Drug-drug interaction software in clinical practice: a systematic review

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.

Antibiotic-Drug Interactions in the Intensive Care Unit: A Literature Review

Interactions between drugs are a common problem in Intensive Care Unit patients, as they mainly have a critical condition that often demands the administration of multiple drugs simultaneously. Antibiotics are among the most frequently used medications, as infectious diseases are often observed in ICU patients. In this review, the most important antibiotic-drug interactions, based on the pharmacokinetic and pharmacodynamic mechanisms, were gathered together and described. In particular, some of the most important interactions with main groups of antibacterial drugs were observed in patients simultaneously prescribed oral anticoagulants, NSAIDs, loop diuretics, and valproic acid. As a result, the activity of drugs can be increased or decreased, as dosage modification might be necessary. It should be noted that these crucial interactions can help predict and avoid negative consequences, leading to better patient recovery. Moreover, since there are other factors, such as fluid therapy or albumins, which may also modify the effectiveness of antibacterial therapy, it is important for anaesthesiologists to be aware of them.

Prevalence, Attributes, and Risk Factors of QT-Interval-Prolonging Drugs and Potential Drug-Drug Interactions in Cancer Patients: A Prospective Study in a Tertiary Care Hospital

Introduction Cancer chemotherapy regimens include multiple classes of adjuvant drugs as supportive therapy. Because of the concurrent intake of other drugs (like antiemetics, antidepressants, analgesics, and antimicrobials), there is a heightened risk for possible QT interval prolongation. There is a dearth of evidence in the literature regarding the usage of QT-prolonging anticancer drugs and associated risk factors that have the propensity to prolong QT interval. The purpose was to explore the extent of the use of QT-interval-prolonging drugs and potential QT-prolonging drug-drug interactions (QT-DDIs) in cancer patients attending OPD in a tertiary-care hospital. Methods This was a hospital-based, cross-sectional, observational study. Risk stratification of QT-prolonging drugs for torsades de pointes (TdP) was done by the Arizona Center for Education and Research on Therapeutics (AzCERT)/CredibleMeds-lists, and potential QT-DDIs were determined with four online DDI-checker-software. Results In 1331 cancer patients, the overall prevalence of potential QT-prolonging drug utilization was 97.3%. Ondansetron, pantoprazole, domperidone, and olanzapine were the most frequent QT-prolonging drugs in cancer patients. The top six antineoplastics with potential QT-prolonging and torsadogenic actions were capecitabine, oxaliplatin, imatinib, bortezomib, 5-fluorouracil, and bendamustine. Evidence-based pragmatic QTc interval prolongation risk assessment tools are imperative for cancer patients. Conclusion This study revealed a high prevalence of QT-prolonging drugs and QT-DDIs among cancer patients who are treated with anticancer and non-anticancer drugs. As a result, it's critical to take precautions, stay vigilant, and avoid QT-prolonging in clinical situations. Evidence-based pragmatic QTc interval prolongation risk assessment tools are needed for cancer patients.

Drug-drug interactions with oral anticoagulants: information consistency assessment of three commonly used online drug interactions databases in Switzerland

Background: Toxicity or treatment failure related to drug-drug interactions (DDIs) are known to significantly affect morbidity and hospitalization rates. Despite the availability of numerous databases for DDIs identification and management, their information often differs. Oral anticoagulants are deemed at risk of DDIs and a leading cause of adverse drug events, most of which being preventable. Although many databases include DDIs involving anticoagulants, none are specialized in them. Aim and method: This study aims to compare the DDIs information content of four direct oral anticoagulants and two vitamin K antagonists in three major DDI databases used in Switzerland: Lexi-Interact, Pharmavista, and MediQ. It evaluates the consistency of DDIs information in terms of differences in severity rating systems, mechanism of interaction, extraction and documentation processes and transparency. Results: This study revealed 2'496 DDIs for the six anticoagulants, with discrepant risk classifications. Only 13.2% of DDIs were common to all three databases. Overall concordance in risk classification (high, moderate, and low risk) was slight (Fleiss' kappa = 0.131), while high-risk DDIs demonstrated a fair agreement (Fleiss' kappa = 0.398). The nature and the mechanism of the DDIs were more consistent across databases. Qualitative assessments highlighted differences in the documentation process and transparency, and similarities for availability of risk classification and references. Discussion: This study highlights the discrepancies between three commonly used DDI databases and the inconsistency in how terminology is standardised and incorporated when classifying these DDIs. It also highlights the need for the creation of specialised tools for anticoagulant-related interactions.

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.

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.

Concordance analysis of two databases to search for potential drug interactions in onco-hematologic patients

INTRODUCTION

Potential drug interactions exert a significant impact on patient safety, especially within intricate onco-hematological treatments, potentially resulting in toxicity or treatment failures. Despite the availability of databases for potential drug interaction investigation, persistent heterogeneity in concordance rates and classifications exists. The additional variability in database agreement poses further complexity, notably in critical contexts like onco-hematology.

AIM

To analyze the concordance of two databases for researching potential drug interaction in prescriptions for hematological patients at a University Hospital in the Midwest region of Brazil.

METHOD

Cross-sectional study developed in a Brazilian hospital. The search for potential drug interaction was conducted in Micromedex® and UpToDate®. The variables were: the presence of potential drug interaction, severity, mechanism, management, and documentation. Data was analyzed in terms of frequency (absolute and relative), Cohen's kappa, and Fleiss kappa.

RESULTS

The presence of potential drug interaction, showed a lack of concordance between the databases (k = -0.115 [95% CI: 0.361-0.532], p = 0.003). Regarding the mechanism, a strong agreement was observed (k = 0.805, p < 0.001 [95% CI: 0.550-0.941]). The management concordance showed a fair agreement, 46.8% (k = 0.22, p < 0.001 [95% CI: 0.099-0.341]). Stratifying the categories, significant concordance was observed in "Adjustment of dose + Monitoring" (k = 0.302, p = 0.018) and "Monitoring" (k = 0.417, p = 0.001), while other categories did not reach statistical significance.

