XMetDB: an open access database for xenobiotic metabolism.

Xenobiotic metabolism is an active research topic but the limited amount of openly available high-quality biotransformation data constrains predictive modeling. Current database often default to commonly available information: which enzyme metabolizes a compound, but neither experimental conditions nor the atoms that undergo metabolization are captured. We present XMetDB, an open access database for drugs and other xenobiotics and their respective metabolites. The database contains chemical structures of xenobiotic biotransformations with substrate atoms annotated as reaction centra, the resulting product formed, and the catalyzing enzyme, type of experiment, and literature references. Associated with the database is a web interface for the submission and retrieval of experimental metabolite data for drugs and other xenobiotics in various formats, and a web API for programmatic access is also available. The database is open for data deposition, and a curation scheme is in place for quality control. An extensive guide on how to enter experimental data into is available from the XMetDB wiki. XMetDB formalizes how biotransformation data should be reported, and the openly available systematically labeled data is a big step forward towards better models for predictive metabolism.

Quantum Mechanics/Molecular Mechanics Modeling of Drug Metabolism: Mexiletine N-Hydroxylation by Cytochrome P450 1A2.

The mechanism of cytochrome P450(CYP)-catalyzed hydroxylation of primary amines is currently unclear and is relevant to drug metabolism; previous small model calculations have suggested two possible mechanisms: direct N-oxidation and H-abstraction/rebound. We have modeled the N-hydroxylation of (R)-mexiletine in CYP1A2 with hybrid quantum mechanics/molecular mechanics (QM/MM) methods, providing a more detailed and realistic model. Multiple reaction barriers have been calculated at the QM(B3LYP-D)/MM(CHARMM27) level for the direct N-oxidation and H-abstraction/rebound mechanisms. Our calculated barriers indicate that the direct N-oxidation mechanism is preferred and proceeds via the doublet spin state of Compound I. Molecular dynamics simulations indicate that the presence of an ordered water molecule in the active site assists in the binding of mexiletine in the active site, but this is not a prerequisite for reaction via either mechanism. Several active site residues play a role in the binding of mexiletine in the active site, including Thr124 and Phe226. This work reveals key details of the N-hydroxylation of mexiletine and further demonstrates that mechanistic studies using QM/MM methods are useful for understanding drug metabolism.

Mechanism of the N-hydroxylation of primary and secondary amines by cytochrome P450.

Cytochrome P450 enzymes (CYPs) metabolize alkyl- and arylamines, generating several different products. For the primary and secondary amines, some of these reactions result in hydroxylated amines, which may be toxic. Thus, when designing new drugs containing amine groups, it is important to be able to predict if a given compound will be a substrate for CYPs, in order to avoid toxic metabolites, and hence to understand the mechanism that is utilized by CYPs. Two possible mechanisms, for the N-hydroxylation of primary and secondary amines mediated by CYPs, are studied by density functional theory (DFT) for four different amines (aniline, N-methylaniline, propan-2-amine, and dimethylamine). The hydrogen abstraction and rebound mechanism is found to be preferred over a direct oxygen transfer mechanism for all four amines. However, in contrast to the same mechanism for the hydroxylation of aliphatic carbon atoms, the rebound step is shown to be rate-limiting in most cases.

Density functional theory study on the formation of reactive benzoquinone imines by hydrogen abstraction.

Many drug compounds are oxidized by cytochrome P450 (CYP) enzymes to form reactive metabolites. This study presents density functional theory calculations of the CYP-mediated metabolism of acetaminophen and a series of related compounds that can form reactive metabolites by hydrogen abstraction. The substitution pattern affects the activation barrier for hydrogen abstraction by up to 30 kJ/mol. A correlation (R(2) = 0.72) between the transition-state energies and the corresponding substrate radical energies has been established. Using this correlation is significantly less time-demanding than using the porphyrin model to determine the activation energies. We have used this correlation on monosubstituted phenols to rationalize the effect of the various substituents in the drug compounds. In addition to facilitating a chemical interpretation, the approach is sufficiently fast and reliable to be used as an in silico method in the design of new compounds with improved metabolic stability.

Trends in predicted chemoselectivity of cytochrome P450 oxidation: B3LYP barrier heights for epoxidation and hydroxylation reactions.

