Predicting drug metabolism--an evaluation of the expert system METEOR

MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators

Xenobiotic metabolism is a key consideration in evaluating the hazards and risks posed by environmental chemicals. A number of software tools exist that are capable of simulating metabolites, but each reports its predictions in a different format and with varying levels of detail. This makes comparing the performance and coverage of the tools a practical challenge. To address this shortcoming, we developed a metabolic simulation framework called MetSim, which comprises three main components. A graph-based schema was developed to allow metabolism information to be harmonized. The schema was implemented in MongoDB to store and retrieve metabolic graphs for subsequent analysis. MetSim currently includes an application programming interface for four metabolic simulators: BioTransformer, the OECD Toolbox, EPA's chemical transformation simulator (CTS), and tissue metabolism simulator (TIMES). Lastly, MetSim provides functions to help evaluate simulator performance for specific data sets. In this study, a set of 112 drugs with 432 reported metabolites were compiled, and predictions were made using the 4 simulators. Fifty-nine of the 112 drugs were taken from the Small Molecule Pathway Database, with the remainder sourced from the literature. The human models within BioTransformer and CTS (Phase I only) and the rat models within TIMES and the OECD Toolbox (Phase I only) were used to make predictions for the chemicals in the data set. The recall and precision (recall, precision) ranked in order of highest recall for each individual tool were CTS (0.54, 0.017), BioTransformer (0.50, 0.008), Toolbox in vitro (0.40, 0.144), TIMES in vivo (0.40, 0.133), Toolbox in vivo (0.40, 0.118), and TIMES in vitro (0.39, 0.128). Combining all of the model predictions together increased the overall recall (0.73, 0.008). MetSim enabled insights into the performance and coverage of in silico metabolic simulators to be more efficiently derived, which in turn should aid future efforts to evaluate other data sets.

Deep Learning Based Drug Metabolites Prediction

Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.

Potential inhibitors of the enzyme acetylcholinesterase and juvenile hormone with insecticidal activity: study of the binding mode via docking and molecular dynamics simulations

Models validation in QSAR, pharmacophore, docking and others can ensure the accuracy and reliability of future predictions in design and selection of molecules with biological activity. In this study, pyriproxyfen was used as a pivot/template to search the database of the Maybridge Database for potential inhibitors of the enzymes acetylcholinesterase and juvenile hormone as well. The initial virtual screening based on the 3D shape resulted in 2000 molecules with Tanimoto index ranging from 0.58 to 0.88. A new reclassification was performed on the overlapping of positive and negative charges, which resulted in 100 molecules with Tanimoto's electrostatic score ranging from 0.627 to 0.87. Using parameters related to absorption, distribution, metabolism and excretion and the pivot molecule, the molecules selected in the previous stage were evaluated regarding these criteria, and 21 were then selected. The pharmacokinetic and toxicological properties were considered and for 12 molecules, the DEREK software not fired any alert of toxicity, which were thus considered satisfactory for prediction of biological activity using the Web server PASS. In the molecular docking with insect acetylcholinesterase, the Maybridge3_002654 molecule had binding affinity of -11.1 kcal/mol, whereas in human acetylcholinesterase, the Maybridge4_001571molecule show in silico affinity of -10.2 kcal/mol, and in the juvenile hormone, the molecule MCULE-8839595892 show in silico affinity value of -11.6 kcal/mol. Subsequent long-trajectory molecular dynamics studies indicated considerable stability of the novel molecules compared to the controls.AbbreviationsQSARquantitative structure-activity relationshipsPASSprediction of activity spectra for substancesCommunicated by Ramaswamy H. Sarma.