CONCLUSION

Our study emphasizes the variability in potential drug interaction research, revealing disparities in severity classification, management recommendations, and documentation practices across databases.

[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.

A Review of the Lidocaine in the Perioperative Period

This review analyzes the controversies surrounding lidocaine (LIDO), a widely recognized local anesthetic, by exploring its multifaceted effects on pain control in the perioperative setting. The article critically analyzes debates about lidocaine's efficacy, safety, and optimal administration methods. While acknowledging its well-documented analgesic attributes, the text highlights the ongoing controversies in its application. The goal is to provide clinicians with a comprehensive understanding of the current discourse, enabling informed decisions about incorporating lidocaine into perioperative protocols. On the other hand, emphasizes the common uses of lidocaine and its potential role in personalized medicine. It discusses the medication's versatility, including its application in anesthesia, chronic pain, and cardiovascular diseases. The text recognizes lidocaine's widespread use in medical practice and its ability to be combined with other drugs, showcasing its adaptability for individualized treatments. Additionally, it explores the incorporation of lidocaine into hyaluronic acid injections and its impact on pharmacokinetics, signaling innovative approaches. The discussion centers on how lidocaine, within the realm of personalized medicine, can offer safer and more comfortable experiences for patients through tailored treatments.

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.

Potential drug-drug interactions in patients with non-small cell lung cancer at a university hospital in Turkey

BACKGROUND

The aim of this study was to determine the drug profile of patients with non-small cell lung cancer (NSCLC) and to identify potential drug-drug interactions (PDDIs) during hospitalization. In particular, PDDIs in categories X and D were determined.

METHODS

This retrospective cross-sectional study was conducted in the oncology services of a university hospital between 2018 and 2021. PDDIs were evaluated using Lexicomp Drug Interactions® software included in UpToDate®.

RESULTS

A total of 199 patients were included in the study. Polypharmacy was present in 92.5% of the patients and the median (min-max) number of drugs used was 8 (2-16). 32% of the patients had D and X PDDIs. A total of 16 PDDIs at risk grade X were found in 15 (7.5%) patients. A total of 81 PDDIs of risk grade D were found in 54 (27.1%) patients and a total of 276 PDDIs of risk grade C were identified in 97 (48.7%) patients. Anticancer drugs (p = 0.008), opioids (p = 0.046), steroids (p = 0.003), 5-HT3 receptor antagonists (p = 0.012), aprepitant (p = 0.025) and antihistamines (p < 0.001) were statistically more frequent among patients with PDDIs than among those without.

CONCLUSION

The results of our study indicated that polypharmacy and PDDIs are common in hospitalized patients with NSCLC cancer. The monitoring of medications is critical for maximizing therapeutic effects and minimizing side effects related to PDDIs. As a part of multidisciplinary team, clinical pharmacists can contribute significantly to preventing, detecting and managing PDDIs.

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.

Examining the relationship between long working hours and the use of prescription sedatives among U.S. workers

OBJECTIVES

The prevalence of long working hours has been accompanied by a corresponding rise in sleep disorders. Sedative-hypnotic agents (SHAs), have been reported as the second most commonly misused drug class in the U.S. The key objective of this study was to examine the relationship between working hours on the use of sleep aids and medications with sedative properties.

METHODS

The 2010-2019 Medical Expenditure Panel Survey data was utilized. SHAs and medications with sedative related properties (MSRPs) were identified. Furthermore, we employed different regression models ranging from multivariable linear regression, Tobit regression, Heckman regression, and multivariable logistic regression, to ensure consistency, robustness, and reliability of associations.

RESULTS

Overall, a sample of 81,518 observations of full-time workers was analyzed. Working 56hours or more per week was significantly associated (p < 0.05) with an increased odds of using SHAs and MSRPs by 13% (Adjusted Odds Ratio, aOR =1.13, 95% Confidence Interval, CI=1.01:1.26) and 9% (aOR=1.09, 95% CI=1.03:1.16), respectively more than that among those who worked fewer hours. Females in our study had a higher likelihood (aOR=1.11, 95% CI=1.05:1.19) of using SHAs when compared to males. Also, professional services had the highest likelihood (aOR=1.31, 95% CI=1.14:1.50) of using SHAs.

CONCLUSION

We found that long working hours were significantly associated with an elevated use of SHAs and MSRPs among U.S. workers. Specifically, female workers and individuals working in professional services had the highest likelihood of using sleep medications.

New Perspective for Drug-Drug Interaction in Perioperative Period

In this review, we aim to discuss current information on drug interactions in the perioperative period. During this period, patients receive several drugs that may interact with each other and affect the efficacy and safety of the treatment. There are three types of drug interactions: pharmacodynamic, pharmacokinetic, and pharmaceutical. It is important to recognize that drug interactions may increase the toxicity of the drug or reduce its efficacy, increasing the risk of complications in the perioperative period. This review describes the most commonly used perioperative drugs approved by the FDA and some of the described interactions between them. Thoroughly reviewing a patient's medication list and identifying potential interactions are essential steps in minimizing risks. Additionally, vigilant monitoring of patients during and after surgery plays a pivotal role in early detection of any signs of drug interactions. This article emphasizes the significance of addressing DDIs in the perioperative period to ensure patient well-being and advocates for the implementation of careful monitoring protocols to promptly identify and manage potential interactions.

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.

A. Incidence, Patterns, and Severity of Potential Drug Interactions Among Cancer Patients on Chemotherapy in a Tertiary Care Hospital

Objective

To study the incidence of potential drug−drug interactions (DDIs) and evaluate their pattern and severity in cancer inpatients.

Materials and Methods

A detailed clinical data and prescriptions of 150 inpatients with different malignancies were subjected to DDI screening using Micromedex software. The frequency of potential DDIs and their types, patterns, and severity were investigated.

