Prediction Of Drug Metabolism Research Articles

Regioselectivity Prediction of CYP1A2-Mediated Phase I Metabolism

A kinetic, reactivity-binding model has been proposed to predict the regioselectivity of substrates meditated by the CYP1A2 enzyme, which is responsible for the metabolism of planar-conjugated compounds such as caffeine. This model consists of a docking simulation for binding energy and a semiempirical molecular orbital calculation for activation energy. Possible binding modes of CYP1A2 substrates were first examined using automated docking based on the crystal structure of CYP1A2, and binding energy was calculated. Then, activation energies for CYP1A2-mediated metabolism reactions were calculated using the semiempirical molecular orbital calculation, AM1. Finally, the metabolic probability obtained from two energy terms, binding and activation energies, was used for predicting the most probable metabolic site. This model predicted 8 out of 12 substrates accurately as the primary preferred site among all possible metabolic sites, and the other four substrates were predicted into the secondary preferred site. This method can be applied for qualitative prediction of drug metabolism mediated by CYP1A2 and other CYP450 family enzymes, helping to develop drugs efficiently.

Biomimetic modeling of oxidative drug metabolism

The prediction of drug metabolism is an important task in drug development. Besides well-established in vitro and in vivo methods using biological matrices, several biomimetic models have been developed. This review summarizes three different nonenzymatic strategies, including metalloporphyrins as surrogates of the active centre of cytochrome P450, Fenton's reagent, and the electrochemical oxidation of drug compounds. Although none of the systems can simulate the whole range of cytochrome P450-catalyzed reactions adequately, the biomimetic models show some advantages over standard in vitro methods. For example, metalloporpyhrin catalysts allow the synthesis of certain metabolites in sufficient amounts and with sufficient purities to permit characterization and further pharmacological and toxicological tests. The electrochemical generation of metabolites coupled on-line to liquid chromatography/mass spectrometry is a promising tool for studying reactive metabolites and can be applied in automated high-throughput screening approaches. In this paper, detailed comparisons with cytochrome P450 catalysis are drawn, advantages and disadvantages of the respective methods are revealed, and possible applications are discussed.

Identification of Aspartic Acid and Histidine Residues Mediating the Reaction Mechanism and the Substrate Specificity of the Human UDP-glucuronosyltransferases 1A

The human UDP-glucuronosyltransferase UGT1A6 is the primary phenol-metabolizing UDP-glucuronosyltransferase isoform. It catalyzes the nucleophilic attack of phenolic xenobiotics on UDP-glucuronic acid, leading to the formation of water-soluble glucuronides. The catalytic mechanism proposed for this reaction is an acid-base mechanism that involves an aspartic/glutamic acid and/or histidine residue. Here, we investigated the role of 14 highly conserved aspartic/glutamic acid residues over the entire sequence of human UGT1A6 by site-directed mutagenesis. We showed that except for aspartic residues Asp-150 and Asp-488, the substitution of carboxylic residues by alanine led to active mutants but with decreased enzyme activity and lower affinity for acceptor and/or donor substrate. Further analysis including mutation of the corresponding residue in other UGT1A isoforms suggests that Asp-150 plays a major catalytic role. In this report we also identified a single active site residue important for glucuronidation of phenols and carboxylic acid substrates by UGT1A enzyme family. Replacing Pro-40 of UGT1A4 by histidine expanded the glucuronidation activity of the enzyme to phenolic and carboxylic compounds, therefore, leading to UGT1A3-type isoform in terms of substrate specificity. Conversely, when His-40 residue of UGT1A3 was replaced with proline, the substrate specificity shifted toward that of UGT1A4 with loss of glucuronidation of phenolic substrates. Furthermore, mutation of His-39 residue of UGT1A1 (His-40 in UGT1A4) to proline led to loss of glucuronidation of phenols but not of estrogens. This study provides a step forward to better understand the glucuronidation mechanism and substrate recognition, which is invaluable for a better prediction of drug metabolism and toxicity in human.