Prediction of epoxide formation in drug metabolism is a difficult but important task, as epoxide formation is linked to drug toxicity. A comparison of the energy barriers for cytochrome P450 mediated epoxidation of alkenes to the barriers for the hydroxylation of an aliphatic carbon atom next to a double bond has been performed using B3LYP and B3LYP-D3. Relevant experimental data on oxidation selectivity has also been assessed. The results show that density functional theory, when using B3LYP-D3, does well in reproducing the experimental trends. Considering that the comparison involves chemical steps with quite different features this is remarkable. We also find that B3LYP consistently underestimates the hydrogen abstraction barriers relative to the epoxidation barriers, and that including a dispersion correction reduces this problem.

Prediction of cytochrome p450 mediated metabolism of designer drugs.

The analysis of designer drugs in human plasma is highly complex, as most of these drugs are metabolized quickly, and often into multiple products. For novel designer drugs, it is common that reference compounds for these metabolites are unavailable at the time of analysis. Hence, the usage of in silico procedures to accurately predict the chemical structures of these metabolites would be very useful. In this study, the differences between several methods for prediction of site of metabolism for cytochrome P450 mediated drug metabolism are described, and their prediction accuracies are analyzed on a set of designer drugs. It is found that ligand-based methods, which are simpler and faster, are better than or at least as good as much more complex structure-based methods.

Use of density functional theory in drug metabolism studies.

INTRODUCTION: The cytochrome P450 enzymes (CYPs) metabolize many drug compounds. They catalyze a wide variety of reactions, and potentially, a large number of different metabolites can be generated. Density functional theory (DFT) has, over the past decade, been shown to be a powerful tool to rationalize and predict the possible metabolites generated by the CYPs as well as other drug-metabolizing enzymes. AREAS COVERED: We review applications of DFT on reactions performed by the CYPs and other drug-metabolizing enzymes able to perform oxidation reactions, with an emphasis on predicting which metabolites are produced. We also cover calculations of binding energies for complexes in which the ligands interact directly with the heme iron atom. EXPERT OPINION: DFT is a useful tool for prediction of the site of metabolism. The use of small models of the enzymes work surprisingly well for most CYP isoforms. This is probably due to the fact that the binding of the substrates is not the major determinant. When binding of the substrate plays a significant role, the well-known issue of determining the free energy of binding is the challenge. How approaches taking the protein environment into account, like docking, MD and QM/MM, can be used are discussed.

FAst MEtabolizer (FAME): A rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes.

FAst MEtabolizer (FAME) is a fast and accurate predictor of sites of metabolism (SoMs). It is based on a collection of random forest models trained on diverse chemical data sets of more than 20 000 molecules annotated with their experimentally determined SoMs. Using a comprehensive set of available data, FAME aims to assess metabolic processes from a holistic point of view. It is not limited to a specific enzyme family or species. Besides a global model, dedicated models are available for human, rat, and dog metabolism; specific prediction of phase I and II metabolism is also supported. FAME is able to identify at least one known SoM among the top-1, top-2, and top-3 highest ranked atom positions in up to 71%, 81%, and 87% of all cases tested, respectively. These prediction rates are comparable to or better than SoM predictors focused on specific enzyme families (such as cytochrome P450s), despite the fact that FAME uses only seven chemical descriptors. FAME covers a very broad chemical space, which together with its inter- and extrapolation power makes it applicable to a wide range of chemicals. Predictions take less than 2.5 s per molecule in batch mode on an Ultrabook. Results are visualized using Jmol, with the most likely SoMs highlighted.

Structure-activity relationships and identification of optmized CC-chemokine receptor CCR1, 5, and 8 metal-ion chelators.

Chemokine receptors are involved in trafficking of leukocytes and represent targets for autoimmune conditions, inflammatory diseases, viral infections, and cancer. We recently published CCR1, CCR8, and CCR5 agonists and positive modulators based on a three metal-ion chelator series: 2,2'-bipyridine, 1,10-phenanthroline, and 2,2';6',2″-terpyridine. Here, we have performed an in-depth structure-activity relationship study and tested eight new optimized analogs. Using density functional theory calculations we demonstrate that the chelator zinc affinities depend on how electron-donating and -withdrawing substituents modulate the partial charges of chelating nitrogens. The zinc affinity was found to constitute the major factor for receptor potency, although the activity of some chelators deviate suggesting favorable or unfavorable interactions. Hydrophobic and halogen substituents are generally better accommodated in the receptors than polar groups. The new analog brominated terpyridine (29) resulted in the highest chelator potencies observed so far CCR1 (EC50: 0.49 µM) and CCR8 (EC50: 0.28 µM). Furthermore, we identified the first selective CCR5 agonist chelator, meta dithiomethylated bipyridine (23). The structure-activity relationships contribute to small-molecule drug development, and the novel chelators constitute valuable tools for studies of structural mechanisms for chemokine receptor activation.