Results

A total of 360 potential DDIs were present in 111 (74%) of 150 inpatients, dominated by female (67.33%) and breast cancer (30%) patients. The incidence of severe interactions was 63.88%, moderate interactions 35.83%, and mild interactions 0.27%. The potential mechanisms of DDIs were 38.33% pharmacodynamic, 48.33% pharmacokinetic, and 13.33% unspecified. The drug interactions were found to be positively correlated ( p < 0.01) with the 6–10 number of prescribed medicines.

Conclusion

According to this study, the number of medicines prescribed to cancer inpatients increased the chance of DDIs. As a result, the drug surveillance program could save a sizable number of patients from the potentially hazardous clinical effects of DDIs.

Olive oil-based quercetin nanoemulsion (QuNE)'s interactions with human serum proteins (HSA and HTF) and its anticancer activity

The current study produced Quercetin nanoemulsions (QuNEs) for the purpose of improving Quercetin solubility in an aqueous polar condition and to analyze QuNE-protein formation (QuNE-human serum albumin (HSA) and QuNE-holo-transferrin (HTF)).QuNE was produced by utilizing an ultrasound-based emulsification method and was characterized by DLS, TEM, and SEM. Its interaction with HSA and HTF proteins was studied by analyzing the results of FRET and RLS spectroscopy, Stern-Volmer plotting, the Van't Hoff equation, CD spectroscopy, and molecular docking methods. Finally, QuNE's cytotoxic impact, cell death type induction, and antioxidant properties were evaluated by applying an MTT assay on a human hepatocyte cancer cell (HepG2), measuring Cas-3 gene expression, and conducting a DPPH antioxidant test, respectively. Compared to the non-entrapped Quercetin, Quercetin-entrapped nano-emulsions formed stable complexes with HSA and HTF by improving hydrophilic-hydrophobic interactions. The binding constant (BC), ΔH⁰, and ΔS⁰ indices for both the QuNE-HSA and QuNE-HTF complexes were measured at (4.92 × 105 and 11.99 × 104 M^-1), (170.96 and -131.19 KJ.mol^-1), and (-464.86 and 342.83J.mol^-1K^-1), respectively.QuNE lowered the HepG2 viability by up-regulating Cas-3 gene expression and thus inducing apoptosis. Moreover, a notable antioxidant impact on the QuNE was detected. Due to its ability in delivering Quercetin to HSA and HTF proteins and stabilizing their protein complexes, QuNE can be used as a suitable primary transporting agent whose formation of stable bio-accessible QuNE-HSA and -HTF protein complexes creates a safe and natural secondary delivery system, which has potential to be used as an efficient anticancer compound.Communicated by Ramaswamy H. Sarma.

Assessment of risk factors associated with potential drug-drug interactions among patients suffering from chronic disorders

Patients suffering from chronic diseases are more likely to experience pDDIs due to older age, prolonged treatment, severe illness and greater number of prescribed drugs. The objective of the current study was to assess the prevalence of pDDIs and risk factors associated with occurrence of pDDIs in chronic disease patients attending outpatient clinics for regular check-ups. Patients suffering from diabetes, chronic obstructive pulmonary disease (COPD), stroke and osteoporosis were included in the study. This study was a cross sectional, observational, prospective study that included 337 patients from outpatient clinics of respiratory ward, cardiac ward and orthopedic ward of Nishter Hospital Multan, Pakistan. The mean number of interactions per patient was 1.68. A greater risk for occurrence of pDDI was associated with older age ≥ 60 years (OR = 1.95, 95% CI = 1.44-2.37, p<0.001); polypharmacy (≥ 5 drugs) (OR = 3.74, 95% CI 2.32-4.54, p<0.001); overburden (OR = 2.23, 95% CI = 1.64-3.16, p<0.01); CCI score (OR = 1.28, 95% CI = 1.04-1.84, p<0.001); multiple prescribers to one patient (OR = 1.18, 95% CI = 1.06-1.41, p<0.01); and trainee practitioner (OR = 1.09, 95% CI = 1.01-1.28, p<0.01). Old age, polypharmacy, overburden healthcare system, higher comorbidity index, multiple prescribers to one patient and trainee practitioner were associated with increased risk of occurrence of pDDIs in chronic disease patients.

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.

Adverse drug reactions and correlations with drug–drug interactions: A retrospective study of reports from 2011 to 2020

Introduction: Adverse drug reactions (ADRs) represent a public health problem worldwide that deserves attention due to the impact on mortality, morbidity, and healthcare costs. Drug–drug interactions (DDIs) are an important contributor to ADRs. Most of the studies focused only on potential DDIs (pDDIs), while the detailed data are limited regarding the ADRs associated with actual DDIs.

Methods: This retrospective study evaluated ADRs reported between 2011 and 2020 in a tertiary hospital. The causality and severity of ADRs were evaluated through the Naranjo Algorithm and Hartwig’s scale, respectively. Preventability classification was based on the modified Schoumock and Thornton scale. For ADRs with at least two suspected drugs, pDDIs were identified according to the Lexi-Interact. We further checked whether the ADR description in the reports corresponded to the clinical consequences of the pDDIs.

Results: A total of 1,803 ADRs were reported, of which 36.77% ADRs were classified as mild, 43.26% as moderate, and 19.97% as severe. The assessment of causality showed that the distributions of definite, probable, and possible categories were 0.33%, 58.68%, and 40.99%, respectively. A total of 53.97% of ADRs were identified as preventable ADRs, while 46.03% were recognized as unpreventable. The severity of ADRs was significantly correlated with age, the number of suspected drugs and preventability. Antimicrobial agents were the most common implicated pharmacological group, and the most frequently affected system was the gastrointestinal system. Considering individual drugs, aspirin was the most frequently reported drug. Among 573 ADRs with at least two suspected drugs, 105 ADRs were caused by actual DDIs, of which only 59 and 6 ADRs were caused by actual DDIs in category D and X, respectively. The most frequent drugs involved in actual DDIs of category D were aspirin and heparin, with the majority of ADRs being gastrointestinal bleeding.

