Site Of Metabolism Research Articles

Using chemical bond-based method to predict site of metabolism for five biotransformations mediated by CYP 3A4, 2D6, and 2C9

Although it has been proposed for decades to predict site of metabolism (SOM) by in silico methods, identifying SOM correctly remains an unsolved fundamental problem and is an active area of research. In our prior works, we proposed a chemical bond-based approach to construction of SOM prediction models by integrating chemical bond descriptors and drug-metabolizing enzymes data. Although it has been evaluated with both 10-fold cross-validation and independent validation, we believe comparisons between this method and prior methods using publicly accessible external datasets are indispensable and more desirable. In the current study, based on chemical bond-based method, metabolism data released by Sheridan et al. and Zaretzki et al. was utilized to establish metabolite prediction models for CYP450 3A4, 2D6, and 2C9. Five major reaction types were involved, including Aliphatic C-hydroxylation, Aromatic C-hydroxylation, N-dealkylation, O-dealkylation, and S-Oxidation. Consequently, all our five models showed impressive performance on predicting SOMs, with accuracy and area under curve exceeded 0.940 and 0.953, respectively. Compared to prior works, our models were better than SOMP both in “SOM-scale” and “molecule-scale”. In conclusion, comparisons between chemical-bond based method and prior works were conducted for the first time, which demonstrated that chemical-bond based method is better than or at least comparable to prior works.

QNA-Based Prediction of Sites of Metabolism.

Metabolism of xenobiotics (Greek xenos: exogenous substances) plays an essential role in the prediction of biological activity and testing for the subsequent research and development of new drug candidates. Integration of various methods and techniques using different computational and experimental approaches is one of the keys to a successful metabolism prediction. While multiple structure-based and ligand-based approaches to metabolism prediction exist, the most important problem arises at the first stage of metabolism prediction: detection of the sites of metabolism (SOMs). In this paper, we describe the application of Quantitative Neighborhoods of Atoms (QNA) descriptors for prediction of the SOMs using potential function method, as well as several different machine learning techniques: naïve Bayes, random forest classifier, multilayer perceptron with back propagation and convolutional neural networks, and deep neural networks.

Aldehyde Oxidase: Reaction Mechanism and Prediction of Site of Metabolism.

Aldehyde oxidase (AO) is a molybdenum-containing enzyme involved in the clearance of drug compounds containing aldehydes and N-containing heterocyclic fragments. AO has gained considerable interest in recent years because of examples of too fast clearance of drug compounds in development. Thus, it is important to be able to predict AO-mediated drug metabolism. Therefore, we have characterized the structural and energetic aspects of different mechanisms with density functional theory using the molybdenum cofactor as a model for the reactive part of the enzyme. For a series of 6-substituted 4-quinazolinones, the trend in activation energies is the same for three tested reaction mechanisms. Using the concerted mechanism as a model for the enzymatic reaction, the transition states (TSs) for the formation of all possible metabolites for a series of known AO substrates were determined. The lowest activation energies correspond in all cases to the experimentally observed sites of metabolism (SOMs). Various molecular properties were calculated and investigated as more easily determinable markers for reactivity. The stabilities of both intermediates and products correlate to some extent with the TS energies and may be used to predict the SOM. The electrostatic-potential-derived charges are also good markers for the prediction of the experimental SOM for this set of compounds and may pave the way for the development of fast methods for the prediction of SOM for AO substrates.

Alignment-Based Prediction of Sites of Metabolism.

Prediction of metabolically labile atom positions in a molecule (sites of metabolism) is a key component of the simulation of xenobiotic metabolism as a whole, providing crucial information for the development of safe and effective drugs. In 2008, an exploratory study was published in which sites of metabolism were derived based on molecular shape- and chemical feature-based alignment to a molecule whose site of metabolism (SoM) had been determined by experiments. We present a detailed analysis of the breadth of applicability of alignment-based SoM prediction, including transfer of the approach from a structure- to ligand-based method and extension of the applicability of the models from cytochrome P450 2C9 to all cytochrome P450 isozymes involved in drug metabolism. We evaluate the effect of molecular similarity of the query and reference molecules on the ability of this approach to accurately predict SoMs. In addition, we combine the alignment-based method with a leading chemical reactivity model to take reactivity into account. The combined model yielded superior performance in comparison to the alignment-based approach and the reactivity models with an average area under the receiver operating characteristic curve of 0.85 in cross-validation experiments. In particular, early enrichment was improved, as evidenced by higher BEDROC scores (mean BEDROC = 0.59 for α = 20.0, mean BEDROC = 0.73 for α = 80.5).

