Ligand‐Based Models for the Isoform Specificity of Cytochrome P450 3A4, 2D6, and 2C9 Substrates.

Terfloth, Lothar; Bienfait, Bruno; Gasteiger, Johann

doi:10.1002/chin.200742214

Cited by 22 publications

(47 citation statements)

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“…In relation to QSAR models for CYP450 substrate recognition, Terfloth et al (2007) investigated the application of several model-building techniques, namely: k-Nearest Neighbours (k-NN), C4.5/J48 decision tree, Multilayer Perceptron Neural Networks (MLP-NN), Radial Basis Function Neural Networks (RBF-NN), Logistic Regression (LR) and Support Vector Machine (SVM), to predict the isoform specificity for CYP450 3A4, 2D6 and 2C9 substrates. The authors used a dataset originally compiled by Manga et al (2005), containing drugs which are predominately metabolised by CYP3A4, CYP2D6 or CYP2C9.…”

Section: Literature Models For Metabolismmentioning

confidence: 99%

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

2010

EFSA Supporting Publications

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Section: Literature Models For Metabolismmentioning

confidence: 99%

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

2010

EFSA Supporting Publications

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Section: Introductionmentioning

confidence: 99%

“…Machine learning methods are particularly useful for data mining of large databases to discover patterns or rules to derive models for problems for which the underlying mechanism is not clear. For example, support vector machine (SVM) methods have been applied to classify inhibitors of the CYP3A4 enzyme with a success rate of approximately 70% for the test set (807/538 compounds in training/test sets) (Kriegl et al, 2005a) and to predict isoform specificity of CYP3A4, CYP2D6, and CYP2C9 substrates with approximately 80% of the test set correctly predicted (146/233 compounds in training/test sets) (Terfloth et al, 2007).Thus, in silico methods seem promising for making reliable models for sets of a large number of compounds. In this study, we used approximately 400 compounds to construct models for CYP1A2 inhibition and to explore the accuracy of various machine learning Downloaded from methods such as SVM, random forest, decision tree, kappa nearest neighbor (kNN), and binary QSAR methods for a test set containing 7000 compounds.…”

mentioning

confidence: 99%

Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques

Poongavanam

Taboureau²,

Oostenbrink³

et al. 2008

Drug Metab Dispos

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ABSTRACT:The cytochrome P450 (P450) superfamily plays an important role in the metabolism of drug compounds, and it is therefore highly desirable to have models that can predict whether a compound interacts with a specific isoform of the P450s. In this work, we provide in silico models for classification of CYP1A2 inhibitors and noninhibitors. Training and test sets consisted of approximately 400 and 7000 compounds, respectively. Various machine learning techniques, such as binary quantitative structure activity relationship, support vector machine (SVM), random forest, kappa nearest neighbor (kNN), and decision tree methods were used to develop in silico models, based on Volsurf and Molecular Operating Environment descriptors. The best models were obtained using the SVM, random forest, and kNN methods in combination with the BestFirst variable selection method, resulting in models with 73 to 76% of accuracy on the test set prediction (Matthews correlation coefficients of 0.51 and 0.52). Finally, a decision tree model based on Lipinski's Rule-of-Five descriptors was also developed. This model predicts 67% of the compounds correctly and gives a simple and interesting insight into the issue of classification. All of the models developed in this work are fast and precise enough to be applicable for virtual screening of CYP1A2 inhibitors or noninhibitors or can be used as simple filters in the drug discovery process.Cytochromes P450 (P450s) are heme-containing enzymes found in both prokaryotes and eukaryotes, and they are involved in a wide range of cellular biotransformation functions. From a pharmaceutical perspective, the most important function is the degradation of drugs (Nebert and Russell, 2002). In general, hydrophobic compounds are converted into more hydrophilic species to facilitate excretion.The most important P450 isoforms involved in metabolism of drugs in humans are CYP1A2, CYP2A6, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4. CYP1A2 constitutes 12% of the total P450 content in the liver and plays an important role in the metabolic clearance of ϳ5% of currently marketed drugs. The substrates for the CYP1A subfamily are generally characterized as neutral, flat, aromatic, and lipophilic (two to four aromatic rings) with at least one putative hydrogen bond donor (Smith et al., 1997), in agreement with the observed contacts in the recent crystal structure of CYP1A2 (Sansen et al., 2007). Examples of drugs that are CYP1A2 substrates are acetaminophen, caffeine, clozapine, haloperidol, olanzapine, propranolol, tacrine, theophylline, and zolmitriptan (drug interactions: cytochrome P450 drug interaction table, Indiana University School of Medicine, http://medicine.iupui.edu/flockhart/table.htm).In silico approaches are attractive because they can be used in an early stage of the drug discovery process and thereby reduce the number of experimental studies and improve the success rates. For this purpose, various traditional in silico modeling methods and more recently developed nonlinear machine learning methods...

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“…Based on trees from Lewis et al [34]., Terfloth et al [35]., Yamashita et al [36]. and Zhang et al [37].…”

Section: Decision Tree For Cyp Isoform Predictionmentioning

confidence: 99%

Precursors for cytochrome P450 profiling breath tests from an in silico screening approach

et al. 2014

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The family of cytochrome P450 enzymes (CYPs) is a major player in the metabolism of drugs and xenobiotics. Genetic polymorphisms and transcriptional regulation give a complex patientindividual CYP activity profile for each human being. Therefore, personalized medicine demands easy and non-invasive measurement of the CYP phenotype. Breath tests detect volatile organic compounds (VOCs) in the patients' exhaled air after administration of a precursor molecule. CYP breath tests established for individual CYP isoforms are based on the detection of 13 CO 2 or 14 CO 2 originating from CYP-catalyzed oxidative degradation reactions of isotopically labeled precursors.We present an in silico work-flow aiming at the identification of novel precursor molecules, likely to result in VOCs other than CO 2 upon oxidative degradation as we aim at label-free precursor molecules. The ligand-based work-flow comprises five parts: (1) CYP profiling was encoded as a decision tree based on 2D molecular descriptors derived from established models in the literature and validated against publicly available data extracted from the DrugBank. (2) Likely sites of metabolism were identified by reactivity and accessibility estimation for abstractable hydrogen radical. (3) Oxidative degradation reactions (O-and N-dealkylations) were found to be most promising in the release of VOCs. Thus, the CYP-catalyzed oxidative degradation reaction was encoded as SMIRKS (a programming language style to implement reactions based on the SMARTS description) to enumerate possible reaction products. (4) A quantitative structure property relation (QSPR) model aiming to predict the Henry constant H was derived from data for 488 organic compounds and identifies potentially VOCs amongst CYP reaction products.(5) A blacklist of naturally occurring breath components was implemented to identify marker molecules allowing straightforward detection within the exhaled air.

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Ligand‐Based Models for the Isoform Specificity of Cytochrome P450 3A4, 2D6, and 2C9 Substrates.

Abstract: ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.

Cited by 22 publications

References 1 publication

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

Applicability of QSAR analysis to the evaluation of the toxicological relevance of metabolites and degradates of pesticide active substances for dietary risk assessment

Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques

Precursors for cytochrome P450 profiling breath tests from an in silico screening approach

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