Conclusion: This study analyzed the pattern of ADRs in detail and obtained clinical evidence about ADRs associated with actual DDIs. These findings may be useful to compare patterns between different centers and to design preventive strategies for ADRs. Continuous education and training should be provided for physicians regarding the knowledge and recognition of ADRs associated with DDIs.

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

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

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.

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

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

Drug-Drug Interactions With Over-The-Counter Medicines: Mind the Unprescribed

BACKGROUND

This review provides a summary of the currently available clinical data on drug-drug interactions (DDIs) involving over-the-counter (OTC) medicines. It aims to educate and increase awareness among health care providers and to support decisions in daily practice.

METHODS

An extensive literature search was performed using bibliographic databases available through PubMed.gov. An initial structured search was performed using the keywords "drug-drug-interaction AND (over-the-counter OR OTC)," without further restrictions except for the language. The initial results were screened for all described DDIs involving OTC drugs, and further information was gathered specifically on these drugs using dedicated database searches and references found in the bibliography from the initial hits.

RESULTS

From more than 1200 initial hits (1972-June 2021), 408 relevant publications were screened for DDIs involving OTC drugs, leading to 2 major findings: first, certain types of drug regimens are more prone to DDIs or have more serious DDI-related consequences, such as antiretroviral, anti-infective, and oral anticancer therapies. Second, although most DDIs involve OTC drugs as the perpetrators, some prescription drugs (statins or phosphodiesterase-5 inhibitors) that currently have OTC status can be identified as the victims in DDIs. The following groups were identified to be frequently involved in DDIs: nonsteroidal anti-inflammatory drugs, food supplements, antacids, proton-pump inhibitors, H2 antihistamines, laxatives, antidiarrheal drugs, and herbal drugs.

CONCLUSIONS

The most significant finding was the lack of high-quality evidence for commonly acknowledged interactions. High-quality interaction studies involving different phenotypes in drug metabolism (cytochrome P450) and distribution (transporters) are urgently needed. This should include modern and critical drugs, such as oral anticancer medications and direct oral anticoagulants.

Drug interaction alerts in older primary care patients, and related medically justified actions

PURPOSE

To describe presented interaction alerts in older patients, and the extent to which these require further medical action for the specific patient or are already being addressed.

METHODS

Interaction alerts presented at a physician consultation, for 274 consecutive primary care patients treated with two or more drugs (median age: 75 years; 59% female), were extracted. These alerts are based on Janusmed, a decision support integrated in the medical records that provides recommendations for managing the interactions. One general practitioner (GP) and one GP/clinical pharmacologist determined in retrospect, first independently and then in consensus, whether the alerts justified further medical action, considering each patient's health condition.

RESULTS

In all, 405 drug interaction alerts in 151 (55%) patients were triggered. Medical action in response was deemed medically justified for 35 (9%) alerts in 26 (17%) patients. These actions most often involved a switch to a less interacting drug from the same drug class (n = 10), a separate intake (n = 9), or the ordering of a laboratory test (n = 8). Out of 531 actions suggested by the alert system, only 38 (7%) were applicable to the specific patient, as, for instance, laboratory parameters were already being satisfactorily monitored or a separate intake implemented.

CONCLUSIONS

More than every other older patient receives drug treatment that triggers drug interaction alerts. Nine in ten alerts were already being addressed or were not relevant in the clinical setting, whereas, for the remaining tenth, some medical action, that for unknown reasons had not been taken, was reasonable. These findings show that interaction alerts are questionable as indicators of problematic prescribing.

Evaluation of Potential Drug Interactions with AiDKlinik® in a Random Population Sample

Purpose

Undesirable drug interactions are frequent, they endanger the success of therapy, and they lead to adverse drug reactions. The present study aimed to evaluate statistically potentially drug interactions in a locally circumscribed, random sample population.

Patients and Methods

In a random sample population of 264 patients taking medications, we performed analyses with the drug information system AiDKlinik^®. Statistical analysis was performed using SAS version 9.4.

Results

Statistically potentially drug interactions were recorded in 82/264 (31.1%) subjects, including 39/82 (47.56%) men, and 43/82 (52.43%) women (χ²= 0.081; p = 0.776). The average number of potential possible interactions detected per person was 1.60 ± 1.21. The regression model with the variables age, body-mass-index and number of long-term-medications shows a significant association between the number of long-term medications taken and the number of moderately severe and severe reactions to drug interactions (F(3.239) = 28.67, p < 0.0001; (t(239) 8.28; p < 0.0001)). After backward elimination, the regression model showed a significant interaction with the number of long-term medications (t (240) = 8.73, p < 0.0001) and body-mass-index (t (240) = 2.02, p = 0.0442). In descriptive analysis, the highest percentages of potential drug interactions occurred in 42/82 (51.22%) subjects with body mass indices (BMIs) >25 kg/m² and in 28/82 (34.15%) subjects aged 61–70 years.

Conclusion

Number of long-term medications use, age, and obesity may lead to increased drug–drug interactions in a random population sample.

Potential and real drug interactions in patients treated with abiraterone or enzalutamide in clinical practice

BACKGROUND

Abiraterone and enzalutamide, androgen receptor pathway inhibitors (ARPI) for the treatment of metastatic castration-resistant prostate cancer (mCRPC), are at high risk of potential drug interactions (PDIs). We aimed to describe PDIs and their management, and triggered adverse events (AEs) in clinical practice.

METHODS

We conducted a cross-sectional study in mCRPC patients who started treatment with abiraterone or enzalutamide in a university hospital between August 1st, 2016 and July 31st, 2020. Lexicomp® was used to identify and analyze PDIs, and the clinical records to assess their management and the occurrence of AEs.

RESULTS

We included 173 patients: 36.8% and 93.0% of patients treated with abiraterone and enzalutamide, respectively, had at least 1 PDI. Globally, 6.3% of PDIs had X-risk (contraindication due to high probability of AE). Treatment was modified in 9.2% of patients and 9.8% suffered AEs due to PDIs. Factors associated with a higher risk of PDIs were polypharmacy (OR=41.0, p=0.003) and treatment with enzalutamide (OR=128.26, p< 0.001).

