A comparison of five common drug-drug interaction software programs regarding accuracy and comprehensiveness

Ticagrelor and Statins: Dangerous Liaisons?

Polypharmacy is often necessary in complex, chronic, comorbid and cardiovascular patients and is a known risk factor for potential drug-drug interaction (DDI) that can cause adverse reactions (toxicity or therapeutic failure). Anti-thrombotic drugs (largely low-dose aspirin and a platelet P2Y12 receptor inhibitor) and statins are among the most co-administered drugs in cardiovascular patients. Ticagrelor is a selective antagonist of the platelet P2Y12-receptor, highly effective in inhibiting platelet aggregation and bio-transformed by the CYP3A4 and substrate of transporters, such as the breast cancer resistance protein (BCRP). Statins have different pharmacokinetic profiles; some undergo CYP3A4-mediated metabolism; rosuvastatin is primarily metabolized by the CYP2C9; and they have different affinities for drug transporters. Rhabdomyolysis is a very rare but severe adverse event, which is specific for statins which can be triggered by DDIs that increase statin's concentrations through blockade of their biotransformation and/or elimination. Large pharmacovigilance and small observational studies reported increased rhabdomyolysis in patients treated with some statins and ticagrelor but not aspirin, clopidogrel or prasugrel. Recent studies in vitro, pharmacokinetic trials and in silico drug modelling identified and validated the BCRP inhibition by ticagrelor, as a mechanism contributing to the DDI with statins, as 'victim' drugs, leading to increased rhabdomyolysis. While the clinical impact of this DDI deserves further investigation, a careful evaluation should be advised when ticagrelor is co-prescribed with some statins.

Prescription Patterns of Inducers and Inhibitors of Cytochrome P450 and Their Potential Drug Interactions in the Real World: A Cross-Sectional Study

INTRODUCTION

Both the induction and inhibition of cytochrome P450 are associated with multiple pharmacological interactions, which can lead to loss of efficacy or increase the risk of adverse drug reactions.

OBJECTIVE

The aim was to determine the prescription patterns of cytochrome P450-inducing and -inhibiting drugs and their contraindicated and major pharmacological interactions in a group of patients from Colombia.

METHODS

This cross-sectional observational study included patients who received drugs that induce or inhibit metabolism and examined their contraindicated and major pharmacological interactions. The patients were identified from a population-based database of drug dispensing. Patients were included between December 1 and December 31, 2021. Inhibitors and inducers of cytochrome P450 were classified based on FDA (Food and Drug Administration) guidelines. Drug interactions were identified using the Micromedex® database. Descriptive, bivariate and multivariable analysis was performed.

RESULTS

A total of 63,433 patients were analyzed. Antiseizure medications (35.9%) and antifungals (27.6%) were the most used inducers and inhibitors. A total of 30.1% of patients had potential contraindicated or greater interactions. The following factors were associated with a higher probability of presenting a potential pharmacological interaction: being male (OR 1.14; 95% CI 1.10-1.19), aged 18-39 years (OR 1.77; 95% CI 1.67-1.89) or 40-64 years (OR 1.64; 95% CI 1.56-1.72), having neurological diseases (OR 1.28; 95% CI 1.21-1.35), having psychiatric diseases (OR 3.84; 95% CI 3.58-4.13), having rheumatologic diseases (OR 1.32; 95% CI 1.23-1.41), receiving comedications with statins (OR 1.14; 95% CI 1.08-1.19), receiving comedications with analgesics (OR 1.33; 95% CI 1.27-1.38), receiving comedications with antiparasitics (OR 2.88; 95% CI 2.66-3.11) and an increase in the number of medications (OR 1.24; 95% CI 1.23-1.25).

CONCLUSION

Among the users of cytochrome P450 inhibitors and inducers, potential contraindications and greater interactions are very common, especially in men under 65 years of age with comorbidities and polypharmacy.

Impact of medication nonadherence and drug-drug interaction testing on the management of primary care patients with polypharmacy: a randomized controlled trial

BACKGROUND

Clinical management of patients with chronic cardiometabolic disease is complicated by polypharmacy. Consequently, when patients clinically deteriorate, physicians are challenged to distinguish both medication nonadherence and drug-drug interactions (DDI) from chronic disease progression.

METHODS

In this randomized controlled trial, we enrolled U.S. board-certified Primary Care Physicians (PCPs) serving patients with cardiometabolic disease. PCPs were randomized and managed their patients with (intervention), or without (control), a novel chronic disease management test (CDMT) that can detect medication nonadherence and DDIs. Patients' medical records were abstracted at baseline and 3-month follow-up. Primary outcomes were the CDMT's impact on both the PCPs' detection of medication nonadherence and DDI, and the frequency of performing medication nonadherence- and DDI-related clinical actions. Secondary outcomes examined the types of clinical actions performed. Primary and secondary outcomes were analyzed by logistic regression using single variable and clustered multivariable modeling to adjust for similarities in patient characteristics, by PCP practice.

RESULTS

Sixteen intervention and 20 control PCPs shared de-identified records for 126 and 207 patients, respectively. There were no significant demographic differences between the two study arms, among PCPs or patients. At baseline, there was no significant difference between the intervention and control PCPs in the percentage of clinical actions performed for medication nonadherence (P = 0.98) and DDI (P = 0.41). At 3-month follow-up (after CDMT), 69.1% of intervention compared to 20.3% of control patients with medication nonadherence had a related clinical action performed (P < 0.001). Regarding DDI, 37.3% of intervention compared to 0.5% of control patients had a relevant clinical action performed in follow-up (P < 0.001). Across the range of medication nonadherence- and DDI-related actions, the intervention compared to the control PCPs were more likely to adjust the medication regimen (24.1% vs. 9.5%) and document medication nonadherence in the patient chart (31.0% vs. 14.3%) at follow-up (P = 0.04).

CONCLUSIONS

Although intervention and control PCPs similarly detected and acted upon medication nonadherence and DDI at baseline, intervention PCPs' detection increased significantly after using the CDMT. Also, the clinical actions performed with CDMT support were more clinically rigorous. These outcomes support the clinical utility of CDMT in the management of symptomatic patients with cardiometabolic disease and polypharmacy.

TRIAL REGISTRATION

https://clinicaltrials.gov/study/NCT05910684 .

Prevalence of potential drug‒drug interactions and associated factors among elderly patients in Ethiopia: a systematic review and meta-analysis

BACKGROUND

The occurrence of potential drug‒drug interactions (pDDIs) is a serious global issue that affects all age groups, with the elderly population being the most vulnerable. This is due to their relatively high rates of comorbidity and polypharmacy, as well as physiological changes that can increase the potential for DDIs and the likelihood of adverse drug reactions. The aim of this study was to estimate the prevalence of pDDIs and associated factors among elderly patients in Ethiopia.

METHODS

A comprehensive literature search using the preferred reporting items for systematic review and meta-analysis statement was conducted on HINARI, Science Direct, Embase, PubMed/MEDLINE, Google Scholar, and Research Gate. Data were extracted via a Microsoft Excel spreadsheet and analyzed via STATA version 11.0. Egger regression tests and funnel plot analysis were used to check publication bias, and the I2 statistic was used to evaluate statistical heterogeneity. Sensitivity and subgroup analyses were also conducted to identify potential causes of heterogeneity.

RESULTS

Seven articles were analyzed, and a total of 1897 pDDIs were identified in 970 patients, resulting in an average of 1.97 DDIs per patient. The number of DDIs per patient ranged from 0.18 to 5.86. The overall prevalence of pDDIs among elderly patients was 50.69% (95% CI 18.77-82.63%). However, the prevalence of pDDIs ranged widely from 2.80 to 90.1%. When the severity of the interactions was considered, the prevalence of potential DDIs was found to be 28.74%, 70.68%, and 34.20% for major, moderate, and minor pDDIs, respectively. Polypharmacy and long hospital stays were identified as factors associated with pDDIs among elderly patients in Ethiopia.

CONCLUSIONS

The overall prevalence of pDDIs among elderly patients was high, with a wide range of prevalence rates. Moderate-severity interactions were the most prevalent. Polypharmacy and long hospital stays were identified as factors associated with pDDIs among elderly patients. The study suggests that DDIs identification database itself could have modified the DDIs prevalence rate. As a result, a single DDIs identification database needs to be authorized; otherwise, clinical knowledge should be taken into account when interpreting the information obtained.

PTB-DDI: An Accurate and Simple Framework for Drug-Drug Interaction Prediction Based on Pre-Trained Tokenizer and BiLSTM Model

The simultaneous use of two or more drugs in clinical treatment may raise the risk of a drug-drug interaction (DDI). DDI prediction is very important to avoid adverse drug events in combination therapy. Recently, deep learning methods have been applied successfully to DDI prediction and improved prediction performance. However, there are still some problems with the present models, such as low accuracy due to information loss during molecular representation or incomplete drug feature mining during the training process. Aiming at these problems, this study proposes an accurate and simple framework named PTB-DDI for drug-drug interaction prediction. The PTB-DDI framework consists of four key modules: (1) ChemBerta tokenizer for molecular representation, (2) Bidirectional Long Short-Term Memory (BiLSTM) to capture the bidirectional context-aware features of drugs, (3) Multilayer Perceptron (MLP) for mining the nonlinear relationship of drug features, and (4) interaction predictor to perform an affine transformation and final prediction. In addition, we investigate the effect of dual-mode on parameter-sharing and parameter-independent within the PTB-DDI framework. Furthermore, we conducted comprehensive experiments on the two real-world datasets (i.e., BIOSNAP and DrugBank) to evaluate PTB-DDI framework performance. The results show that our proposed framework has significant improvements over the baselines based on both datasets. Based on the BIOSNAP dataset, the AUC-ROC, PR-AUC, and F1 scores are 0.997, 0.995, and 0.984, respectively. These metrics are 0.896, 0.873, and 0.826 based on the DrugBank dataset. Then, we conduct the case studies on the three newly approved drugs by the Food and Drug Administration (FDA) in 2024 using the PTB-DDI framework in dual modes. The obtained results indicate that our proposed framework has advantages for predicting drug-drug interactions and that the dual modes of the framework complement each other. Furthermore, a free website is developed to enhance accessibility and user experience.