Site of Metabolism Prediction Based on ab initio Derived Atom Representations.

Machine learning models for site of metabolism (SoM) prediction offer the ability to identify metabolic soft spots in low-molecular-weight drug molecules at low computational cost and enable data-based reactivity prediction. SoM prediction is an atom classification problem. Successful construction of machine learning models requires atom representations that capture the reactivity-determining features of a potential reaction site. We have developed a descriptor scheme that characterizes an atom's steric and electronic environment and its relative location in the molecular structure. The partial charge distributions were obtained from fast quantum mechanical calculations. We successfully trained machine learning classifiers on curated cytochrome P450 metabolism data. The models based on the new atom descriptors showed sustained accuracy for retrospective analyses of metabolism optimization campaigns and lead optimization projects from Bayer Pharmaceuticals. The results obtained demonstrate the practicality of quantum-chemistry-supported machine learning models for hit-to-lead optimization.

Aldehyde Oxidase Mediated Metabolism in Drug-like Molecules: A Combined Computational and Experimental Study.

Aldehyde oxidase (AOX) is an important drug-metabolizing enzyme. However, the current in vitro models for evaluating AOX metabolism are sometimes misleading, and preclinical animal models generally fail to predict human AOX-mediated metabolism. In this study, we report a combined computational and experimental investigation of drug-like molecules that are potential aldehyde oxidase substrates, of which multiple sites of metabolism (SOMs) mediated by AOX and their preferences for the reaction can be unambiguously identified. In addition, the proposed strategy was used to evaluate the metabolism of newly designed c-Met inhibitors, and a success switch-off of AOX metabolism was observed. Overall, this study provide useful information to guide lead optimization and drug discovery based on AOX-mediated metabolism.

Dissecting the Cytochrome P450 1A2- and 3A4-Mediated Metabolism of Aflatoxin B1 in Ligand and Protein Contributions.

Aflatoxin B1 (AFB1) is a chemically intriguing compound because it has several potential sites of metabolism (SOMs), although only some of them are observed experimentally. Cytochrome P450 (CYP) 3A4 and 1A2 are the major isoforms involved in its metabolism. Here, we systematically investigate reactivity and accessibility of all possible SOMs in these two CYPs to elucidate AFB1 metabolism. DFT calculations were used to determine activation energies for each possible reaction. Aliphatic hydroxylation on position 9A and 3α are energetically favored, whereas position 9 is the preferred site for epoxidation. Docking studies, molecular dynamics (MD) simulations, and free energy (MM/GBSA) calculations were applied to elucidate the accessibility of each SOM. The most stable binding modes in CYP3A4 favor the formation of the 3α-hydroxylated and 8,9-exo-epoxide metabolites. Conversion of the methoxy group is also sterically possible, but not observed experimentally due to its low reactivity. In the CYP1A2 active site, AFB1 cannot orient position 3 towards the catalytic center, whereas the 8,9-exo-epoxide and 9A-hydroxylated metabolites are formed from the most stable and the 8,9-endo-epoxide from a less stable binding mode, respectively. The results agree with experimental data and suggest that both reactivity and the shape of the enzyme active site need to be considered to understand the distribution of SOMs and to improve current SOM prediction methods.

Predicting the Metabolic Sites by Flavin-Containing Monooxygenase on Drug Molecules Using SVM Classification on Computed Quantum Mechanics and Circular Fingerprints Molecular Descriptors.

As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA− representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes.

Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism Using Quantum Mechanical Simulations

We describe methods for predicting cytochrome P450 (CYP) metabolism incorporating both pathway-specific reactivity and isoform-specific accessibility considerations. Semiempirical quantum mechanical (QM) simulations, parametrized using experimental data and ab initio calculations, estimate the reactivity of each potential site of metabolism (SOM) in the context of the whole molecule. Ligand-based models, trained using high-quality regioselectivity data, correct for orientation and steric effects of the different CYP isoform binding pockets. The resulting models identify a SOM in the top 2 predictions for between 82% and 91% of compounds in independent test sets across seven CYP isoforms. In addition to predicting the relative proportion of metabolite formation at each site, these methods estimate the activation energy at each site, from which additional information can be derived regarding their lability in absolute terms. We illustrate how this can guide the design of compounds to overcome issues with rapid CYP metabolism.

Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System.

