A Combined Approach to Drug Metabolism and Toxicity Assessment

Abstract

ABSTRACT:The challenge of predicting the metabolism or toxicity of a drug in humans has been approached using in vivo animal models, in vitro systems, high throughput genomics and proteomics methods, and, more recently, computational approaches. Understanding the complexity of biological systems requires a broader perspective rather than focusing on just one method in isolation for prediction. Multiple methods may therefore be necessary and combined for a more accurate prediction. In the field of drug metabolism and toxicology, we have seen the growth, in recent years, of computational quantitative structure-activity relationships (QSARs), as well as empirical data from microarrays. In the current study we have further developed a novel computational approach, MetaDrug, that 1) predicts metabolites for molecules based on their chemical structure, 2) predicts the activity of the original compound and its metabolites with various absorption, distribution, metabolism, excretion, and toxicity models, 3) incorporates the predictions with human cell signaling and metabolic pathways and networks, and 4) integrates networks and metabolites, with relevant toxicogenomic or other high throughput data. We have demonstrated the utility of such an approach using recently published data from in vitro metabolism and microarray studies for aprepitant, 2(S)-((3,5-bis(trifluoromethyl)benzyl)-oxy)-3(S)phenyl-4-((3-oxo-1,2,4-triazol-5-yl)methyl)morpholine (L-742694), trovofloxacin, 4-hydroxytamoxifen, and artemisinin and other artemisinin analogs to show the predicted interactions with cytochromes P450, pregnane X receptor, and P-glycoprotein, and the metabolites and the networks of genes that are affected. As a comparison, we used a second computational approach, MetaCore, to generate statistically significant gene networks with the available expression data. These case studies demonstrate the combination of QSARs and systems biology methods.Predicting the metabolism and toxicity of a drug in humans can use resources that include in vivo animal models, in vitro systems, and high throughput genomics and proteomics methods (Gerhold et al., 2001;Thomas et al., 2001) to generate empirical data for analysis and decision making. The amount and complexity of the data being generated are increasing, requiring not only judicious decisions about which experimental methods to use but also novel tools for visualization and analysis. More recently, within drug disposition and toxicology, in vitro approaches for generating data with drug-metabolizing enzymes, transporters, ion channels, and receptors have been used for computational approaches, including quantitative structureactivity relationships (QSARs) (Ekins and Swaan, 2004). These methods have been used widely and applied for predicting absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) (Ekins et al., 2005d) properties, at the level of either the individual protein (e.g., P450s; Balakin et al., 2004a,b) or specific properties (e.g., absorption; Zhao et al., 2001;Niwa,...

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

“…A combination of Tanimoto similarity, PCA, clustering, and Mahalanobis distance has been used to determine prediction confidence (Sheridan et al, 2004;Dimitrov et al, 2005;Ekins et al, 2006a;Tetko et al, 2006Tetko et al, , 2008Chekmarev et al, 2008Chekmarev et al, , 2009Kortagere et al, 2008Kortagere et al, , 2009. A prediction should not be provided if the test molecule is too far away from a training set member as defined by the user based on a combination of distance as well as a similarity metric of choice.…”

Twenty-six years of HIV science: an overview of anti-HIV drugs metabolism

“…A given compound is fragmented and then passed through all rules to identify putative metabolically labile sites. Expert systems and their databases, such as MetabolExpert (Darvas, 1988), META (Klopman, Dimayuga, Talafous, 1994), METEOR (Testa et al, 2005), MetaDrug (Ekins et al, 2006), and PK/DB (Moda et al, 2008), are examples of in silico methods used to predict drug biotransformation pathways and possible metabolites, that provide a ranked list of most likely metabolites.…”

“…A given compound is fragmented and then passed through all rules to identify putative metabolically labile sites. Expert systems and their databases, such as MetabolExpert (Darvas, 1988), META (Klopman, Dimayuga, Talafous, 1994), METEOR (Testa et al, 2005), MetaDrug (Ekins et al, 2006), and PK/DB (Moda et al, 2008), are examples of in silico methods used to predict drug biotransformation pathways and possible metabolites, that provide a ranked list of most likely metabolites.Metasite (Cruciani et al, 2005) is a computational program for metabolite formation prediction, which combines structural information, by matching the structural complementarity of the substrate and the protein, and rule-based and reactivity methods.The availability of more and more 3D protein structures of P450 paves the way for molecular modeling approaches that zoom in on the atomic details of enzymeligand interactions. Docking is the most commonly used structure-based method, able to predict the site of metabolism of the substrate based on which atoms are exposed or close to the catalytic centre (the heme iron) in order for metabolism to take place (Stjernschantz, Vermeulen, Oostenbrink, 2008).…”

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

“…MetaDrug represents one such method, combining a manually annotated database of human drug metabolism information including xenobiotic reactions, enzyme substrates, and enzyme inhibitors with kinetic data (Ekins et al, 2005b(Ekins et al, , 2006. This database has enabled the generation of rules for predicting likely metabolic reactions.…”

“…This database has enabled the generation of rules for predicting likely metabolic reactions. The parent molecule and metabolites may then be scored through integrated QSAR models and rules for molecule reactivity before visualizing molecules as nodes on a network diagram (Ekins et al, 2005b(Ekins et al, , 2006.…”