Assessment of drug-drug interactions among patients with hematologic malignancy: A clinical pharmacist-led study

INTRODUCTION

Patients with hematologic malignancies often receive multiple medications, leading to potential drug-drug interactions (DDIs). Identifying and managing these DDIs is crucial for ensuring patient safety and effective care. This study aimed to identify and describe DDIs and associated factors in hematologic malignancy patients.

METHODS

This prospective interventional study was conducted at a referral center and included hospitalized patients with hematologic malignancies who were receiving at least four concurrent medications. A pharmacist initially compiled a comprehensive list of all medications through patient interviews and medication reviews, and subsequently, identified and categorized potential DDIs using the Lexi-interact® and Micromedex® databases. The clinical pharmacist then evaluated the clinical impact of the identified DDIs in every individual patient and provided appropriate interventions to resolve them.

RESULTS

A total of 200 patients met the inclusion criteria for the study, with 1281 DDIs identified across 337 distinct types. The majority of identified DDIs exhibited major severity (52.1%) and pharmacokinetic mechanisms (50.3%), with an unspecified onset (79.4%) and fair evidence (67%). Of the identified DDIs, 81.1% were considered clinically significant, prompting 1059 pharmacotherapy interventions by the clinical pharmacist. Additionally, a significant relationship was observed between the number of drugs used during hospitalization and the occurrence of DDIs (P < 0.001, r = 0.633).

CONCLUSION

DDIs are highly prevalent among hospitalized patients with hematologic malignancies, with their occurrence increasing alongside the number of medications administrated. The intervention of a clinical pharmacist is crucial to evaluate the clinical impact of these DDIs and implement effective interventions for their management.

Review of drug-drug interactions in patients with prostate cancer

OBJECTIVE

The objective of this review is to provide an overview of common drug-drug interactions (DDIs) associated with prostate cancer treatments and outline recommendations for managing polypharmacy.

DATA SOURCES

A literature search of PubMed, Embase, and CINAHL was carried out to identify pharmacokinetic and pharmacodynamic changes caused by DDIs that are relevant for prostate cancer patients, DDIs between prostate cancer therapies and co-administered medications (both prescription and over-the-counter), and measures to prevent DDIs. Medication package inserts were used to identify the impact of DDI on the prostate cancer therapy and suggested interventions.

DATA SUMMARY

No DDIs are expected for the LHRH agonists leuprolide acetate, histrelin, goserelin, or leuprolide mesylate. However, DDIs have been reported for GnRH antagonists, anti-androgens, PARP inhibitors, and taxanes. Although there are no confirmed DDIs for sipuleucel-T to date, it is not generally recommended to use sipuleucel-T concurrently with immunosuppressive medications. Interventions to prevent DDIs include the use of software that can detect clinically significant DDIs, up-to-date medication reconciliation, the inclusion of dedicated clinical pharmacists in cancer treatment teams, and patient/caregiver education.

CONCLUSIONS

Prostate cancer patients have a high risk of potential DDIs due to numerous new anti-cancer therapies, the increased use of treatment combinations, and the likelihood of comorbid conditions also requiring drug therapy. Drug-drug interaction screening software, up-to-date medication reconciliation, inclusion of oncology pharmacists on healthcare teams, and patient/caregiver education will aid the development of treatment plans that focus on achieving an optimal risk-benefit profile whilst reducing the risk of DDIs.

Possible drug-drug interactions among elderly patients receiving antiviral therapy for chronic hepatitis B

AIM

To identify possible drug-drug interactions in patients taking medications for other comorbidities while on antiviral therapy for chronic hepatitis B.

METHODS

The study enrolled patients with chronic hepatitis B aged ≥60 years who were treated with antiviral therapy in five hospitals in Turkey between January 1 and March 1, 2023. The Lexicomp® Drug Interactions program was used to identify possible drug-drug interactions.

RESULTS

The study included 213 patients (119 [55.9%] men). The mean age was 68.5 years. A potential drug-drug interaction was identified in 112 patients (52.6%). The most common type of interaction was type C ("follow the treatment") (71.54%). The number of potential drug-drug interactions increased with an increase in the number of drugs used by the patients. A robust and affirmative correlation was observed between the number of medications used and the number of possible drug-drug interactions (r=0.791, P<0.001). Adverse interactions (interactions of types C and D, 3.7% of cases) were limited to patients receiving tenofovir disoproxil fumarate.

CONCLUSION

Nonsteroidal anti-inflammatory medications should be used cautiously in elderly patients with chronic hepatitis B treated with tenofovir disoproxil fumarate due to the increased risk of renal toxicity.

Clinical Significance and Patterns of Potential Drug-Drug Interactions in Cardiovascular Patients: Focus on Low-Dose Aspirin and Angiotensin-Converting Enzyme Inhibitors

Objective: This study assessed the patterns and clinical significance of potential drug-drug interactions (pDDIs) in patients with diseases of the cardiovascular system. Methods: Electronic health records (EHRs), established in 2018-2023, were selected using the probability serial nested sampling method (n = 1030). Patients were aged 27 to 95 years (65.0% men). Primary diagnosis of COVID-19 was present in 17 EHRs (1.7%). Medscape Drug Interaction Checker was used to characterize pDDIs. The Mann-Whitney U test and chi-square test were used for statistical analysis. Results: Drug numbers per record ranged from 1 to 23 in T-List and from 1 to 20 in P-List. In T-List, 567 drug combinations resulted in 3781 pDDIs. In P-List, 584 drug combinations resulted in 5185 pDDIs. Polypharmacy was detected in 39.0% of records in T-List versus 65.9% in P-List (p-value < 0.05). The rates of serious and monitor-closely pDDIs due to 'aspirin + captopril' combinations were significantly higher in P-List than in T-List (p-value < 0.05). The rates of serious pDDIs due to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations were significantly lower in P-List compared with the corresponding rates in T-List (p-value < 0.05). Serious pDDIs due to administration of aspirin with fosinopril, perindopril, and ramipril were detected less frequently in T-List (p-value < 0.05). Conclusions: Obtained data may suggest better patient adherence to 'aspirin + enalapril' and 'aspirin + lisinopril' combinations, which are potentially superior to the combinations of aspirin with fosinopril, perindopril, and ramipril. An abundance of high-order pDDIs in real-world clinical practice warrants the development of a decision support system aimed at reducing pharmacotherapy-associated risks while integrating patient pharmacokinetic, pharmacodynamic, and pharmacogenetic information.

Survey of Potential Drug Interactions, Use of Non-Medical Health Products, and Immunization Status among Patients Receiving Targeted Therapies

In recent years, several changes have occurred in the management of chronic immunological conditions with the emerging use of targeted therapies. This two-phase cross-sectional study was conducted through structured in-person interviews in 2018-2019 and 2022. Additional data sources included ambulatory medical records and the itemized reimbursement reporting interface of the National Health Insurance Fund. Drug interactions were analyzed using the UpToDate Lexicomp, Medscape drug interaction checker, and Drugs.com databases. The chi-square test was used, and odds ratios (ORs) were calculated. In total, 185 patients participated. In 53% of patients (n = 53), a serious drug-drug interaction (DDI) was identified (mean number: 1.07 ± 1.43, 0-7), whereas this value was 38% (n = 38) for potential drug-supplement interactions (mean number: 0.58 ± 0.85, 0-3) and 47% (n = 47) for potential targeted drug interactions (0.72 ± 0.97, 0-5) in 2018. In 2022, 78% of patients (n = 66) were identified as having a serious DDI (mean number: 2.27 ± 2.69, 0-19), 66% (n = 56) had a potential drug-supplement interaction (mean number: 2.33 ± 2.69, 0-13), and 79% (n = 67) had a potential targeted drug interactions (1.35 ± 1.04, 0-5). Older age (>60 years; OR: 2.062), female sex (OR: 3.387), and polypharmacy (OR: 5.276) were identified as the main risk factors. Screening methods and drug interaction databases do not keep pace with the emergence of new therapeutics.

Agreement of Different Drug-Drug Interaction Checkers for Proton Pump Inhibitors

Importance

Proton pump inhibitors (PPIs) are a widely prescribed class of drugs, potentially interacting with a large number of medicines, especially among older patients with multimorbidity and polypharmacy. Beyond summary of product characteristics (SPCs), interaction checkers (ICs) are routinely used tools to help clinicians in medication review interventions.

Objective

To assess the consistency of information on drugs potentially interacting with PPIs as reported in their SPCs and different ICs.

Design, Setting, and Participants

This cross-sectional study was conducted using data from SPCs for 5 PPIs (omeprazole, esomeprazole, lansoprazole, pantoprazole, and rabeprazole) and 5 ICs (ie, INTERCheck WEB, Micromedex, Lexicomp, Epocrates, and drugs.com). Information from the SPCs and the ICs were extracted between July 15 and 30, 2023.

Main Outcomes and Measures

The main outcome was the level of agreement among SPCs and the 5 ICs in identifying drugs potentially interacting with PPIs and attributing drug-drug interaction (DDI) severity categories. The level of agreement was computed using Gwet AC1 statistic on the 5 ICs and by comparing 4-sets and 2-sets of ICs. As a sensitivity analysis, the level of agreement in listing PPI-related DDIs was evaluated using Cohen κ and Fleiss κ coefficients.