CONCLUSIONS

At least two-thirds of patients treated with ARPI suffered a PDI. Overall, abiraterone would have a more favorable PDI profile. Knowing these interaction profiles may be helpful to develop a more efficient therapeutic follow-up and to select the safest treatment.

Polypharmacy and the occurrence of potential drug-drug interactions among geriatric patients at the outpatient pharmacy department of a regional hospital in Durban, South Africa

Background

Polypharmacy is the administration of an excessive number of medicines and a significant irrational medicine use practice. Little is known about this practice in South Africa. This study aimed to determine the level of polypharmacy and potential drug–drug interactions amongst the geriatric patient population in a facility in South Africa.

Method

A cross-sectional retrospective prescription chart review for 250 geriatric patients was conducted at the outpatient pharmacy department of a regional hospital. Variables extracted included demographic information, diagnosis, type of prescriber contact, and polypharmacy. Potential drug–drug interactions were determined with web-based multi-drug interaction checkers.

Results

The average (SD) number of diagnosed clinical problems was 3.54 ± 1.26, with hypertension, diabetes mellitus, and heart disease occurring most frequently. The level of polypharmacy was high with patients receiving an average (SD) of 12.13 ± 4.25 prescribed medicines from 3032 prescribed medicines. The level of polypharmacy was highest within the age categories, 60–64, and 70–74 years of age, respectively. The level of potential drug–drug interactions was also high with an average (SD) of 10.30 ± 7.48 from 2570 potential drug interactions. The majority of these interactions were moderate (72.5%) and pharmacodynamic (73.2%) by nature of the clinical severity of action and mechanism of action, respectively. Polypharmacy and type of prescriber contact were statistically significant contributors to the occurrence of potential drug–drug interactions, (F (2, 249) = 68.057, p < 0.05). However, in a multivariate analysis of variables to determine the strength of the association, polypharmacy was determined to be the strongest contributor to the occurrence of potential drug–drug interactions (p < 0.05) when compared with the type of prescriber contact (p value = 0.467). Therefore, irrespective of the type of prescriber contact, polypharmacy increases the potential for drug interactions among the sampled patient population.

Conclusion

A comprehensive consideration of disease management guidelines, patient factors, and rational medicine review could be measurable strategies towards improving medicine use. This would also limit the occurrence of significant drug interactions among the geriatric patient population. A national study is required to determine if differences occur across hospitals and regions.

Bradyarrhythmias in Cardio-Oncology

The relationship between bradyarrhythmias and cancer therapies has not been well described but is increasingly recognized. There have been extensive advances in oncological pharmacotherapy, with several new classes of drugs available including targeted agents, immune checkpoint inhibitors and CAR T cell therapy. This increasing repertoire of available drugs has revolutionized overall prognosis and survival of cancer patients but the true extent of their cardiovascular toxicity is only beginning to be understood. Previous studies and published reviews have traditionally focused on conventional chemotherapies and in arrhythmias in general, particularly tachyarrhythmias. The number of patients with both cancer and cardiovascular problems is increasing globally and oncologists and cardiologists need to be adept at managing arrythmia based scenarios. Greater collaboration between the two specialties including studies with prospective data collection in Cardio-Oncology are much needed to fill in knowledge gaps in this arena. This case-based review summarizes current available evidence of cancer treatment-related bradyarrhythmia incidence (including its different subtypes), possible mechanisms and outcomes. Furthermore, we propose a stepwise surveillance and management protocol for patients with suspected bradyarrhythmia related to cancer treatment.

Metabolism‑related pharmacokinetic drug‑drug interactions with poly (ADP‑ribose) polymerase inhibitors (Review)

Poly (ADP‑ribose) polymerase (PARP) inhibitors, including olaparib, niraparib, rucaparib, talazoparib and veliparib, have emerged as one of the most exciting new treatments for solid tumors, particularly in patients with breast‑related cancer antigen 1/2 mutations. Oral administration is convenient and shows favorable compliance with the majority of patients, but it may be affected by numerous factors, including food, metabolic enzymes and transporters. These interactions may be associated with serious adverse drug reactions or may reduce the treatment efficacy of PARP inhibitors. In fact, numerous pharmacokinetic (PK)‑based drug‑drug interactions (DDIs) involve the metabolism of PARP inhibitors, particularly those metabolized via cytochrome P450 enzymes. The present review aims to characterize and summarize the metabolism‑related PK‑based DDIs of PARP inhibitors, and to provide specific recommendations for reducing the risk of clinically significant DDIs.

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Drug-Drug Interactions in Patients with COVID-19: A Retrospective Study at a Tertiary Care Hospital in Eastern India

Introduction: Concomitant atrial fibrillation (AF) in non-ST segment elevation acute coronary syndrome (NSTE-ACS) patients complicates the decision-making process regarding short- and long-term antithrombotic strategies. Patient profiles and usage rates of different antithrombotic combinations in this patient subgroup in Romania are poorly described. Introduction: Coronavirus disease 2019 (COVID-19) is an emerging viral infection without any approved treatment. Investigational therapies for COVID-19 may cause clinically important drug-drug interactions (DDIs). We aimed to study drug-drug interactions (DDIs) and their risk factors in hospitalised COVID-19 patients. Methods: We conducted a retrospective study in a tertiary care hospital dedicated to COVID-19 patients. The Lexi-Interact database was used to investigate clinically important DDIs. The database output, including interacting drug pairs, risk rating, reliability rating, mechanism, and management, was evaluated. Results: Medical records of 200 COVID-19 patients were analysed. All patients had at least one clinically important DDI. More than half of interactions were associated with hydroxychloroquine and azithromycin, the most commonly prescribed medications for the management of COVID-19. Concomitant drugs for comorbid conditions leading to polypharmacy were significantly associated with the occurrence of this. Conclusion: There is a higher chance of DDI, which necessitates ongoing care evaluation and therapy adjustment. Drugs used to treat COVID-19 should be carefully selected.