DrugBank: a knowledgebase for drugs, drug actions and drug targets

DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With ∼4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more ‘experimental’ drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food–drug interactions, drug–drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca

Prediction of Intestinal First-Pass Drug Metabolism

Despite a lower content of many drug metabolising enzymes in the intestinal epithelium compared to the liver (e.g. intestinal CYP3A abundance in the intestine is 1% that of the liver), intestinal metabolic extraction may be similar to or exceed hepatic extraction. Modelling of events on first-pass through the intestine requires attention to the complex interplay between passive permeability, active transport, binding, relevant blood flows and the intrinsic activity and capacity of enzyme systems. We have compared the predictive accuracy of the "well-stirred" gut model with that of the "Q(Gut)" model. The former overpredicts the fraction escaping first-pass gut metabolism; the latter improves the predictions by accounting for interplay between permeability and metabolism.

The Prediction of Drug Metabolism, Tissue Distribution, and Bioavailability of 50 Structurally Diverse Compounds in Rat Using Mechanism-Based Absorption, Distribution, and Metabolism Prediction Tools

The aim of this study was to assess a physiologically based modeling approach for predicting drug metabolism, tissue distribution, and bioavailability in rat for a structurally diverse set of neutral and moderate-to-strong basic compounds (n = 50). Hepatic blood clearance (CL(h)) was projected using microsomal data and shown to be well predicted, irrespective of the type of hepatic extraction model (80% within 2-fold). Best predictions of CL(h) were obtained disregarding both plasma and microsomal protein binding, whereas strong bias was seen using either blood binding only or both plasma and microsomal protein binding. Two mechanistic tissue composition-based equations were evaluated for predicting volume of distribution (V(dss)) and tissue-to-plasma partitioning (P(tp)). A first approach, which accounted for ionic interactions with acidic phospholipids, resulted in accurate predictions of V(dss) (80% within 2-fold). In contrast, a second approach, which disregarded ionic interactions, was a poor predictor of V(dss) (60% within 2-fold). The first approach also yielded accurate predictions of P(tp) in muscle, heart, and kidney (80% within 3-fold), whereas in lung, liver, and brain, predictions ranged from 47% to 62% within 3-fold. Using the second approach, P(tp) prediction accuracy in muscle, heart, and kidney was on average 70% within 3-fold, and ranged from 24% to 54% in all other tissues. Combining all methods for predicting V(dss) and CL(h) resulted in accurate predictions of the in vivo half-life (70% within 2-fold). Oral bioavailability was well predicted using CL(h) data and Gastroplus Software (80% within 2-fold). These results illustrate that physiologically based prediction tools can provide accurate predictions of rat pharmacokinetics.

Thermodynamics of Ligand Binding to P450 2B4 and P450eryF Studied by Isothermal Titration Calorimetry

Structural plasticity and cooperativity in ligand recognition are two key aspects of the catalytic diversity of cytochrome P450 enzymes. As more mammalian P450 crystal structures have emerged, computational modeling has become a major tool to predict drug metabolism and interactions. There is a need for real solution thermodynamic data to support modeling and crystallographic observations. Using isothermal titration calorimetry (ITC) we successfully evaluated the conformational flexibility of P450 2B4 in binding imidazole inhibitors of different size and chemistry and dissected the stoichiometry and energetics of ligand binding allostery in P450eryF. Thermodynamic signatures obtained by ITC nicely correlated with structural and modeling results. Thus, ITC is a powerful tool to study structure-function relationships in P450s.