Results

Considering SPCs and the 5 ICs, a total of 518 potentially interacting drugs with omeprazole were reported, 455 for esomeprazole, 433 for lansoprazole, 421 for pantoprazole, and 405 for rabeprazole. As compared with the ICs, the SPCs reported a much smaller number of drugs potentially interacting with PPIs, with proportions ranging from 2.7% (11 potentially interacting drugs) for rabeprazole to 7.6% (33 potentially interacting drugs) for lansoprazole of the total identified drugs at risk of interaction with a PPI. The overall level of agreement among the 5 ICs for identifying potential interactions was poor (from 0.23 [95% CI, 0.21-0.25] for omeprazole to 0.27 [95% CI, 0.24-0.29] for pantoprazole and 0.27 [95% CI, 0.25-0.29] for rabeprazole). Similarly, the level of agreement was low in 4-set and 2-set analyses as well as when restricting the analysis to the potential DDIs identified as severe (range, 0.30-0.32).

Conclusions and Relevance

This cross-sectional study found significant disagreement among different ICs and SPCs, highlighting the need to focus on standardizing DDI databases. Therefore, to ensure evaluation and prevention of clinically relevant DDIs, it is recommended to revise multiple ICs and consult with specialists, such as clinical pharmacologists, particularly for patients with complex medical conditions.

Potentially Inappropriate Medication: A Pilot Study in Institutionalized Older Adults

Institutionalized older adults often face complex medication regimens, increasing their risk of adverse drug events due to polypharmacy, overprescribing, medication interactions, or the use of Potentially Inappropriate Medications (PIM). However, data on medication use and associated risks in this population remain scarce. This pilot study aimed to characterize the sociodemographic, clinical and pharmacotherapeutic profiles, and the use of PIM among institutionalized elders residing in Residential Structures for Elderly People (ERPI) in the Faro municipality, located in the Portuguese region of the Algarve. We conducted a cross-sectional study in a non-randomized sample of 96 participants (mean age: 86.6 ± 7.86 years) where trained researchers reviewed medication profiles and identified potentially inappropriate medications using the EU(7)-PIM list. Over 90% of participants exhibited polypharmacy (≥5 medications), with an average of 9.1 ± 4.15 medications per person. About 92% had potential drug interactions, including major and moderate interactions. More than 86% used at least one potentially inappropriate medication, most commonly central nervous system drugs. This pilot study demonstrates that institutionalized older adults may be at high risk of potential medication-related problems. Implementing comprehensive medication review programs and promoting adapted prescribing practices are crucial to optimize medication use and improve the well-being of this vulnerable population.

Polypharmacy and drug interactions in older patients with cancer receiving chemotherapy: associated factors

BACKGROUND

Polypharmacy in older adults with cancer receiving chemotherapy leads to increased risks of drug interactions, translating in potential hazardous health outcomes. This study aims to assess the prevalence of polypharmacy, drug-drug interactions (DDIs), and severe-drug interactions (SDIs) in older patients with cancer. Antineoplastic agents (ANAs) involvement and possible risk contexts (comorbidities with cardiac risk, and high-risk medications) were also analysed.

METHODS

Observational study with older adults (≥ 65 years) diagnosed with cancer, who were treated with antineoplastic agents (ANAs); it was conducted in three hospitals from the north of Portugal. Data collection was obtained using self-reports and medical records. DDIs were identified and classified using Micromedex® software. Descriptive and association analyze statistics were performed. Statistical hypothesis tests with p value less than 0.05 were considered significant. All statistical procedures and analysis were performed with R version 4.1.3.

RESULTS

We enrolled 552 patients. Polypharmacy prevalence was 88.40%; 76.45% and 56.16% of the patients presented with DDIs and SDIs, respectively. SDIs with ANAs were found in 21.20% of the patients. High-risk medications were associated with a higher risk of polypharmacy, DDIs, and SDIs. Polypharmacy and DDIs were higher in patients with hypertension or diabetes. SDIs were higher in patients with diabetes.

CONCLUSION

Polypharmacy, potential DDIs and SDIs were highly prevalent in older adults with cancer. A careful review of the medication administered is necessary to decrease it. These findings warrant further research to optimize medication in this population and decrease problems related to medication, which may lead to emergency room visits and hospitalisations, compromising patient safety and/or ongoing treatments.

Drug-Drug Interactions and Their Association with Adverse Health Outcomes in the Older Community-Dwelling Population: A Prospective Cohort Study

BACKGROUND

Evidence on associations between drug-drug interactions (DDIs) and health outcomes in the older community-dwelling population is limited.

OBJECTIVE

We estimate potentially clinically important DDI prevalence and examine the association between DDIs and (1) adverse drug events (ADEs), (2) emergency hospital attendance and (3) health-related quality of life (HRQoL) in an older community-dwelling population in Ireland.

METHODS

This is a prospective cohort study of community-dwelling older adults (N = 904) aged ≥ 70 years from 15 general practices in Ireland recruited in 2010 (wave-1) and followed-up over 2 years (wave-2; 2012-2013), with linked national pharmacy claims data. Individuals dispensed two or more drugs (wave-1: N = 842; wave-2: N = 763) were included. DDI prevalence at baseline, follow-up and 6 months prior to each health outcome was estimated. Multi-level regression was used to model the association between DDI-exposure and health outcomes at follow-up. DDI prevalence, adjusted incidence-rate ratios (aIRR), adjusted odds ratios (aOR), β coefficients and robust standard error (RSE) from multi-level regression analyses, and 95% confidence intervals (CIs) are reported.

RESULTS

At wave-1, n = 196 (23.3% [95% CI 20.5-26.3]), individuals were potentially exposed to ≥ 1 DDI, increasing to n = 345 (45.2% [41.7-48.9]) at wave-2. At 2-year follow-up, the median number of ADEs was 3 (interquartile range [IQR 2-5]); 229 (30.1%) had ≥ 1 emergency hospital attendance, and the mean EQ-5D was 0.74 (± 0.23). Evidence for the association between DDI-exposure and emergency hospital attendance at follow-up was lacking (aOR = 1.38 [0.42-4.53]). DDI-exposure was associated with an increasing number of ADEs (aIRR = 1.26 [1.03-1.55]), and decreasing EQ-5D utility (β = - 0.07, [-0.11 to -0.04], RSE = 0.02). Aspirin-warfarin, clarithromycin-prednisolone, amiodarone-furosemide, clarithromycin-salbutamol, rosuvastatin-warfarin, amiodarone-bisoprolol, and aspirin-nicorandil were common DDIs 6 months preceding these health outcomes.

CONCLUSIONS

We found a two-fold increase in DDI prevalence between wave 1 and 2. DDI exposure was associated with increasing ADEs and declining HRQoL at 2-year follow-up. Common DDIs involved anticoagulants, cardiovascular and antimicrobial drugs, which should be targeted for medicine optimisation.

Determination of drug-related problems in the hematology service: a prospective interventional study

BACKGROUND

Patients with hematological malignancies often require multidrug therapy using a variety of antineoplastic agents and supportive care medications. This increases the risk of drug-related problems (DRPs). Determining DRPs in patients hospitalized in hematology services is important for patients to achieve their drug treatment goals and prevent adverse effects. This study aims to identify DRPs by the clinical pharmacist in the multidisciplinary team in patients hospitalized in the hematology service of a university hospital in Turkey.

METHODS

This study was conducted prospectively between December 2022 and May 2023 in the hematology service of Suleyman Demirel University Research and Application Hospital in Isparta, Turkey. DRPs were determined using the Pharmaceutical Care Network Europe (PCNE) 9.1 Turkish version.

RESULTS

This study included 140 patients. Older age, longer hospital stay, presence of acute lymphoblastic leukemia, presence of comorbidities, higher number of medications used, and polypharmacy rate were statistically significantly higher in the DRP group than in the non-DRP group (p < 0.05). According to multivariate logistic regression analysis, the probability of DRP in patients with polypharmacy was statistically significant 7.921 times (95% CI: 3.033-20.689) higher than in patients without polypharmacy (p < 0.001).Every 5-day increase in the length of hospital stay increased the likelihood of DRP at a statistically significant level (OR = 1.476, 95% CI: 1.125-1.938 p = 0.005). In this study, at least one DRP was detected in 69 (49.3%) patients and the total number of DRPs was 152. Possible or actual adverse drug events (96.7%) were the most common DRPs. The most important cause of DRPs was drug choice (94.7%), and the highest frequency within its subcategories was the combination of inappropriate drugs (93.4%).

CONCLUSIONS

This study shows the importance of including a clinical pharmacist in a multidisciplinary team in identifying and preventing DRPs in the hematology service.

Drug use survey to identify significant drug-drug interactions and assess clinical importance in the outpatient setting of a tertiary care hospital

OBJECTIVES

Drug-drug interactions (DDIs) are a common problem in pharmacotherapy, particularly in situations where multiple disorders must be treated at the same time. We conducted a drug use survey in the general medicine outpatient department of a tertiary care hospital with the objective of assessing the potential for DDI in individual prescriptions for adult patients.

MATERIALS AND METHODS

Drugs prescribed in the current physician-patient encounter were considered in conjunction with medicines already being received by the patient as well as those discontinued in the past 1 month. Free online DDI checkers (available at https://www.drugs.com/drug_interactions.html and https://reference.medscape.com/) were used to identify potential DDI and categorize them into mild, moderate, and severe categories. We did not consider food, alcohol, or smoking-related interactions.

