Evidence map›Paper›PMID 27735849›Full record

ArticleInternational journal of molecular sciences2016

Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System.

Shuai-Bing He, Man-Man Li, Bai-Xia Zhang, Xiao-Tong Ye, Ran-Feng Du, Yun Wang, Yan-Jiang Qiao

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. An In Silico Model for Predicting Drug-Induced Hepatotoxicity.International journal of molecular sciences · 2019
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Shuai-Bing HeKey Laboratory of Traditional Chinese Medicine-Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China. wenyuxuan2530@163.com.
Man-Man LiKey Laboratory of Traditional Chinese Medicine-Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China. limanman@bucm.edu.cn.
Bai-Xia ZhangCollege of Chinese Medicine, Hebei University, Baoding 071002, China. baixia575@163.com.
Xiao-Tong YeKey Laboratory of Traditional Chinese Medicine-Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China. yexiaotongs@163.com.
Ran-Feng DuKey Laboratory of Traditional Chinese Medicine-Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China. duranfeng@126.com.
Yun WangKey Laboratory of Traditional Chinese Medicine-Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China. wangyun@bucm.edu.cn.
Yan-Jiang QiaoKey Laboratory of Traditional Chinese Medicine-Information Engineer of State Administration of Traditional Chinese Medicine, School of Chinese Pharmacy, Beijing University of Chinese Medicine, Beijing 100102, China. yjqiao@bucm.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)-a novel concept, which integrates information about site of metabolism (SOM) and enzyme-was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic

Indexed as

Models, TheoreticalArea Under CurveBiotransformationCytochrome P-450 CYP2C9Cytochrome P-450 CYP2D6Cytochrome P-450 CYP3AHumansMicrosomes, LiverPharmaceutical PreparationsROC CurveSupport Vector MachineCytochrome P-450 CYP2C9Cytochrome P-450 CYP2D6Cytochrome P-450 CYP3APharmaceutical PreparationsclassificationCYP2C9CYP2D6CYP3A4feature selectionmetabolism predictionmicrosomal metabolic reaction system

Identifiers

PMID27735849
PMCPMC5085718

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.