scholarly journals Drug interactions of azole antifungals

2021 ◽  
Vol 0 ◽  
pp. 1-5
Author(s):  
Navya Vanaja Sahadevan
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Antifungal Agents ◽  
The Other ◽  
Azole Antifungals ◽  
Wide Range ◽  
Drug Drug Interaction

Drug interactions can occur when two or more medications are simultaneously given, and one drug increases or decreases the effectiveness of the other. Azole antifungal agents show a wide range of interactions with other drugs. Failure to recognize a drug–drug interaction may produce harm to the patient, including enhanced toxicity of the concomitantly administered medication. Most of the interactions of azole antifungals are of pharmacokinetic type. This article reviews the clinically relevant drug interactions of commonly used antifungals - fluconazole and itraconazole.

scholarly journals Drug–drug interaction database for safe prescribing of systemic antifungal agents

2021 ◽  
Vol 8 ◽  
pp. 204993612110106
Author(s):  
Saarah Niazi-Ali ◽  
Graham T. Atherton ◽  
Marcin Walczak ◽  
David W. Denning
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Antifungal Agents ◽  
The Body ◽  
Online Database ◽  
Potential Ddis ◽  
Interaction Database ◽  
Widespread Acceptance ◽  
Iatrogenic Harm ◽  
Drug Drug Interaction

Introduction: A drug–drug interaction (DDI) describes the influence of one drug upon another or the change in a drug’s effect on the body when the drug is taken together with a second drug. A DDI can delay, decrease or enhance absorption or metabolism of either drug. Several antifungal agents have a large number of potentially deleterious DDIs. Methods: The antifungal drug interactions database https://antifungalinteractions.org/was first launched in 2012 and is updated regularly. It is available as web and app versions to allow information on potential drug interactions with antifungals with a version for patients and another for health professionals. A new and updated database and interface with apps was created in 2019. This allows clinicians and patients to rapidly check for DDIs. The database is fully referenced to allow the user to access further information if needed. Currently DDIs for fluconazole, itraconazole, voriconazole, posaconazole, isavuconazole, terbinafine, amphotericin B, caspofungin, micafungin and anidulafungin are cross-referenced against 2398 other licensed drugs, a total of nearly 17,000 potential DDIs. Results: The database records 541 potentially severe DDIs, 1129 moderate and 1015 mild DDIs, a total of 2685 (15.9%). Conclusion: As the online database and apps are free to use, we hope that widespread acceptance and usage will reduce medical misadventure and iatrogenic harm from unconsidered DDIs.

Pharmacokinetic Drug-drug Interaction of Antibiotics Used in Sepsis Care in China

2020 ◽  
Vol 21 ◽  
Author(s):  
Xuan Yu ◽  
Zixuan Chu ◽  
Jian Li ◽  
Rongrong He ◽  
Yaya Wang ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Penicillin G ◽  
Literature Mining ◽  
Human Pharmacokinetics ◽  
P450 Enzyme ◽  
Drug Drug Interaction ◽  
Pharmacokinetic Drug ◽  
Sepsis Care

Background: Many antibiotics have a high potential for having an interaction with drugs, as perpetrator and/or victim, in critically ill patients, and particularly in sepsis patients. Methods: The aim of this review is to summarize the pharmacokinetic drug-drug interaction (DDI) of 45 antibiotics commonly used in sepsis care in China. Literature mining was conducted to obtain human pharmacokinetics/dispositions of the antibiotics, their interactions with drug metabolizing enzymes or transporters, and their associated clinical drug interactions. Potential DDI is indicated by a DDI index > 0.1 for inhibition or a treated-cell/untreated-cell ratio of enzyme activity being > 2 for induction. Results: The literature-mined information on human pharmacokinetics of the identified antibiotics and their potential drug interactions is summarized. Conclusion: Antibiotic-perpetrated drug interactions, involving P450 enzyme inhibition, have been reported for four lipophilic antibacterials (ciprofloxacin, erythromycin, trimethoprim, and trimethoprim-sulfamethoxazole) and three lipophilic antifungals (fluconazole, itraconazole, and voriconazole). In addition, seven hydrophilic antibacterials (ceftriaxone, cefamandole, piperacillin, penicillin G, amikacin, metronidazole, and linezolid) inhibit drug transporters in vitro. Despite no reported clinical PK drug interactions with the transporters, caution is advised in the use of these antibacterials. Eight hydrophilic antibacterials (all β-lactams; meropenem, cefotaxime, cefazolin, piperacillin, ticarcillin, penicillin G, ampicillin, and flucloxacillin), are potential victims of drug interactions due to transporter inhibition. Rifampin is reported to perpetrate drug interactions by inducing CYP3A or inhibiting OATP1B; it is also reported to be a victim of drug interactions, due to the dual inhibition of CYP3A4 and OATP1B by indinavir. In addition, three antifungals (caspofungin, itraconazole, and voriconazole) are reported to be victims of drug interactions because of P450 enzyme induction. Reports for other antibiotics acting as victims in drug interactions are scarce.

