In silico approaches to predicting drug metabolism, toxicology and beyond

Ekins, Sean

doi:10.1042/bst0310611

Cited by 61 publications

(34 citation statements)

References 24 publications

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“…There have been several computational models of metabolic stability in the literature. For example, a recursive partitioning model containing 875 molecules with HLM metabolic stability was used to predict and rank the clearance of 41 drugs (Ekins, 2003). A k-nearest neighbor model of metabolic stability data using human S9 homogenate for 631 diverse molecules was able to adequately classify metabolism of a further set of more than 100 molecules (Shen et al, 2003).…”

Section: Discussionmentioning

confidence: 99%

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Gupta

Gifford²,

Liston³

et al. 2010

Drug Metab Dispos

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Section: Discussionmentioning

confidence: 99%

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Gupta

Gifford²,

Liston³

et al. 2010

Drug Metab Dispos

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“…Several drugs have been withdrawn from the market in the past decade due to cardiovascular toxicity associated with undesirable blockade of this channel. Since 2002, there have been numerous studies that have described individual three-dimensional (3D) quantitative structure-activity relationship (QSAR) models, statistical models, or pharmacophores for hERG (21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32)(33)(34). These different studies and others have encompassed a wide set of data generation and modeling techniques as well as an array of molecules for model building and testing as recently reviewed (35).…”

Section: Introductionmentioning

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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“…There are several ways to evaluate the predictive ability of a computational model; leaving groups out and scrambling the descriptors with the biological activity are perhaps the most widely used. The most valuable test is an external set of molecules that have been excluded from the modelbuilding process (Ekins, 2003). In this study, nine CYP3A4-mediated and five CYP2D6-mediated N-dealkylation reactions with known V max values were collected from the literature and used to test the respective models.…”

Section: Resultsmentioning

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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In silico approaches to predicting drug metabolism, toxicology and beyond

Cited by 61 publications

References 24 publications

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Using Open Source Computational Tools for Predicting Human Metabolic Stability and Additional Absorption, Distribution, Metabolism, Excretion, and Toxicity Properties

Shape Signatures: New Descriptors for Predicting Cardiotoxicity In Silico

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

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