Drug-drug Interaction Screening Research Articles

Review of drug-drug interactions in patients with prostate cancer.

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. 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. 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. 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.

Detection and management of clinically relevant drug-drug interactions with direct oral anticoagulants: an intervention study in community pharmacies

IntroductionDirect oral anticoagulants (DOACs) are increasingly used and can be involved in clinically relevant drug-drug interactions (DDIs) that increase the risk of major bleeding or thromboembolism. Skilled drug interaction management is essential to ensure safe and effective use of DOACs. In this study, we aimed to investigate the impact of the detection and management of DDIs with DOACs in a real-life community pharmacy setting on the pharmacotherapy of DOAC users. MethodsWe conducted an intervention study in 201 community pharmacies in Belgium. On random days, patients purchasing DOACs or drugs known to interact with them were screened. When a DDI with the DOAC was detected, the pharmacist contacted the prescribing physician to discuss the management of the interaction. A previously developed practice-oriented DDI list accompanied by management plans for ambulatory care was used for both screening and management of the DDIs. ResultsIn total, 751 patients were included, among whom 875 DDIs were identified, primarily pharmacodynamic DDIs (95.7 %). Predominant interacting drug classes included selective serotonin or serotonin and norepinephrine reuptake inhibitors (32.9 %), antiplatelets (30.9 %), and non-steroidal anti-inflammatory drugs (28.9 %). In 43.0 % of DDIs, an intervention was decided upon. At three-month follow-up, proposed pharmacotherapy changes had been implemented in 79.1 % of these DDIs. ConclusionsThis study demonstrates that active screening and management of DDIs with DOACs in community pharmacies, in close collaboration with prescribing physicians, resulted in changes in pharmacotherapy in a substantial number of patients. This may contribute significantly to the safer utilisation of DOACs in high-risk populations.

Detection and management of clinically relevant drug-drug interactions with direct oral anticoagulants in ambulatory care: a prospective interventional study in community pharmacies

Abstract Background/Introduction Direct oral anticoagulants (DOACs) are increasingly used but can be involved in clinically relevant drug-drug interactions (DDIs) that increase the risk of major bleeding or thromboembolism. Community pharmacists, who have access to patients’ complete medication profiles, including over-the-counter medication and medication from different prescribers, can play a crucial role in detecting and managing DDIs with DOAC in ambulatory care. Purpose This study aimed to detect and manage DDIs with DOAC in ambulatory care, because such real-life clinical practice studies are currently lacking. Methods A prospective interventional study was conducted between October 2021 and August 2022 in 201 community pharmacies in Belgium. On random days, every patient purchasing a DOAC or an interacting drug was screened for the presence of a DDI with DOAC. The pharmacist contacted the physician to discuss the management of the interaction. For the screening and management of DDI with DOAC, a previously developed practice-oriented DDI list for ambulatory care was used [1]. Results In total, 751 patients (74.5±10.7 years) were included, in whom 875 DDIs were identified. The three most commonly involved interacting drugs were (selective) serotonin (norepinephrine) reuptake inhibitors (32.9%), antiplatelets (30.9%), and non-steroidal anti-inflammatory drugs (28.9%). Most DDIs (83.1%) were not new (patients were already using these drugs) and 8.7% of interacting drugs were dispensed over-the-counter. In almost one-third of DDIs (28.0%), the interacting drug and the DOAC were prescribed by different physicians. Prescribers rated the clinical risks of the identified DDIs as high (15.9%), moderate (33.1%), and low (38.7%), whereas 12.3% were unaware of the potential risks. Remarkably, in 18 patients who were concomitantly taking an antiplatelet and DOAC, the prescriber reported forgetting to stop the antiplatelet or not being aware that the patient was taking both medications. Three patients were also found to be using another anticoagulant along with DOAC due to miscommunication between prescribers. When discussing how to manage the DDI, pharmacotherapy was mostly unchanged (60.2%). Physicians cited reasons such as the interacting drug being deemed necessary (37.2%), the patient already using a proton pump inhibitor (PPI) (19.5%), or the patient using both interacting drugs already for a long time without problems (18.6%). However, in 39.8% of cases, the discussion resulted in a pharmacotherapy change. The most common changes included stopping the interacting drug (21.5%), initiating a PPI (17.6%), or replacing the interacting drug with a safer alternative (14.5%). Conclusion This study illustrated that community pharmacists, in collaboration with physicians, can contribute to reducing avoidable patient harm from DDIs with DOACs.

