A New Statistical Approach to Predicting Aromatic Hydroxylation Sites. Comparison with Model‐Based Approaches.

Borodina, Yu. V.; Rudik, Anastasia V.; Филимонов, Д. А.; Харчевникова, Н. В.; Дмитриев, А. В.; Blinova, V. G.; Poroikov, Vladimir

doi:10.1002/chin.200506224

Cited by 2 publications

(3 citation statements)

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“…One approach uses a statistical analysis (15) of the regiochemical outcomes of aromatic hydroxylation as determined from the MDL database to predict the outcome for a novel structure. A second approach, called Metasite (15,18), starts with the published 3D structures of cytochrome P450s and fits a set of on-the-fly calculated drug conformations into the active sites. This approach yields a set of predicted oxidative metabolite structures for each enzyme.…”

Section: In Silico Prediction Of Metabolismmentioning

confidence: 99%

Analytical Tools and Approaches for Metabolite Identification in Early Drug Discovery

Chen¹,

Monshouwer²,

Fitch³

2006

Pharm Res

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Determination of the chemical structures of metabolites is a critical part of the early pharmaceutical discovery process. Understanding the structures of metabolites is useful both for optimizing the metabolic stability of a drug as well as rationalizing the drug safety profile. This review describes the current state of the art in this endeavor. The likely outcome of metabolism is first predicted by comparison to the literature. Then metabolites are synthesized in a variety of in vitro systems. The various approaches to LC/UV/MS are applied to learn information about these metabolites and structure hypotheses are made. Structures are confirmed by synthesis or NMR. The special topic of reactive metabolite structure determination is briefly addressed.

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Section: In Silico Prediction Of Metabolismmentioning

confidence: 99%

Analytical Tools and Approaches for Metabolite Identification in Early Drug Discovery

Chen¹,

Monshouwer²,

Fitch³

2006

Pharm Res

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show abstract

“…Some approaches have combined metabolite data and rules for suggesting metabolic pathways across multiple species (Erhardt, 2003). Such databases may also be useful for calculating the probability for a given metabolic reaction (Boyer and Zamora, 2002) to then indicate potential metabolites and the sites of metabolism using statistical or algorithmic approaches (Borodina et al, 2004). Although these types of comprehensive databases generally enable numerous search options to retrieve molecule structures and published information, the predictive capabilities seem limited at present (Wishart et al, 2006).…”

mentioning

confidence: 99%

“…In the area of metabolism prediction, these techniques encompass pharmacophores (Ekins et al, 2001), quantitative structure-activity relationships (QSARs) (Shen et al, 2003;Balakin et al, 2004), electronic models (Korzekwa et al, 2004), and commercial drug metabolism databases (Borodina et al, 2004), as well as other methods that have been comprehensively reviewed elsewhere (de Graaf et al, 2005;Ekins et al, 2005a;de Groot, 2006). Some approaches have combined metabolite data and rules for suggesting metabolic pathways across multiple species (Erhardt, 2003).…”

mentioning

confidence: 99%

Classification of Metabolites with Kernel-Partial Least Squares (K-PLS)

Embrechts¹,

Ekins²

2006

Drug Metab Dispos

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ABSTRACT:Numerous experimental and computational approaches have been developed to predict human drug metabolism. Since databases of human drug metabolism information are widely available, these can be used to train computational algorithms and generate predictive approaches. In turn, they may be used to assist in the identification of possible metabolites from a large number of molecules in drug discovery based on molecular structure alone. In the current study we have used a commercially available database (MetaDrug) and extracted a fraction of the human drug metabolism data. These data were used along with augmented atom descriptors in a predictive machine learning model, kernel-partial least squares (K-PLS). A total of 317 molecules, including parent drugs and their primary and secondary (sequential) metabolites, were used to build these models corresponding to individual metabolism rules, representing the formation of discrete metabolites, e.g., N-dealkylation. Each model was internally validated to assess the capability to classify other molecules that were left out. Using receiver operator curve statistics models for N-dealkylation, Odealkylation, aromatic hydroxylation, aliphatic hydroxylation, Oglucuronidation, and O-sulfation gave area under the curve values from 0.75 to 0.84 and were able to predict between 61 and 79% active molecules upon leave-one-out testing. This preliminary study indicates that K-PLS and possibly other similar machine learning methods (such as support vector machines) can be applied to predicting human drug metabolite formation in a classification manner. Improvements can be achieved using considerably larger datasets that contain more positive examples for the less frequently occurring metabolite rules, as well as the external evaluation of novel molecules.With the emphasis now on increasing the efficiency of drug discovery, there is interest in using predictive computational approaches to complement in vitro and in vivo studies. In the area of metabolism prediction, these techniques encompass pharmacophores (Ekins et al., 2001), quantitative structure-activity relationships (QSARs) (Shen et al., 2003;Balakin et al., 2004), electronic models (Korzekwa et al., 2004), and commercial drug metabolism databases (Borodina et al., 2004), as well as other methods that have been comprehensively reviewed elsewhere (de Graaf et al., 2005;Ekins et al., 2005a;de Groot, 2006). Some approaches have combined metabolite data and rules for suggesting metabolic pathways across multiple species (Erhardt, 2003). Such databases may also be useful for calculating the probability for a given metabolic reaction (Boyer and Zamora, 2002) to then indicate potential metabolites and the sites of metabolism using statistical or algorithmic approaches (Borodina et al., 2004). Although these types of comprehensive databases generally enable numerous search options to retrieve molecule structures and published information, the predictive capabilities seem limited at present (Wishart et al., 2006). A major limita...

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A New Statistical Approach to Predicting Aromatic Hydroxylation Sites. Comparison with Model‐Based Approaches.

Abstract: 2005 Computers in chemistry V 0380 A New Statistical Approach to Predicting Aromatic Hydroxylation Sites. Comparison with Model-Based Approaches. -(BORODINA, Y.; RUDIK, A.; FILIMONOV, D.; KHARCHEVNIKOVA, N.; DMITRIEV, A.; BLINOVA, V.; POROIKOV*, V.; J. Chem. Inf.

Cited by 2 publications

References 1 publication

Analytical Tools and Approaches for Metabolite Identification in Early Drug Discovery

Analytical Tools and Approaches for Metabolite Identification in Early Drug Discovery

Classification of Metabolites with Kernel-Partial Least Squares (K-PLS)

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