Comparison of eight screening tools to detect interactions between herbal supplements and oncology agents

2020 ◽  
Vol 26 (8) ◽  
pp. 1843-1849
Author(s):  
Faisal Shakeel ◽  
Fang Fang ◽  
Kelley M Kidwell ◽  
Lauren A Marcath ◽  
Daniel L Hertz
Keyword(s):  
Drug Interaction ◽  
Positive Predictive Value ◽  
Drug Interactions ◽  
Predictive Value ◽  
Screening Tools ◽  
Herbal Supplements ◽  
Interaction Detection ◽  
Natural Medicine ◽  
Interaction Screening ◽  
Drug Drug Interaction

Introduction Patients with cancer are increasingly using herbal supplements, unaware that supplements can interact with oncology treatment. Herb–drug interaction management is critical to ensure optimal treatment outcomes. Several screening tools exist to detect drug–drug interactions, but their performance to detect herb–drug interactions is not known. This study compared the performance of eight drug–drug interaction screening tools to detect herb–drug interaction with anti-cancer agents. Methods The herb–drug interaction detection performance of four subscription (Micromedex, Lexicomp, PEPID, Facts & Comparisons) and free (Drugs.com, Medscape, WebMD, RxList) drug–drug interaction tools was assessed. Clinical relevance of each herb–drug interaction was determined using Natural Medicine and each drug–drug interaction tool. Descriptive statistics were used to calculate sensitivity, specificity, positive predictive value, and negative predictive value. Linear regression was used to compare performance between subscription and free tools. Results All tools had poor sensitivity (<0.20) for detecting herb–drug interaction. Lexicomp had the highest positive predictive value (0.98) and best overall performance score (0.54), while Medscape was the best performing free tool (0.52). The worst subscription tools were as good as or better than the best free tools, and as a group subscription tools outperformed free tools on all metrics. Using an average subscription tool would detect one additional herb–drug interaction for every 10 herb–drug interactions screened by a free tool. Conclusion Lexicomp is the best available tool for screening herb–drug interaction, and Medscape is the best free alternative; however, the sensitivity and performance for detecting herb–drug interaction was far lower than for drug–drug interactions, and overall quite poor. Further research is needed to improve herb–drug interaction screening performance.

Potential Drug Interactions— Fact or Fallacy?

1975 ◽  
Vol 9 (11) ◽  
pp. 586-590 ◽  
Author(s):  
Curtis D. Black ◽  
Nicholas G. Popovich
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Patient Care ◽  
Clinical Studies ◽  
Literature Search ◽  
Potential Drug ◽  
Careful Evaluation ◽  
Current Drug ◽  
Drug Drug Interaction

At present, the pharmacist is faced with a perplexing number of potential drug interactions as they relate to patient care. The purpose of the investigation was to evaluate current drug-drug interaction literature, specifically gastrointestinal drug interactions. Literature search and review evaluated the authoritative basis on which conclusions were made. From this, a review was written to illustrate fallacies and misconceptions that could be derived from the literature with the intent it would serve as a guide in interpreting and evaluating drug-drug interactions. The overall study illustrates the vast need for careful evaluation of drug interaction literature before erroneous recommendations are made on conceivably inconclusive clinical studies.

scholarly journals DRUG-DRUG INTERACTION;

2017 ◽  
Vol 24 (03) ◽  
pp. 357-365
Author(s):  
Hina Hasnain ◽  
Huma Ali ◽  
Farya Zafar ◽  
Ali Akbar Sial ◽  
Kamran Hameed ◽  
...  
Keyword(s):  
Patient Outcome ◽  
Adverse Event ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Multidisciplinary Approach ◽  
Drug Information ◽  
Drug Effects ◽  
Information Provision ◽  
Drug Drug Interaction ◽  
Drug Order

Drug-drug interaction (DDI) is a specific type of adverse event, which developsdue to multiple regimen therapy, and that may lead to significant hospitalization and death.Clinical and economic impact of drug interactions are increasingly accredited as a chiefconcern in critical care. Potentiating effects of DDIs in intensive care units are far more criticaldue to complex medications regimen, high risk severely ill population and associated metabolicand physiological disturbances which can impede drug effects. Pharmacist contribution isclassified as clarification of drug order, appropriate drug information provision, and advice forsubstitute treatment. A multidisciplinary approach is very necessary in developing a pharmacotherapeuticregimen designed to optimize patient outcome and minimize any potential dugdrug interactions. This review encompasses the prevalence, categorization, significance interm of patient safety and prescription efficacy, clinical and economic burdens, national andinternational data comparisons related to drug-drug interactions.