MM-GANN-DDI: Multimodal Graph-Agnostic Neural Networks for Predicting Drug–Drug Interaction Events

Personalized treatment of complex diseases relies on combined medication. However, the occurrence of unexpected drug–drug interactions (DDIs) in these combinations can lead to adverse effects or even fatalities. Although recent computational methods exhibit promising performance in DDI screening, their practical implementation faces two significant challenges: (i) the availability of comprehensive datasets to support clinical application, and (ii) the ability to infer DDI types for new drugs beyond the existing dataset coverage. To mitigate these challenges, we propose MM-GANN-DDI: a Multimodal Graph-Agnostic Neural Network for Predicting Drug–Drug Interaction Events. We first mine six drug modalities and incorporate a graph attention (GAT) mechanism to fuse these modalities with the topological features of the DDI graph. We further propose a novel graph neural network training mechanism called graph-agnostic meta-training (GAMT), which effectively leverages topological information from the DDI graph and efficiently predicts DDI types for new drugs beyond the available dataset. Specifically, GAMT samples meta-graphs from the original DDI graph, splitting them into support and query sets to simulate seen and unseen drugs. Two-level optimizations are applied to enhance the model’s generalization capability.We evaluate our model on two datasets (DB-v1 and DB-v2) across three tasks. Our MM-GANN-DDI demonstrates competitive performance on all three tasks. Notably, in Task 2, which focuses on predicting DDI types for drugs outside the dataset, our proposed model outperforms other methods, exhibiting an improvement of 4.6 percentage points in AUPR on DB-v1 and 5.9 percentage points on DB-v2. Additionally, our model surpasses state-of-the-art methods and classic approaches in terms of accuracy, F1 score, precision, and recall. Ablation experiments provide further validation of the effectiveness of the proposed model design. Importantly, our model exhibits the potential to discover unobserved DDIs, demonstrating its practical application in clinical medication.

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

Aim This study aimed tocritically 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.

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.

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.

Primary care electronic medication record discrepancies in patients starting treatment at a hospital-based ambulatory care pharmacy and impact on prevalence of potential drug–drug interactions

ObjectiveOur objective was to evaluate the prevalence of discrepancies between primary care electronic medication records (EMR) and patient reported medication (PRM) in ambulatory patients starting a hospital dispensing treatment (HDT)...

Potential drug\u2013drug interactions in patients with cardiovascular diseases: findings from a prospective observational study

BackgroundPatients with cardiovascular diseases (CVD) are at high risk of experiencing drug–drug interactions (DDIs). The objective of this study was to evaluate the frequency, level and risk factors associated with potential-DDIs (pDDIs) in hospitalized CVD patients at cardiology departments of two tertiary care hospitals in Quetta, Pakistan.MethodsIn the current prospective observational study, a total of 300 eligible CVD inpatients were evaluated for pDDIs using Lexicomp Interact®. The pDDIs were classified into class A (no known interaction); B (no action needed); C (monitor therapy: it is documented that the benefits of an interaction outweigh the risk, appropriately monitor therapy in order to avoid potential adverse outcomes); D (consider therapy modification: it is documented that proper actions must be taken to reduce the toxicity resulting from an interaction); X (avoid combination: the risk of an interaction outweighs the benefits and are usually contraindicated). Multivariate binary logistic regression analysis was used to find factors associated with the presence of Class-D and/or X pDDIs. A p-value < 0.05 was considered statistically significant.ResultsWith a median of 8.50 pDDIs per patient, all patients (100%) had ≥ 1 pDDIs. Out of total 2787 pDDIs observed, 74.06% (n = 2064) were of moderate and (n = 483) 17.33% of major severity. Class C pDDIs were most common (n = 1971, 70.72%) followed by D (n = 582, 20.88%), B (n = 204, 7.32%) and X (n = 30, 1.08%). Suffering from cardiovascular diseases other than myocardial infarction (OR 0.053, p-value < 0.001) and receiving > 12 drugs (OR 4.187, p-value = 0.009) had statistical significant association with the presence of class D and/or X pDDIs.ConclusionIn the current study, pDDIs were highly prevalent. The inclusion of DDI screening tools, availability of clinical pharmacists and paying special attention to the high-risk patients may reduce the frequency of pDDIs at the study sites.

