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 A Comparison of Tertiary Drug Resources’ Consistency Regarding Drug-Drug Interactions of Adjunctive Analgesics

2020 ◽  
pp. 875512252095133
Author(s):  
Andrew Lang ◽  
Michael A. Veronin ◽  
Justin P. Reinert
Keyword(s):  
Health Care ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Patient Care ◽  
Health Care Providers ◽  
Drug Information ◽  
Interaction Analysis ◽  
Information Resources ◽  
Care Providers ◽  
Drug Drug Interaction

Background: Health care providers routinely rely on tertiary drug information resources to affirm knowledge or proactively verify the safety and efficacy of medications. Though all patient care areas are affected, the reliability of these resources is perhaps nowhere as poignant as it is in high-acuity settings, including the emergency department and the intensive care unit. As providers seek to identify adjunctive analgesics for acute pain in these areas, they must be able to rely on the integrity to whichever resource their institution has granted access. Objective: To determine the congruency of drug-drug interaction information found on 3 tertiary drug resources. Methods: A drug-drug interaction analysis was conducted on Micromedex, Lexicomp, and Medscape. Adjunctive analgesics included dexmedetomidine and ketamine, which were compared with the intravenous opioid products morphine, fentanyl, and hydromorphone. Results: Significant discrepancies were appreciated with regard to the severity of drug-drug interactions. In addition, the heterogeneity in which reaction severity and likelihood are described by each respective resource makes direct comparisons difficult. Interaction warnings for dexmedetomidine and fentanyl included a “major interaction” from Micromedex, whereas Lexicomp did not identify a risk and Medscape only recommended increased monitoring on the grounds of respiratory and central nervous system depression. Conclusions: Health care providers must remain vigilant when reviewing tertiary drug information resources. Pharmacists possess the training and skills necessary to assist interdisciplinary medical teams in providing optimal patient care through evaluating and applying the information gleaned from these resources.

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

2021 ◽  
Vol 12 ◽  
pp. 204209862110412
Author(s):  
Levin Thomas ◽  
Sumit Raosaheb Birangal ◽  
Rajdeep Ray ◽  
Sonal Sekhar Miraj ◽  
Murali Munisamy ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Molecular Modelling ◽  
Molecular Level ◽  
Drug Information ◽  
Potential Drug ◽  
African Countries ◽  
Antitubercular Drugs ◽  
Tuberculosis Patients ◽  
Drug Drug Interaction

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 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 predicted to be infected with COVID-19 during this period, there is a higher risk for the occurrence of medication interactions between the medicines used for COVID-19 and tuberculosis. Hence, identifying and managing these interactions is vital to ensure the safety of patients undergoing COVID-19 and tuberculosis treatment simultaneously. Methods: We studied the major medication interactions that could likely happen between the various medicines that are currently given for COVID-19 and tuberculosis treatment using the medication interaction checker of a drug information software (Micromedex®). In addition, thorough molecular modelling was done to confirm and understand the interactions found from the medication interaction checker database using specific docking software. Molecular docking is a method that predicts the preferred orientation of one medicine molecule to a second molecule, when bound to each other to form a stable complex. Knowledge of the preferred orientation may be used to determine the strength of association or binding affinity between two medicines using scoring functions to determine the extent of the interactions between medicines. The combined knowledge from Micromedex and molecular modelling data was used to properly predict the potential medicine interactions between currently used COVID-19 and antitubercular medicines. Results: We found a total of 91 medication interactions from Micromedex. Majority of our molecular modelling findings matched with the interaction information obtained from the drug information software. QT prolongation, an abnormal heartbeat, was identified as one of the most common interactions. Our findings suggest that antitubercular medicines, mainly rifampin and second-line agents, suggest high alert and scrutiny while prescribing with the repurposed COVID-19 medicines. Conclusion: Our current study highlights the need for further well-designed studies confirming the current information for recommending safe prescribing in patients with both infections.

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.

scholarly journals Herb–Drug Interactions: Worlds Intersect with the Patient at the Center

Medicines ◽  
2021 ◽  
Vol 8 (8) ◽  
pp. 44
Author(s):  
Mary Beth Babos ◽  
Michelle Heinan ◽  
Linda Redmond ◽  
Fareeha Moiz ◽  
Joao Victor Souza-Peres ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Case Report ◽  
Drug Interactions ◽  
Clinical Studies ◽  
Drug Information ◽  
Case Reports ◽  
Full Information ◽  
Information Need ◽  
Herbal Products ◽  
Reduce Risk

This review examines three bodies of literature related to herb–drug interactions: case reports, clinical studies, evaluations found in six drug interaction checking resources. The aim of the study is to examine the congruity of resources and to assess the degree to which case reports signal for further study. A qualitative review of case reports seeks to determine needs and perspectives of case report authors. Methods: Systematic search of Medline identified clinical studies and case reports of interacting herb–drug combinations. Interacting herb–drug pairs were searched in six drug interaction resources. Case reports were analyzed qualitatively for completeness and to identify underlying themes. Results: Ninety-nine case-report documents detailed 107 cases. Sixty-five clinical studies evaluated 93 mechanisms of interaction relevant to herbs reported in case studies, involving 30 different herbal products; 52.7% of these investigations offered evidence supporting reported reactions. Cohen’s kappa found no agreement between any interaction checker and case report corpus. Case reports often lacked full information. Need for further information, attitudes about herbs and herb use, and strategies to reduce risk from interaction were three primary themes in the case report corpus. Conclusions: Reliable herb–drug information is needed, including open and respectful discussion with patients.

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 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.

scholarly journals Hypothermia in an Adolescent Due to Probable Drug-Drug Interaction Involving Clobazam

2019 ◽  
Vol 24 (2) ◽  
pp. 156-159 ◽  
Author(s):  
Malinda G. Parman ◽  
Amy P. Holmes
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Active Metabolite ◽  
Therapeutic Index ◽  
Serum Concentrations ◽  
Drug Drug Interaction

We report on a 16-year-old female who developed hypothermia as a result of a drug-drug interaction that produced supratherapeutic serum concentrations of clobazam. Although clobazam and its active metabolite (N-desmethylclobazam) are metabolized by cytochrome 2C19 (CYP2C19), literature suggests that clobazam-associated drug interactions involving this isoenzyme are not clinically relevant because of its wide therapeutic index. This report describes clobazam-associated hypothermia due to supratherapeutic serum concentrations of clobazam that resulted from the combination of 2 CYP2C19 inhibitors.

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