Computer systems for the prediction of xenobiotic metabolism

Langowski, J. J.; Long, Anthony D.

doi:10.1016/s0169-409x(02)00011-x

Cited by 133 publications

(72 citation statements)

References 18 publications

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“…These selected descriptors are consistent with the findings that the substrates of CYP2C9 are primarily polar compounds that contain an aromatic group and that drug-CYP2C9 interaction is mediated by both hydrogen bonding 9 and π-π interactions at the binding site. 13 The differences in the distribution of intermolecular forces between inhibitors and substrates of CYP2C9 suggest that the inhibitors have fewer hydrogen bonds but increased electrostatic interactions at the active site compared to the substrates. Potential Training Errors and Misclassified Compounds.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Cytochrome P450 3A4, 2D6, and 2C9 Inhibitors and Substrates by Using Support Vector Machines

Yap

Chen

2005

J. Chem. Inf. Model.

152

185

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Statistical learning methods have been used in developing filters for predicting inhibitors of two P450 isoenzymes, CYP3A4 and CYP2D6. This work explores the use of different statistical learning methods for predicting inhibitors of these enzymes and an additional P450 enzyme, CYP2C9, and the substrates of the three P450 isoenzymes. Two consensus support vector machine (CSVM) methods, "positive majority" (PM-CSVM) and "positive probability" (PP-CSVM), were used in this work. These methods were first tested for the prediction of inhibitors of CYP3A4 and CYP2D6 by using a significantly higher number of inhibitors and noninhibitors than that used in earlier studies. They were then applied to the prediction of inhibitors of CYP2C9 and substrates of the three enzymes. Both methods predict inhibitors of CYP3A4 and CYP2D6 at a similar level of accuracy as those of earlier studies. For classification of inhibitors of CYP2C9, the best CSVM method gives an accuracy of 88.9% for inhibitors and 96.3% for noninhibitors. The accuracies for classification of substrates and nonsubstrates of CYP3A4, CYP2D6, and CYP2C9 are 98.2 and 90.9%, 96.6 and 94.4%, and 85.7 and 98.8%, respectively. Both CSVM methods are potentially useful as filters for predicting inhibitors and substrates of P450 isoenzymes. These methods generally give better accuracies than single SVM classification systems, and the performance of the PP-CSVM method is slightly better than that of the PM-CSVM method.

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Section: Discussionmentioning

confidence: 99%

Prediction of Cytochrome P450 3A4, 2D6, and 2C9 Inhibitors and Substrates by Using Support Vector Machines

Yap

Chen

2005

J. Chem. Inf. Model.

152

185

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“…It is a knowledge-based system that uses a database of possible biotransformations to determine the metabolic fate of the query compound. 135 The user can select from a list of possible biotransformations the ones to be used in the prediction. The transformations are separated into phase I and phase II groups and subcategorized in chemically based groups like redox, epoxidation, decarboxylation, glucuronidation, acetylation, etc.…”

Section: In Silico Modelsmentioning

confidence: 99%

Review of the Availability ofIn VitroandIn SilicoMethods for Assessing Dermal Bioavailability

DumontCoralie

PrietoPilar

AsturiolDavid

et al. 2015

Applied In Vitro Toxicology

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The exposure of the skin to consumer products, drugs, and environmental chemicals can result in their penetrating the skin barrier and entering systemic circulation, potentially resulting in adverse effects in the skin and other organs. The assessment of dermal penetration and bioavailability (including penetration, metabolism, and entry into the systemic circulation) is therefore an important consideration in the risk assessment of chemicals. The skin is a heterogeneous organ with a multilayer structure. Based on its architecture and physiology, substances can penetrate through three major ways but can also be blocked in the different skin layers and in the skin appendages, which act as reservoir. In addition to that, as the skin is a metabolically competent organ, substances can undergo metabolism. After a brief description of the skin architecture, this review will focus on the skin penetration mechanisms and skin metabolic capacities. The skin absorption has traditionally been tested in vivo on animals. However, with the new legislation (i.e., Registration, Evaluation, Authorisation, and Restriction of Chemicals Regulation or Cosmetics Regulation), alternatives to animal testing have to be implemented. In a second part, this review will provide a description of the main in vitro and in silico or computational models available to study skin absorption and skin metabolism (i.e., ex vivo skin models, artificial membrane barriers, primary cells and cell lines, Quantitative Structure-Activity Relationship [QSAR], simulators for the prediction of skin metabolism).

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“…It correctly predicts 95% of the training data and 90% of a semi-independent validation data set, and can be used as a valid filter in virtual screening [165]. Several approaches that use databases to predict metabolism are available or under development, including expert systems, such as MetabolExpert, META or Meteor and Metabolism [166,167].…”

Section: In Silico Prediction Of Adme Propertiesmentioning

confidence: 99%

Recent Development and Application of Virtual Screening in Drug Discovery: An Overview

Hou¹,

Xu²

2012

Frontiers in Medicinal Chemistry - (Volume 3)

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Virtual screening, especially the structure-based virtual screening, has emerged as a reliable, cost-effective and time-saving technique for the discovery of lead compounds. Here, the basic ideas and computational tools for virtual screening have been briefly introduced, and emphasis is placed on aspects of recent development of docking-based virtual screening, scoring functions in molecular docking and ADME/Tox-based virtual screening in the past three years (2000 to 2003). Moreover, successful examples are provided to further demonstrate the effectiveness of virtual screening in drug discovery.

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Computer systems for the prediction of xenobiotic metabolism

Cited by 133 publications

References 18 publications

Prediction of Cytochrome P450 3A4, 2D6, and 2C9 Inhibitors and Substrates by Using Support Vector Machines

Prediction of Cytochrome P450 3A4, 2D6, and 2C9 Inhibitors and Substrates by Using Support Vector Machines

Review of the Availability ofIn VitroandIn SilicoMethods for Assessing Dermal Bioavailability

Recent Development and Application of Virtual Screening in Drug Discovery: An Overview

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