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Applied Clinical Informatics May 2020Alert presentation of clinical decision support recommendations is a common method for providing information; however, many alerts are overridden suggesting presentation...
OBJECTIVE
Alert presentation of clinical decision support recommendations is a common method for providing information; however, many alerts are overridden suggesting presentation design improvements can be made. This study attempts to assess pediatric prescriber information needs for drug-drug interactions (DDIs) alerts and to evaluate the optimal presentation timing and presentation in the medication ordering process.
METHODS
Six case scenarios presented interactions between medications used in pediatric specialties of general medicine, infectious disease, cardiology, and neurology. Timing varied to include alert interruption at medication selection versus order submission; or was noninterruptive. Interviews were audiotaped, transcribed, and independently analyzed to derive central themes.
RESULTS
Fourteen trainee and attending clinicians trained in pediatrics, cardiology, and neurology participated. Coders derived 8 central themes from 929 quotes. Discordance exists between medication prescribing frequency and DDI knowledge; providers may commonly prescribe medications for which they do not recognize DDIs. Providers wanted alerts at medication selection rather than at order signature. Alert presentation themes included standardizing text, providing interaction-specific incidence/risk information, DDI rating scales, consolidating alerts, and providing alternative therapies. Providers want alerts to be actionable, for example, allowing medication discontinuation and color visual cues for essential information. Despite alert volume, participants did not "mind being reminded because there is always the chance that at that particular moment (they) do not remember it" and acknowledged the importance of alerts as "essential in terms of patient safety."
CONCLUSION
Clinicians unanimously agreed on the importance of receiving DDI alerts to improve patient safety. The perceived alert value can be improved by incorporating clinician preferences for timing and presentation.
Topics: Drug Interactions; Health Personnel; Hospitals; Humans; Pediatrics; Perception; Reminder Systems; Surveys and Questionnaires; Time Factors
PubMed: 32698231
DOI: 10.1055/s-0040-1714276 -
PloS One 2022Drug-drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug attributes or...
Drug-drug interaction (DDI) prediction has received considerable attention from industry and academia. Most existing methods predict DDIs from drug attributes or relationships with neighbors, which does not guarantee that informative drug embeddings for prediction will be obtained. To address this limitation, we propose a multitype drug interaction prediction method based on the deep fusion of drug features and topological relationships, abbreviated DM-DDI. The proposed method adopts a deep fusion strategy to combine drug features and topologies to learn representative drug embeddings for DDI prediction. Specifically, a deep neural network model is first used on the drug feature matrix to extract feature information, while a graph convolutional network model is employed to capture structural information from the adjacency matrix. Then, we adopt delivery operations that allow the two models to exchange information between layers, as well as an attention mechanism for a weighted fusion of the two learned embeddings before the output layer. Finally, the unified drug embeddings for the downstream task are obtained. We conducted extensive experiments on real-world datasets, the experimental results demonstrated that DM-DDI achieved more accurate prediction results than state-of-the-art baselines. Furthermore, in two tasks that are more similar to real-world scenarios, DM-DDI outperformed other prediction methods for unknown drugs.
Topics: Drug Interactions; Neural Networks, Computer; Pharmaceutical Preparations
PubMed: 36037188
DOI: 10.1371/journal.pone.0273764 -
The American Journal of Managed Care Nov 2022Drug-drug interactions (DDIs) are among the most common causes of adverse drug reactions and are further complicated by genetic variants of drug-metabolizing enzymes....
OBJECTIVES
Drug-drug interactions (DDIs) are among the most common causes of adverse drug reactions and are further complicated by genetic variants of drug-metabolizing enzymes. The aim of this study is to quantify and describe potential DDIs, drug-gene interactions (DGIs), and drug-drug-gene interactions (DDGIs) in a community-based population.
STUDY DESIGN
This was an analysis of deidentified retail pharmacy prescription data for 4761 individuals.
METHODS
Data were first assessed for DDIs, and individuals were stratified to a risk category using the logic of a commercially available digital DDGI tool. To calculate the frequency of potential DGIs and DDGIs, genotypes were imputed and randomly allocated to the cohort 100 times via Monte Carlo simulation according to each variant's frequency in the general population.
RESULTS
The probability of a DDI of any impact was 26.0% and increased to 49.6% (95% CI, 48.4%-50.7%) when drug-metabolizing phenotypes were ascribed according to the distribution of variants of 11 genes as found in a Caucasian population. There was a 7.8% probability of major DDIs, which increased to a 10.1% (95% CI, 9.5%-10.8%) probability with the addition of genetic contributions. The probability of DDGIs of any impact was correlated with the number of medications. Antidepressants, antiemetics, blood products and modifiers, analgesics, and antipsychotics had the highest probability of DDGIs.
