Designing functional materials: DNA/Poly(3,4-ethylenedioxythiophene) interfaces for advanced DNA direct electrochemistry and DNA-Drug interaction detection

2021 ◽  
Vol 272 ◽  
pp. 115382
Author(s):  
Filiz Kuralay ◽  
Nilgün Dükar ◽  
Yaşar Bayramlı
Keyword(s):  
Drug Interaction ◽  
Direct Electrochemistry ◽  
Functional Materials ◽  
Interaction Detection

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 Cold Contact Method as a Simple Drug Interaction Detection System

2011 ◽  
pp. 92-97
Author(s):  
Ilma Nugrahani ◽  
Sukmadjaja Asyarie ◽  
Sundani Soewandhi ◽  
Slamet Ibrahim
Keyword(s):  
Drug Interaction ◽  
Detection System ◽  
Contact Method ◽  
Interaction Detection

scholarly journals Analysis of overridden alerts in a drug-drug interaction detection system

2008 ◽  
Vol 20 (6) ◽  
pp. 400-405 ◽  
Author(s):  
F. Mille ◽  
C. Schwartz ◽  
F. Brion ◽  
J.-E. Fontan ◽  
O. Bourdon ◽  
...  
Keyword(s):  
Drug Interaction ◽  
Detection System ◽  
Interaction Detection ◽  
Drug Drug Interaction

scholarly journals Structure-Based Drug-Drug Interaction Detection via Expressive Graph Convolutional Networks and Deep Sets (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13927-13928
Author(s):  
Mengying Sun ◽  
Fei Wang ◽  
Olivier Elemento ◽  
Jiayu Zhou
Keyword(s):  
Drug Interaction ◽  
Detection Method ◽  
Molecular Structures ◽  
Convolutional Networks ◽  
Interaction Detection ◽  
Drug Drug Interaction

In this work, we proposed a DDI detection method based on molecular structures using graph convolutional networks and deep sets. We proposed a more discriminative convolutional layer compared to conventional GCN and achieved permutation invariant prediction without losing the capability of capturing complicated interactions.

Assessment of Drug Interactions with Online Electronic Checkers in Multi-Pathological Patients

Pharmacology ◽  
2021 ◽  
pp. 1-5
Author(s):  
Bernardino Roca ◽  
Manuel Roca
Keyword(s):  
Drug Interaction ◽  
Drug Interactions ◽  
Cross Sectional Study ◽  
Sectional Study ◽  
Cross Sectional ◽  
Medicine Department ◽  
Interaction Detection ◽  
Drug Substances ◽  
Positive Correlations ◽  
Hospital General

<b><i>Introduction:</i></b> Multi-pathological patients are at high risk of drug interactions and side effects. We aimed to assess the usefulness of 3 online drug interaction checkers. <b><i>Methods:</i></b> In a cross-sectional study, carried out in the Medicine Department of Hospital General of Castellon, Spain, in February 2020, we assessed drug interaction detection with 3 online electronic checkers, Drugs.com, Lexicomp®, and Medscape, and compared results obtained with the 3 tools. From every hospitalized patient, we obtained the list of drugs he or she had been taking until admission. Bivariable tests were used for analysis. <i>p</i> values &#x3c;0.05 were considered significant. <b><i>Results:</i></b> We included data from 134 patients; 68 (51%) were male; median (and interquartile range) of their age was 82 (76–88) years. A total of 1,082 substance drugs were entered in the checkers. The number of highest grade interactions found with every program was Drugs.com 85, Lexicomp® 33, and Medscape 67. Positive correlations were found between age and number of drug substances prescribed (<i>p</i> = 0.009) and between number of drug substances prescribed and interactions found with any of the 3 checkers (<i>p</i> &#x3c; 0.001 in all 3 cases). Regarding highest grade interactions, agreement among all 3 checkers was poor. <b><i>Conclusions:</i></b> The 3 online checkers we assessed found a large number of interactions. The 3 programs gave very discrepant results. <b><i>Impact on Practice Statements:</i></b> The analyzed programs, Drugs.com, Lexicomp®, and Medscape Interactions, found a large number of drug interactions in the studied patients. The 3 programs gave very discrepant results among them.

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