Prediction of UGT-mediated Metabolism Using the Manually Curated MetaQSAR Database

Even though glucuronidations are the most frequent metabolic reactions of conjugation, both in quantitative and qualitative terms, they have rather seldom been investigated using computational approaches. To fill this gap, we have used the manually collected MetaQSAR metabolic reaction database to generate two models for the prediction of UGT-mediated metabolism, both based on molecular descriptors and implementing the Random Forest algorithm. The first model predicts the occurrence of the reaction and was internally validated with a Matthew correlation coefficient (MCC) of 0.76 and an area under the ROC curve (AUC) of 0.94, and further externally validated using a test set composed of 120 additional xenobiotics (MCC of 0.70 and AUC of 0.90). The second model distinguishes between O- and N-glucuronidations and was optimized by the random undersampling procedure to improve the predictive accuracy during the internal validation, with the recall measure of the minority class increasing from 0.55 to 0.78.

Identification of Potential Inhibitors from Pyriproxyfen with Insecticidal Activity by Virtual Screening

Aedes aegypti is the main vector of dengue fever transmission, yellow fever, Zika, and chikungunya in tropical and subtropical regions and it is considered to cause health risks to millions of people in the world. In this study, we search to obtain new molecules with insecticidal potential against Ae. aegypti via virtual screening. Pyriproxyfen was chosen as a template compound to search molecules in the database Zinc_Natural_Stock (ZNSt) with structural similarity using ROCS (rapid overlay of chemical structures) and EON (electrostatic similarity) software, and in the final search, the top 100 were selected. Subsequently, in silico pharmacokinetic and toxicological properties were determined resulting in a total of 14 molecules, and these were submitted to the PASS online server for the prediction of biological insecticide and acetylcholinesterase activities, and only two selected molecules followed for the molecular docking study to evaluate the binding free energy and interaction mode. After these procedures were performed, toxicity risk assessment such as LD50 values in mg/kg and toxicity class using the PROTOX online server, were undertaken. Molecule ZINC00001624 presented potential for inhibition for the acetylcholinesterase enzyme (insect and human) with a binding affinity value of -10.5 and -10.3 kcal/mol, respectively. The interaction with the juvenile hormone was -11.4 kcal/mol for the molecule ZINC00001021. Molecules ZINC00001021 and ZINC00001624 had excellent predictions in all the steps of the study and may be indicated as the most promising molecules resulting from the virtual screening of new insecticidal agents.

Integrated Framework for Identifying Toxic Transformation Products in Complex Environmental Mixtures

Complex environmental mixtures consist of hundreds to thousands of unknown and unregulated organic compounds that may have toxicological relevance, including transformation products (TPs) of anthropogenic organic pollutants. Non-targeted analysis and suspect screening analysis offer analytical approaches for potentially identifying these toxic transformation products. However, additional tools and strategies are needed in order to reduce the number of chemicals of interest and focus analytical efforts on chemicals that may pose risks to humans and the environment. This brief review highlights recent developments in this field and suggests an integrated framework that incorporates complementary instrumental techniques, computational chemistry, and toxicity analysis, for prioritizing and identifying toxic TPs in the environment.

Mind the Gap! A Journey towards Computational Toxicology

Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale. However, the predictive potential of these models presents strengths and weaknesses. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. On the other, the unconscious use of in silico methods can mislead end-users with elusive results. The focus of this review is on the basic scientific and regulatory recommendations in the derivation and application of computational models. Attention is paid to examine the interplay between computational toxicology and drug discovery and development. Avoiding the easy temptation of an overoptimistic future, we report our view on what can, or cannot, realistically be done. Indeed, studies of safety/toxicity represent a key element of chemical prioritization programs carried out by chemical industries, and primarily by pharmaceutical companies.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Metabolic activation and drug-induced liver injury: in vitro approaches for the safety risk assessment of new drugs