Evaluation of serious bleeding signals during concomitant use of clopidogrel and hypnotic drugs

BackgroundIn a previous drug-drug interaction (DDI) screening study intended to generate hypotheses, clopidogrel + either eszopiclone or zolpidem (vs. clopidogrel alone) were associated with serious bleeding. ObjectivesTo confirm or refute these DDI signals and examine associations with other hypnotics in an independent population of United States Medicaid beneficiaries MethodsWe employed a bi-directional self-controlled case series design in eligible individuals concomitantly exposed to one of 12 hypnotics (precipitants, exposures of interest) plus either clopidogrel (the object drug) or pravastatin (the negative control object drug). The outcome was hospital presentation with serious bleeding. Using conditional Poisson regression, we calculated confounder-adjusted rate ratios (RRs) and 95% confidence intervals for serious bleeding during clopidogrel + precipitant use (vs. clopidogrel alone). To distinguish a DDI from a precipitant’s inherent effect on bleeding, we divided effect measures by the adjusted RR for the corresponding pravastatin + precipitant pair to obtain ratios of RR (RRRs). ResultsAmong 23,194 users of clopidogrel and 3824 of pravastatin who experienced serious bleeding during an active prescription for one of these agents, confounder-adjusted RRRs for serious bleeding were 6.63 (0.39–113.01) and 0.77 (0.53–1.11) with eszopiclone and zolpidem, respectively, whereas confounder-adjusted RRRs for other hypnotics ranged from 0.18 (0.04–0.85) for triazolam to 1.79 (0.16–20.44) for zaleplon. Statistical imprecision therefore precluded us from confirming or refuting these prior signals with eszopiclone and zolpidem. ConclusionsWhile we could not confirm or refute previously identified DDI signals, numerically elevated RRRs for serious bleeding with several clopidogrel + hypnotic pairs warrant further examination.

Drug-drug interactions in subjects enrolled in SWOG trials of oral chemotherapy

BackgroundPatients with cancer are at increased risk of drug-drug interactions (DDI), which can increase treatment toxicity or decrease efficacy. It is especially important to thoroughly screen DDI in oncology clinical trial subjects to ensure trial subject safety and data accuracy. This study determined the prevalence of potential DDI involving oral anti-cancer trial agents in subjects enrolled in two SWOG clinical trials.MethodsCompleted SWOG clinical trials of commercially available agents with possible DDI that had complete concomitant medication information available at enrollment were included. Screening for DDI was conducted through three methods: protocol-guided screening, Lexicomp® screening, and pharmacist determination of clinical relevance. Descriptive statistics were calculated.ResultsSWOG trials S0711 (dasatinib, n = 83) and S0528 (everolimus/lapatinib, n = 84) were included. Subjects received an average of 6.6 medications (standard deviation = 4.9, range 0–29) at enrollment. Based on the clinical trial protocols, at enrollment 18.6% (31/167) of subjects had a DDI and 12.0% (20/167) had a DDI that violated a protocol exclusion criterion. According to Lexicomp®, 28.7% of subjects (48/167) had a DDI classified as moderate or worse, whereas pharmacist review indicated that 7.2% of subjects (12/167) had a clinically relevant interaction. The majority of clinically relevant DDI identified were due to the coadministration of acid suppression therapies with dasatinib (83.3%, 10/12).ConclusionsThe high DDI prevalence in subjects enrolled on SWOG clinical trials, including a high prevalence that violate trial exclusion criteria, support the need for improved processes for DDI screening to ensure trial subject safety and trial data accuracy.