WhichCyp: prediction of cytochromes P450 inhibition.

SUMMARY: In this work we present WhichCyp, a tool for prediction of which cytochromes P450 isoforms (among 1A2, 2C9, 2C19, 2D6 and 3A4) a given molecule is likely to inhibit. The models are built from experimental high-throughput data using support vector machines and molecular signatures. AVAILABILITY: The WhichCyp server is freely available for use on the web at http://drug.ku.dk/whichcyp, where the WhichCyp Java program and source code is also available for download.

The contribution of atom accessibility to site of metabolism models for cytochromes P450.

Three different types of atom accessibility descriptors are investigated in relation to site of metabolism predictions. To enable the integration of local accessibility we have constructed 2DSASA, a method for the calculation of the atomic solvent accessible surface area that is independent of 3D coordinates. The method was implemented in the SMARTCyp site of metabolism prediction models and improved the results by up to 4 percentage points for nine cytochrome P450 isoforms. The final models are made available at http://www.farma.ku.dk/smartcyp.

Enrichment of True Positives from Structural Alerts Through the Use of Novel Atomic Fragment Based Descriptors.

To enhance the discrimination rate for methods applying structural alerts and biotransformation rules in the prediction of toxicity and drug metabolism we have developed a set of novel fragment based atomic descriptors. These atomic descriptors encode the properties of the fragments separating an atom from the closest end of a branch or the molecule. The end of a branch and the end of a molecule, as well as the selection of the fragments, are made by an algorithm that uses only the distance matrix of the molecule. The novel descriptors are applied to a small set of biotransformation rules and are shown to be able to reduce the number of unconfirmed positives by up to 58 %.

RS-WebPredictor: a server for predicting CYP-mediated sites of metabolism on drug-like molecules.

SUMMARY: Regioselectivity-WebPredictor (RS-WebPredictor) is a server that predicts isozyme-specific cytochrome P450 (CYP)-mediated sites of metabolism (SOMs) on drug-like molecules. Predictions may be made for the promiscuous 2C9, 2D6 and 3A4 CYP isozymes, as well as CYPs 1A2, 2A6, 2B6, 2C8, 2C19 and 2E1. RS-WebPredictor is the first freely accessible server that predicts the regioselectivity of the last six isozymes. Server execution time is fast, taking on average 2s to encode a submitted molecule and 1s to apply a given model, allowing for high-throughput use in lead optimization projects. AVAILABILITY: RS-WebPredictor is accessible for free use at http://reccr.chem.rpi.edu/Software/RS-WebPredictor/

Predicting drug metabolism by cytochrome P450 2C9: comparison with the 2D6 and 3A4 isoforms.

By the use of knowledge gained through modeling of drug metabolism mediated by the cytochrome P450 2D6 and 3A4 isoforms, we constructed a 2D-based model for site-of-metabolism prediction for the cytochrome P450 2C9 isoform. The similarities and differences between the models for the 2C9 and 2D6 isoforms are discussed through structural knowledge from the X-ray crystal structures and trends in experimental data. The final model was validated on an independent test set, resulting in an area under the curve value of 0.92, and a site of metabolism was found among the top two ranked atoms for 77% of the compounds.

RS-Predictor models augmented with SMARTCyp reactivities: robust metabolic regioselectivity predictions for nine CYP isozymes.