During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)—a novel concept, which integrates information about site of metabolism (SOM) and enzyme—was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic C-hydroxylation, aromatic C-hydroxylation, N-dealkylation and O-dealkylation, were involved. For validation, the overall accuracies of all four biotransformations exceeded 0.95. For receiver operating characteristic (ROC) analysis, each of these models gave a significant area under curve (AUC) value >0.98. In addition, an external test was performed based on dataset published previously. As a result, 87.7% of the potential SOMs were correctly identified by our four models. In summary, four MMRS-based models were established, which can be used to predict the metabolism mediated by CYP3A4, 2D6, and 2C9 with high accuracy.

Site of metabolism prediction for oxidation reactions mediated by oxidoreductases based on chemical bond.

The metabolites of exogenous and endogenous compounds play a pivotal role in the domain of metabolism research. However, they are still unclear for most chemicals in our environment. The in silico methods for predicting the site of metabolism (SOM) are considered to be efficient and low-cost in SOM discovery. However, many in silico methods are focused on metabolism processes catalyzed by several specified Cytochromes P450s, and only apply to substrates with special skeleton. A SOM prediction model always deserves more attention, which demands no special requirements to structures of substrates and applies to more metabolic enzymes. By incorporating the use of hybrid feature selection techniques (CHI, IG, GR, Relief) and multiple classification procedures (KStar, BN, IBK, J48, RF, SVM, AdaBoostM1, Bagging), SOM prediction models for six oxidation reactions mediated by oxidoreductases were established by the integration of enzyme data and chemical bond information. The advantage of the method is the introduction of unlabeled SOM. We defined the SOM which not reported in the literature as unlabeled SOM, where negative SOM was filtered. Consequently, for each type of reaction, a series of SOM prediction models were built based on information about metabolism of 1237 heterogeneous chemicals. Then optimal models were attained through comparisons among these models. Finally, independent test set was used to validate optimal models. It demonstrated that all models gave accuracies above 0.90. For receiver operating characteristic analysis, the area under curve values of all these models over 0.906. The results suggested that these models showed good predicting power. All the models will be available when contact with wangyun@bucm.edu.cn. wangyun@bucm.edu.cn or yjqiao@yahoo.com. Supplementary data are available at Bioinformatics online.

Exploring new chemical functionalities to improve aromatase inhibition of steroids

In this work, new potent steroidal aromatase inhibitors both in microsomes and in breast cancer cells have been found. The synthesis of the 3,4-(ethylenedioxy)androsta-3,5-dien-17-one (12), a new steroid containing a heterocycle dioxene fused in the A-ring, led to the discovery of a new reaction for which a mechanism is proposed. New structure–activity relationships were established. Some 5β-steroids, such as compound 4β,5β-epoxyandrostan-17-one (9), showed aromatase inhibitory activity, because they adopt a similar A-ring conformation as those of androstenedione, the natural substrate of aromatase. Moreover, new chemical features to increase planarity were disclosed, specifically the 3α,4α-cyclopropane ring, as in 3α,4α-methylen-5α-androstan-17-one (5) (IC50=0.11μM), and the Δ9–11 double bond in the C-ring, as in androsta-4,9(11)-diene-3,17-dione (13) (IC50=0.25μM). In addition, induced-fit docking (IFD) simulations and site of metabolism (SoM) predictions helped to explain the recognition of new potent steroidal aromatase inhibitors within the enzyme. These insights can be valuable tools for the understanding of the molecular recognition process by the aromatase and for the future design of new steroidal inhibitors.

Insights into regioselective metabolism of mefenamic acid by cytochrome P450 BM3 mutants through crystallography, docking, molecular dynamics, and free energy calculations.

Cytochrome P450 BM3 (CYP102A1) mutant M11 is able to metabolize a wide range of drugs and drug-like compounds. Among these, M11 was recently found to be able to catalyze formation of human metabolites of mefenamic acid and other nonsteroidal anti-inflammatory drugs (NSAIDs). Interestingly, single active-site mutations such as V87I were reported to invert regioselectivity in NSAID hydroxylation. In this work, we combine crystallography and molecular simulation to study the effect of single mutations on binding and regioselective metabolism of mefenamic acid by M11 mutants. The heme domain of the protein mutant M11 was expressed, purified, and crystallized, and its X-ray structure was used as template for modeling. A multistep approach was used that combines molecular docking, molecular dynamics (MD) simulation, and binding free-energy calculations to address protein flexibility. In this way, preferred binding modes that are consistent with oxidation at the experimentally observed sites of metabolism (SOMs) were identified. Whereas docking could not be used to retrospectively predict experimental trends in regioselectivity, we were able to rank binding modes in line with the preferred SOMs of mefenamic acid by M11 and its mutants by including protein flexibility and dynamics in free-energy computation. In addition, we could obtain structural insights into the change in regioselectivity of mefenamic acid hydroxylation due to single active-site mutations. Our findings confirm that use of MD and binding free-energy calculation is useful for studying biocatalysis in those cases in which enzyme binding is a critical event in determining the selective metabolism of a substrate.

