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.

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.

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.

Framework for the Design of Cannabis-Mediated Phase I Drug-Drug Interaction Studies

2021 ◽  
Vol 16 ◽  
Author(s):  
Diana L. Shuster ◽  
Gina Pastino ◽  
Dirk Cerneus
Keyword(s):  
Drug Interaction ◽  
Drug Development ◽  
The United States ◽  
The Body ◽  
Cannabis User ◽  
Special Population ◽  
Care Providers ◽  
Clinical Drug Development ◽  
Drug Drug Interaction ◽  
Clinical Drug

: Cannabis has become legal in much of the United States similarly to many other countries, for either recreational or medical use. The use of cannabis products is rapidly increasing while the body of knowledge of its myriad of effects still lags. In vitro and clinical data show that cannabis’ main constituents, delta-9-tetrahydrocannabinol and cannabidiol, can affect the pharmacokinetics (PK), safety and pharmacodynamics (PD) of other drugs. Within the context of clinical drug development, the widespread and frequent use of cannabis products has essentially created another special population; that is, the cannabis user. We propose that all clinical drug development programs include a Phase 1 study to assess the drug-drug interaction potential of cannabis as a precipitant on the PK, safety and if applicable, the PD of all new molecular entities (NMEs) in a combination of healthy adult subjects as well as frequent and infrequent cannabis users. This data should be required to inform drug labeling and aid health care providers in treating any patient, as cannabis has quickly become another common concomitant medication and cannabis users, a new special population.

scholarly journals HCIP: An Online database for prediction CYP450 Enzyme Inhibition potential of bioactive compounds

2021 ◽  
Vol 11 (2) ◽  
pp. 253-255
Author(s):  
Sabarathinam Sarvesh ◽  
Preethi L ◽  
Haripritha Meganathan ◽  
M Arjun Gokulan ◽  
Dhivya Dhanasekaran ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Enzyme Inhibition ◽  
Bioactive Compounds ◽  
Bioactive Compound ◽  
Concomitant Administration ◽  
Cyp3a4 Inhibitor ◽  
Online Database ◽  
Computer Aided ◽  
Cyp450 Enzyme

Background: Concomitant administration of herbal medicine and conventional may lead to severe metabolism-oriented herb-drug interactions. However, detecting herb-drug interaction is expensive and higher time-consuming. Several computer-aided techniques have been proposed in recent years to predict drug interactions. However, most of the methods cannot predict herb-drug interactions effectively. Methods: Canonical SMILES of bioactive compounds was gathered from the PubChem online database, and its inhibition details were gathered PKCSM from the webserver. Results: By searching the bioactive compound name in the search bar of “The Herb-CYP450 Enzyme Inhibition Predictor online database” (HCIP- http://hcip.in/), it will provide the liver enzyme inhibition profile of the selected bioactive compound. For example; Guggulsterone:  CYP3A4 inhibitor.  Conclusion: The Herb-CYP450 Enzyme Inhibition Predictor online database is very peculiar and easy to determine the inhibition profile of the targeted bioactive compound. Keywords: CYP450; Enzyme inhibition; Bioactive Compounds; Online database; Herb-Drug Interaction

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.

The prevalence of potentially beneficial and harmful drug-drug interactions in intensive care units

2019 ◽  
Vol 34 (1) ◽  
Author(s):  
Hossein Ali Mehralian ◽  
Jafar Moghaddasi ◽  
Hossein Rafiei
Keyword(s):  
Intensive Care ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Intensive Care Units ◽  
Medical Records ◽  
Potential Ddis ◽  
Cross Sectional ◽  
Text Book ◽  
The Mean ◽  
Potential Interactions

Abstract Background The present study was conducted with the aim of investigating the prevalence of potentially beneficial and harmful drug-drug interactions (DDIs) in intensive care units (ICUs). Methods The present cross-sectional prospective study was conducted in two ICUs in Shahr-e Kord city, Iran. The study sample was consisted of 300 patients. The Drug Interaction Facts reference text book [Tatro DS. Drug interaction facts. St Louis, MO: Walters Kluwer Health, 2010.] was used to determine the type and the frequency of the DDIs. Results The participants consisted of 189 patients men and 111 women. The mean age of patients was 44.2 ± 24.6 years. Totally, 60.5% of patients had at least one drug-drug interaction in their profile. The total number of DDIs found was 663 (the mean of the total number of drug-drug interactions was 2.4 interactions per patient). Of all the 663 interactions, 574 were harmful and others were beneficial. In terms of starting time, 98 of the potential interactions were rapid and 565 of them were delayed. In terms of severity, 511 of the potential interactions were moderate. Some of the drugs in the patients’ medical records including phenytoin, dopamine, ranitidine, corticosteroid, dopamine, heparin, midazolam, aspirin, magnesium, calcium gluconate, and antibiotics, the type of ventilation, the type of nutrition and the duration of hospital stay were among the factors that were associated with high risk of potential DDIs (p < 0.05). Conclusions The prevalence of potentially beneficial and harmful DDIs, especially harmful drug-drug interactions, is high in ICUs and it is necessary to reduce these interactions by implementing appropriate programs and interventions.

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.

Drug-drug interaction database SFINX – first results from north Estonia Medical Centre, Estonia

2015 ◽  
Vol 37 (8) ◽  
pp. e5-e6
Author(s):  
T. Marandi ◽  
L. Orav ◽  
A. Kändmaa ◽  
S. Nahkur
Keyword(s):  
Drug Interaction ◽  
Medical Centre ◽  
Interaction Database ◽  
First Results ◽  
Drug Drug Interaction
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