Prediction of potential drug interactions between repurposed COVID-19 and antitubercular drugs: an integrational approach of drug information software and computational techniques data

Introduction:

Tuberculosis is a major respiratory disease globally with a higher prevalence in Asian and African countries than rest of the world. With a larger population of tuberculosis patients anticipated to be co-infected with COVID-19 infection, an ongoing pandemic, identifying, preventing and managing drug–drug interactions is inevitable for maximizing patient benefits for the current repurposed COVID-19 and antitubercular drugs.

Methods:

We assessed the potential drug–drug interactions between repurposed COVID-19 drugs and antitubercular drugs using the drug interaction checker of IBM Micromedex®. Extensive computational studies were performed at a molecular level to validate and understand the drug–drug interactions found from the Micromedex drug interaction checker database at a molecular level. The integrated knowledge derived from Micromedex and computational data was collated and curated for predicting potential drug–drug interactions between repurposed COVID-19 and antitubercular drugs.

Results:

A total of 91 potential drug–drug interactions along with their severity and level of documentation were identified from Micromedex between repurposed COVID-19 drugs and antitubercular drugs. We identified 47 pharmacodynamic, 42 pharmacokinetic and 2 unknown DDIs. The majority of our molecular modelling results were in line with drug–drug interaction data obtained from the drug information software. QT prolongation was identified as the most common type of pharmacodynamic drug–drug interaction, whereas drug–drug interactions associated with cytochrome P450 3A4 (CYP3A4) and P-glycoprotein (P-gp) inhibition and induction were identified as the frequent pharmacokinetic drug–drug interactions. The results suggest antitubercular drugs, particularly rifampin and second-line agents, warrant high alert and monitoring while prescribing with the repurposed COVID-19 drugs.

Conclusion:

Predicting these potential drug–drug interactions, particularly related to CYP3A4, P-gp and the human Ether-à-go-go-Related Gene proteins, could be used in clinical settings for screening and management of drug–drug interactions for delivering safer chemotherapeutic tuberculosis and COVID-19 care. The current study provides an initial propulsion for further well-designed pharmacokinetic-pharmacodynamic-based drug–drug interaction studies.

Plain Language Summary

Assessment of factors associated with potential drug-drug interactions in patients with tuberculosis and HIV/AIDS

INTRODUCTION:

The concomitant use of antituberculosis and antiretroviral drugs, as well as drugs to treat other diseases, can cause drug-drug interactions. This study aimed to describe potential drug-drug interactions (pDDI) in patients with TB and HIV/AIDS co-infection, as well as to analyze possible associated factors.

METHODS:

This study was performed in a reference hospital for infectious and contagious diseases in the southeastern region of Brazil and evaluated adult patients co-infected with tuberculosis and HIV/AIDS. A cross-sectional study was conducted in which sociodemographic, clinical, and pharmacotherapeutic characteristics were assessed. The pDDI were identified using the Drug-Reax software. Association analysis was performed using either a chi-squared test or a Fisher’s exact test. Correlation analysis was performed using the Spearman’s coefficient.

RESULTS:

The study included 81 patients, of whom 77 (95.1%) were exposed to pDDI. The most frequent interactions were between antituberculosis and antiretroviral drugs, which can cause therapeutic ineffectiveness and major adverse reactions. A positive correlation was established between the number of associated diseases, the number of drugs used, and the number of pDDI. An association was identified between contraindicated and moderate pDDI with excessive polypharmacy and hospitalization.

CONCLUSIONS:

We found a high frequency of pDDI, especially among those hospitalized and those with excessive polypharmacy. These findings highlight the importance of pharmacists in the pharmacotherapeutic monitoring in these patients.

Cognitive Computing-Based CDSS in Medical Practice

Importance. The last decade has witnessed the advances of cognitive computing technologies that learn at scale and reason with purpose in medicine studies. From the diagnosis of diseases till the generation of treatment plans, cognitive computing encompasses both data-driven and knowledge-driven machine intelligence to assist health care roles in clinical decision-making. This review provides a comprehensive perspective from both research and industrial efforts on cognitive computing-based CDSS over the last decade.Highlights. (1) A holistic review of both research papers and industrial practice about cognitive computing-based CDSS is conducted to identify the necessity and the characteristics as well as the general framework of constructing the system. (2) Several of the typical applications of cognitive computing-based CDSS as well as the existing systems in real medical practice are introduced in detail under the general framework. (3) The limitations of the current cognitive computing-based CDSS is discussed that sheds light on the future work in this direction.Conclusion. Different from medical content providers, cognitive computing-based CDSS provides probabilistic clinical decision support by automatically learning and inferencing from medical big data. The characteristics of managing multimodal data and computerizing medical knowledge distinguish cognitive computing-based CDSS from other categories. Given the current status of primary health care like high diagnostic error rate and shortage of medical resources, it is time to introduce cognitive computing-based CDSS to the medical community which is supposed to be more open-minded and embrace the convenience and low cost but high efficiency brought by cognitive computing-based CDSS.

Sodium-glucose cotransporter 2 inhibitor-induced euglycaemic diabetic ketoacidosis in heart failure with preserved ejection fraction

The number of patients receiving sodium-glucose cotransporter 2 inhibitors (SGLT2is), especially those with heart failure, is increasing worldwide. SGLT2is control glycaemia by triggering glycosuria with simultaneous facilitation of a more ketogenic metabolic profile. Patients therefore are more prone to develop euglycaemic diabetic ketoacidosis (euDKA), an entity largely unknown beyond diabetes care professionals. We present a heart failure with preserved ejection fraction (HFpEF) patient with known Type 2 diabetes. He was treated with dapagliflozin and presented acutely with dyspnoea, hyperglycaemia, and ketoacidosis. After standard treatment for diabetic ketoacidosis, hyperglycaemia was corrected, while metabolic ketoacidosis persisted, and thus, euDKA was suspected. With adequate therapy, the patient recovered completely and was discharged without any sequelae. To the best of our knowledge, our case is the first to describe SGLT2i-induced euDKA in HFpEF patients. Regarding no previous reports of euDKA in heart failure with reduced ejection fraction, our report is highly relevant for ongoing SGLT2i trials in HFpEF and clinical practice in general.

