Kohonen Maps for Prediction of Binding to Human Cytochrome P450 3a4

Balakin, Konstantin V.; Ekins, Sean; Bugrim, Andrey; Ivanenkov, Yan A.; Королев, Д. С.; Nikolsky, Yuri; Skorenko, Andrey V.; Ivashchenko, A. V.; Savchuk, Nikolay P.; Nikolskaya, Tatiana

doi:10.1124/dmd.104.000356

Cited by 47 publications

(27 citation statements)

References 30 publications

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“…In an unsupervised training algorithm, the neurons self-organize until their pairwise neighborhoods represent the correct topology of the original data set. Kohonen maps have recently been applied to successfully model cytochrome P450-mediated drug metabolism (67,68) and hERG inhibition (40). The generation of the Kohonen SOMs (51) was conducted using the Smart Mining software v1.01 (ChemDiv, Inc., San Diego, CA, www.chemosoft.com).…”

Section: Neural Network Modeling Using Self-organizing Kohonen Mapsmentioning

confidence: 99%

Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

Chekmarev

Kholodovych

Balakin

et al. 2008

Chem. Res. Toxicol.

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Shape Signatures is a new computational tool that is being evaluated for applications in computational toxicology and drug discovery. The method employs a customized ray-tracing algorithm to explore the volume enclosed by the surface of a molecule and then uses the output to construct compact histograms (i.e., signatures) that encode for molecular shape and polarity. In the present study, we extend the application of the Shape Signatures methodology to the domain of computational models for cardiotoxicity. The Shape Signatures method is used to generate molecular descriptors that are then utilized with widely used classification techniques such as k nearest neighbors (k-NN), support vector machines (SVM), and Kohonen self-organizing maps (SOM). The performances of these approaches were assessed by applying them to a data set of compounds with varying affinity toward the 5-HT 2B receptor as well as a set of human ether-a-go-go-related gene (hERG) potassium channel inhibitors. Our classification models for 5-HT 2B represented the first attempt at global computational models for this receptor and exhibited average accuracies in the range of 73−83%. This level of performance is comparable to using commercially available molecular descriptors. The overall accuracy of the hERG Shape Signatures-SVM models was 69−73%, in line with other computational models published to date. Our data indicate that Shape Signatures descriptors can be used with SVM and Kohonen SOM and perform better in classification problems related to the analysis of highly clustered and heterogeneous property spaces. Such models may have utility for predicting the potential for cardiotoxicity in drug discovery mediated by the 5-HT 2B receptor and hERG.

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Section: Neural Network Modeling Using Self-organizing Kohonen Mapsmentioning

confidence: 99%

Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

Chekmarev

Kholodovych

Balakin

et al. 2008

Chem. Res. Toxicol.

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“…This work is a continuation of numerous studies in the field of development of computational models for these P450s (Smith et al, 1997a,b;Lewis et al, 1998;de Groot et al, 1999. Korolev et al, 2003. Balakin et al, 2004Szklarz et al, 2000;Ekins et al, 2001Ekins et al, , 2003de Groot and Ekins, 2002).…”

Section: Discussionmentioning

confidence: 99%

“…In contrast, another approach that can be taken is to carefully collate a large amount of substrate-product reactions for human P450s and then categorize the metabolic reactions according to the particular type of chemical transformation [e.g., N-dealkylation, O-dealkylation, and sulfur (II) oxidation] (Korolev et al, 2003). Recently, we have developed computational algorithms for assessment of the general probability of P450-mediated transformation for drug-like compounds (Korolev et al, 2003) and for prediction of the K m of drugs to the active sites of P450 enzymes using this data base (Balakin et al, 2004).…”

