Knowledge and Practices of Nurses Working in Intensive Care on Drug-Drug Interaction

Purpose:This study was conducted to examine the knowledge and practices of nurses working in the intensive care unit about drug-drug interaction. Materials and Methods: This study was conducted with 186 intensive care nurses working in shifts in 12 intensive care units. "Nurse information form", "Drug-drug interaction questionnaire" prepared by the researcher, and "nurse observation form" were used in data collection. The data obtained from the study were evaluated in computer environment using frequency, percentage distribution and Chi-square test. Results:In the study, 76.3% of the nurses working in the intensive care unit reported that they did not know which of the drug pairs they encountered frequently caused drug-drug interactions. When the drugs administered by the nurses were examined, it was determined that 34.4% of them had the potential for drug-drug interaction. It was found that nurses' potential drug-drug interaction practices, staff status and working styles, and working time in the institution, profession and intensive care unit were not effective. It was determined that there were significant differences between the intensive care units (p=0.043) in the drug-drug interaction practices encountered. Conclusion:It was determined that the nurses did not have the desired level of knowledge about drug-drug interactions and interacting drug pairs, and there were differences between the intensive care units where they worked. It is recommended to increase the level of knowledge by giving regular trainings to nurses on drug-drug interaction

COMEDICATION OF RABEPRAZOLE SODIUM CAUSES POTENTIAL DRUG-DRUG INTERACTION WITH DIABETIC DRUG LINAGLIPTIN: In-vitro AND In-silico APPROACHES

Drug-drug interaction is a notable concern among physicians when prescribing multi-therapy to the patients as concomitant administration of multi-drugs might cause unexpected adverse drug reactions. The main objective of this research is to predict a potential drug-drug interaction between two frequently used drugs by diabetic patients, an antidiabetic drug (linagliptin) and a proton pump inhibitor (rabeprazole sodium). Here, several in vitro techniques, including thermal (melting point, thermogravimetric analysis [TGA]), morphological (scanning electron microscopy [SEM] and X-ray powder diffraction [XRPD] analysis), highly sophisticated synchronous fluorescence, and in silico methods were applied to anticipate the potential drug-drug interaction between these stated drugs quickly. The melting point and TGA study revealed thermochemical properties, thermal stability profiles, and degradation patterns upon temperature rising of the formed complex and these precursor drugs. The SEM and XRPD have provided the morphological changes like particle shape and size distribution of the desired molecule that might be caused due to the potential drug-drug interactions. Besides, the drastic reduction of the quenching rate constant of linagliptin during interaction with bovine serum albumin in synchronous fluorescence also endorsed the potential drug-drug interaction. Furthermore, the drug-receptor docking analysis demonstrated that the binding affinity of the precursor ligands might be reduced due to the predicted drug-drug interaction. However, the current evidence warrants extensive investigation to confirm the above-stated potential drug-drug interaction in the larger animal model. Finally, clinical data need to be closely monitored during the treatment of diabetic patients prescribed with linagliptin and rabeprazole sodium

Participatory design for drug-drug interaction alerts

The utilization of decision support systems, in the point of care, to alert drug-drug interactions has been shown to improve quality of care. Still, the use of these systems has not been as expected, it is believed, because of the difficulties in their knowledge databases; errors in the generation of the alerts and the lack of a suitable design. This study expands on the development of alerts using participatory design techniques based on user centered design process. This work was undertaken in three stages (inquiry, participatory design and usability testing) it showed that the use of these techniques improves satisfaction, effectiveness and efficiency in an alert system for drug-drug interactions, a fact that was evident in specific situations such as the decrease of errors to meet the specified task, the time, the workload optimization and users overall satisfaction in the system.Fil: Luna, Daniel Roberto. Instituto Tecnologico de Buenos Aires. Departamento de Investigacion y Doctorado.; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires; ArgentinaFil: Otero, Carlos Martin. Instituto Tecnológico de Buenos Aires; Argentina. Hospital Italiano. Departamento de Informática En Salud.; ArgentinaFil: Almerares, Alfredo. Hospital Italiano. Departamento de Informática En Salud.; Argentina. University of Oregon; Estados UnidosFil: Stanziola, Enrique Luis. Hospital Italiano. Departamento de Informática En Salud.; ArgentinaFil: Risk, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Oregon Health And Science University; . Instituto Tecnologico de Buenos Aires. Departamento de Investigacion y Doctorado.; ArgentinaFil: González Bernaldo De Quirós, Fernán. Hospital Italiano. Departamento de Informática En Salud.; Argentin

Drug-Drug Interaction Alerts: Emphasizing the Evidence

Many analysts and users of contemporary clinical decision support ( CDS ) systems have expressed grave concerns about the technology’s efficacy and functionality. Alerts generated by CDS systems are often inaccurate, and an excess of alerts leads some physicians to experience alert fatigue and to turn off CDS altogether. This article formulates recommendations to improve drug-drug interaction (DDI) alerts. The paper comments upon a proposal by Susan Ridgely and Michael Greenberg, who call for the development of a consensus-based clinically significant drug-drug interaction list that could generate limited liability protection for users. We argue that instead of creating a list of always-contraindicated DDIs, experts should develop DDI alerts that offer essential information about DDI risks, supporting evidence, mitigating factors, and appropriate courses of action. Thus, DDI warnings should provide users with concise but comprehensive information. They should not deprive clinicians of discretion, but rather, enable them to make more knowledgeable and effective prescribing decisions. In addition, the article analyzes several other DDI-related issues. It details a process for determining which DDIs should generate alerts in CDS systems. It also examines the extent to which DDI alerts should serve as a basis for liability protection and suggests how data about DDI alert accuracy could be used as evidence in malpractice litigation