Drug-induced liver injury (DILI) is a significant leading cause of hepatic dysfunction, drug failure during clinical trials and post-market withdrawal of approved drugs. Many cases of DILI are unexpected reactions of an idiosyncratic nature that occur in a small group of susceptible individuals. Intensive research efforts have been made to understand better the idiosyncratic DILI and to identify potential risk factors. Metabolic bioactivation of drugs to form reactive metabolites is considered an initiation mechanism for idiosyncratic DILI. Reactive species may interact irreversibly with cell macromolecules (covalent binding, oxidative damage), and alter their structure and activity. This review focuses on proposed in vitro screening strategies to predict and reduce idiosyncratic hepatotoxicity associated with drug bioactivation. Compound incubation with metabolically competent biological systems (liver-derived cells, subcellular fractions), in combination with methods to reveal the formation of reactive intermediates (e.g., formation of adducts with liver proteins, metabolite trapping or enzyme inhibition assays), are approaches commonly used to screen the reactivity of new molecules in early drug development. Several cell-based assays have also been proposed for the safety risk assessment of bioactivable compounds. Copyright © 2015 John Wiley & Sons, Ltd.

Antihypertensive drugs metabolism: an update to pharmacokinetic profiles and computational approaches

Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements.

Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy response especially due to the P450 dependent metabolic status they present.

Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and (ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches.

This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to predict the metabolic profile of several antihypertensive drugs.

Prediction of cytochrome P450 mediated metabolism

Cytochrome P450 enzymes (CYPs) form one of the most important enzyme families involved in the metabolism of xenobiotics. CYPs comprise many isoforms, which catalyze a wide variety of reactions, and potentially, a large number of different metabolites can be formed. However, it is often hard to rationalize what metabolites these enzymes generate. In recent years, many different in silico approaches have been developed to predict binding or regioselective product formation for the different CYP isoforms. These comprise ligand-based methods that are trained on experimental CYP data and structure-based methods that consider how the substrate is oriented in the active site or/and how reactive the part of the substrate that is accessible to the heme group is. We will review key aspects for various approaches that are available to predict binding and site of metabolism (SOM), what outcome can be expected from the predictions, and how they could potentially be improved.

A review of metabolic and enzymatic engineering strategies for designing and optimizing performance of microbial cell factories

Microbial cell factories (MCFs) are of considerable interest to convert low value renewable substrates to biofuels and high value chemicals. This review highlights the progress of computational models for the rational design of an MCF to produce a target bio-commodity. In particular, the rational design of an MCF involves: (i) product selection, (ii) de novo biosynthetic pathway identification (i.e., rational, heterologous, or artificial), (iii) MCF chassis selection, (iv) enzyme engineering of promiscuity to enable the formation of new products, and (v) metabolic engineering to ensure optimal use of the pathway by the MCF host. Computational tools such as (i) de novo biosynthetic pathway builders, (ii) docking, (iii) molecular dynamics (MD) and steered MD (SMD), and (iv) genome-scale metabolic flux modeling all play critical roles in the rational design of an MCF. Genome-scale metabolic flux models are of considerable use to the design process since they can reveal metabolic capabilities of MCF hosts. These can be used for host selection as well as optimizing precursors and cofactors of artificial de novo biosynthetic pathways. In addition, recent advances in genome-scale modeling have enabled the derivation of metabolic engineering strategies, which can be implemented using the genomic tools reviewed here as well.

Drug metabolism in silico - the knowledge-based expert system approach. Historical perspectives and current strategies

Drug metabolism in silico is briefly discussed in terms of the importance of understanding the mechanistic basis of drug molecule biotransformation in vivo and its consequences in terms of changes in the properties of metabolites relative to those of the parent compound. A basic overview of an expert system is presented, along with how these general principles apply to expert systems for the prediction of xenobiotic metabolism. A brief history of the development of these systems is also presented. Methods for increasing both the sensitivity and selectivity of prediction are outlined and the benefits of using complementary prediction systems in a conjoint manner are proposed.