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confidence: 99%

QUANTITATIVE STRUCTURE-METABOLISM RELATIONSHIP MODELING OF METABOLICN-DEALKYLATION REACTION RATES

Balakin¹,

Ekins²,

Bugrim³

et al. 2004

Drug Metab Dispos

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ABSTRACT:It is widely recognized that preclinical drug discovery can be improved via the parallel assessment of bioactivity, absorption, distribution, metabolism, excretion, and toxicity properties of molecules. High-throughput computational methods may enable such assessment at the earliest, least expensive discovery stages, such as during screening compound libraries and the hit-to-lead process. As an attempt to predict drug metabolism and toxicity, we have developed an approach for evaluation of the rate of N-dealkylation mediated by two of the most important human cytochrome P450s (P450), namely CYP3A4 and CYP2D6. We have taken a novel approach by using descriptors generated for the whole molecule, the reaction centroid, and the leaving group, and then applying neural network computations and sensitivity analysis to generate quantitative structure-metabolism relationship models. The quality of these models was assessed by using the crossvalidated correlation coefficients of 0.82 for CYP3A4 and 0.79 for CYP2D6 as well as external test molecules for each enzyme. The relative performance of different neural networks was also compared, and modular neural networks with two hidden layers provided the best predictive ability. Functional dependencies between the neural network input and output variables, generalization ability, and limitations of the described approach are also discussed. These models represent an initial approach to predicting the rate of P450-mediated metabolism and may be applied and integrated with other models for P450 binding to produce a systems-based approach for predicting drug metabolism.Quantitative structure-metabolism relationship (QSMR) models allow the estimation of complex metabolism-related phenomena from relatively simple calculated molecular properties or descriptors. Such models can be used for the design of structural analogs of bioactive compounds with improved pharmacokinetic properties (Bouska et al., 1997;Madsen et al., 2002;Humphreys et al., 2003), evaluation of excretion kinetics (Holmes et al., 1995;Bollard et al., 1996;Cupid et al., 1996), estimation of approximate rates of metabolic conversion for prodrugs or soft drug candidates (Buchwald and Bodor, 1999;Bodor, 1999), and assessment of potential toxic effects of novel compounds. (Di Carlo et al., 1986a,b;Di Carlo, 1990;Altomare et al., 1992).The computational prediction of the metabolic fate of novel compounds is a nontrivial problem. First, an indiscriminate pooling of metabolic data from different species in the commercially available databases substantially distorts any attempt at generalization (Darvas, 1988). The metabolic pathways and corresponding networks can be very different even in close mammalian species, so that any use of such pooled data is problematic (Mulder, 1990). Second, in vitro and in vivo data may differ substantially even for the same species. The metabolic fate of a drug delivered to the human liver after intravenous administration is often quite different from that observed in the liver micros...

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“…Other possibilities for modeling studies include the move toward methods that do not require detailed ligand alignments (Balakin et al, 2004) and incorporate more information derived from determined protein structures and/or homology models. Improved homology models are also leading to more progress in molecular dynamics approaches in describing human P450 isoforms whose structure has not yet been determined (e.g., 2D6) (Venhorst et al, 2003;Kemp et al, 2004).…”

Section: New Uses Of 3d-qsarmentioning

confidence: 99%

Three-Dimensional Quantitative Structure-Activity Relationship Analysis of Cytochromes P450: Effect of Incorporating Higher-Affinity Ligands and Potential New Applications

Locuson¹,

Wahlstrom²

2005

Drug Metab Dispos

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ABSTRACT:Recently, two new classes of reversible inhibitors, the benzbromarones (BZBRs) and the N-3 substituted phenobarbitals (PBs), were used to study the active site characteristics of CYP2C9 and 2C19, respectively. Since these ligands are some of the first CYP2C ligands to extend into the low nanomolar K i range (K i < 100 nM), they were subjected to three-dimensional quantitative structureactivity relationship (3D-QSAR) analysis. Given that BZBRs or the PB ligands bind very tightly, it can be assumed that these structures complement the binding pocket(s) for these enzymes. Thus, the resulting models should output a 3D arrangement of interaction sites predicted to be important for near optimal binding to the CYP2C9 and CYP2C19 enzymes. These predicted interaction regions may then improve the ability to predict drug-drug interactions. The resulting models generated from these new high affinity ligands are discussed, as are novel uses of 3D-QSAR and molecular modeling techniques that may be useful in the study of cytochromes P450 specifically.In ligand-based molecular modeling, knowledge accumulated from experimentation is used to build and test potential models for predicting ligand-protein interactions and hence drug-drug interactions, the sites of drug metabolism, toxicity, and other parameters. Recently, CYP2C9 and CYP2C19 inhibitors with 1 to 2 orders of magnitude lower K i values than previously characterized compounds have been reported (Suzuki et al., 2002;Locuson et al., 2003). These new inhibitors should help expand various models for CYP2C metabolism. Several groups have published 3D quantitative structure-activity relationship analysis (3D-QSAR) models for P450s, including multiple models for CYP2C9. Therefore, it is relevant to address how the newer, more potent inhibitors, which fit the same alignment rules used previously, have altered existing QSAR models for CYP2C9 and, in some cases, provided distinctions between the chemical properties that appear to define inhibitors of CYP2C9 and CYP2C19. Finally, new applications of 3D-QSAR modeling and how it may enhance P450 metabolism research is discussed. What Can High Affinity Ligands of P450s Tell Us?The pursuit for, and study of, high affinity P450 ligands remains an important endeavor. High affinity ligands for each P450 enzyme can help define the enzyme in the form of a pharmacophore, improving the identification of drug leads with the highest potential for drugdrug interactions based on their structure. In practice, this is a lofty goal because determining drug interaction potential in any quantitative manner requires an accurate, universal binding model that can predict any compound's affinity for a given enzyme. Establishing such a model, however, can be difficult because: 1) models used for prediction can only be based on what is currently known, and 2) P450s display complex binding behavior and conformational flexibility. Investigators may have the ability to screen new drugs for their drug interaction potential with the use of selective su...

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Kohonen Maps for Prediction of Binding to Human Cytochrome P450 3a4

Cited by 47 publications

References 30 publications

Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

QUANTITATIVE STRUCTURE-METABOLISM RELATIONSHIP MODELING OF METABOLICN-DEALKYLATION REACTION RATES

Three-Dimensional Quantitative Structure-Activity Relationship Analysis of Cytochromes P450: Effect of Incorporating Higher-Affinity Ligands and Potential New Applications

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