Predicting Drug Metabolism–dependent Toxicity for Humans with a Genetically Engineered Cell Battery

This paper covers the presentation of an invited lecture - the FRAME Annual Lecture - given in London on 8 November 2006. Investigating the metabolism of chemicals in general, and of drugs and pollutants in particular, is of key importance to understanding pharmacological and toxicological effects. Over more than 15 years, the genes encoding the enzymes involved, have been individually cloned and expressed after gene transfer into V79 Chinese hamster cells, yielding a collection of cell lines - the so called V79 Cell Battery. With this technology, it has become possible to study the relevant enzymes individually, thus avoiding complex in vivo situations. By cloning genes from different species, including humans, species-species comparison became possible, yielding results of immediate predictive value for humans. Since V79 cells had already been approved by the OECD for toxicity studies since the 1980s, the metabolically competent V79 cell lines are of even greater value, as metabolism and toxicity testing are linked in the very same cells in a highly defined fashion. The results obtained so far with the genetically engineered V79 cell lines justify their acceptance as alternatives to animal experimentation in drug development and in the toxicity testing of chemicals, serving the goals of the Three Rs and, in particular, the most important R: Replacement.

Editorial [Hot Topic: Predicting Drug Metabolism In Silico (Guest Editors: Drs. Michael Sorich and Paul Smith)

Editorial [Hot Topic: Predicting Drug Metabolism In Silico (Guest Editors: Drs. Michael Sorich and Paul Smith)

Predicting Drug Metabolism Induction In Silico

The inducibility of drug-metabolizing enzymes and transporters by numerous xenobiotics has become a vital issue to be considered in the drug development process. Activation of so-called orphan nuclear receptors has been identified to result in increased expression of these detoxifying systems and consequently altered drug levels in the human body. In order to anticipate such mechanisms already in early stages of drug design and to avoid dangerous drug-drug interactions, reliable in silico simulation tools are highly desirable. This review aims to give an introduction into induction of drug metabolism and transport and focuses on computer-assisted molecular modeling prediction techniques, on their applicability and limitations, on recent case studies, and on success stories.

Structure-based methods for the prediction of drug metabolism

There is a tantalising possibility that we may be able to predict the metabolism of a drug directly from its structure, thus obviating the requirement for animal tests in this area. There are a number of techniques that can be used to estimate a range of events associated with metabolism, and may allow us to achieve this aim. This paper considers the role of (quantitative) structureactivity relationships, and pharmacophore and homology modelling in the prediction of metabolism. Examples are also presented where such approaches have been formalised into expert systems. Clearly, many advances have been made in this area in recent years. Discussed herein is the importance of fully integrating the diverse systems and approaches available to fulfil the aspiration to predict metabolism directly from structure.

Why is it Challenging to Predict Intestinal Drug Absorption and Oral Bioavailability in Human Using Rat Model

To study the correlation of intestinal absorption for drugs with various absorption routes between human and rat, and to explore the underlying molecular mechanisms for the similarity in drug intestinal absorption and the differences in oral bioavailability between human and rat. The intestinal permeabilities of 14 drugs and three drug-like compounds with different absorption mechanisms in rat and human jejunum were determined by in situ intestinal perfusion. A total of 48 drugs were selected for oral bioavailability comparison. Expression profiles of transporters and metabolizing enzymes in both rat and human intestines (duodenum and colon) were measured using GeneChip analysis. No correlation (r(2) = 0.29) was found in oral drug bioavailability between rat and human, while a correlation (r(2) = 0.8) was observed for drug intestinal permeability with both carrier-mediated absorption and passive diffusion mechanisms between human and rat small intestine. Moderate correlation (with r(2) > 0.56) was also found for the expression levels of transporters in the duodenum of human and rat, which provides the molecular mechanisms for the similarity and correlation of drug absorption between two species. In contrast, no correlation was found for the expressions of metabolizing enzymes between rat and human intestine, which indicates the difference in drug metabolism and oral bioavailability in two species. Detailed analysis indicates that many transporters (such as PepT1, SGLT-1, GLUT5, MRP2, NT2, and high affinity glutamate transporter) share similar expression levels in both human and rat with regional dependent expression patterns, which have high expression in the small intestine and low expression in the colon. However, discrepancy was also observed for several other transporters (such as MDR1, MRP3, GLUT1, and GLUT3) in both the duodenum and colon of human and rat. In addition, the expressions of metabolizing enzymes (CYP3A4/CYP3A9 and UDPG) showed 12 to 193-fold difference between human and rat intestine with distinct regional dependent expression patterns. The data indicate that rat and human show similar drug intestinal absorption profiles and similar transporter expression patterns in the small intestine, while the two species exhibit distinct expression levels and patterns for metabolizing enzymes in the intestine. Therefore, a rat model can be used to predict oral drug absorption in the small intestine of human, but not to predict drug metabolism or oral bioavailability in human.