RS-Predictor is a tool for creating pathway-independent, isozyme-specific, site of metabolism (SOM) prediction models using any set of known cytochrome P450 (CYP) substrates and metabolites. Until now, the RS-Predictor method was only trained and validated on CYP 3A4 data, but in the present study, we report on the versatility the RS-Predictor modeling paradigm by creating and testing regioselectivity models for substrates of the nine most important CYP isozymes. Through curation of source literature, we have assembled 680 substrates distributed among CYPs 1A2, 2A6, 2B6, 2C19, 2C8, 2C9, 2D6, 2E1, and 3A4, the largest publicly accessible collection of P450 ligands and metabolites released to date. A comprehensive investigation into the importance of different descriptor classes for identifying the regioselectivity mediated by each isozyme is made through the generation of multiple independent RS-Predictor models for each set of isozyme substrates. Two of these models include a density functional theory (DFT) reactivity descriptor derived from SMARTCyp. Optimal combinations of RS-Predictor and SMARTCyp are shown to have stronger performance than either method alone, while also exceeding the accuracy of the commercial regioselectivity prediction methods distributed by Optibrium and Schrödinger, correctly identifying a large proportion of the metabolites in each substrate set within the top two rank-positions: 1A2 (83.0%), 2A6 (85.7%), 2B6 (82.1%), 2C19 (86.2%), 2C8 (83.8%), 2C9 (84.5%), 2D6 (85.9%), 2E1 (82.8%), 3A4 (82.3%), and merged (86.0%). Comprehensive datamining of each substrate set and careful statistical analyses of the predictions made by the different models revealed new insights into molecular features that control metabolic regioselectivity and enable accurate prospective prediction of likely SOMs.

Binding affinities in the SAMPL3 trypsin and host-guest blind tests estimated with the MM/PBSA and LIE methods.

We have estimated affinities for the binding of 34 ligands to trypsin and nine guest molecules to three different hosts in the SAMPL3 blind challenge, using the MM/PBSA, MM/GBSA, LIE, continuum LIE, and Glide score methods. For the trypsin challenge, none of the methods were able to accurately predict the experimental results. For the MM/GB(PB)SA and LIE methods, the rankings were essentially random and the mean absolute deviations were much worse than a null hypothesis giving the same affinity to all ligand. Glide scoring gave a Kendall's τ index better than random, but the ranking is still only mediocre, τ = 0.2. However, the range of affinities is small and most of the pairs of ligands have an experimental affinity difference that is not statistically significant. Removing those pairs improves the ranking metric to 0.4-1.0 for all methods except CLIE. Half of the trypsin ligands were non-binders according to the binding assay. The LIE methods could not separate the inactive ligands from the active ones better than a random guess, whereas MM/GBSA and MM/PBSA were slightly better than random (area under the receiver-operating-characteristic curve, AUC = 0.65-0.68), and Glide scoring was even better (AUC = 0.79). For the first host, MM/GBSA and MM/PBSA reproduce the experimental ranking fairly good, with τ = 0.6 and 0.5, respectively, whereas the Glide scoring was considerably worse, with a τ = 0.4, highlighting that the success of the methods is system-dependent.

Ligand-Based Site of Metabolism Prediction for Cytochrome P450 2D6.

A ligand-based method based on the SMARTCyp approach that predicts the sites of cytochrome P450 2D6-mediated metabolism of druglike molecules has been developed. The method uses only two descriptors besides the reactivity from SMARTCyp: the distance to a protonated nitrogen atom and the distance to the end of the molecule. Hence, the site of metabolism is predicted directly from the 2D structure of a molecule, without requiring calculation of electronic properties or generation of 3D structures. Testing on an independent test set gives an area under the curve value of 0.94, and a site of metabolism is found among the top two ranked atoms for 91% of the compounds.

Theoretical Study of the Cytochrome P450 Mediated Metabolism of Phosphorodithioate Pesticides.

The toxicity of phosphorodithioate pesticides is due to the formation of the active oxane product through desulfurization by cytochrome P450 enzymes, both in humans and insects. During this desulfurization, inhibition of cytochrome P450 and a loss of heme has been observed. Here, we study the mechanism of desulfurization and inhibition with density functional theory, using the B3LYP functional with and without dispersion correction. The results show that a reaction mechanism initiated by sulfur oxidation is most likely, with a reaction barrier of 47 kJ/mol. The sulfur oxidation is followed by a ring-closing mechanism with a barrier of 28 kJ/mol relative to the sulfur-oxidized intermediate. The enzymatic contribution to the ring-closing is very small. It is also shown that the apparent loss of heme might be due to the formation of a previously unknown inhibition complex, which changes the aromatic conjugation of the porphyrin ring. We also show that including dispersion correction has significant effects on a ring closure transition state (∼30 kJ/mol), whereas effects on the other steps in the reaction are relatively small (4-15 kJ/mol).