A comprehensive review of integrative pharmacology-based investigation: A paradigm shift in traditional Chinese medicine

Over the past decade, traditional Chinese medicine (TCM) has widely embraced systems biology and its various data integration approaches to promote its modernization. Thus, integrative pharmacology-based traditional Chinese medicine (TCMIP) was proposed as a paradigm shift in TCM. This review focuses on the presentation of this novel concept and the main research contents, methodologies and applications of TCMIP. First, TCMIP is an interdisciplinary science that can establish qualitative and quantitative pharmacokinetics-pharmacodynamics (PK-PD) correlations through the integration of knowledge from multiple disciplines and techniques and from different PK-PD processes in vivo. Then, the main research contents of TCMIP are introduced as follows: chemical and ADME/PK profiles of TCM formulas; confirming the three forms of active substances and the three action modes; establishing the qualitative PK-PD correlation; and building the quantitative PK-PD correlations, etc. After that, we summarize the existing data resources, computational models and experimental methods of TCMIP and highlight the urgent establishment of mathematical modeling and experimental methods. Finally, we further discuss the applications of TCMIP for the improvement of TCM quality control, clarification of the molecular mechanisms underlying the actions of TCMs and discovery of potential new drugs, especially TCM-related combination drug discovery.

Clinical pharmacology and drug-drug interactions of lenvatinib in thyroid cancer

Lenvatinib is a non-selective tyrosine kinase inhibitor (TKI) with high in vitro potency against vascular endothelial growth factor receptors. Although this drug is used to treat several cancer types, it is the most effective TKI used in patients with thyroid cancer. Lenvatinib is well tolerated and the most common adverse drug reactions can be adequately managed by dose adjustment. Particularly, blood pressure and cardiac function monitoring, as well as antihypertensive treatment optimization, may be required in patients treated with lenvatinib. Dose reduction should be taken into account in patients with body weight <60 kg or severe hepatic failure. No significant change in lenvatinib pharmacokinetics has been observed with other patient-related factors and very few data are available on lenvatinib pharmacogenetics. Lenvatinib can be administered orally regardless of food and no clinically relevant drug-drug interactions have been reported.

Real-life drug-drug and herb-drug interactions in outpatients taking oral anticancer drugs: comparison with databases

PURPOSE

Due to polypharmacy and the rising popularity of complementary and alternative medicines (CAM), oncology patients are particularly at risk of drug-drug interactions (DDI) or herb-drug interactions (HDI). The aims of this study were to assess DDI and HDI in outpatients taking oral anticancer drug.

METHOD

All prescribed and non-prescribed medications, including CAM, were prospectively collected by hospital pharmacists during a structured interview with the patient. DDI and HDI were analyzed using four interaction software programs: Thériaque^®, Drugs.com^®, Hédrine, and Memorial Sloan Kettering Cancer Center (MSKCC) database. All detected interactions were characterized by severity, risk and action mechanism. The need for pharmaceutical intervention to modify drug use was determined on a case-by-case basis.

RESULTS

294 patients were included, with a mean age of 67 years [55-79]. The median number of chronic drugs per patient was 8 [1-29] and 55% of patients used at least one CAM. At least 1 interaction was found for 267 patients (90.8%): 263 (89.4%) with DDI, 68 (23.1%) with HDI, and 64 (21.7%) with both DDI and HDI. Only 13% of the DDI were found in Thériaque^® and Drugs.com^® databases, and 125 (2.5%) were reported with similar level of risk on both databases. 104 HDI were identified with only 9.5% of the interactions found in both databases. 103 pharmaceutical interventions were performed, involving 61 patients (20.7%).

CONCLUSION

Potentially clinically relevant drug interaction were frequently identified in this study, showing that several databases and structured screening are required to detect more interactions and optimize medication safety.

Drug-Associated QTc Prolongation in Geriatric Hospitalized Patients: A Cross-Sectional Study in Internal Medicine

Objective

The primary objectives of this prospective cross-sectional study were to estimate the prevalence of drug-related long QT syndrome (LQTS) and the prevalence of use of QT-prolonging drugs in older patients admitted to an internal medicine unit.

Methods

We screened consecutive patients hospitalized in an internal medicine unit over a 2-year period. A 12-lead electrocardiogram using an electrocardiograph with automated measurement of QT interval was recorded. Patient characteristics (age, sex, body mass index), drug treatments, and variables associated with QT interval prolongation, including hypothyroidism, type 2 diabetes mellitus, and cardiac disease, were also recorded. In addition, we also measured serum levels of potassium, calcium, magnesium, and creatinine at admission. The list of medications known to cause or to contribute to LQTS was obtained from CredibleMeds^®.

Results

A total of 243 patients were enrolled: mean ± standard deviation age, 79.65 ± 8.27 years; males, n = 121 (40.8%); mean corrected QT (QTc) interval, 453.70 ± 43.77 ms. Overall, 89/243 (36.6%) patients had a prolonged QTc interval, with 29/243 (11.9%) having QTc interval prolongation > 500 ms (11.9%). A vast majority were prescribed at least one QT-prolonging drug (218/243 [89.7%]), whereas 74/218 (30.5%) were receiving at least one medication with a known risk of Torsades des Pointes (TdP). Proton pump inhibitors were the second most commonly prescribed class of drugs. After logistic regression, male sex was independently associated with LQTS (odds ratio 2.85; 95% confidence interval 1.56–5.22; p = 0.001).

Conclusions

The prevalence of LQTS with QTc interval > 500 ms in geriatric inpatients was > 10%, and QT-prolonging drugs were frequently used on admission (more than 30% of patients were receiving drugs with a known risk of TdP).