Effects of protein flexibility and active site water molecules on the prediction of sites of metabolism for cytochrome P450 2C19 substrates.

Structure-based prediction of sites of metabolism (SOMs) mediated by cytochrome P450s (CYPs) is of great interest in drug discovery and development. However, protein flexibility and active site water molecules remain a challenge for accurate SOM prediction. CYP2C19 is one of the major drug-metabolizing enzymes and has attracted considerable attention because of its polymorphism and capability of metabolizing ∼7% clinically used drugs. In this study, we systematically evaluated the effects of protein flexibility and active site water molecules on SOM prediction for CYP2C19 substrates. Multiple conformational sampling techniques including GOLD flexible residues sampling, molecular dynamics (MD) and tCONCOORD side-chain sampling were adopted for assessing the influence of protein flexibility on SOM prediction. The prediction accuracy could be significantly improved when protein flexibility was considered using the tCONCOORD sampling method, which indicated that the side-chain conformation was important for accurate prediction. However, the inclusion of the crystallographic or MD-derived water molecule(s) does not necessarily improve the prediction accuracy. Finally, a combination of docking results with SMARTCyp was found to be able to increase the SOM prediction accuracy.

SOMP: web server for in silico prediction of sites of metabolism for drug-like compounds.

A new freely available web server site of metabolism predictor to predict the sites of metabolism (SOM) based on the structural formula of chemicals has been developed. It is based on the analyses of 'structure-SOM' relationships using a Bayesian approach and labelled multilevel neighbourhoods of atoms descriptors to represent the structures of over 1000 metabolized xenobiotics. The server allows predicting SOMs that are catalysed by 1A2, 2C9, 2C19, 2D6 and 3A4 isoforms of cytochrome P450 and enzymes of the UDP-glucuronosyltransferase family. The average invariant accuracy of prediction that was calculated for the training sets (using leave-one-out cross-validation) and evaluation sets is 0.9 and 0.95, respectively. Freely available on the web at http://www.way2drug.com/SOMP.

XenoSite server: a web-available site of metabolism prediction tool.

Cytochrome P450 enzymes (P450s) are metabolic enzymes that process the majority of FDA-approved, small-molecule drugs. Understanding how these enzymes modify molecule structure is key to the development of safe, effective drugs. XenoSite server is an online implementation of the XenoSite, a recently published computational model for P450 metabolism. XenoSite predicts which atomic sites of a molecule--sites of metabolism (SOMs)--are modified by P450s. XenoSite server accepts input in common chemical file formats including SDF and SMILES and provides tools for visualizing the likelihood that each atomic site is a site of metabolism for a variety of important P450s, as well as a flat file download of SOM predictions. XenoSite server is available at http://swami.wustl.edu/xenosite.

Review of current chemoinformatic tools for modeling important aspects of CYPs-mediated drug metabolism. Integrating metabolism data with other biological profiles to enhance drug discovery.

The study of the metabolism of xenobiotics by the human body is an essential stage in the complex and expensive process of drug discovery, being one of the main causes of disapproval and/or withdrawal of drugs. Regarding this, enzymes known as cytochromes P450 (CYPs) play a very decisive role in the biotransformation of many chemicals. For this reason, the use of chemoinformatics to predict and /or analyze from different points of view CYPs-mediated drug metabolism, can help to reduce time and financial resources. This work is focused on the most remarkable advances in the last 5 years of the chemoinformatics tools towards the virtual analysis of CYPsmediated drug metabolism. First, a brief section is dedicated to the applicability of chemoinformatics in different areas associated with drug metabolism. Then, both the models for prediction of CYPs substrates and those allowing the assessment of sites of metabolism (SOM) are discussed. At the same time, the principal limitations of the current chemoinformatic tools are pointed out. Finally, and taking into account that metabolism is an essential step in the whole process of designing any drug, we introduce here as a case of study, the first multitasking model for quantitative-structure biological effect relationships (mtk-QSBER). The purpose of this model is to integrate different types of biological profiles such as ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles and antistaphylococci activities. The mtk-QSBER model was created by employing a heterogeneous dataset of more than 66000 cases tested in 6510 different experimental conditions. The model displayed a total accuracy higher than 94%. To the best of our knowledge, this is the first attempt to complement metabolism assays with other relevant biological data in order to speed up the discovery of efficacious antistaphylococci agents.