CONCLUSIONS
The probability of drug interaction risk increased when phenotypes associated with genetic polymorphisms were attributed to the population. These data suggest that pharmacogenomic assessment may be useful in predicting drug interactions and severity when evaluating patient medication profiles.
Topics: Humans; Pharmacies; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Genotype
PubMed: 36374614
DOI: 10.37765/ajmc.2022.89259 -
BMC Medical Informatics and Decision... Mar 2020Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has...
BACKGROUND
Adverse drug events (ADEs) often occur as a result of drug-drug interactions (DDIs). The use of data mining for detecting effects of drug combinations on ADE has attracted growing attention and interest, however, most studies focused on analyzing pairwise DDIs. Recent efforts have been made to explore the directional relationships among high-dimensional drug combinations and have shown effectiveness on prediction of ADE risk. However, the existing approaches become inefficient from both computational and illustrative perspectives when considering more than three drugs.
METHODS
We proposed an efficient approach to estimate the directional effects of high-order DDIs through frequent itemset mining, and further developed a novel visualization method to organize and present the high-order directional DDI effects involving more than three drugs in an interactive, concise and comprehensive manner. We demonstrated its performance by mining the directional DDIs associated with myopathy using a publicly available FAERS dataset.
RESULTS
Directional effects of DDIs involving up to seven drugs were reported. Our analysis confirmed previously reported myopathy associated DDIs including interactions between fusidic acid with simvastatin and atorvastatin. Furthermore, we uncovered a number of novel DDIs leading to increased risk for myopathy, such as the co-administration of zoledronate with different types of drugs including antibiotics (ciprofloxacin, levofloxacin) and analgesics (acetaminophen, fentanyl, gabapentin, oxycodone). Finally, we visualized directional DDI findings via the proposed tool, which allows one to interactively select any drug combination as the baseline and zoom in/out to obtain both detailed and overall picture of interested drugs.
CONCLUSIONS
We developed a more efficient data mining strategy to identify high-order directional DDIs, and designed a scalable tool to visualize high-order DDI findings. The proposed method and tool have the potential to contribute to the drug interaction research and ultimately impact patient health care.
AVAILABILITY AND IMPLEMENTATION
http://lishenlab.com/d3i/explorer.html.
Topics: Data Mining; Databases, Factual; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Humans; Pharmaceutical Preparations
PubMed: 32183790
DOI: 10.1186/s12911-020-1053-z -
Journal of Pharmacy & Pharmaceutical... 2013Cranberry juice is a popular beverage with many health benefits. It has anthocyanins to supplement dietary needs. Based on in vitro evidence cranberry juice is an... (Review)
Review
Cranberry juice is a popular beverage with many health benefits. It has anthocyanins to supplement dietary needs. Based on in vitro evidence cranberry juice is an inhibitor of CYP enzymes and at higher amounts as potent as ketoconazole (CYP3A) and fluconazole (CYP2C9). There is, however, a discrepancy between in vitro and in vivo observations with respect to a number of substrates (cyclosporine, warfarin, flurbiprofen, tizanidine, diclofenac, amoxicillin, ceflacor); with the exception of a single report on midazolam, where there was a moderate increase in the AUC of midazolam in subjects pre-treated with cranberry juice. However, another study questions the clinical relevancy of in vivo pharmacokinetic interaction between cranberry juice and midazolam. The controversy may be due to a) under in vitro conditions all anthocyanin principles may be available to have a concerted effort in CYP inhibition; however, limited anthocyanin principles may be bioavailable with varying low levels in the in vivo studies; b) a faster clearance of the active anthocyanin principles under in vivo conditions may occur, leading to low threshold levels for CYP inhibition; c) efficient protein binding and/or rapid tissue uptake of the substrate may have precluded the drug availability to the enzymes in the in vivo studies. With respect to pharmacodynamic aspects, while the debate continues on the issue of an interaction between warfarin and cranberry juice, the summation of the pharmacodynamics data obtained in patients and healthy subjects from different prospectively designed and controlled clinical trials does not provide overwhelming support for the existence of a pharmacodynamic drug interaction for normal cranberry juice ingestion. However, it is apparent that consumption of large quantities of cranberry juice (about 1-2 L per day) or cranberry juice concentrates in supplements for an extended time period (>3-4 weeks) may temporally alter the effect of warfarin. Therefore, the total avoidance of cranberry juice by warfarin users may not be warranted by the published studies. However, in certain situations of higher intake of cranberry juice or concentrate there may be a need to monitor both warfarin doses and its effect.