Supplementary Information

The online version contains supplementary material available at 10.1007/s40801-021-00234-x.

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

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

Polypharmacy and severe potential drug-drug interactions among older adults with cardiovascular disease in the United States

BACKGROUND

Polypharmacy continues to be a topic of concern among older adults and puts patients at increased risk of potential drug-drug interactions (DDIs) and negative health outcomes. The objective of this study was to assess the prevalence of polypharmacy among older adults with cardiovascular disease (CVD) and to identify severe potential DDIs.

METHODS

A retrospective chart review was conducted in a tertiary care center over a three-month period where we reviewed home medications of older adults upon hospital admission. Inclusion criteria were age ≥ 65 years, history of CVD, and admission to the cardiology service. Polypharmacy was defined as 5 or more medications taken concomitantly, hyper-polypharmacy was defined as 10 or more medications taken concomitantly, and severe potential DDIs were considered to be those belonging to category D or X using Lexicomp® Drug Information Handbook. Category D interaction states that modification of therapy should be considered while category X states that the combination should be absolutely avoided.

RESULTS

A total of 404 patients with a mean age of 76.6 ± 7.4 years were included. Patients were taking an average of 11.6 ± 4.5 medications at home and 385 (95%) received polypharmacy, 278 (69%) received hyper-polypharmacy, and 313 (77.5%) had at least one severe potential DDI. Under category D, the most common potential DDIs were drugs with additive central nervous system (CNS) depressant effect and drugs that increase the risk of QT prolongation. Under category X, the most common potential DDIs were non-selective β-blockers that may diminish the bronchodilator effect of β2 agonists and drugs with anticholinergic properties that enhance the ulcerogenic effect of oral solid potassium.

CONCLUSIONS

Polypharmacy, hyper-polypharmacy, and severe potential DDIs are very common in older adults with CVD. Clinicians should vigilantly review patients' drug records and adjust therapy accordingly to prevent adverse drug reactions and negative health outcomes.

Effects of CYP3A inhibitors ketoconazole, voriconazole, and itraconazole on the pharmacokinetics of sunitinib and its main metabolite in rats

Sunitinib is a small molecule inhibitor of multiple receptor tyrosine kinases such as platelet derived growth factor receptor, vascular endothelial growth factor receptor, kit receptor and other receptors. The US Food and Drug Administration (FDA) has approved sunitinib for the treatment of advanced renal cell carcinoma and gastrointestinal stromal tumors. It has been reported that sunitinib was mainly metabolized by CYP3A but its pharmacokinetic interactions have not been revealed. In this study, we investigated whether CYP3A inhibitors (ketoconazole, voriconazole, and itraconazole) could influence the pharmacokinetics of sunitinib and its equipotent metabolite N-desethyl sunitinib in a drug-drug interaction study in Sprague Dawley (SD) rats. The results showed that ketoconazole and voriconazole significantly increased the exposure of sunitinib, decreased the exposure of N-desethyl sunitinib, and inhibited the metabolism of sunitinib in rats. However, itraconazole showed only a weak effect on pharmacokinetics and metabolism. Coadministration of sunitinib with ketoconazole and voriconazole should be avoided if possible or if not, there should be therapeutic drug monitoring of the levels of sunitinib and N-desethyl sunitinib. Therefore, drug-drug interaction should be considered when sunitinib is administered in conjunction with CYP3A inhibitors, which might lead to toxicity.

Current Knowledge about Providing Drug-Drug Interaction Services for Patients-A Scoping Review

Drug-drug interactions (DDIs) pose a major problem to patient safety. eHealth solutions have the potential to address this problem and generally improve medication management by providing digital services for health care professionals and patients. Clinical decision support systems (CDSS) to alert physicians or pharmacists about DDIs are common, and there is an extensive body of research about CDSS for professionals. Information about DDIs is commonly requested by patients, but little is known about providing similar support to patients. The aim of this scoping review was to explore and describe current knowledge about providing digital DDI services for patients. Using a broad search strategy and an established framework for scoping reviews, 19 papers were included. The results show that although some patients want to check for DDIs themselves, there are differences between patients, in terms of demands and ability. There are numerous DDI services available, but the existence of large variations regarding service quality implies potential safety issues. The review includes suggestions about design features but also indicates a substantial knowledge gap highlighting the need for further research about how to best design and provide digital DDI to patients without risking patient safety or having other unintended consequences.

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

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

Does DDI-Predictor Help Pharmacists to Detect Drug-Drug Interactions and Resolve Medication Issues More Effectively?

The characterization of drug-drug interactions (DDIs) may require the use of several different tools, such as the thesaurus issued by our national health agency (i.e., ANSM), the metabolic pathways table from the Geneva University Hospital (GUH), and DDI-Predictor (DDI-P). We sought to (i) compare the three tools’ respective abilities to detect DDIs in routine clinical practice and (ii) measure the pharmacist intervention rate (PIR) and physician acceptance rate (PAR) associated with the use of DDI-P. The three tools’ respective DDI detection rates (in %) were measured. The PIRs and PARs were compared by using the area under the curve ratio given by DDI-P (R_AUC) and applying a chi-squared test. The DDI detection rates differed significantly: 40.0%, 76.5%, and 85.2% for ANSM (The National Agency for the Safety of Medicines and Health Products), GUH and DDI-P, respectively (p < 0.0001). The PIR differed significantly according to the DDI-P’s R_AUC: 90.0%, 44.2% and 75.0% for R_AUC ≤ 0.5; R_AUC 0.5–2 and R_AUC > 2, respectively (p < 0.001). The overall PAR was 85.1% and did not appear to depend on the R_AUC category (p = 0.729). Our results showed that more pharmacist interventions were issued when details of the strength of the DDI were available. The three tools can be used in a complementary manner, with a view to refining medication adjustments.

A Minimal Information Model for Potential Drug-Drug Interactions

Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.