Literature DB >> 20446902

Graphic rule for drug metabolism systems.

Kuo-Chen Chou1.   

Abstract

Using graphic rules to deal with kinetic systems is an elegant approach by combining the graph representation (schematic representation) and rigorous mathematical derivation. It bears the following advantages: (1) providing an intuitive picture or illuminative insights; (2) helping grasp the key points from complicated details; (3) greatly simplifying many tedious, laborious, and error-prone calculations; and (4) able to double-check the final results. In this mini review, the non-steady state graphic rule in enzyme-catalyzed kinetics and protein-folding kinetics was extended to cover drug-metabolic systems. As a demonstration, a step-by-step illustration is presented showing how to use the graphic rule to derive the concentrations of the parent drug and its metabolites vs. time for the seliciclib, vildagliptin, and cyclin-dependent kinase inhibitor (AG-024322) metabolic systems, respectively. It can be seen from these paradigms that the graphic rule is particularly useful to analyze complicated drug metabolic systems and ensure the correctness of the derived results. Meanwhile, the intuitive feature of graphic representation may facilitate analyzing and classifying drug metabolic systems; e.g., according to their directed graphs, the metabolism of seliciclib and the metabolism of vildagliptin can be categorized as 0-->5 mechanism while that of AG-024322 as 0-->4-->3 mechanism.

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Year:  2010        PMID: 20446902     DOI: 10.2174/138920010791514261

Source DB:  PubMed          Journal:  Curr Drug Metab        ISSN: 1389-2002            Impact factor:   3.731


  33 in total

1.  Study of drug function based on similarity of pathway fingerprint.

Authors:  Hao Ye; Kailin Tang; Linlin Yang; Zhiwei Cao; Yixue Li
Journal:  Protein Cell       Date:  2012-03-17       Impact factor: 14.870

2.  iN6-methylat (5-step): identifying DNA N6-methyladenine sites in rice genome using continuous bag of nucleobases via Chou's 5-step rule.

Authors:  Nguyen Quoc Khanh Le
Journal:  Mol Genet Genomics       Date:  2019-05-04       Impact factor: 3.291

3.  Protein remote homology detection by combining Chou's distance-pair pseudo amino acid composition and principal component analysis.

Authors:  Bin Liu; Junjie Chen; Xiaolong Wang
Journal:  Mol Genet Genomics       Date:  2015-04-21       Impact factor: 3.291

4.  iNuc-PhysChem: a sequence-based predictor for identifying nucleosomes via physicochemical properties.

Authors:  Wei Chen; Hao Lin; Peng-Mian Feng; Chen Ding; Yong-Chun Zuo; Kuo-Chen Chou
Journal:  PLoS One       Date:  2012-10-29       Impact factor: 3.240

5.  Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.

Authors:  Bi-Qing Li; Tao Huang; Lei Liu; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2012-04-04       Impact factor: 3.240

6.  Predicting the network of substrate-enzyme-product triads by combining compound similarity and functional domain composition.

Authors:  Lei Chen; Kai-Yan Feng; Yu-Dong Cai; Kuo-Chen Chou; Hai-Peng Li
Journal:  BMC Bioinformatics       Date:  2010-05-31       Impact factor: 3.169

7.  Classification and analysis of regulatory pathways using graph property, biochemical and physicochemical property, and functional property.

Authors:  Tao Huang; Lei Chen; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2011-09-28       Impact factor: 3.240

8.  Prediction of body fluids where proteins are secreted into based on protein interaction network.

Authors:  Le-Le Hu; Tao Huang; Yu-Dong Cai; Kuo-Chen Chou
Journal:  PLoS One       Date:  2011-07-29       Impact factor: 3.240

9.  Predicting Anatomical Therapeutic Chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities.

Authors:  Lei Chen; Wei-Ming Zeng; Yu-Dong Cai; Kai-Yan Feng; Kuo-Chen Chou
Journal:  PLoS One       Date:  2012-04-13       Impact factor: 3.240

10.  Signal propagation in protein interaction network during colorectal cancer progression.

Authors:  Yang Jiang; Tao Huang; Lei Chen; Yu-Fei Gao; Yudong Cai; Kuo-Chen Chou
Journal:  Biomed Res Int       Date:  2013-03-20       Impact factor: 3.411

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