Topics: Animals; Beverages; Cytochrome P-450 Enzyme System; Food-Drug Interactions; Fruit; Herb-Drug Interactions; Humans; Vaccinium macrocarpon
PubMed: 23958198
DOI: 10.18433/j3ng6z -
Journal of Clinical Pharmacy and... Oct 2022Antiretrovirals have a high drug interaction potential, which can lead to increased toxicity and/or decreased efficacy. Multiple databases are available to assess...
WHAT IS KNOWN AND OBJECTIVE
Antiretrovirals have a high drug interaction potential, which can lead to increased toxicity and/or decreased efficacy. Multiple databases are available to assess drug-drug interactions. The aim of our study was to compare interaction identification for commonly used ARVs and concomitant medications between six different online drug-drug interaction databases.
COMMENT
This was a cross-sectional review using each of the following six databases: LexiComp®, Clinical Pharmacology®, Micromedex®, Epocrates®, University of Liverpool, and University of Toronto. Sixteen antiretroviral drugs and 100 of the DrugStats Database "Top 200 of 2019" list of medications were included. Each of the six databases identified a different number of actual or potential interactions. The number of interactions ranged from 211 to 283.
WHAT IS NEW AND CONCLUSIONS
A variety of databases exist with inconsistent identification of actual or potential drug-drug interactions amongst them. It may be beneficial to cross-reference multiple databases prior to making decisions regarding patient care.
Topics: Anti-Retroviral Agents; Cross-Sectional Studies; Databases, Factual; Drug Interactions; HIV Infections; Humans
PubMed: 36059105
DOI: 10.1111/jcpt.13750 -
AMIA ... Annual Symposium Proceedings.... 2022: Polypharmacy can be a source of adverse drug events including those caused by drug to drug interaction (DDI) exposures. Web-based DDI databases are available to...
: Polypharmacy can be a source of adverse drug events including those caused by drug to drug interaction (DDI) exposures. Web-based DDI databases are available to researchers for the identification of potential DDI exposures. Rather than relying on potentially incomplete DDI databases, large clinical data repositories (CDR) which are integrated data sources fed with millions of heterogeneous electronic health records (EHRs) containing real-world data should be leveraged for data driven DDI identification. : To explore and validate the viability of clinical data repositories as data driven resources for clinically important adverse drug events detection and surveillance. : This work leverages a minimum clinical data set from the University of Minnesota's CDR to identify drugs that have statin to drug interaction (SDI) potential and compares the findings with results of web based DDI databases. Using an SDI identification matrix, we identified several potential novel SDI drugs that were not mentioned in the web-based sources but explored through our study as drugs with SDI potential. : Drugs flagged by our SDI identification matrix but not mentioned in the web-based sources include Lysine, Ketotifen, Latanoprost, Methylcellulose, Oxazepam, Linseed Oil, and others. : Our findings identified potential gaps regarding the completeness, currency, and overall reliability of open source and commercial DDI databases. CDRs can be a primary source for identifying drug to drug interactions. : clinical data repository, drug to drug interaction databases, drug to drug interaction, statin to drug interaction, polypharmacy, statin to drug interaction identification matrix, adverse drug event, statin.
Topics: Humans; Hydroxymethylglutaryl-CoA Reductase Inhibitors; Reproducibility of Results; Drug Interactions; Drug-Related Side Effects and Adverse Reactions; Risk Assessment
PubMed: 37128384
DOI: No ID Found -
Epilepsy Research Jul 2020Brivaracetam is an antiepileptic drug (AED) indicated for the treatment of focal seizures, with improved safety and tolerability vs first-generation AEDs. Brivaracetam... (Review)
Review
Brivaracetam is an antiepileptic drug (AED) indicated for the treatment of focal seizures, with improved safety and tolerability vs first-generation AEDs. Brivaracetam binds with high affinity to synaptic vesicle protein 2A in the brain, which confers its antiseizure activity. Brivaracetam is rapidly absorbed and extensively biotransformed, and exhibits linear and dose-proportional pharmacokinetics at therapeutic doses. Brivaracetam does not interact with most metabolizing enzymes and drug transporters, and therefore does not interfere with drugs that use these metabolic routes. The favorable pharmacokinetic profile of brivaracetam and lack of clinically relevant drug-drug interactions with commonly prescribed AEDs or oral contraceptives allows administration without dose adjustment, and avoids potential untoward events from decreased efficacy of an AED or oral contraceptive due to a drug-drug interaction. Few agents have been reported to affect the pharmacokinetics of brivaracetam. The strong enzyme-inducing AEDs carbamazepine, phenytoin and phenobarbital/primidone have been shown to moderately lower brivaracetam plasma concentrations, with no adjustment of brivaracetam dose needed. Dose adjustment should be considered when brivaracetam is coadministered with the more potent CYP inducer, rifampin. Additionally, caution should be used when adding or ending treatment with the strong enzyme inducer, St. John's wort. In summary, brivaracetam (50-200 mg/day) has a favorable pharmacokinetic profile and is associated with few clinically relevant drug-drug interactions.