RESULTS

A total of 153 prescriptions, having two or more drugs, were collected, and they accounted for 1052 prescribed drugs. Among them, 613 (58.27%) were prescribed in index visits, and the rest 438 (41.63%) were preexisting medication. The number of drugs prescribed in index visits ranged from 1 to 9 (mean ± standard deviation [SD] 4.0 ± 1.86; median 4). Potential DDIs were identified in 103 (67.32%) instances. The total number of interactions identified was 412. Of these, 19.66% had minor, 77.67% moderate, and 7.19% major clinical implications. Potential DDI count in each prescription was found from 0 to 13 in number (mean ± SD 2.7 ± 3.12; median 2.0). This number correlated strongly with the number of drugs being received by individual subjects (Rho 0.744; P < 0.001).

CONCLUSIONS

Potential DDIs are a reality in day to day prescribing practice. Substantial proportion of these DDIs may have significant clinical implications. Prescribers need to be sensitized to this issue. Combining human expertise with technological solutions such as automated drug interaction alerts can help rectify the situation. Similar surveys are needed on a periodic basis to improve medication safety for patients.

Pharmacotherapeutic Considerations in the Treatment of Nontuberculous Mycobacterial Infections: A Primer for Clinicians

Nontuberculous mycobacteria (NTM) can cause a variety of infections, including serious pulmonary disease. Treatment encompasses polypharmacy, with a targeted regimen of 2-5 active medications, depending on site of infection, species, and clinical characteristics. Medications may include oral, intravenous, and inhalational routes. Medication acquisition can be challenging for numerous reasons, including investigational status, limited distribution models, and insurance prior authorization. Additionally, monitoring and managing adverse reactions and drug interactions is a unique skill set. While NTM is primarily medically managed, clinicians may not be familiar with the intricacies of medication selection, procurement, and monitoring. This review offers insights into the pharmacotherapeutic considerations of this highly complex disease state, including regimen design, medication acquisition, safety monitoring, relevant drug-drug interactions, and adverse drug reactions.

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.

Factors associated with potential drug-drug interactions in psychiatric inpatients

OBJECTIVE

The aim of this study was to investigate the prevalence and severity of potential drug-drug interactions (pDDIs) in hospitalised patients with major psychiatric disorders and to identify factors associated with their occurrence.

METHODS

The research was designed as an observational, cross-sectional study conducted at the Clinic for Mental Disorders (CMD) 'Dr. Laza Lazarevic', Belgrade, Serbia. Medscape, Epocrates and Lexicomp bases were used to detect potential drug interactions among inpatients. Multivariate regression analysis was used to reveal risk and protective factors associated with the number of pDDIs.

RESULTS

The study included 511 patients, average age 44.63±11.81 years. The average number of pDDIs per patient ranged from 5.9±4.7 (Medscape) to 8.2±5.4 (Epocrates) and 8.5±5.1 (Lexicomp). The following risk factors were identified by all three interaction checkers used: C-reactive protein, number of pharmacological subgroups, number of prescribed drugs, antibiotics, antacids, vitamins, number of associated comorbidities, route, form and dose of the drug.

CONCLUSIONS

When making clinical decisions to reduce drug problems, including DDIs, one should consult several interaction databases, which should be reviewed by a multidisciplinary team consisting of an experienced clinical pharmacist, physician, nurse, and so on.

Prevalence and clinical significance of potential drug-drug interactions among lung transplant patients

Background: Drug-drug interactions (DDIs) are a major but preventable cause of adverse drug reactions. There is insufficient information regarding DDIs in lung transplant recipients. Objective: This study aimed to determine the prevalence of potential DDIs (pDDIs) in intensive care unit (ICU) lung transplant recipients, identify the real DDIs and the most frequently implicated medications in this vulnerable population, and determine the risk factors associated with pDDIs. Methods: This retrospective cross-sectional study included lung transplant recipients from January 2018 to December 2021. Pertinent information was retrieved from medical records. All prescribed medications were screened for pDDIs using the Lexicomp® drug interaction software. According to this interaction software, pDDIs were classified as C, D, or X (C = monitor therapy, D = consider therapy modification, X = avoid combination). The Drug Interaction Probability Scale was used to determine the causation of DDIs. All statistical analysis was performed in SPSS version 26.0. Results: 114 patients were qualified for pDDI analysis, and total pDDIs were 4051. The most common type of pDDIs was category C (3323; 82.0%), followed by D (653; 16.1%) and X (75; 1.9%). Voriconazole and posaconazole were the antifungal medicine with the most genuine DDIs. Mean tacrolimus concentration/dose (Tac C/D) before or after co-therapy was considerably lower than the Tac C/D during voriconazole or posaconazole co-therapy (p < 0.001, p = 0.027). Real DDIs caused adverse drug events (ADEs) in 20 patients. Multivariable logistic regression analyses found the number of drugs per patient (OR, 1.095; 95% CI, 1.048-1.145; p < 0.001) and the Acute Physiology and Chronic Health Evaluation II (APACHE Ⅱ) score (OR, 1.097; 95% CI, 1.021-1.179; p = 0.012) as independent risk factors predicting category X pDDIs. Conclusion: This study revealed a high incidence of both potential and real DDIs in ICU lung transplant recipients. Immunosuppressive drugs administered with azole had a high risk of causing clinically significant interactions. The number of co-administered drugs and APACHE Ⅱ score were associated with an increased risk of category × drug interactions. Close monitoring of clinical and laboratory parameters is essential for ensuring successful lung transplantation and preventing adverse drug events associated with DDIs.

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.

Assessment of Substrate Status of Drugs Metabolized by Polymorphic Cytochrome P450 (CYP) 2 Enzymes: An Analysis of a Large-Scale Dataset

BACKGROUND

The analysis of substrates of polymorphic cytochrome P450 (CYP) enzymes is important information to enable drug-drug interactions (DDIs) analysis and the relevance of pharmacogenetics in this context in large datasets. Our aim was to compare different approaches to assess the substrate properties of drugs for certain polymorphic CYP2 enzymes.

METHODS

A standardized manual method and an automatic method were developed and compared to assess the substrate properties for the metabolism of drugs by CYP2D6, 2C9, and 2C19. The automatic method used a matching approach to three freely available resources. We applied the manual and automatic methods to a large real-world dataset deriving from a prospective multicenter study collecting adverse drug reactions in emergency departments in Germany (ADRED).

RESULTS

In total, 23,878 medication entries relating to 895 different drugs were analyzed in the real-world dataset. The manual method was able to assess 12.2% (n = 109) of drugs, and the automatic method between 12.1% (n = 109) and 88.9% (n = 796), depending on the resource used. The CYP substrate classifications demonstrated moderate to almost perfect agreements for CYP2D6 and CYP2C19 (Cohen's Kappa (κ) 0.48-0.90) and fair to moderate agreements for CYP2C9 (κ 0.20-0.48).

CONCLUSION

A closer look at different classifications between methods revealed that both methods are prone to error in different ways. While the automated method excels in time efficiency, completeness, and actuality, the manual method might be better able to identify CYP2 substrates with clinical relevance.

[Polypharmacy in acute and chronic kidney diseases]

The prevalence for chronic kidney disease (CKD) has steadily increased over the past decades. It is a gradually progressive disease that is associated with several comorbidities including cardiovascular diseases, hypertension, anemia, disorders of bone and mineral metabolism, electrolyte imbalance and acid-base abnormalities. All these comorbidities require adequate medication. Therefore, patients with CKD have a high risk for polypharmacy, which is defined as five or more medications daily. Polypharmacy causes a greatly increased risk for adverse drug effects and severe drug-drug interactions, which if not closely controlled and the individual doses adapted to the decreased renal function during the progression of the CKD, can result in increased morbidity and mortality. Therefore, several aspects of the medication need to be considered and constantly addressed. This article summarizes the problems arising from inadequate polypharmacy in CKD patients, including undesired adverse drug effects, drug interactions, the complexity of medication plans, treatment burden and nonadherence to the treatment. Furthermore, the most important steps to identify patients with inadequate polypharmacy are discussed, whereby complications can also be avoided and the benefits of the medication can be increased. Finally, the polypharmacy in acute kidney injury is dealt with.

Update and Application of a Deep Learning Model for the Prediction of Interactions between Drugs Used by Patients with Multiple Sclerosis

Patients with multiple sclerosis (MS) often take multiple drugs at the same time to modify the course of disease, alleviate neurological symptoms and manage co-existing conditions. A major consequence for a patient taking different medications is a higher risk of treatment failure and side effects. This is because a drug may alter the pharmacokinetic and/or pharmacodynamic properties of another drug, which is referred to as drug-drug interaction (DDI). We aimed to predict interactions of drugs that are used by patients with MS based on a deep neural network (DNN) using structural information as input. We further aimed to identify potential drug-food interactions (DFIs), which can affect drug efficacy and patient safety as well. We used DeepDDI, a multi-label classification model of specific DDI types, to predict changes in pharmacological effects and/or the risk of adverse drug events when two or more drugs are taken together. The original model with ~34 million trainable parameters was updated using >1 million DDIs recorded in the DrugBank database. Structure data of food components were obtained from the FooDB database. The medication plans of patients with MS (n = 627) were then searched for pairwise interactions between drug and food compounds. The updated DeepDDI model achieved accuracies of 92.2% and 92.1% on the validation and testing sets, respectively. The patients with MS used 312 different small molecule drugs as prescription or over-the-counter medications. In the medication plans, we identified 3748 DDIs in DrugBank and 13,365 DDIs using DeepDDI. At least one DDI was found for most patients (n = 509 or 81.2% based on the DNN model). The predictions revealed that many patients would be at increased risk of bleeding and bradycardic complications due to a potential DDI if they were to start a disease-modifying therapy with cladribine (n = 242 or 38.6%) and fingolimod (n = 279 or 44.5%), respectively. We also obtained numerous potential interactions for Bruton's tyrosine kinase inhibitors that are in clinical development for MS, such as evobrutinib (n = 434 DDIs). Food sources most often related to DFIs were corn (n = 5456 DFIs) and cow's milk (n = 4243 DFIs). We demonstrate that deep learning techniques can exploit chemical structure similarity to accurately predict DDIs and DFIs in patients with MS. Our study specifies drug pairs that potentially interact, suggests mechanisms causing adverse drug effects, informs about whether interacting drugs can be replaced with alternative drugs to avoid critical DDIs and provides dietary recommendations for MS patients who are taking certain drugs.

