scholarly journals Qualitative Analysis of the Role of Metabolites in Inhibitory Drug−Drug Interactions: Literature Evaluation Based on the Metabolism and Transport Drug Interaction Database

2009 ◽  
Vol 22 (2) ◽  
pp. 294-298 ◽  
Author(s):  
Nina Isoherranen ◽  
Houda Hachad ◽  
Catherine K. Yeung ◽  
Rene H. Levy
Keyword(s):  
Drug Interaction ◽  
Qualitative Analysis ◽  
Drug Interactions ◽  
Interaction Database ◽  
Inhibitory Drug

scholarly journals Screening Ingredients from Herbs against Pregnane X Receptor in the Study of Inductive Herb-Drug Interactions: Combining Pharmacophore and Docking-Based Rank Aggregation

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zhijie Cui ◽  
Hong Kang ◽  
Kailin Tang ◽  
Qi Liu ◽  
Zhiwei Cao ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Target Genes ◽  
Pharmacophore Model ◽  
Pregnane X Receptor ◽  
Rank Aggregation ◽  
Pharmacophore Modelling ◽  
Interaction Database ◽  
Drug Metabolizing Enzyme ◽  
Metabolizing Enzyme

The issue of herb-drug interactions has been widely reported. Herbal ingredients can activate nuclear receptors and further induce the gene expression alteration of drug-metabolizing enzyme and/or transporter. Therefore, the herb-drug interaction will happen when the herbs and drugs are coadministered. This kind of interaction is called inductive herb-drug interactions. Pregnane X Receptor (PXR) and drug-metabolizing target genes are involved in most of inductive herb-drug interactions. To predict this kind of herb-drug interaction, the protocol could be simplified to only screen agonists of PXR from herbs because the relations of drugs with their metabolizing enzymes are well studied. Here, a combinational in silico strategy of pharmacophore modelling and docking-based rank aggregation (DRA) was employed to identify PXR’s agonists. Firstly, 305 ingredients were screened out from 820 ingredients as candidate agonists of PXR with our pharmacophore model. Secondly, DRA was used to rerank the result of pharmacophore filtering. To validate our prediction, a curated herb-drug interaction database was built, which recorded 380 herb-drug interactions. Finally, among the top 10 herb ingredients from the ranking list, 6 ingredients were reported to involve in herb-drug interactions. The accuracy of our method is higher than other traditional methods. The strategy could be extended to studies on other inductive herb-drug interactions.

scholarly journals Potential Drug interactions with Drugs used for Bipolar Disorder: A Comparison of 6 Drug Interaction Database Programs

2020 ◽  
Vol 53 (05) ◽  
pp. 220-227
Author(s):  
Scott Monteith ◽  
Tasha Glenn ◽  
Michael Gitlin ◽  
Michael Bauer
Keyword(s):  
Bipolar Disorder ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Mood Stabilizer ◽  
Kappa Statistic ◽  
Category Rating ◽  
Interaction Database ◽  
Percent Agreement ◽  
Psychiatric Drugs ◽  
Kappa Agreement

Abstract Background Patients with bipolar disorder frequently experience polypharmacy, putting them at risk for clinically significant drug-drug interactions (DDI). Online drug interaction database programs are used to alert physicians, but there are no internationally recognized standards to define DDI. This study compared the category of potential DDI returned by 6 commercial drug interaction database programs for drug interaction pairs involving drugs commonly prescribed for bipolar disorder. Methods The category of potential DDI provided by 6 drug interaction database programs (3 subscription, 3 open access) was obtained for 125 drug interaction pairs. The pairs involved 103 drugs (38 psychiatric, 65 nonpsychiatric); 88 pairs included a psychiatric and nonpsychiatric drug; 37 pairs included 2 psychiatric drugs. Every pair contained at least 1 mood stabilizer or antidepressant. The category provided by 6 drug interaction database programs was compared using percent agreement and Fleiss kappa statistic of interrater reliability. Results For the 125 drug pairs, the overall percent agreement among the 6 drug interaction database programs was 60%; the Fleiss kappa agreement was slight. For drug interaction pairs with any category rating of severe (contraindicated), the kappa agreement was moderate. For drug interaction pairs with any category rating of major, the kappa agreement was slight. Conclusion There is poor agreement among drug interaction database programs for the category of potential DDI involving psychiatric drugs. Drug interaction database programs provide valuable information, but the lack of consistency should be recognized as a limitation. When assistance is needed, physicians should check more than 1 drug interaction database program.