Topics: Anticonvulsants; Brain; Carbamazepine; Drug Interactions; Humans; Pyrrolidinones; Seizures
PubMed: 32361205
DOI: 10.1016/j.eplepsyres.2020.106327 -
Drug Design, Development and Therapy 2022The combined administration of tadalafil, a phosphodiesterase-5 inhibitor, and amlodipine, a calcium channel blocker, can be a promising therapeutic option for... (Randomized Controlled Trial)
Randomized Controlled Trial
PURPOSE
The combined administration of tadalafil, a phosphodiesterase-5 inhibitor, and amlodipine, a calcium channel blocker, can be a promising therapeutic option for hypertension patients with erectile dysfunction. This study aimed to examine the pharmacokinetic drug interaction between tadalafil and amlodipine and the tolerability of their combined administration in healthy male subjects.
SUBJECTS AND METHODS
Healthy volunteers (N = 24) were randomly assigned to one of the six sequences that consisted of three treatments: tadalafil (5 mg) alone, amlodipine (10 mg) alone, and tadalafil plus amlodipine. The study drugs were administered orally for 9 d, and the collected serial blood samples were analyzed up to 72 h after the last dosing. Pharmacokinetic parameters were calculated using non-compartmental analysis.
RESULTS
For tadalafil, geometric mean ratios (GMRs) (90% confidence interval (CI)) of the combined therapy over the monotherapy were 1.57 (1.46-1.68) for AUC and 1.34 (1.24-1.45) for C. For amlodipine, the GMRs (90% CI) of AUC and C were 0.93 (0.90-0.97) and 0.95 (0.91-0.99), respectively. The severity of all observed adverse events (AEs) related to the study drugs was mild, and the frequency of AEs of the combined administration was not significantly different from the monotherapy.
CONCLUSION
A substantial pharmacokinetic drug interaction between tadalafil and amlodipine was observed with respect to the concentration of tadalafil when administered concomitantly. However, the dose range of the combined administration of tadalafil and amlodipine in the present study was well tolerated by the subjects.
Topics: Administration, Oral; Amlodipine; Area Under Curve; Cross-Over Studies; Drug Interactions; Healthy Volunteers; Humans; Male; Tadalafil
PubMed: 35221673
DOI: 10.2147/DDDT.S348897 -
PeerJ 2023Unlike conventional drug substances, herbal medicines are composed of a complex of biologically active compounds. Therefore, the potential occurrence of herb-drug... (Review)
Review
Unlike conventional drug substances, herbal medicines are composed of a complex of biologically active compounds. Therefore, the potential occurrence of herb-drug interactions is even more probable than for drug-drug interactions. Interactions can occur on both the pharmacokinetic and pharmacodynamic level. Herbal medicines may affect the resulting efficacy of the concomitantly used (synthetic) drugs, mainly on the pharmacokinetic level, by changing their absorption, distribution, metabolism, and excretion. Studies on the pharmacodynamic interactions of herbal medicines and conventional drugs are still very limited. This interaction level is related to the mechanism of action of different plant constituents. Herb-drug interactions can cause changes in drug levels and activities and lead to therapeutic failure and/or side effects (sometimes toxicities, even fatal). This review aims to provide a summary of recent information on the potential drug interactions involving commonly used herbal medicines that affect the central nervous system () and conventional drugs. The survey databases were used to identify primary scientific publications, case reports, and secondary databases on interactions were used later on as well. Search keywords were based on plant names (botanical genera), officinal herbal drugs, herbal drug preparations, herbal drug extracts.
Topics: Herb-Drug Interactions; Plants, Medicinal; Plant Extracts; Phytotherapy; Central Nervous System
PubMed: 38025741
DOI: 10.7717/peerj.16149