scholarly journals Safe Non-opioid and Non-barbiturate containing Acute Medication Combinations in Headache: An Exhaustive Approach Based on DrugBank Database

2020 ◽  
Author(s):  
Victor Kaytser ◽  
Pengfei Zhang
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Drug Combinations ◽  
Exhaustive Search ◽  
Common Elements ◽  
Acute Medication ◽  
Drug Drug Interaction ◽  
Drugbank Database ◽  
Exhaustive List

Structured AbstractBackgroundRational polypharmacy in abortive medications use is often inevitable for patients with refractory headaches.ObjectiveWe seek to enumerate an exhaustive list of headaches abortive medications that are without drug-drug interactions.MethodsWe updated a list of acute medications based on the widely used Jefferson Headache Manual with novel abortive medications including ubrogepant, lasmiditan, and rimegepant. Opioids and barbiturate containing products are excluded. We then conducted an exhaustive search of all pair-wise interactions for this list of medication via DrugBank API. Using this interaction list, we filtered all possible two, three, and four drug combinations of abortive medications. The resultant list of medication was then reapplied to DrugBank to verify the lack of known drug-drug interaction.ResultsThere are 192 medication combinations that do not contain any drug-drug interactions. Most common elements in these combinations are ubrogepant, prochlorperazine, followed by tizanidine. There are 67 three-drug combinations that do not contain interaction. Only 2 of the four-drug combinations do not yield some form of drug-drug interactions.ConclusionThis list of headaches abortive medications without drug-drug interactions is a useful tool for clinicians seeking to more effectively manage refractory headaches.

scholarly journals Universal Patient Identifier and Interoperability for Detection of Serious Drug Interactions: Retrospective Study

10.2196/23353 ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. e23353
Author(s):  
Howard Michael Sragow ◽  
Eileen Bidell ◽  
Douglas Mager ◽  
Shaun Grannis
Keyword(s):  
United States ◽  
Drug Interaction ◽  
Drug Interactions ◽  
The United States ◽  
Security And Privacy ◽  
Patient Identification ◽  
Pharmacy Benefit Manager ◽  
Pharmacy Benefit ◽  
Privacy Concerns ◽  
Drug Drug Interaction

Background The United States, unlike other high-income countries, currently has no national unique patient identifier to facilitate health information exchange. Because of security and privacy concerns, Congress, in 1998, prevented the government from promulgating a unique patient identifier. The Health and Human Services funding bill that was enacted in 2019 requires that Health and Human Services report their recommendations on patient identification to Congress. While there are anecdotes of incomplete health care data due to patient misidentification, to date there have been insufficient large-scale analyses measuring improvements to patient care that a unique patient identifier might provide. This lack of measurement has made it difficult for policymakers to balance security and privacy concerns against the value of potential improvements. Objective We sought to determine the frequency of serious drug-drug interaction alerts discovered because a pharmacy benefits manager uses a universal patient identifier and estimate undiscovered serious drug-drug interactions because pharmacy benefit managers do not yet fully share patient records. Methods We conducted a retrospective study of serious drug-drug interaction alerts provided from September 1, 2016 to August 31, 2019 to retail pharmacies by a national pharmacy benefit manager that uses a unique patient identifier. We compared each alert to the contributing prescription and determined whether the unique patient identifier was necessary in order to identify the crossover alert. We classified each alert’s disposition as override, abandonment, or replacement. Using the crossover alert rate and sample population size, we inferred a rate of missing serious drug-drug interaction alerts for the United States. We performed logistic regression in order to identify factors correlated with crossover and alert outcomes. Results Among a population of 49.7 million patients, 242,646 serious drug-drug interaction alerts occurred in 3 years. Of these, 2388 (1.0%) crossed insurance and were discovered because the pharmacy benefit manager used a unique patient identifier. We estimate that up to 10% of serious drug-drug alerts in the United States go undetected by pharmacy benefit managers because of unexchanged information or pharmacy benefit managers that do not use a unique patient identifier. These information gaps may contribute, annually, to up to 6000 patients in the United States receiving a contraindicated medication. Conclusions Comprehensive patient identification across disparate data sources can help protect patients from serious drug-drug interactions. To better safeguard patients, providers should (1) adopt a comprehensive patient identification strategy and (2) share patient prescription history to improve clinical decision support.

scholarly journals A Minimal Information Model for Potential Drug-Drug Interactions

2021 ◽  
Vol 11 ◽  
Author(s):  
Harry Hochheiser ◽  
Xia Jing ◽  
Elizabeth A. Garcia ◽  
Serkan Ayvaz ◽  
Ratnesh Sahay ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Best Practice ◽  
Task Force ◽  
Information Model ◽  
Potential Drug ◽  
Community Group ◽  
Drug Drug Interaction ◽  
Potential Interactions ◽  
Minimal Information

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.

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