DDIWAS: High-throughput electronic health record-based screening of drug-drug interactions.

We developed and evaluated Drug-Drug Interaction Wide Association Study (DDIWAS). This novel method detects potential drug-drug interactions (DDIs) by leveraging data from the electronic health record (EHR) allergy list. To identify potential DDIs, DDIWAS scans for drug pairs that are frequently documented together on the allergy list. Using deidentified medical records, we tested 616 drugs for potential DDIs with simvastatin (a common lipid-lowering drug) and amlodipine (a common blood-pressure lowering drug). We evaluated the performance to rediscover known DDIs using existing knowledge bases and domain expert review. To validate potential novel DDIs, we manually reviewed patient charts and searched the literature. DDIWAS replicated 34 known DDIs. The positive predictive value to detect known DDIs was 0.85 and 0.86 for simvastatin and amlodipine, respectively. DDIWAS also discovered potential novel interactions between simvastatin-hydrochlorothiazide, amlodipine-omeprazole, and amlodipine-valacyclovir. A software package to conduct DDIWAS is publicly available. In this proof-of-concept study, we demonstrate the value of incorporating information mined from existing allergy lists to detect DDIs in a real-world clinical setting. Since allergy lists are routinely collected in EHRs, DDIWAS has the potential to detect and validate DDI signals across institutions.

Evaluation of an optimized context-aware clinical decision support system for drug-drug interaction screening

ObjectiveEvaluation of the effect of six optimization strategies in a clinical decision support system (CDSS) for drug-drug interaction (DDI) screening on alert burden and alert acceptance and description of clinical pharmacist intervention acceptance. MethodsOptimizations in the new CDSS were the customization of the knowledge base (with addition of 67 extra DDIs and changes in severity classification), a new alert design, required override reasons for the most serious alerts, the creation of DDI-specific screening intervals, patient-specific alerting, and a real-time follow-up system of all alerts by clinical pharmacists with interventions by telephone was introduced. The alert acceptance was evaluated both at the prescription level (i.e. prescription acceptance, was the DDI prescribed?) and at the administration level (i.e. administration acceptance, did the DDI actually take place?). Finally, the new follow-up system was evaluated by assessing the acceptance of clinical pharmacist’s interventions. ResultsIn the pre-intervention period, 1087 alerts (92.0 % level 1 alerts) were triggered, accounting for 19 different DDIs. In the post-intervention period, 2630 alerts (38.4 % level 1 alerts) were triggered, representing 86 different DDIs. The relative risk forprescription acceptance in the post-intervention period compared to the pre-intervention period was 4.02 (95 % confidence interval (CI) 3.17–5.10; 25.5 % versus 6.3 %). The relative risk for administration acceptance was 1.16 (95 % CI 1.08–1.25; 54.4 % versus 46.7 %). Finally, 86.9 % of the clinical pharmacist interventions were accepted. ConclusionSix concurrently implemented CDSS optimization strategies resulted in a high alert acceptance and clinical pharmacist intervention acceptance. Administration acceptance was remarkably higher than prescription acceptance.

Sensitivity and specificity of drug interaction databases to detect interactions with recently approved oral antineoplastics.