Effects of protein flexibility on the site of metabolism prediction for CYP2A6 substrates

Structure-based prediction for the site of metabolism (SOM) of a compound metabolized by human cytochrome P450s (CYPs) is highly beneficial in drug discovery and development. However, the flexibility of the CYPs’ active site remains a huge challenge for accurate SOM prediction. Compared with other CYPs, the active site of CYP2A6 is relatively small and rigid. To address the impact of the flexibility of CYP2A6 active site residues on the SOM prediction for substrates, in this work, molecular dynamics (MD) simulations and molecular docking were used to predict the SOM of 96 CYP2A6 substrates. Substrates with known SOM were docked into the snapshot structures from MD simulations and the crystal structures of CYP2A6. Compared to the crystal structures, the protein structures obtained from MD simulations showed more accurate prediction for SOM. Our results indicated that the flexibility of the active site of CYP2A6 significantly affects the SOM prediction results. Further analysis for the 40 substrates with definite Km values showed that the prediction accuracy for the low Km substrates is comparable to that by ligand-based methods.

Predicting drug metabolism by CYP1A1, CYP1A2, and CYP1B1: insights from MetaSite, molecular docking and quantum chemical calculations.

Recently, CYP1 enzymes are documented for selective metabolism of anticancer leads in cancer prevention and/or progression. Elucidation of specificity of substrates/inhibitors of CYP1 isoforms plays a vital role in design of more selective and potent anticancer leads. However, an area of concern is the broad range of substrate specificities and planar nature of substrates with limited dataset which makes it difficult to predict their site of metabolism (SOM) accurately. In the present study, various models for prediction of site of metabolism in case of CYP1A1, CYP1A2, and CYP1B1 substrates were developed using MetaSite, molecular docking, and quantum chemical descriptors. The predictive accuracy of MetaSite, molecular docking, and quantum chemical descriptors in identifying experimental site of metabolism was analyzed at three levels; top rank, top three ranks, and top five ranks. Two quantum chemical descriptors, chemical hardness and local nucleophilicity are proposed for the prediction of CYP-mediated SOM for the first time. The predictive accuracy shown by chemical hardness at top three ranks was 83.3, 85.7, and 84.6 % for CYP1A1, CYP1A2 and CYP1B1, respectively, whereas local nucleophilicity gave poor predictions of 50, 42.8, and 46.2 %, respectively. The predictability of chemical hardness descriptor outperformed at all three levels of ranks for CYP1A1, CYP1A2, and CYP1B1. Hence, we propose chemical hardness as an useful quantum chemical descriptor for prediction of metabolically vulnerable prints in CYP1A1, CYP1A2, and CYP1B1 mediated metabolism and support the optimization efforts in drug discovery and development programs.

Cytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiers

BackgroundThe prediction of sites and products of metabolism in xenobiotic compounds is key to the development of new chemical entities, where screening potential metabolites for toxicity or unwanted side-effects is of crucial importance. In this work 2D topological fingerprints are used to encode atomic sites and three probabilistic machine learning methods are applied: Parzen-Rosenblatt Window (PRW), Naive Bayesian (NB) and a novel approach called RASCAL (Random Attribute Subsampling Classification ALgorithm). These are implemented by randomly subsampling descriptor space to alleviate the problem often suffered by data mining methods of having to exactly match fingerprints, and in the case of PRW by measuring a distance between feature vectors rather than exact matching. The classifiers have been implemented in CUDA/C++ to exploit the parallel architecture of graphical processing units (GPUs) and is freely available in a public repository.ResultsIt is shown that for PRW a SoM (Site of Metabolism) is identified in the top two predictions for 85%, 91% and 88% of the CYP 3A4, 2D6 and 2C9 data sets respectively, with RASCAL giving similar performance of 83%, 91% and 88%, respectively. These results put PRW and RASCAL performance ahead of NB which gave a much lower classification performance of 51%, 73% and 74%, respectively.Conclusions2D topological fingerprints calculated to a bond depth of 4-6 contain sufficient information to allow the identification of SoMs using classifiers based on relatively small data sets. Thus, the machine learning methods outlined in this paper are conceptually simpler and more efficient than other methods tested and the use of simple topological descriptors derived from 2D structure give results competitive with other approaches using more expensive quantum chemical descriptors. The descriptor space subsampling approach and ensemble methodology allow the methods to be applied to molecules more distant from the training data where data mining would be more likely to fail due to the lack of common fingerprints. The RASCAL algorithm is shown to give equivalent classification performance to PRW but at lower computational expense allowing it to be applied more efficiently in the ensemble scheme.