Potential drug-drug interactions among U.S. adults treated with nirmatrelvir/ritonavir: A cross-sectional study of the National Covid Cohort Collaborative (N3C)

STUDY OBJECTIVE

To estimate the prevalence of potential moderate to severe drug-drug interactions (DDIs) involving nirmatrelvir/ritonavir, identify interacting medications, and evaluate risk factors associated with potential DDIs.

DESIGN

Cross-sectional study.

DATA SOURCE

Electronic health records from the National COVID Cohort Collaborative Enclave, one of the largest COVID-19 data resources in the United States.

PATIENTS

Outpatients aged ≥18 years and started nirmatrelvir/ritonavir between December 23, 2021 and March 31, 2022.

INTERVENTION

Nirmatrelvir/ritonavir.

MEASUREMENTS

The outcome is potential moderate to severe DDIs, defined as starting interacting medications reported by National Institutes of Health 30 days before or 10 days after starting nirmatrelvir/ritonavir.

MAIN RESULTS

Of 3214 outpatients who started nirmatrelvir/ritonavir, the mean age was 56.8 ± 17.1 years, 39.5% were male, and 65.8% were non-Hispanic white. Overall, 521 (16.2%) were potentially exposed to at least one moderate to severe DDI, most commonly to atorvastatin (19.2% of all DDIs), hydrocodone (14.0%), or oxycodone (14.0%). After adjustment for covariates, potential DDIs were more likely among individuals who were older (odds ratio [OR] 1.16 per 10-year increase, 95% confidence interval [CI] 1.08-1.25), male (OR 1.36, CI 1.09-1.71), smokers (OR 1.38, CI 1.10-1.73), on more co-medications (OR 1.35, CI 1.31-1.39), and with a history of solid organ transplant (OR 3.63, CI 2.05-6.45).

CONCLUSIONS

One in six of individuals receiving nirmatrelvir/ritonavir were at risk of a potential moderate or severe DDI, underscoring the importance of clinical and pharmacy systems to mitigate such risks.

Comparative analysis of potential drug-drug interactions in a public and private hospital among chronic kidney disease patients in Khyber Pakhtunkhwa: A retrospective cross-sectional study

INTRODUCTION

Chronic kidney disease (CKD) is a significant public health challenge due to its rising incidence, mortality, and morbidity. Patients with kidney diseases often suffer from various comorbid conditions, making them susceptible to potential drug-drug interactions (pDDIs) due to polypharmacy and multiple prescribers. Inappropriate prescriptions for CKD patients and their consequences in the form of pDDIs are a major challenge in Pakistan.

AIM

This study aimed to compare the incidence and associated risk factors of pDDIs among a public and private sector hospital in Khyber Pakhtunkhwa, Pakistan.

METHOD

A retrospective cross-sectional study design was conducted to compare pDDIs among public and private sector hospitals from January 2023 to February 2023. Patients profile data for the full year starting from January 1 2022 to December 302022, was accessed All adult patients aged 18 years and above, of both genders, who currently have or have previously been diagnosed with end-stage renal disease (ESRD) were included. For assessing pDDIs, patient data was retrieved and checked using Lexicomp UpToDate® for severity and documentation of potential drug-drug interactions.

RESULTS

A total of 358 patients' data was retrieved (with n = 179 in each hospital); however, due to incomplete data, n = 4 patients were excluded from the final analysis. The prevalence of pDDIs was found to be significantly higher in private hospitals (84.7%) than in public hospitals (26.6%), with a p-value <0.001. Patients in the age category of 41-60 years (AOR = 6.2; p = 0.008) and those prescribed a higher number of drugs (AOR = 1.2; p = 0.027) were independently associated with pDDIs in private hospitals, while the higher number of prescribed drugs (AOR = 2.9; p = <0.001) was an independent risk factor for pDDIs in public hospitals. The majority of pDDIs (79.0%) were of moderate severity, and a significant number of patients (15.1%) also experienced major pDDIs, with a p-value <0.001. The majority of pDDIs had fair documentation for reliability rating in both public and private hospitals.

CONCLUSION

The prevalence of pDDIs was higher among CKD patients at private hospitals, and most of the pDDIs were of moderate severity. A considerable number of patients also experienced major pDDIs. The risk of experiencing pDDIs was found to be higher in older patients and among those prescribed a higher number of drugs.

Screening the Drug-Drug Interactions Between Antimicrobials and Other Prescribed Medications Using Google Bard and Lexicomp® Online™ Database

Aim This study aimed to critically appraise the drug-drug interaction (DDI) screening performance of Google Bard (Google AI, Mountain View, California, United States) by comparing it with the authorized Lexicomp® Online™ database (Wolters Kluwer Health, Philadelphia, Pennsylvania, United States). Methods This cross-sectional study was conducted between April 2023 and August 2023, and enrolled 414 prescriptions that had been collected randomly between April 2023 and June 2023. These prescriptions were processed individually by Lexicomp online and Google Bard to screen for DDIs between antimicrobials and other prescribed medications. Results The total number of DDIs based on Lexicomp and Google Bard were 90 and 68, respectively. Cohen's Kappa (κ) values showed that there was a nil to slight agreement between Lexicomp and Google Bard regarding the DDI risk rating (κ=0.01). Regarding the severity rate, there was a slight agreement between them (κ=0.02), but in terms of reliability rate, there was no agreement (κ =-0.02). Conclusion This study unveiled differences between Lexicomp and Google Bard regarding their DDI identification, severity rating, and reliability rates. It is fundamental to consider that both tools have their strengths and weaknesses and, therefore, should not be individually depended on for final clinical decisions. However, Lexicomp can be considered authoritative in screening DDIs, but Google Bard currently lacks the necessary precision and reliability for conducting such screenings.

Prescribed Versus Taken Polypharmacy and Drug-Drug Interactions in Older Cardiovascular Patients during the COVID-19 Pandemic: Observational Cross-Sectional Analytical Study

The study aimed to assess clinical pharmacology patterns of prescribed and taken medications in older cardiovascular patients using electronic health records (EHRs) (n = 704) (2019-2022). Medscape Drug Interaction Checker was used to identify pairwise drug-drug interactions (DDIs). Prevalence rates of DDIs were 73.5% and 68.5% among taken and prescribed drugs, respectively. However, the total number of DDIs was significantly higher among the prescribed medications (p < 0.05). Serious DDIs comprised 16% and 7% of all DDIs among the prescribed and taken medications, respectively (p < 0.05). Median numbers of DDIs between the prescribed vs. taken medications were Me = 2, IQR 0-7 vs. Me = 3, IQR 0-7 per record, respectively. Prevalence of polypharmacy was significantly higher among the prescribed medications compared with that among the taken drugs (p < 0.05). Women were taking significantly more drugs and had higher prevalence of polypharmacy and DDIs (p < 0.05). No sex-related differences were observed in the list of prescribed medications. ICD code U07.1 (COVID-19, virus identified) was associated with the highest median DDI number per record. Further research is warranted to improve EHR structure, implement patient engagement in reporting adverse drug reactions, and provide genetic profiling of patients to avoid potentially serious DDIs.

Antifungal Drug-Drug Interactions with Commonly Used Pharmaceutics in European Pediatric Patients with Acute Lymphoblastic Leukemia

Leukemia is one of the leading childhood malignancies, with acute lymphoblastic leukemia (ALL) being the most common type. Invasive fungal disease is a concerning problem also at pediatric hemato-oncology units. Available guidelines underline the need for antifungal prophylaxis and give recommendations for proper treatment in various clinical scenarios. Nonetheless, antifungal agents are often involved in drug-drug interaction (DDI) occurrence. The prediction of those interactions in the pediatric population is complicated because of the physiological differences in adults, and the lack of pharmacological data. In this review, we discuss the potential DDIs between antifungal agents and commonly used pharmaceutics in pediatric hemato-oncology settings, with special emphasis on the use of liposomal amphotericin B and ALL treatment. We obtained information from Micromedex^® and Drugs.com^® interaction checking databases and checked the EudraVigilance^® database to source the frequency of severe adverse drug reactions that resulted from antifungal drug interactions. Several major DDIs were identified, showing a favorable safety profile of echinocandins and liposomal amphotericin B. Interestingly, although there are numerous available drug interaction checking tools facilitating the identification of potential serious DDIs, it is important to use more than one tool, as the presented searching results may differ between particular checking programs.

Risk Factors for Potential Drug-Drug Interactions in Outpatients with Dyslipidemia

Background

Patients with dyslipidemia are usually multimorbid and require polypharmacy. Therefore, it is important to identify potential drug-drug interactions (pDDIs) in time to prevent their consequences. We aimed to identify and analyze risk factors contributing to their occurrence to guide health professionals.

Methods

A prospective cross-sectional study of 216 outpatients with dyslipidemia was conducted from May 2021 to April 2022 in Podgorica, the capital of Montenegro. pDDIs were identified using Medscape, Epocrates, and Drugs online interaction checkers. Multivariate regression analysis was performed to evaluate the potential predictors of interactions.