Drug-drug interactions (DDIs) with oral antineoplastics (OAs) are of increasing concern given the rapid increase in OA approvals and use in cancer patients. A small pilot study of 20 DDIs with OAs showed significant variability in commonly used DDI screening databases in sensitivity of detecting potentially clinically relevant DDIs. This study builds upon that work by expanding the number of potential DDIs analyzed and including a specificity analysis. Newly approved OAs from 2016 to May 2019 (n = 22) were included in this analysis. Prescribing information for each drug was reviewed. A list of explicit and theoretical drug interactions was created for each OA by the two investigators. A board-certified oncology pharmacist adjudicated all DDI pairs for potential clinical significance. In total, 229 DDI pairs were used to analyze sensitivity of 5 DDI databases (Lexicomp®, Micromedex®, Medscape, Eporactes®, & Drugs.com). Additionally, 64 "dummy" or false DDI pairs were created to analyze specificity. Sensitivity and specific were analyzed using Cochran's Qtest, while accuracy was analyzed using chi-square test. There was significant variability among the databases with regards to sensitivity (p < 0.0001), specificity (p < 0.0001), and accuracy (p < 0.0001). In terms of accuracy (max score = 400), Lexicomp®(355), Epocrates® (344), and Drugs.com (352) scored higher than MicroMedex® (270) and Medscape (280). Considerable variability exists among DDI screening databases with regards to OAs and potential drug interactions. Clinicians should be vigilant in both screening for DDIs with OAs and describing DDIs encountered in clinical practice.

Drug–drug interactions in neonatal intensive care: A prospective cohort study

To characterize the prevalence and profile of drug-drug interactions (DDIs), the drugs most related to major DDIs and risk factors of their prescription in a neonatal intensive care unit (NICU). Neonates admitted to a NICU who had at least one medication prescribed and a hospital stay >24h were included in a prospective cohort study (August 2017 to July 2018). All medications prescribed during the hospitalization were collected from all neonates (n=220), with the screening for DDIs. Prevalence and type of DDIs was identified. Network analysis was used to identify the drugs more implicated with DDIs. Logistic regression was used for the analysis of risk factors (p<0.05). Over 70% of neonates were exposed to DDIs and 29% were exposed to major DDIs. The network analysis identified furosemide, fentanyl, aminophylline and fluconazole as most implicated with DDI, fentanyl was especially associated with major DDIs. The number of drugs (OR 1.60, p<0.01), caesarean delivery (OR 2.68, p<0.05), gestational age (OR 1.03, p<0.01) and APGAR score (OR 0.78, p<0.01) were identified as risk factors for exposure to DDI. Neonates in intensive care have a high exposure to DDIs and the occurrence of major DDIs is related specifically to the prescription of fentanyl. The number of prescribed drugs, gestational age, cesarean delivery and low APGAR score in the first minute were identified as risk factors for DDIs in NICU.

Improvement Initiative to Develop and Implement a Tool for Detecting Drug-Drug Interactions During Oncology Clinical Trial Enrollment Eligibility Screening.

Screening subjects for drug-drug interactions (DDIs) before enrollment in oncology clinical trials is integral to ensuring safety, but standard procedures or tools are not readily available to screen DDI in this setting. Our objectives were to develop a DDI screening tool for use during oncology clinical trial enrollment and to test usability in single-center and multicenter pilot studies. A multistage approach was used for this quality improvement intervention. Semistructured interviews with individuals responsible for DDI screening were conducted to develop a prototype tool. The tool was used for screening DDI in subjects enrolling in National Clinical Trials Network trials of commercially available agents during a single-center 3-month pilot. Improvements were made, and a 3-month multicenter pilot was conducted at volunteer SWOG Cancer Research Network sites. Participants were surveyed to determine tool usability and efficiency. A tool was developed from semistructured interviews. A critical feature was reporting which medications had specific pharmacokinetic and pharmacodynamic characteristics including transporter and cytochrome P450 substrates, inhibitors, or inducers and QT prolongation. In the 12-site study, average (SD) DDI screening time for each patient decreased by 15.7 (10.2) minutes (range, 3-35 minutes; P < 0.001). Users reported the tool highly usable, with >90% agreeing with all positive usability characterizations and disagreeing with all negative complexity characterizations. A DDI screening tool for oncology clinical trial enrollment was created and its usability confirmed. Further testing with more diverse investigator sites and study drugs during eligibility screening is warranted to improve safety and data accuracy within clinical trials.