Substrate selectivity of human intestinal UDP-glucuronosyltransferases (UGTs): in silico and in vitro insights

The current drug development process aims to produce safe, effective drugs within a reasonable time and at a reasonable cost. Phase II metabolism (glucuronidation) can affect drug action and pharmacokinetics to a considerable extent and so its studies and prediction at initial stages of drug development are very imperative. Extensive glucuronidation is an obstacle to oral bioavailability because the first-pass glucuronidation [or premature clearance by UDP-glucuronosyltransferases (UGTs)] of orally administered agents frequently results in poor oral bioavailability and lack of efficacy. Modeling of new chemical entities/drugs for UGTs and their kinetic data can be useful in understanding the binding patterns to be used in the design of better molecules. This review concentrates on first-pass glucuronidation by intestinal UGTs, including their topology, expression profile, and pharmacogenomics. In addition, recent advances are discussed with respect to substrate selectivity at the binding pocket, structural requirements, and mechanism of enzyme actions.

Software aided approaches to structure-based metabolite identification in drug discovery and development

Technological advances in mass spectrometry (MS) such as accurate mass high resolution instrumentation have fundamentally changed the approach to systematic metabolite identification over the past decade. Despite technological break-through on the instrumental side, metabolite identification still requires tedious manual data inspection and interpretation of huge analytical datasets. The process of metabolite identification has become largely facilitated and partly automated by cheminformatics approaches such as knowledge base metabolite prediction using, for example, Meteor, MetaDrug, MetaSite and StarDrop that are typically applied pre-acquisition. Likewise, emerging new technologies in postacquisition data analysis like mass defect filtering (MDF) have moved the technology driven analytical methodology to metabolite identification toward generic, structure-based workflows. The biggest challenge for automation however remains the structural assignment of drug metabolites. Software-guided approaches for the unsupervised metabolite identification still cannot compete with expert user manual data interpretation yet. Recently MassMetaSite has been introduced for the automated ranked output of metabolite structures based on the combination of metabolite prediction and interrogation of analytical mass spectrometric data. This approach and others are promising milestones toward an unsupervised process to metabolite identification and structural characterization moving away from a sample focused per-compound approach to a structure-driven generic workflow.

Strategies for the generation, validation and application of in silico ADMET models in lead generation and optimization

INTRODUCTION

The most desirable chemical starting point in drug discovery is a hit or lead with a good overall profile, and where there may be issues; a clear SAR strategy should be identifiable to minimize the issue. Filtering based on drug-likeness concepts are a first step, but more accurate theoretical methods are needed to i) estimate the biological profile of molecule in question and ii) based on the underlying structure-activity relationships used by the model, estimate whether it is likely that the molecule in question can be altered to remove these liabilities.

AREAS COVERED

In this paper, the authors discuss the generation of ADMET models and their practical use in decision making. They discuss the issues surrounding data collation, experimental errors, the model assessment and validation steps, as well as the different types of descriptors and statistical models that can be used. This is followed by a discussion on how the model accuracy will dictate when and where it can be used in the drug discovery process. The authors also discuss how models can be developed to more effectively enable multiple parameter optimization.

EXPERT OPINION

Models can be applied in lead generation and lead optimization steps to i) rank order a collection of hits, ii) prioritize the experimental assays needed for different hit series, iii) assess the likelihood of resolving a problem that might be present in a particular series in lead optimization and iv) screen a virtual library based on a hit or lead series to assess the impact of diverse structural changes on the predicted properties.

The application of structure-activity relationships to the prediction of the mutagenic activity of chemicals

Prediction of mutagenicity by computer is now routinely used in research and by regulatory authorities. Broadly, two different approaches are in wide use. The first is based on statistical analysis of data to find patterns associated with mutagenic activity. The resultant models are generally termed quantitative structure-activity relationships (QSAR). The second is based on capturing human knowledge about the causes of mutagenicity and applying it in ways that mimic human reasoning. These systems are generally called knowledge-based system. Other methods for finding patterns in data, such as the application of neural networks, are in use but less widely so.