Results

pDDIs were detected in 212 (98.1%) participants, whereas pDDIs with high clinical significance were detected in 25.46%, 40.74%, and 58.8% of subjects by Drugs, Epocrates, and Medscape, respectively. Polypharmacy emerged as a risk factor for the occurrence of pDDIs in all three checkers in each category of clinical significance. The use of non-steroidal anti-inflammatory drugs and antiplatelet drugs contributes to the incidence of severe pDDIs B=1.014, 95%CI 0.681-1.346, P=0.000 and B=0.492, 95%CI 0.286-0.698, P=0.000, by Epocrates and Medscape respectively. The number of prescribers per patient was a protective factor against moderate pDDI B= -0.858, 95%CI -1.572-(-0.144), P=0.019 and B= -0.956, 95%CI -1.671-(-0.241), P=0.009, by Medscape and Epocrates, respectively, but a risk factor for the occurrence of minor pDDIs B=0.373, 95%CI 0.033-0.712 P=0.032 and B=0.143, 95%CI 0.042-0.244, P=0.006, by the same checkers.

Conclusion

Knowledge of the risk factors contributing to the occurrence of pDDIs is important for the development and implementation of strategies for their prevention, and given the high prevalence of dyslipidemia, understanding these factors seems crucial nowadays.

Real world co-prescribing contraindicated drugs with fluconazole and itraconazole

PURPOSE

This study aimed to investigate co-prescribing of contraindicated drugs with fluconazole and itraconazole using real-world nationwide data.

METHODS

This retrospective cross-sectional study was performed using claims data collected by the Health Insurance Review and Assessment Service (HIRA) of Korea during 2019-2020. To determine the drugs that should be avoided in patients taking fluconazole or itraconazole, Lexicomp® and Micromedex® were used. The co-prescribed medications, co-prescription rates, and potential clinical consequences of the contraindicated drug-drug interactions (DDIs) were investigated.

RESULTS

Of the 197 118 prescriptions of fluconazole, 2847 co-prescriptions with drugs classified as contraindicated DDI by either Micromedex® or Lexicomp® were identified. Further, of the 74 618 prescriptions of itraconazole, 984 co-prescriptions with contraindicated DDI were identified. Solifenacin (34.9%), clarithromycin (18.1%), alfuzosin (15.1%), and donepezil (10.4%) were frequently found in the co-prescriptions of fluconazole, whereas tamsulosin (40.4%), solifenacin (21.3%), rupatadine (17.8%), and fluconazole (8.8%) were frequently found in the co-prescriptions of itraconazole. In 1105 and 95 co-prescriptions of fluconazole and itraconazole, accounting for 31.3% of all co-prescriptions, potential DDIs were associated with a risk of corrected QT interval (QTc) prolongation. Of the total 3831 co-prescriptions, 2959 (77.2%) and 785 (20.5%) were classified as contraindicated DDI by Micromedex® alone and by Lexicomp® alone, respectively, whereas 87 (2.3%) were classified as contraindicated DDI by both Micromedex® and Lexicomp®.

CONCLUSIONS

Many co-prescriptions were associated with the risk of DDI-related QTc prolongation, warranting the attention of healthcare providers. Narrowing the discrepancy between databases that provide information on DDIs is required for optimized medicine usage and patient safety.

Potential drug-drug interactions and their associated factors in hospitalized COVID-19 patients with comorbidities

Background

Hospitalized COVID-19 patients with comorbidities receive more complex drug therapy. This increases the probability of potential drug-drug interactions (pDDIs). Studies on pDDIs in hospitalized patients with COVID-19 in countries with limited resources like Indonesia during the later period of the disease are still limited. This study aims to identify the pattern of pDDIs in hospitalized COVID-19 patients with comorbidities and their associated factors, especially in the second wave of the disease in Indonesia.

Methods

This study was a longitudinal-retrospective study observing hospitalized COVID-19 patients with comorbidities using medical record data in June-August 2021 at a public hospital in a region in Indonesia. pDDIs were identified using the Lexicomp^® database. Data were descriptively analyzed. Factors associated with important pDDIs were analyzed in multivariate logistic regression model.

Results

A total of 258 patients with a mean age of 56.99 ± 11.94 years met the inclusion criteria. Diabetes mellitus was the most common comorbidity experienced by 58.14% of the patients. More than 70% of the patients had one comorbidity and the average number of administered drugs was 9.55 ± 2.71 items per patient. Type D pDDIs, which required modification of therapeutic regimens, amounted to 21.55% of the total interactions. Only the number of drugs was significantly and independently associated with type D pDDIs (adjusted odds ratio 1.47 [1.23-1.75], p < 0.01).

Conclusion

The drugs involved in the pDDIs of hospitalized COVID-19 patients with comorbidities may differ depending on the disease periods, hospital settings, or countries. This study was small, single center, and of short duration. However, it may give a glimpse of important pDDIs during the delta variant of COVID-19 in a similar limited-resource setting. Further studies are needed to confirm the clinical significance of these pDDIs.

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.

Assessment of the scope, completeness, and consistency of various drug information resources related to COVID-19 medications in pregnancy and lactation

BACKGROUND

Drug use in pregnancy and lactation is challenging. It becomes more challenging in pregnant and lactating women with certain critical clinical conditions such as COVID-19, because of inconsistent drug safety data. Therefore, we aimed to evaluate the various drug information resources for the scope, completeness, and consistency of the information related to COVID-19 medications in pregnancy and lactation.

METHODS

Data related to COVID-19 medications from various drug information resources such as text references, subscription databases, and free online tools were used for the comparison. The congregated data were analyzed for scope, completeness, and consistency.

RESULTS

Scope scores were highest for Portable Electronic Physician Information Database (PEPID), Up-to-date, and drugs.com compared to other resources. The overall completeness scores were higher for Micromedex and drugs.com (p < 0.05 compared to all other resources). The inter-reliability analysis for overall components by Fleiss kappa among all the resources was found to be 'slight' (k < 0.20, p < 0.0001). The information related to the older drugs in most of the resources, provides in-depth details on various components such as pregnancy safety, clinical data related to lactation, the effect of the drug distribution into breast milk, reproductive potential/infertility risk and the pregnancy category/recommendations. However, the information related to these components for newer drugs was superficial and incomplete, with insufficient data and inconclusive evidence, which is a statistically significant observation. The strength of observer agreement for the various COVID-19 medications ranged from poor to fair and moderate for the various recommendation categories studied.

CONCLUSION

This study reports discrepancies in the information related to pregnancy, lactation, drug level, reproductive risk, and pregnancy recommendations among the resources directing to refer to more than one resource for information about the safe and quality use of medications in this special population.The present study also emphasizes the need for development of comprehensive, evidence-based, and precise information guide that can promote safe and effective drug use in this special population.

The Capability of ChatGPT in Predicting and Explaining Common Drug-Drug Interactions

Background Drug-drug interactions (DDIs) can have serious consequences for patient health and well-being. Patients who are taking multiple medications may be at an increased risk of experiencing adverse events or drug toxicity if they are not aware of potential interactions between their medications. Many times, patients self-prescribe medications without knowing DDI. Objective The objective is to investigate the effectiveness of ChatGPT, a large language model, in predicting and explaining common DDIs. Methods A total of 40 DDIs lists were prepared from previously published literature. This list was used to converse with ChatGPT with a two-stage question. The first question was asked as "can I take X and Y together?" with two drug names. After storing the output, the next question was asked. The second question was asked as "why should I not take X and Y together?" The output was stored for further analysis. The responses were checked by two pharmacologists and the consensus output was categorized as "correct" and "incorrect." The "correct" ones were further classified as "conclusive" and "inconclusive." The text was checked for reading ease scores and grades of education required to understand the text. Data were tested by descriptive and inferential statistics. Results Among the 40 DDI pairs, one answer was incorrect in the first question. Among correct answers, 19 were conclusive and 20 were inconclusive. For the second question, one answer was wrong. Among correct answers, 17 were conclusive and 22 were inconclusive. The mean Flesch reading ease score was 27.64±10.85 in answers to the first question and 29.35±10.16 in answers to the second question, p = 0.47. The mean Flesh-Kincaid grade level was 15.06±2.79 in answers to the first question and 14.85±1.97 in answers to the second question, p = 0.69. When we compared the reading levels with hypothetical 6th grade, the grades were significantly higher than expected (t = 20.57, p < 0.0001 for first answers and t = 28.43, p < 0.0001 for second answers). Conclusion ChatGPT is a partially effective tool for predicting and explaining DDIs. Patients, who may not have immediate access to the healthcare facility for getting information about DDIs, may take help from ChatGPT. However, on several occasions, it may provide incomplete guidance. Further improvement is required for potential usage by patients for getting ideas about DDI.

Optimization of Therapy and the Risk of Probiotic Use during Antibiotherapy in Septic Critically Ill Patients: A Narrative Review

Optimizing the entire therapeutic regimen in septic critically ill patients should be based not only on improving antibiotic use but also on optimizing the entire therapeutic regimen by considering possible drug–drug or drug–nutrient interactions. The aim of this narrative review is to provide a comprehensive overview on recent advances to optimize the therapeutic regimen in septic critically ill patients based on a pharmacokinetics and pharmacodynamic approach. Studies on recent advances on TDM-guided drug therapy optimization based on PK and/or PD results were included. Studies on patients <18 years old or with classical TDM-guided therapy were excluded. New approaches in TDM-guided therapy in septic critically ill patients based on PK and/or PD parameters are presented for cefiderocol, carbapenems, combinations beta-lactams/beta-lactamase inhibitors (piperacillin/tazobactam, ceftolozane/tazobactam, ceftazidime/avibactam), plazomicin, oxazolidinones and polymyxins. Increased midazolam toxicity in combination with fluconazole, nephrotoxic synergism between furosemide and aminoglycosides, life-threatening hypoglycemia after fluoroquinolone and insulin, prolonged muscle weakness and/or paralysis after neuromuscular blocking agents and high-dose corticosteroids combinations are of interest in critically ill patients. In the real-world practice, the use of probiotics with antibiotics is common; even data about the risk and benefits of probiotics are currently spares and inconclusive. According to current legislation, probiotic use does not require safety monitoring, but there are reports of endocarditis, meningitis, peritonitis, or pneumonia associated with probiotics in critically ill patients. In addition, probiotics are associated with risk of the spread of antimicrobial resistance. The TDM-guided method ensures a true optimization of antibiotic therapy, and particular efforts should be applied globally. In addition, multidrug and drug–nutrient interactions in critically ill patients may increase the likelihood of adverse events and risk of death; therefore, the PK and PD particularities of the critically ill patient require a multidisciplinary approach in which knowledge of clinical pharmacology is essential.