Evaluation of knowledge, attitude and practice of healthcare providers towards life-threatening drug-drug interactions in Penang General Hospital, Malaysia

IntroductionThe evaluation of knowledge, attitude, and practice (KAP) of health care providers on the life-threatening drug-drug interactions (DDIs) and the implementation of specific interventions may enhance patients’ safety. The current study was designed to evaluate the KAP of healthcare providers (HCPs) on life-threatening DDIs using different drug information sources. MethodsValidated questionnaire contains demographic data, knowledge and practice questions related to the life-threatening DDIs, and attitude towards the preferable sources of drug information. The survey forms were distributed to pharmacists as well as ward doctors in departments of cardiology, nephrology, neurology, and infectious diseases, in Penang General Hospital, Penang, Malaysia. ResultsThe overall response rate for 220 distributed questionnaires was 65.45%. Knowledge level is associated with healthcare specialty (p < 0.001), with 100% houseman officer having low knowledge, years of experience (p < 0.05) (50% of the HCPs with11–15 years of experience had high knowledge) and postgraduate study (p < 0.05), which 78.2% of HCPs without postgraduate studies had a low knowledge level. An association was obtained between knowledge level and attitude towards the use of computerized interaction checker (25% and 66.7% of respondents with high knowledge level had agreed and strongly agreed, respectively, to use computerized interaction checker, p < 0.05). Respondents with high knowledge level had agreed (33%) and strongly agreed (33%) to practice screening of DDIs at admission, p < 0.05. ConclusionPrescribers may have insufficient knowledge to avoid potential life-threatening DDIs. These findings affirm the necessity of well-designed computerized alerting systems, in addition to highlighting on pharmacists’ role as the competent source of drug information.

Prevalence of high-risk drug-drug interactions in paediatric inpatients: a retrospective, single-centre cohort analysis

Drug-drug interaction (DDI) screening programmes aim to increase the safety of medication by issuing alerts based on the severity of DDIs, since an increased risk of adverse drug events has been reported for some DDIs (clinically relevant alerts). However, not all DDI alerts may be clinically relevant, depending on the clinical decision support system (CDSS) interaction tool used and the target population. There are few data about the frequency and relevance of DDIs in the paediatric population. The objective of this study was to evaluate the prevalence and appropriateness of high-risk DDI alerts (drug combinations that are rated as &ldquo;contraindicated&rdquo; or &ldquo;contraindicated by precaution&rdquo; according to the Swiss CDSS interaction tool Pharmavista&reg; (HCI Solutions AG, Bern, Switzerland)) in paediatric inpatients. We carried out a retrospective, single-centre study examining a cohort of paediatric cases hospitalised between January and May 2017 on the surgery/orthopaedic and oncology wards at the University of Basel Children&rsquo;s Hospital (UKBB), Switzerland. Drugs administered to the patients concomitantly were obtained from the medical records. DDI screening was performed using Pharmavista&reg;. All DDIs detected were documented with their severity grading for each hospital day per case. The clinical relevance of DDI alerts for drug combinations rated as contraindicated or contraindicated by precaution was critically evaluated by a literature review. A total of 300 patient cases were assessed for &ldquo;contraindicated&rdquo; or &ldquo;contraindicated by precaution&rdquo; DDI alerts. Of these, none had &ldquo;contraindicated&rdquo; and five had DDI alerts rated as &ldquo;contraindicated by precaution&rdquo; (1.7%, 95% CI 0.6&ndash;4.1%). The corresponding drug combinations were tramadol/fentanyl/morphine-nalbuphine (n = 3), droperidol-ondansetron (n = 1) and methotrexate-metamizole (n = 1), given for a duration of 1&ndash;2 days. Adverse drug events (ADEs) due to these three combinations (QT prolongation with the combination droperidol-ondansetron, reduced effect of opioid agonists with nalbuphine and increased haematotoxicity with methotrexate-metamizole) were not documented in the patients&rsquo; medical records. The low prevalence of contraindicated DDIs suggests that Pharmavista&reg; has a low risk of over-alerting when used in a Swiss paediatric hospital. However, the current literature suggests that the severity rating of established contraindicated DDIs could be partially downgraded, and that patient/population-specific evaluations of DDI alerts are needed.

