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 The Cold Contact Method as a Simple Drug Interaction Detection System

2008 ◽  
Vol 2008 ◽  
pp. 1-4 ◽  
Author(s):  
Ilma Nugrahani ◽  
Sukmadjaja Asyarie ◽  
Sundani Nurono Soewandhi ◽  
Slamet Ibrahim
Keyword(s):  
Simple Technique ◽  
Detection System ◽  
Drug Dosage ◽  
Dosage Forms ◽  
Physical Interaction ◽  
Contact Method ◽  
Melting Point Determination ◽  
Interaction Detection ◽  
Amoxicillin Clavulanate ◽  
Point Determination

The physical interaction between 2 substances frequently occurs along the mixing and manufacturing of solid drug dosage forms. The physical interaction is generally based on coarrangement of crystal lattice of drug combination. The cold contact method has been developed as a simple technique to detect physical interaction between 2 drugs. This method is performed by observing new habits of cocrystal that appear on contact area of crystallization by polarization microscope and characterize this cocrystal behavior by melting point determination. Has been evaluated by DSC, this method is proved suitable to identify eutecticum interaction of pseudoephedrine HCl-acetaminophen, peritecticum interaction of methampyrone-phenylbutazon, and solid solution interaction of amoxicillin-clavulanate, respectively.

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.

Design of Biomolecule Interaction Detection System Based on Fiber Biosensor

2020 ◽  
Vol 20 (16) ◽  
pp. 8922-8929
Author(s):  
Wenhao Zhang ◽  
Tiechao Jiang
Keyword(s):  
Detection System ◽  
Interaction Detection

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.

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