDIs in cancer patients from Pakistan.

OBJECTIVE

To evaluate frequency, pattern, mechanism and factors associated with pDDIs in cancer patients treated at a tertiary care hospital in Pakistan.

METHODS

In this cross-sectional analytical study, a total of 253 eligible ambulatory cancer patients treated at Center for Nuclear Medicine and Radiotherapy Hospital Quetta were evaluated for pDDIs using IBM Micromedex® Drug Interactions. SPSS (version 26) was used for conducting multivariate analysis to find factors associated with the presence pDDIs. A p-value <0.05 was considered statistically significant.

RESULTS

A total of 141/253 (55.7%) patients were exposed to at-least one pDDI. A total of 251 pDDIs were noted with a median of one pDDI/per patient (interquartile range:1-2) Majority interactions were of major severity (72.9%), pharmacodynamic (49.8%) and had fair documentation level (64.1%). Anti-cancer drugs were involved in 73.0% pDDIs with doxorubicin as the most commonly involved (40.0%) anti-cancer followed by cyclophosphamide (27.6%) and cisplatin (13.5%). Potential cardiac adverse events made the bulk (33.8%) of predicted events. Receiving >2 anti-cancer (OR = 5.19, p-value = 0.001) and >6 ancillary drugs (OR = 4.16, p-value = 0.033) emerged as the risk factors of pDDIs.

CONCLUSIONS

The prevalence of pDDIs was within the range reported in published literature. Solid medication review, availability of DDI detecting tools and clinical pharmacist, and paying special attention to the high-risk patients may reduce the frequency of pDDIs at the study site.

Determination of potential drug-drug interactions in prescription orders dispensed in a community pharmacy setting using Micromedex® and Lexicomp®: a retrospective observational study

Background Community pharmacists have a role in identifying drug-drug interactions (DDIs) when processing prescription orders and dispensing medications to patients. The harmful effects of DDIs can be prevented or minimized by using an electronic DDI checker to screen for potential DDIs (pDDIs). However, different DDI checkers have variable rates of detecting pDDIs. Aim To estimate the prevalence of pDDIs in prescriptions dispensed in a community pharmacy setting using two electronic DDI databases and to evaluate the association between the pDDIs and contributory factors. Method Eligible prescription orders dispensed by a community pharmacy chain in Qatar from January to July 2020 were included in this retrospective observational study. For each prescription, Micromedex^® and Lexicomp^® were simultaneously used to identify pDDIs, and the interactions categorized based on severity and risk rating. Results Seven hundred-twenty prescriptions met the inclusion criteria, of which Micromedex^® and Lexicomp^® respectively identified 125 prescriptions (17.4%) and 230 prescriptions (31.9%) as having at least one pDDI. Moderate strength of agreement was found between Lexicomp^® and Micromedex^® in identifying pDDIs (Cohen's Kappa = 0.546). Micromedex^® classified 61.6% of DDIs as major severity, while Lexicomp^® classified 30.8% as major severity. The number of concurrent medications per prescription was significantly and positively associated with pDDI. Conclusion This study demonstrates a high prevalence of pDDIs among prescriptions dispensed in a community pharmacy setting. It is advisable that community pharmacists in Qatar, who typically do not have access to computerized patient profiles, use these DDI checkers to ensure all pDDIs are communicated to respective prescribers for appropriate action.

Comparing Potential Drug-Drug Interactions in Companion Animal Medications Using Two Electronic Databases

Multiple-drug prescriptions can cause drug–drug interactions (DDIs), which increase risks associated with healthcare in veterinary medicine. Moreover, many human medicines are used in canine patients under the responsibility of veterinarians and may cause severe problems due to off-label use. Currently, many electronic databases are being used as tools for potential DDI prediction, for example, Micromedex and Drugs.com, which may benefit the prediction of potential DDIs for drugs used in canine. The purpose of this study was to examine different abilities for the identification of potential DDIs in companion animal medicine, especially in canine patients, by Micromedex and Drugs.com. Micromedex showed 429 pairs of potential DDIs, while Drugs.com showed 842 pairs of potential DDIs. The analysis comparing results between the two databases showed 139 pairs (12.28%) with the same severity and 993 pairs (87.72%) with different severities. The major mechanisms of contraindicated and major potential DDIs were cytochrome P450 induction–inhibition and QT interval prolongation. Veterinarians should interpret potential DDIs from several databases with caution and keep in mind that the results might not be reliable due to differences in sensitivity to drugs, drug-metabolizing enzymes, and elimination pathway between animals and humans.