Contextualized Drug–Drug Interaction Management Improves Clinical Utility Compared With Basic Drug–Drug Interaction Management in Hospitalized Patients

Drug–drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context-dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI-CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P &lt; 0.01), with 4.0 PIs/1,000 MOs (P &lt; 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.</p

Contextualized Drug–Drug Interaction Management Improves Clinical Utility Compared With Basic Drug–Drug Interaction Management in Hospitalized Patients

Drug–drug interactions (DDIs) frequently trigger adverse drug events or reduced efficacy. Most DDI alerts, however, are overridden because of irrelevance for the specific patient. Basic DDI clinical decision support (CDS) systems offer limited possibilities for decreasing the number of irrelevant DDI alerts without missing relevant ones. Computerized decision tree rules were designed to context-dependently suppress irrelevant DDI alerts. A crossover study was performed to compare the clinical utility of contextualized and basic DDI management in hospitalized patients. First, a basic DDI-CDS system was used in clinical practice while contextualized DDI alerts were collected in the background. Next, this process was reversed. All medication orders (MOs) from hospitalized patients with at least one DDI alert were included. The following outcome measures were used to assess clinical utility: positive predictive value (PPV), negative predictive value (NPV), number of pharmacy interventions (PIs)/1,000 MOs, and the median time spent on DDI management/1,000 MOs. During the basic DDI management phase 1,919 MOs/day were included, triggering 220 DDI alerts/1,000 MOs; showing 57 basic DDI alerts/1,000 MOs to pharmacy staff; PPV was 2.8% with 1.6 PIs/1,000 MOs costing 37.2 minutes/1,000 MOs. No DDIs were missed by the contextualized CDS system (NPV 100%). During the contextualized DDI management phase 1,853 MOs/day were included, triggering 244 basic DDI alerts/1,000 MOs, showing 9.6 contextualized DDIs/1,000 MOs to pharmacy staff; PPV was 41.4% (P &lt; 0.01), with 4.0 PIs/1,000 MOs (P &lt; 0.01) and 13.7 minutes/1,000 MOs. The clinical utility of contextualized DDI management exceeds that of basic DDI management.</p

A Comprehensive Whole-Body Physiologically Based Pharmacokinetic Drug-Drug-Gene Interaction Model of Metformin and Cimetidine in Healthy Adults and Renally Impaired Individuals

Background Metformin is a widely prescribed antidiabetic BCS Class III drug (low permeability) that depends on active transport for its absorption and disposition. It is recommended by the US Food and Drug Administration as a clinical substrate of organic cation transporter 2/multidrug and toxin extrusion protein for drug–drug interaction studies. Cimetidine is a potent organic cation transporter 2/multidrug and toxin extrusion protein inhibitor. Objective The objective of this study was to provide mechanistic whole-body physiologically based pharmacokinetic models of metformin and cimetidine, built and evaluated to describe the metformin-SLC22A2 808G>T drug–gene interaction, the cimetidine-metformin drug–drug interaction, and the impact of renal impairment on metformin exposure. Methods Physiologically based pharmacokinetic models were developed in PK-Sim® (version 8.0). Thirty-nine clinical studies (dosing range 0.001–2550 mg), providing metformin plasma and urine data, positron emission tomography measurements of tissue concentrations, studies in organic cation transporter 2 polymorphic volunteers, drug–drug interaction studies with cimetidine, and data from patients in different stages of chronic kidney disease, were used to develop the metformin model. Twenty-seven clinical studies (dosing range 100–800 mg), reporting cimetidine plasma and urine concentrations, were used for the cimetidine model development. Results The established physiologically based pharmacokinetic models adequately describe the available clinical data, including the investigated drug–gene interaction, drug–drug interaction, and drug–drug–gene interaction studies, as well as the metformin exposure during renal impairment. All modeled drug–drug interaction area under the curve and maximum concentration ratios are within 1.5-fold of the observed ratios. The clinical data of renally impaired patients shows the expected increase in metformin exposure with declining kidney function, but also indicates counter-regulatory mechanisms in severe renal disease; these mechanisms were implemented into the model based on findings in preclinical species. Conclusions Whole-body physiologically based pharmacokinetic models of metformin and cimetidine were built and qualified for the prediction of metformin pharmacokinetics during drug–gene interaction, drug–drug interaction, and different stages of renal disease. The model files will be freely available in the Open Systems Pharmacology model repository. Current guidelines for metformin treatment of renally impaired patients should be reviewed to avoid overdosing in CKD3 and to allow metformin therapy of CKD4 patients