Mechanisms of reaction in cytochrome P450: hydroxylation of camphor in P450cam

The fundamental nature of reactivity in cytochrome P450 enzymes is currently controversial. Modelling of bacterial P450cam has suggested an important role for the haem propionates in the catalysis, though this finding has been questioned. Understanding the mechanisms of this enzyme family is important both in terms of basic biochemistry and potentially in the prediction of drug metabolism. We have modelled the hydroxylation of camphor by P450cam, using combined quantum mechanics/molecular mechanics (QM/MM) methods. A set of reaction pathways in the enzyme was determined. We were able to pinpoint the source of the discrepancies in the previous results. We show that when a correct ionization state is assigned to Asp297, no spin density appears on the haem propionates and the protein structure in this region remains preserved. These results indicate that the haem propionates are not involved in catalysis.

DrugBank: a comprehensive resource for in silico drug discovery and exploration

DrugBank is a unique bioinformatics/cheminformatics resource that combines detailed drug (i.e. chemical) data with comprehensive drug target (i.e. protein) information. The database contains >4100 drug entries including >800 FDA approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, >14 000 protein or drug target sequences are linked to these drug entries. Each DrugCard entry contains >80 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data. Many data fields are hyperlinked to other databases (KEGG, PubChem, ChEBI, PDB, Swiss-Prot and GenBank) and a variety of structure viewing applets. The database is fully searchable supporting extensive text, sequence, chemical structure and relational query searches. Potential applications of DrugBank include in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. DrugBank is available at .

NON-MICHAELIS-MENTEN KINETICS IN CYTOCHROME P450-CATALYZED REACTIONS

The cytochrome P450 monooxygenases (CYPs) are the dominant enzyme system responsible for xenobiotic detoxification and drug metabolism. Several CYP isoforms exhibit non-Michaelis-Menten, or "atypical," steady state kinetic patterns. The allosteric kinetics confound prediction of drug metabolism and drug-drug interactions, and they challenge the theoretical paradigms of allosterism. Both homotropic and heterotropic ligand effects are now widely documented. It is becoming apparent that multiple ligands can simultaneously bind within the active sites of individual CYPs, and the kinetic parameters change with ligand occupancy. In fact, the functional effect of any specific ligand as an activator or inhibitor can be substrate dependent. Divergent approaches, including kinetic modeling and X-ray crystallography, are providing new information about how multiple ligand binding yields complex CYP kinetics.

Computational prediction of human drug metabolism

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrugTM (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure–activity relationship models for major drug metabolising enzymes, and an extensive database of human protein–xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.

Predicting Drug Metabolism - An Evaluation of the Expert SystemMETEOR

The paper begins with a discussion of the goals of metabolic predictions in early drug research, and some difficulties toward this objective, mainly the various substrate and product selectivities characteristic of drug metabolism. The major in silico approaches to predict drug metabolism are then classified and summarized. A discrimination is, thus, made between 'local' and 'global' systems. In its second part, an evaluation of METEOR, a rule-based expert system used to predict the metabolism of drugs and other xenobiotics, is reported. The published metabolic data of ten substrates were used in this evaluation, the overall results being discussed in terms of correct vs. disputable (i.e., false-positive and false-negative) predictions. The predictions for four representative substrates are presented in detail (Figs. 1-4), illustrating the interest of such an evaluation in identifying where and how predictive rules can be improved.