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

From the identification of HIV as the agent causing AIDS, to the development of effective antiretroviral drugs, the scientific achievements in HIV research over the past twenty-six years have been formidable. Currently, there are twenty-five anti-HIV compounds which have been formally approved for clinical use in the treatment of AIDS. These compounds fall into six categories: nucleoside reverse transcriptase inhibitors (NRTIs), nucleotide reverse transcriptase inhibitors (NtRTIs), non-nucleoside reverse transcriptase inhibitors (NNRTIs), protease inhibitors (PIs), cell entry inhibitors or fusion inhibitors (FIs), co-receptor inhibitors (CRIs), and integrase inhibitors (INIs). Metabolism by the host organism is one of the most important determinants of the pharmacokinetic profile of a drug. Formation of active or toxic metabolites will also have an impact on the pharmacological and toxicological outcomes. Therefore, it is widely recognized that metabolism studies of a new chemical entity need to be addressed early in the drug discovery process. This paper describes an overview of the metabolism of currently available anti-HIV drugs.

Understanding the determinants of selectivity in drug metabolism through modeling of dextromethorphan oxidation by cytochrome P450

Cytochrome P450 enzymes play key roles in the metabolism of the majority of drugs. Improved models for prediction of likely metabolites will contribute to drug development. In this work, two possible metabolic routes (aromatic carbon oxidation and O-demethylation) of dextromethorphan are compared using molecular dynamics (MD) simulations and density functional theory (DFT). The DFT results on a small active site model suggest that both reactions might occur competitively. Docking and MD studies of dextromethorphan in the active site of P450 2D6 show that the dextromethorphan is located close to heme oxygen in a geometry apparently consistent with competitive metabolism. In contrast, calculations of the reaction path in a large protein model [using a hybrid quantum mechanical-molecular mechanics (QM/MM) method] show a very strong preference for O-demethylation, in accordance with experimental results. The aromatic carbon oxidation reaction is predicted to have a high activation energy, due to the active site preventing formation of a favorable transition-state structure. Hence, the QM/MM calculations demonstrate a crucial role of many active site residues in determining reactivity of dextromethorphan in P450 2D6. Beyond substrate binding orientation and reactivity of Compound I, successful metabolite predictions must take into account the detailed mechanism of oxidation in the protein. These results demonstrate the potential of QM/MM methods to investigate specificity in drug metabolism.

Database searching for structural identification of metabolites in complex biofluids for mass spectrometry-based metabonomics

MS and HPLC are commonly used for compound characterization and obtaining structural information; in the field of metabonomics, these two analytical techniques are often combined to characterize unknown endogenous or exogenous metabolites present in complex biological samples. Since the structures of a majority of these metabolites are not actually identified, the result of most metabonomic studies is a list of m/z values and retention times. However, without knowing actual structures, the biological significance of these 'features' cannot be determined. The process of identifying the structures of unknown compounds can be time intensive, costly and frequently requires the use of multiple orthogonal analytical techniques - this laborious procedure seems insurmountable for the long lists of unknowns that must be identified for each study. In addition, the limited sample volume and the extremely low concentration of most endogenous analytes frequently make purification and identification by other instrumentation nearly impossible. This review is intended to explore the problems and progress with current tools that are available for MS-based structure identification for both endogenous and exogenous metabolites.

Biokinetic modeling and in vitro-in vivo extrapolations

The introduction of in vitro methodologies in the toxicological risk assessment process requires a number of prerequisites regarding both the toxicodynamics and the biokinetics of the compounds under study. In vitro systems will need to be relevant for measuring those structural and physiological changes that are good indicators for adverse effects. Furthermore, the dose metric found to have an effect in the in vitro system should be relevant. One element in defining the appropriate dose metric is related to the kinetic behavior of the compound in the in vitro system: binding to proteins, binding to plastic, evaporation, and the interaction between the culture medium and the cells. Ways to measure and model "in vitro biokinetics" are described. Second, the appropriate dose metric in vitro, e.g., the effective concentration, will need to be extrapolated to relevant in vivo exposure scenarios. The application of physiologically based biokinetic modelling is essential in such extrapolations. The parameters needed to build these models often can be estimated based on nonanimal data, namely chemical properties (QSARs) and in vitro experiments.

