Properties of cytochrome P450 isoenzymes and their substrates part 2: properties of cytochrome P450 substrates

Smith, Dennis A.; Ackland, Mark J.; Jones, Barry

doi:10.1016/s1359-6446(97)01085-4

Cited by 105 publications

(86 citation statements)

References 15 publications

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“…Noninhibitors, such as compound B, are characterized by slightly smaller hydrophobic regions and much larger polar regions. These characteristics fit well with the general idea of CYP1A2 ligands being hydrophobic with one hydrogen bond acceptor (Smith et al, 1997;Sansen et al, 2007). A compound with characteristics such as those of compound C is likely to be predicted as a noninhibitor, because it has a large polar region and therefore fails in the prediction.…”

Section: External Test Set Of Drug Compoundssupporting

confidence: 68%

“…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).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: External Test Set Of Drug Compoundssupporting

confidence: 68%

Section: Introductionmentioning

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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“…[25] This lipophilicity is of major importance in the binding affinity to the P450 and in this enzyme selectivity or in the clearance. [25][26][27][28] Lipophilicity could be the main factor in the binding of the substrates to P450s, probably due to desolvatation, where the value of Log(P) or of Log(D 7.4 ) is a useful marker of the global lipophilic character. [29] The dynamic binding to the substrate suggests an entropy controlled desolvatation, which could be related to the substrate lipophilicity.…”

Section: Full Papermentioning

confidence: 99%

The Cytochrome P450 3A4 has three Major Conformations: New Clues to Drug Recognition by this Promiscuous Enzyme

Benkaidali

André

Moroy

et al. 2017

Molecular Informatics

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Dedication: This paper is dedicated to the memory of Professor Boubekeur Maouche. Abstract:We computed the channels of the 3A4 isoform of the cytochrome P450 3A4 (CYP) on the basis of 24 crystal structures extracted from the Protein Data Bank (PDB). We identified three major conformations (denoted C, O1 and O2) using an enhanced version of the CCCPP software that we developed for the present work, while only two conformations (C and O 2 ) are considered in the literature.We established the flowchart of definition of these three conformations in function of the structural and physicochemical parameters of the ligand. The channels are characterized with qualitative and quantitative parameters, and not only with their surrounding secondary structures as it is usually done in the literature

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“…It is possible that, depending on the molecule, these three terms make a greater or lesser contribution to the overall log P value, and the effect of dissociation for acidic or basic compounds should also be taken into account. This can be appreciated when one considers the case of observed log P values at a given pH and also in comparisons with the log D 7.4 values of ionizable compounds [41].…”

Section: Appendix a Derivation Of Solvation And Desolvation Energiesmentioning

confidence: 99%

Molecular Binding Interactions: Their Estimation and Rationalization in QSARs in Terms of Theoretically Derived Parameters

Lewis

Broughton

2002

The Scientific World JOURNAL

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An extensive survey of molecular binding interactions and parameters used in QSARs is reported, which includes consideration of lipophilicity and the derivation of Linear Free Energy Relationships associated with drug-receptor binding, together with an overview of the various contributions to binding energy. The lipophilic parameter, log P, and its relevance to desolvation energy is outlined and explanation of the parameters derived from electronic structure calculation is provided, leading into a summary of molecular dynamics simulations. KEY WORDS: drug-receptor binding interactions, quantitative structure-activity relationships (QSARs)

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Properties of cytochrome P450 isoenzymes and their substrates part 2: properties of cytochrome P450 substrates

Cited by 105 publications

References 15 publications

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

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

The Cytochrome P450 3A4 has three Major Conformations: New Clues to Drug Recognition by this Promiscuous Enzyme

Molecular Binding Interactions: Their Estimation and Rationalization in QSARs in Terms of Theoretically Derived Parameters

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