Prediction of Drug Metabolism and Interactions on the Basis of in vitro Investigations

Drug metabolism profoundly affects drug action, because almost all drugs are metabolised in the body and thus their concentrations and elimination rates are dependent on metabolic activity. Drug metabolism contributes substantially to interindividual differences in drug response and is also often involved in drug interactions, resulting in either therapeutic failure or adverse effects. Knowledge about the metabolism of a new chemical entity and its affinity to drug-metabolising enzymes helps in the drug development process by providing important information for the selection of a lead compound from among a number of substances pharmacologically equally effective in their therapeutic response. In drug development protocols, metabolism characteristics should be assessed very early during the development process. This has been made possible by the advances made especially in analytical capabilities and in in vitro technologies that are employed to predict in vivo metabolite profile, pharmacokinetic parameters and drug-drug interaction potential.

Prediction of Drug Metabolism: The Case of Cytochrome P450 2D6

Cytochromes P450 (Cyt P450s) constitute the most important biotransformation enzymes involved in the biotransformation of drugs and other xenobiotics. Because drug metabolism by Cyt P450s plays such an important role in the disposition and in the pharmacological and toxicological effects of drugs, early consideration of ADME-properties is increasingly seen as essential for the discovery and the development of new drugs and drug candidates. The primary aim of this paper is to present various computational approaches used to rationalize and predict the activity and substrate selectivity of Cyt P450s, as well as the possibilities and limitations of these approaches, now and in the future. Attention is also paid to the experimental validation of these approaches by using high-throughput screening (HTS) of affinities to drug-drug interactions at the level of Cyt P450-isoenzymes. Since human Cyt P450 2D6 is one of the most important drug metabolizing enzymes and since in this regard much pioneering work has been done with this Cyt P450, Cyt P450 2D6 is chosen as a model for this discussion. Apart from early mechanism-based ab initio calculations on substrates of Cyt P450 2D6, pharmacophore modeling of ligands (i.e. both substrates and inhibitors) of Cyt P450 2D6 and protein homology modeling have been used successfully for the rationalisation and prediction of metabolite formation by this Cyt P450 isoenzyme. Significant protein structure-related species differences have been reported recently. It is concluded that not one computational approach is capable of rationalizing and reliably predicting metabolite formation by Cyt P450 2D6, but that it is rather the combination of the various complimentary approaches. It is moreover concluded, that experimental validation of the computational models and predictions is often still lacking. With the advent of novel, easily and well applicable in vitro based high throughput assays for ligand binding and turnover this limitation could be overcome soon, however. When effective links with other new and recent developments, such as bioinformatics, neural network computing, genomics and proteomics can be created, in silico rationalisation and prediction of drug metabolism by Cyt P450s is likely to become one of the key technologies in early drug discovery and development processes.

Bioinformatics in Drug Development and Assessment

Bioinformatics is playing an increasingly important role in nearly all aspects of drug discovery, drug assessment, and drug development. This growing importance lies not only in the role that bioinformatics plays in handling large volumes of data, but also in the utility of bioinformatics tools to predict, analyze, or help interpret clinical and preclinical findings. This review focuses on describing and evaluating some of the newer or more important bioinformatics resources (i.e., databases and software) that are of growing importance to understanding or predicting drug metabolism, especially with respect to the absorption, distribution, metabolism, excretion, (ADME), and toxicity (T) of both existing drugs and potential drug leads. Detailed descriptions and critical assessments of a number of potentially useful bioinformatics/cheminformatics databases and predictive ADMET software tools are provided. Additionally, several pharmaceutically important applications of both the databases and software are highlighted. Given the rapid growth in this area and the rapid changes that are taking place, a special emphasis is placed on freely available or Web-accessible resources.