A statistical procedure to selectively detect metabolite signals in LC-MS data based on using variable isotope ratios

The tracing of metabolite signals in LC-MS data using stable isotope-labeled compounds has been described in the literature. However, the filtering efficiency and confidence when mining metabolite signals in complex LC-MS datasets can be improved. Here, we propose an additional statistical procedure to increase the compound-derived signal mining efficiency. This method also provides a highly confident approach to screen out metabolite signals because the correlation of varying concentration ratios of native/stable isotope-labeled compounds and their instrumental response ratio is used. An in-house computational program [signal mining algorithm with isotope tracing (SMAIT)] was developed to perform the statistical procedure. To illustrate the SMAIT concept and its effectiveness for mining metabolite signals in LC-MS data, the plasticizer, di-(2-ethylhexyl) phthalate (DEHP), was used as an example. The statistical procedure effectively filtered 15 probable metabolite signals from 3617 peaks in the LC-MS data. These probable metabolite signals were considered structurally related to DEHP. Results obtained here suggest that the statistical procedure could be used to confidently facilitate the detection of probable metabolites from a compound-derived precursor presented in a complex LC-MS dataset.

A holistic strategy for characterizing the safety of metabolites through drug discovery and development

The subject of metabolites in safety testing has had much debate in the recent past and has shown itself to be a complex issue with no simple solutions to providing absolute assurance of drug safety. Much of the attention has focused on the ability to identify metabolites and then demonstrate that their risk has been adequately characterized, either through their exposure in toxicology species or, failing this, by direct safety testing. In this review, we summarize our forward operational strategy that combines the principles summarized in the FDA Guidance, together with discussions at scientific meetings and literature opinions. It is a balance between the primary goal of assuring patient safety with one of reasonable investment. A key principle in striking this balance is to build stepwise information on metabolites through the drug discovery and development continuum. This allows assessments to be made from early nonclinical studies onward as to whether or not metabolite safety is underwritten by exposure in toxicology species. This strategy does not require absolute quantitation of the metabolites in early clinical trials but relies upon comparison of relative exposures between animals and humans using the capabilities of modern analytical techniques. Through this strategy, human disproportionate metabolites can be identified to allow a decision regarding the need for absolute quantitation and direct safety testing of the metabolite. Definitive radiolabeled studies would be initiated following proof of pharmacology or efficacy in humans, and nonclinical safety coverage would be adequately assessed prior to large-scale clinical trials. In cases where metabolite safety is not supported through the parent compound toxicology program, approaches for the direct safety testing of metabolites with regard to general and reproductive toxicology, safety pharmacology, and genetic safety have been defined.

In silico methods for predicting metabolism and mass fragmentation applied to quetiapine in liquid chromatography/time-of-flight mass spectrometry urine drug screening

Current in silico tools were evaluated for their ability to predict metabolism and mass spectral fragmentation in the context of analytical toxicology practice. A metabolite prediction program (Lhasa Meteor), a metabolite detection program (Bruker MetaboliteDetect), and a fragmentation prediction program (ACD/MS Fragmenter) were used to assign phase I metabolites of the antipsychotic drug quetiapine in the liquid chromatography/time-of-flight mass spectrometry (LC/TOFMS) accurate mass data from ten autopsy urine samples. In the literature, the main metabolic routes of quetiapine have been reported to be sulfoxidation, oxidation to the corresponding carboxylic acid, N- and O-dealkylation and hydroxylation. Of the 14 metabolites predicted by Meteor, eight were detected by LC/TOFMS in the urine samples with use of MetaboliteDetect software and manual inspection. An additional five hydroxy derivatives were detected, but not predicted by Meteor. The fragment structures provided by ACD/MS Fragmenter software confirmed the identification of the metabolites. Mean mass accuracy and isotopic pattern match (SigmaFit) values for the fragments were 2.40 ppm (0.62 mDa) and 0.010, respectively. ACD/MS Fragmenter, in particular, allowed metabolites with identical molecular formulae to be differentiated without a need to access the respective reference standards or reference spectra. This was well exemplified with the hydroxy/sulfoxy metabolites of quetiapine and their N- and O-dealkylated forms. The procedure resulted in assigning 13 quetiapine metabolites in urine. The present approach is instrumental in developing an extensive database containing exact monoisotopic masses and verified retention times of drugs and their urinary metabolites for LC/TOFMS drug screening.