Are patients with a nasally placed feeding tube at risk of potential drug-drug interactions? A multicentre cross-sectional study.

AimsThe primary aims were to determine the rate of potential drug-drug interactions (pDDIs) in patients with nasally placed feeding tubes (NPFT) and the factors significantly associated with pDDIs. The secondary aim was to assess the change in pDDIs for patients between admission and discharge.Material and methodsThis multicentre study applied a cross-sectional design and was conducted in six Brazilian hospitals, from October 2016 to July 2018. Data from patients with NPFT were collected through electronic forms. All regular medications prescribed were recorded. Medications were classified according to the World Health Organization (WHO) Anatomical Therapeutic Chemical code. Drug-drug interaction screening software was used to screen patients’ medications for pDDIs. Negative binomial regression was used to account for the over dispersed nature of the pDDI count. Since the number of pDDIs was closely related to the number of prescribed medications, we modelled the rate of pDDIs with the count of pDDIs as the numerator and the number of prescribed medications as the denominator; six variables were considered for inclusion: time (admission or discharge), patient age, patient gender, age-adjusted Charlson Comorbidity Index (CCI) score, type of prescription (electronic or handwritten) and patient care complexity. To account for correlation within the two time points (admission and discharge) for each patient a generalised estimating equations approach was used to adjust the standard error estimates. To test the change in pDDI rate between admission and discharge a full model of six variables was fitted to generate an adjusted estimate.ResultsIn this study, 327 patients were included. At least one pDDI was found in more than 91% of patients on admission and discharge and most of these pDDIs were classified as major severity. Three factors were significantly associated with the rate of pDDIs per medication: patient age, patient care complexity and prescription type (handwritten vs electronic). There was no evidence of a difference in pDDI rate between admission and discharge.ConclusionPatients with a NPFT are at high risk of pDDIs. Drug interaction screening tools and computerized clinical decision support systems could be effective risk mitigation strategies for this patient group.

Detecting drug communities and predicting comprehensive drug\u2013drug interactions via balance regularized semi-nonnegative matrix factorization

BackgroundBecause drug–drug interactions (DDIs) may cause adverse drug reactions or contribute to complex-disease treatments, it is important to identify DDIs before multiple-drug medications are prescribed. As the alternative of high-cost experimental identifications, computational approaches provide a much cheaper screening for potential DDIs on a large scale manner. Nevertheless, most of them only predict whether or not one drug interacts with another, but neglect their enhancive (positive) and depressive (negative) changes of pharmacological effects. Moreover, these comprehensive DDIs do not occur at random, but exhibit a weakly balanced relationship (a structural property when considering the DDI network), which would help understand how high-order DDIs work.ResultsThis work exploits the intrinsically structural relationship to solve two tasks, including drug community detection as well as comprehensive DDI prediction in the cold-start scenario. Accordingly, we first design a balance regularized semi-nonnegative matrix factorization (BRSNMF) to partition the drugs into communities. Then, to predict enhancive and degressive DDIs in the cold-start scenario, we develop a BRSNMF-based predictive approach, which technically leverages drug-binding proteins (DBP) as features to associate new drugs (having no known DDI) with other drugs (having known DDIs). Our experiments demonstrate that BRSNMF can generate the drug communities, which exhibit more reasonable sizes, the property of weak balance as well as pharmacological significances. Moreover, they demonstrate the superiority of DBP features and the inspiring ability of the BRSNMF-based predictive approach on comprehensive DDI prediction with 94% accuracy among top-50 predicted enhancive and 86% accuracy among bottom-50 predicted degressive DDIs.ConclusionsOwing to the regularization of the weak balance property of the comprehensive DDI network into semi-nonnegative matrix factorization, our proposed BRSNMF is able to not only generate better drug communities but also provide an inspiring comprehensive DDI prediction in the cold-start scenario.