Abstract 4400: Translational drug interaction database (TDID): A knowledgebase for drug interactions

2020 ◽  
Author(s):  
Shijun Zhang ◽  
Heng-Yi Wu ◽  
Rohith Vanam ◽  
chien-WeiChiang ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Interaction Database

scholarly journals Drug Interaction Database Sensitivity With Oral Antineoplastics: An Exploratory Analysis

2017 ◽  
Vol 13 (3) ◽  
pp. e217-e222 ◽  
Author(s):  
John B. Bossaer ◽  
Christan M. Thomas
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Clinical Judgment ◽  
Primary Objective ◽  
Exploratory Analysis ◽  
Multiple Resources ◽  
Interaction Database ◽  
Sensitivity Distribution ◽  
Significant Difference ◽  
Clinically Significant

Purpose: Drug interactions are a concern in oncology with the shift toward oral antineoplastics (OAs). Using electronic databases to screen for drug interactions with OAs is a common practice. There is little literature to guide clinicians on the reliability of these systems with OAs. The primary objective of this study was to explore the sensitivity of commonly available drug interaction databases in detecting drug interactions with OAs. Methods: A list of 20 drug interactions with OAs was developed by two Board-certified oncology pharmacists. The list included multiple types of drug interactions. The sensitivity in detecting these interactions by MicroMedex, Facts & Comparisons, Lexi-Interact, and Epocrates were evaluated. These databases were chosen based on their local availability and widespread use in practice. Drugs.com was evaluated as a surrogate for a patient-accessible drug interaction database. The Cochran Q test was used to assess the sensitivity distribution across the five groups. Results: Lexi-Interact and Drugs.com had a sensitivity of 95% for the 20 tested drug interaction pairs. Epocrates had a sensitivity of 90%, and both Micromedex and Facts & Comparisons had a sensitivity of 70%. There was a statistically significant difference ( P = .016) in the distribution across the databases in detecting clinically significant drug interactions. Conclusion: Commonly used databases for identifying drug interactions with oral antineoplastics vary significantly in their sensitivity. Clinicians should not rely on a single database and should consider using multiple resources as well as sound clinical judgment. Further work is needed in this area.

Predicting Inhibitory Drug—Drug Interactions and Evaluating Drug Interaction Reports Using Inhibition Constants

2005 ◽  
Vol 39 (6) ◽  
pp. 1064-1072 ◽  
Author(s):  
Kenneth A Bachmann ◽  
Jeffrey D Lewis
Keyword(s):  
Cytochrome P450 ◽  
Drug Interaction ◽  
Drug Interactions ◽  
Web Sites ◽  
Case Reports ◽  
Data Extraction ◽  
Data Sources ◽  
Study Selection ◽  
Inhibitory Drug

OBJECTIVE: To review the use of inhibitory constants (Ki) determined from in vitro experiments in the prediction of the significance of inhibitory drug—drug interactions (DDIs). DATA SOURCES: Searches of MEDLINE (1966—August 2004) and manual review of journals, conference proceedings, reference textbooks, and Web sites were performed using the key search terms cytochrome P450, drug—drug interaction, inhibition constant, and Ki. STUDY SELECTION AND DATA EXTRACTION: All articles identified from the data sources were evaluated, and information deemed relevant was included for this review. DATA SYNTHESIS: The cytochrome P450 isoenzymes factor prominently in the explanation of numerous DDIs. Although the regulation of these enzymes by one drug can affect the pharmacokinetics of other drugs, the consequences may not necessarily be significant either in terms of pharmacokinetic or clinical outcomes. Yet, many DDI monographs originate as unconfirmed case reports that implicate the influence of one drug on the CYP-mediated metabolism of another, and these often uncorroborated mechanisms can eventually become regarded as dogma. One consequence of this process is the overprediction of potentially important DDIs. The pharmaceutical industry, Food and Drug Administration, and pharmaceutical scientists have developed a strategy for predicting the significance of inhibitory DDIs at the earliest possible stages of drug development based on a new chemical entity's Ki value, determined in vitro. CONCLUSIONS: We suggest that the use of Ki values of drugs purported to behave as CYP inhibitors be incorporated in the assessment of case reports that ascribe DDIs to inhibition of metabolism of one drug by another.

scholarly journals Regression-Based Model for the Evaluation of CYP2D6-Mediated Drug-Drug Interactions

2020 ◽  
Author(s):  
Roberto Lozano ◽  
Alberto Frutos ◽  
Alberto Apesteguia ◽  
Alejandro Martinez ◽  
Maria-Angeles Alcazar ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Linear Regression ◽  
Drug Interactions ◽  
Regression Model ◽  
Area Under Curve ◽  
Mean Value ◽  
Inhibition Constant ◽  
Constant Ratio ◽  
The Mean ◽  
Inhibitory Drug

Linear regression-based model between area-under-curve (AUCr) and thetherapeutic range-to-inhibition constant ratio (TR-to-Ki). As result, a linear log-log regression model,between the averaged AUCr, calculated as the mean from the values of different DDIs between severalvictim-drugs with the same inhibitor (AUCr avg), and the mean TR-to-Ki ratio (TRm-to-Ki), calculated asthe mean value between the maximum and minimum TR of the inhibitor divided by its Ki, obtained thebest correlation (r2= 0.72; p=0.0116). Accordingly, a drug-drug interaction involving reversible inhibitorydrugs of CYP2D6 could be manage by adjusting dose of victim and/or inhibitory drug to the magnitudeof the desired change, by applying data of TR and Ki of inhibitor to the equation of regression linepresented here.

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