Allergic contact dermatitis--formation, structural requirements, and reactivity of skin sensitizers

Contact allergy is caused by a wide range of chemicals after skin contact. Its clinical manifestation, allergic contact dermatitis (ACD), is developed upon repeated contact with the allergen. This perspective focuses on two areas that have yielded new useful information during the last 20 years: (i) structure-activity relationship (SAR) studies of contact allergy based on the concept of hapten-protein binding and (ii) mechanistic investigations regarding activation of nonsensitizing compounds to contact allergens by air oxidation or skin metabolism. The second area is more thoroughly reviewed since the full picture has previously not been published. Prediction of the sensitizing capacity of a chemical is important to avoid outbreaks of ACD in the population. Much research has been devoted to the development of in vitro and in silico predictive testing methods. Today, no method exists that is sensitive enough to detect weak allergens and that is robust enough to be used for routine screening. To cause sensitization, a chemical must bind to macromolecules (proteins) in the skin. Expert systems containing information about the relationship between the chemical structure and the ability of chemicals to haptenate proteins are available. However, few designed SAR studies based on mechanistic investigations of prohaptens have been published. Many compounds are not allergenic themselves but are activated in the skin (e.g., metabolically) or before skin contact (e.g., via air oxidation) to form skin sensitizers. Thus, more basic research is needed on the chemical reactions involved in the antigen formation and the immunological mechanisms. The clinical importance of air oxidation to activate nonallergenic compounds has been demonstrated. Oxidized fragrance terpenes, in contrast to the pure terpenes, gave positive patch test reactions in consecutive dermatitis patients as frequently as the most common standard allergens. This shows the importance of using compounds to which people are exposed when screening for ACD in dermatology clinics.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Predicting the Oxidative Metabolism of Statins: An Application of the MetaSite® Algorithm

PURPOSE

This study was undertaken to examine the MetaSite algorithm by comparing its predictions with experimentally characterized metabolites of statins produced by cytochromes P450 (CYPs).

METHODS

Seven statins were investigated, namely atorvastatin, cerivastatin, fluvastatin, pitavastatin and pravastatin which are (or were) used in their active hydroxy-acid form, and lovastatin and simvastatin which are used as the lactone prodrug. But given the fast lactone-hydroxy-acid equilibrium undergone by statins, both forms were investigated for each of the seven drugs. The MetaSite version 2.5.3 used here contains the homology 3D-models of CYP1A2, CYP2C19, CYP2C9, CYP2D6 and CYP3A4. In addition, we also used the crystallographic 3D-structure of human CYP2C9 and CYP3A4. To allow a better interpretation of results, the probability function PsMi calculated by MetaSite (namely the probability of atom i to be a site of metabolism) was explicitly decomposed into its two components, namely a recognition score Ei (the accessibility of atom i) and the chemical reactivity Ri of atom i toward oxidation reactions.

RESULTS

The current version of MetaSite is known to work best with prior experimental knowledge of the cytochrome(s) P450 involved. And indeed, experimentally confirmed sites of oxidation were correctly given a high priority by MetaSite. In particular 77% of correct predictions (including false positive but, as discussed, this is not necessarily a shortcoming) were obtained when considering the first five metabolites indicated by MetaSite.

CONCLUSION

To the best of our knowledge, this is the first independent report on the software. It is expected to contribute to the development of improved versions, but above all it demonstrates that the usefulness of such softwares critically depends on human experts.

Analytical Tools and Approaches for Metabolite Identification in Early Drug Discovery

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.

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) structure-activity 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.