Potential Drug Interactions of Repurposed COVID-19 Drugs with Lung Cancer Pharmacotherapies

Lung cancer patients are at heightened risk for developing COVID-19 infection as well as complications due to multiple risk factors such as underlying malignancy, anti-cancer treatment induced immunosuppression, additional comorbidities and history of smoking. Recent literatures have reported a significant proportion of lung cancer patients coinfected with COVID-19. Chloroquine, hydroxychloroquine, lopinavir/ritonavir, ribavirin, oseltamivir, remdesivir, favipiravir, and umifenovir represent the major repurposed drugs used as potential experimental agents for COVID-19 whereas azithromycin, dexamethasone, tocilizumab, sarilumab, famotidine and ceftriaxone are some of the supporting agents that are under investigation for COVID-19 management. The rationale of this review is to identify potential drug-drug interactions (DDIs) occurring in lung cancer patients receiving lung cancer medications and repurposed COVID-19 drugs using Micromedex and additional literatures. This review has identified several potential DDIs that could occur with the concomitant treatments of COVID-19 repurposed drugs and lung cancer medications. This information may be utilized by the healthcare professionals for screening and identifying potential DDIs with adverse outcomes, based on their severity and documentation levels and consequently design prophylactic and management strategies for their prevention. Identification, reporting and management of DDIs and dissemination of related information should be a major consideration in the delivery of lung cancer care during this ongoing COVID-19 pandemic for better patient outcomes and updating guidelines for safer prescribing practices in this coinfected condition.

Potential drug-drug interactions of antiretrovirals and antimicrobials detected by three databases

Standard treatment for HIV infection involves a combination of antiretrovirals. Additionally, opportunistic infections in HIV infected patients require further antimicrobial medications that might cause drug-drug interactions (DDIs). The objective of this study was to to compare the recognition of DDIs between antiretrovirals and antimicrobials by three proprietary databases and evaluate their concordance. 114 items of antiretrovirals and antimicrobials from the National List of Essential Medicines of Thailand 2018 were used in the study. However, 21 items were not recognised by Micromedex, Drugs.com, and Liverpool HIV interactions. Only 93 items were available for the detection of potential DDIs by the three databases. Potential DDIs detected from the three databases included 292 pairs. Liverpool showed the highest number of DDIs with 285 pairs compared with 259 pairs by drugs.com and 133 pairs by Micromedex. Regarding the severity classifications, Liverpool reported 10% Contraindicated; Micromedex reported 14% contraindicated and 59% major; Drugs.com reported 21% major. The Fleiss’ kappa agreements were fair to poor among the three databases, higher agreement was observed for DDIs classified as severe. This study highlights the need to harmonize the evaluation and interpretation of DDI risk in order to produce standardized information to support prescribers.

Prevalence, severity, and nature of risk factors associated with drug-drug interactions in geriatric patients receiving cancer chemotherapy: A prospective study in a tertiary care teaching hospital

INTRODUCTION

Polypharmacy increases hazard of drug-drug interactions(DDIs), hospitalization, treatment toxicity, and mortality in elderly individuals with cancer. The present study explores and analyzes prevalence and severity of DDIs in geriatric cancer patients subjected to anticancer chemotherapy, their mechanisms, stratification of severity, and correlation between DDI risk and number of medications taken.

METHODS

This was a cross-sectional study conducted between January-July 2019 at the Medical Oncology/Hematology and Radiation-Oncology Departments, All India Institute of Medical Sciences(AIIMS) Rishikesh. The study included a convenience sampling of 126 geriatric cancer patients.

RESULTS

126 patients were enrolled in present study. DDIs were identified in 97.6% of elderly cancer patients, and 88.9% had at least one DDI with antineoplastic medications. Highest number of DDIs involving antineoplastic medications in any given patient was 12. DDIs involving medications used for treatment of non-cancerous diseases were observed in 83.3% of patients; highest number of interactions identified in any given patient was 15. Out of 473 interactions, 237(50.1%) DDIs were attributable to pharmacodynamic mechanisms of action. 126(27%) of DDIs involved pharmacokinetic mechanisms and 110(23.6%) involved unknown mechanisms. In this present study, total number of DDIs could be positively correlated with total number of medications and number of health problems.

CONCLUSIONS

Geriatric cancer patients are at high risk of DDIs ascribable to polypharmacy. Physicians may utilize online DDI checking softwares to alert themselves, characterize potential DDIs, and modify medications judiciously. An integrative and algorithmic approach with inclusion of geriatricians, oncologists, cardiologists, general practitioners, and clinical pharmacologists/ pharmacists is imperative to optimize drug therapy.