Prevalence of Drug-Drug Interactions in Older Community-Dwelling Individuals: A Systematic Review and Meta-analysis

BACKGROUND

Drug-drug interactions (DDIs) can lead to medication-related harm, and the older population is at greatest risk. We conducted a systematic review and meta-analysis to estimate DDI prevalence and identify common DDIs in older community-dwelling adults.

METHODS

PubMed and EMBASE were searched for observational studies published between 01/01/2010 and 10/05/2021 reporting DDI prevalence in community-dwelling individuals aged ≥ 65 years. Nursing home and inpatient hospital studies were excluded. Study quality was assessed using the Joanna Briggs Institute critical appraisal tool. Meta-analysis was performed using a random-effects model with logit transformation. Heterogeneity was evaluated using Cochran's Q and I². DDI prevalence and 95% confidence intervals (CIs) are presented. All analyses were performed in R (version 4.1.2).

RESULTS

There were 5144 unique articles identified. Thirty-three studies involving 17,011,291 community-dwelling individuals aged ≥ 65 years met inclusion criteria. Thirty-one studies reported DDI prevalence at the study-participant level, estimates ranged from 0.8% to 90.6%. The pooled DDI prevalence was 28.8% (95% CI 19.3-40.7), with significant heterogeneity (p < 0.10; I² = 100%; tau² = 2.13) largely explained by the different DDI identification methods. Therefore, 26 studies were qualitatively synthesised and seven studies were eligible for separate meta-analyses. In a meta-analysis of three studies (N = 1122) using Micromedex^®, pooled DDI prevalence was 57.8% (95% CI 52.2-63.2; I² = 69.6%, p < 0.01). In a meta-analysis of two studies (N = 809,113) using Lexi-Interact^®, pooled DDI prevalence was 30.3% (95% CI 30.2-30.4; I² = 6.8%). In a meta-analysis of two studies (N = 947) using the 2015 American Geriatrics Society Beers criteria^®, pooled DDI prevalence was 16.6% (95% CI 5.6-40.2; I² = 97.5%, p < 0.01). Common DDIs frequently involved cardiovascular drugs, including ACE inhibitor-potassium-sparing diuretic; amiodarone-digoxin; and amiodarone-warfarin.

CONCLUSIONS

DDIs are prevalent among older community-dwelling individuals; however, the methodology used to estimate these events varies considerably. A standardised methodology is needed to allow meaningful measurement and comparison of DDI prevalence.

Prevalence and associated factors of drug-drug interactions in elderly outpatients in a tertiary care hospital: a cross-sectional study based on three databases

Background

Drug-drug interactions (DDIs) are factors of adverse drug reactions and are more common in elderly patients. Identifying potential DDIs can prevent the related risks. Fewer studies of potential DDIs in prescribing for elderly patients in outpatient clinics. This study aimed to investigate the prevalence and associated factors with potential DDIs and potentially clinically significant DDIs (csDDIs) among elderly outpatients based on 3 DDIs databases.

Methods

A cross-sectional study was carried out on outpatients (≥65 years old) of a tertiary care hospital in China between January and March 2022. Patients' prescriptions, including at least 1 systemic drug, were consecutively collected. The potential DDIs were identified by Lexicomp^®, Micromedex^®, and DDInter. Patient-related clinical parameter recorded at the prescriptions and DDIs with higher risk rating was analyzed. Variables showing association in univariate analysis (P<0.2) were included in logistic regression analysis. Weighted kappa analysis was used to analyze the consistencies of different databases.

Results

A total of 19,991 elderly outpatients were involved in the study, among whom 21,527 drug combinations including 486 drugs occurred. Lexicomp^®, Micromedex^®, and DDInter respectively identified 32.22%, 32.93%, and 22.62% of patients have at least one potential DDIs, meanwhile, 9.16%, 14.53%, and 4.56% of patients have at least one potential csDDIs. Under any evaluation criteria, polypharmacy and neurology visits were risk factors for csDDIs. Lexicomp^® has the highest coverage rate (87.86%) for drugs. Micromedex^® identified the most csDDIs (740 drug combinations). Drugs used in diabetes and psycholeptics were frequently found in the csDDIs of 2 commercial databases. The consistency between Lexicomp^® and Micromedex^® was moderate (weighted kappa 0.473). DDInter had fair consistencies with the other databases.

Conclusions

This study showed the prevalence of potential DDIs is high in elderly outpatients and potential csDDIs were prevalent. Considering the relative risk, pre-warning of potential DDIs before outpatient prescribing is necessary. As the consistencies among identification criteria are not good, more research is needed to focus on actual adverse outcomes to promote accurate prevention of csDDIs.

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

Frequency, severity, and factors associated with clinically significant drug-drug interactions among patients with cancer attending Mbarara Regional Referral Hospital Cancer Unit, Uganda

BACKGROUND

Cancer is a major public health problem with pharmacotherapy being the cornerstone of its management. Cancer patients receive multiple drugs concurrently risking Drug-Drug Interactions (DDIs). DDIs, though avoidable, can significantly contribute to morbidity, mortality, and increased healthcare costs in this population of patients. Currently, there is no published study from Uganda on clinically significant DDIs (cs-DDIs) among cancer patients. This study identifies frequency, severity, and factors associated with cs-DDIs at Mbarara Regional Referral Hospital Cancer Unit (MRRHCU).

METHOD

A cross-sectional study was conducted among 300 cancer patients receiving chemotherapy from a tertiary care hospital in western Uganda from January-February 2022. A questionnaire and data collection form were used to collect patient data. Lexicomp® Drug interaction software was used to screen the patient drug information for DDIs and assess their severity. Predictors of DDIs were identified using logistic regression using SPSS (Statistical Package for Social Sciences).

RESULT

Three hundred participants were enrolled with a mean age of 48 ± 23.3 years. One hundred eighty-one patients experienced 495 cs-DDIs; with a mean of 1.7 ± 2.2. The prevalence of cs-DDI was 60.3% (55.0-66.0% at 95% CI). Digestive organ neoplasms were the most commonly (80, 26.7%) diagnosed category, and 'plant alkaloids and other natural products were the most frequently (143, 47.7%) used chemotherapeutic drug classes. About three-quarters of cs-DDIs were rated as category C risk (367, 74.1%) whereas over two-thirds (355, 71.7%) were moderate in severity.. Being female (aOR = 2.43 [1.23-4.48 at 95% CI]; P-value = 0.011) and use of ≥ 6 drugs concurrently (aOR = 18.82 [9.58-36.95 at 95% CI]; P-value < 0.001)) were significantly associated with cs-DDIs.

CONCLUSION

More than half of the participants experienced at-least one cs-DDI which is generally higher than what was reported in high-income settings. About three-quarters were category C and moderate in severity, and require enhanced monitoring for safety and treatment outcome. Being female and using ≥ 6 drugs were significantly associated with cs-DDIs.

COVID-19 and antiepileptic drugs: an approach to guide practices when nirmatrelvir/ritonavir is co-prescribed

Management and dose adjustment are a major concern for clinicians in the absence of specific clinical outcome data for patients on antiepileptic drugs (AEDs), in the event of short-term (5 days) nirmatrelvir/ritonavir co-exposure. Therefore, in this report, we identified drugs that require dose adjustment because of drug-drug interactions (DDIs) between nirmatrelvir/ritonavir and AEDs. We hereby used four databases (Micromedex Drug Interaction, Liverpool Drug Interaction Group for COVID-19 Therapies, Medscape Drug Interaction Checker, and Lexicomp Drug Interactions) and DDI-Predictor.In the light of applying the DDI-Predictor, for carbamazepine, clobazam, oxcarbazepine, eslicarbazepine, phenytoin, phenobarbital, pentobarbital, rufinamide, and valproate as CYP3A4 inducers, we recommend that a dose adjustment of short-term nirmatrelvir/ritonavir as a substrate (victim) drug would be more appropriate instead of these AEDs to avoid impending DDI-related threats in patients with epilepsy.

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.

Potential drug-drug interactions in drug therapy for older adults with chronic coronary syndrome at hospital discharge: A real-world study

Introduction: Polypharmacy are commonly observed among older adults with cardiovascular disease. However, multiple medications lead to increased risk of drug-drug interactions (DDIs). Therefore, identification and prevention actions related to harmful DDIs are expected in older adults. The study aimed to describe the prevalence of potential DDIs (pDDIs) in discharge prescriptions among older adults with chronic coronary syndrome (CCS).

Methods: A single-center cross-sectional study was performed in a tertiary public hospital in Beijing, China. CCS patients aged 65 years and above who were admitted to cardiology wards over a 3-month period and alive at discharge were included. Electronic medical records and discharge prescriptions were reviewed. pDDIs were evaluated through the Lexi-Interact online.

Results: pDDIs were identified in 72.9% of the 402 individuals (n = 293). A total of 864 pDDIs were obtained. 72.1% of patients were found with C DDIs (n = 290) and 20.3% were categorized in D and X DDIs (n = 82). The only X DDI was between cyclosporine and atorvastatin. Under category D, glycemia alterations within antidiabetics and increased chances of bleeding with antithrombotic were the most common. Concomitant use of clopidogrel and calcium channel blockers was a frequent situation within category C, followed by synergic blood pressure lowering agents and increased rosuvastatin concentration induced by clopidogrel.

Conclusion: DDIs exposure was common in older CCS. DDIs screening tools should be introduced to alert potential adverse effects. Prescribers need to rigorously review or modulate therapies to prevent DDI-related adverse outcomes. Clinical pharmacists should be more involved in complex drug regimen management.

Polypharmacy and Cardiovascular Diseases: Consideration for Older Adults and Women

PURPOSE OF REVIEW

The intent of this review is to provide an update in polypharmacy in older adults and women with a focus on common determinants and strategies to mitigate polypharmacy.

RECENT FINDINGS

Polypharmacy is becoming a critical focus in the management of cardiovascular diseases. It may emerge unintentionally while managing multimorbidity in older adults or in the vulnerable subgroup of patients, such as pregnant and lactating females. Clinicians should utilize several approaches such as deprescribing, sex-specific risk assessment, and encouraging healthy lifestyle to minimize inappropriate and unnecessary use of medications. A shared decision-making model along with coordination and collaboration among healthcare providers should be utilized in the selection and management of pharmacotherapies.

Efficacy of Short-Term High Dose Pulsed Dapsone Combination Therapy in the Treatment of Chronic Lyme Disease/Post-Treatment Lyme Disease Syndrome (PTLDS) and Associated Co-Infections: A Report of Three Cases and Literature Review

Lyme disease and associated co-infections are increasing worldwide and approximately 20% of individuals develop chronic Lyme disease (CLD)/Post-Treatment Lyme Disease Syndrome (PTLDS) despite early antibiotics. A seven- to eight-week protocol of double dose dapsone combination therapy (DDDCT) for CLD/PTLDS results in symptom remission in approximately 50% of patients for one year or longer, with published culture studies indicating higher doses of dapsone demonstrate efficacy against resistant biofilm forms of Borrelia burgdorferi. The purpose of this study was, therefore, to evaluate higher doses of dapsone in the treatment of resistant CLD/PTLDS and associated co-infections. A total of 25 patients with a history of Lyme and associated co-infections, most of whom had ongoing symptoms despite several courses of DDDCT, took one or more courses of high dose pulsed dapsone combination therapy (200 mg dapsone × 3-4 days and/or 200 mg BID × 4 days), depending on persistent symptoms. The majority of patients noticed sustained improvement in eight major Lyme symptoms, including fatigue, pain, headaches, neuropathy, insomnia, cognition, and sweating, where dapsone dosage, not just the treatment length, positively affected outcomes. High dose pulsed dapsone combination therapy may represent a novel therapeutic approach for the treatment of resistant CLD/PTLDS, and should be confirmed in randomized, controlled clinical trials.

Polypharmacy and potential drug–drug interactions among Greenland’s care home residents

Background:

As lifetime expectancy in Greenland is steadily increasing, so is the proportion of elderly Greenlanders. Old age is associated with polypharmacy, and in this study, we aim to describe the prevalence and characteristics of polypharmacy among the care home residents in Greenland.

Methods:

Eight care homes in Greenland were visited between 2010 and 2016. Questionnaires including information on prescribed medication and comorbidities were collected and analyzed. Drugs were categorized according to Anatomical Therapeutic Chemical (ATC) category, and potential drug–drug interactions (pDDIs) were assessed using the Danish Interaction Database. Polypharmacy was defined as five or more prescribed drugs.

Results:

All 244 eligible residents were included in the study. The median number of prescribed drugs per resident was six, and women were prescribed more drugs than men (median six versus five). More than 60% of all residents fulfilled the criteria for polypharmacy. The residents in the polypharmacy group had a higher body mass index (26.9 versus 24.3) and more chronic diseases (median two versus one), and more often pulmonary (14% versus 1%) or endocrine disease (22% versus 2%) than in the non-polypharmacy group. The most prescribed drugs belonged to ATC category N (nervous system, 78% of the residents). Finally, pDDIs were found among 61% of the residents and were more common in the capital (77%), which also had the highest proportion of residents with polypharmacy (77%).

Conclusion:

This is the first study to describe the patterns of polypharmacy and pDDIs among the elderly in care homes in Greenland. Our findings indicate that polypharmacy is as common in Greenland as elsewhere in the Western world, but there are local differences in the prevalence.

Plain Language Summary

Polypharmacy among the elderly in care homes in Greenland

The lifetime expectancy of the Greenlandic population is increasing, and so is the number of elderly Greenlanders. Previous studies have shown that the elderly have a higher risk of being treated with five drugs or more which is called polypharmacy. Polypharmacy can cause unwanted interactions and side effects. In this study, we examine the characteristics of the residents in Greenlandic care homes belonging to this group.

Using questionnaires, we gathered information from 244 residents from care homes in eight different towns and settlements in Greenland. Data included types of medication prescribed to the resident, age, gender, cause of stay, and medical history, which allowed us to compare the results between genders and towns.

We found that among 244 residents, more than half of all residents were prescribed five or more different drugs, and women were generally prescribed more drugs than men. Those prescribed five or more drugs had a higher body mass index and more diseases than those prescribed fewer drugs. We also found that certain types of medication, mainly painkillers, were the most prescribed. Finally, residents in the care home in Greenland’s capital Nuuk were more often prescribed five or more drugs than elsewhere in Greenland, indicating local differences in Greenland.

Our results give an essential insight into the health and medication of the most fragile elderly in Greenland. Polypharmacy seems to be as common here as elsewhere in the Western world and is a point of focus.

Prevalence of potential drug - drug interactions in the cardiothoracic intensive care unit patients in a Chinese tertiary care teaching hospital

Background

With an increasing number of reviews describing clinically significant drug–drug interactions (DDIs), the scope and severity of interactions involving commonly used drugs in cardiothoracic intensive care units (CCUs) remain unclear. This study aims to identify risk factors and determine the incidence of potential DDIs in intensive care units.

Methods

DDIs were identified based on the profile of the prescribed drug and classified according to the Micromedex drug interaction database. Potential risk factors associated with DDIs have been identified.

Results

A total of 3193 medication episodes were evaluated, and 680 DDIs (21.3%) were found. A total of 203 patients were recruited into the study, with an average of 3.4 DDIs per patient [95% confidence interval (3.2 − 3.6)]. A total of 84.2% of the patients experienced at least one DDI. Anticoagulant and antiplatelet agents were involved in 33.5% (228/680) of the potential drug − drug interactions in the CCU. Univariate analysis and multiple logistic regression analysis showed that the age of the patient and the number of medications prescribed were significantly correlated with the occurrence of DDIs. In multiple linear regression analysis, the number of DDIs had a significant correlation only with the number of prescription drugs.

Conclusions

A high prevalence of DDIs was observed, especially in intensive care units without pharmacist intervention and computerized drug monitoring systems, highlighting the need for active surveillance to prevent potential adverse events.

Prevalence and predictors of clinically significant statin-drug interactions among Yemeni patients taking statins for primary and secondary prevention of cardiovascular disease

BACKGROUND

Statins are extensively used in clinical practice for the primary and secondary prevention of cardiovascular diseases. Statins are usually taken in combination with other medications. This may increase the risk of statin-drug interactions. The study aimed to evaluate the prevalence, patterns, and predictors of clinically significant statin-drug interactions among patients on statin therapy.

MATERIAL AND METHODS

A cross-sectional study was conducted at the cardiology, endocrine, and internal medicine outpatient clinics in four tertiary care hospitals in Sana'a, Yemen. Lexicomp Drug Interaction database was used to analyze the prescriptions for potential statin-drug interactions. Binary and multivariable logistic regression were utilized for analysis.

RESULTS

Of the total number of patients (634), 114 individuals (18%) had a total of 122 statin-drug interactions. According to Lexicomp risk classification, 102 (83.6%) DDIs were class C (monitor therapy), 19 (15.6%) were class D (therapy modification), and only one (0.8%) class X (avoid combination). Simvastatin use was significantly associated with the presence of category D and X DDIs (15.9% vs. 1.6%, p < .001). Polypharmacy (OR = 2.571, p < .001) and having ≥3 comorbidities (OR = 2.512, p < .001) were the only variables associated with the presence of statin-drug interactions (C, D, and/or X).

CONCLUSION

Patients with polypharmacy and those with three or more comorbidities had a higher risk for statin-drug interactions. Therefore, routine screening by physicians and pharmacists for potential interactions should occur before prescribing or dispensing any medication to avoid clinically significant statin-drug interactions.

Anti-DDI Resource: A Dataset for Potential Negative Reported Interaction Combinations to Improve Medical Research and Decision-Making

Potential drug-drug interactions (DDIs) are a core concern across medical decision support systems. Among healthcare practitioners, the common practice for screening these interactions is via computer software. However, as real-world negative reporting is missing, counterexamples that serve as contradictory evidence may exist. In this study, we have developed an anti-DDI resource, a set of drug combinations having negative reported interactions. This resource was created from a set of the top 200 most-used drugs, resulting in 14365 prospective negative reported DDI pairs. During analysis and filtering, 2110 DDIs (14.69%) were found in publicly free DDI resources, another 11130 (77.48%) were filtered by a rule-based inference engine incorporating ten mechanisms of interaction, and 208 were identified through commercial resources. Additionally, 90 pairs were removed due to recent FDA approvals or being unapplicable in clinical use. The final set of 827 drug pairs represents combinations potentially having negative reported interactions. The anti-DDI resource is intended to provide a distinctly different direction from the state of the art and establish a ground focus more centered on the evaluation and utilization of existing knowledge for performing thorough assessments. Our negative reported DDIs resource shall provide healthcare practitioners with a level of certainty on DDIs that is worth investigating.