New combined model for the prediction of regioselectivity in cytochrome P450/3A4 mediated metabolism

Cytochrome P450 3A4 metabolizes nearly 50% of the drugs currently in clinical use with a broad range of substrate specificity. Early prediction of metabolites of xenobiotic compounds is crucial for cost efficient drug discovery and development. We developed a new combined model, MLite, for the prediction of regioselectivity in the cytochrome P450 3A4 mediated metabolism. In the model, the ensemble catalyticphore- based docking method was implemented for the accessibility prediction, and the activation energy estimation method of Korzekwa et al. was used for the reactivity prediction. Four major metabolic reactions, aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation reaction, were included and the reaction data, metabolite information, were collected for 72 well-known substrates. The 47 drug molecules were used as the training set, and the 25 well-known substrates were used as the test set for the ensemble catalyticphore-based docking method. MLite predicted correctly about 76% of the first two sites in the ranking list of the test set. This predictability is comparable with that of another combined model, MetaSite, and the recently published QSAR model proposed by Sheridan et al. MLite also offers information about binding configurations of the substrate-enzyme complex. This may be useful in drug modification by the structure-based drug design.     

Modeling diamagnetic and magnetooptic properties of organic compounds with the TOSS-MODE approach

The topological substructural molecular design (TOSS-MODE) approach is used to describe the diamagnetic susceptibility of organic compounds. Two data sets composed of 233 aliphatic and 85 aromatic compounds were studied for which good linear correlations were found. The contributions of many different structural fragments and atomic groups were computed by the current approach. The predictive ability of the models developed was tested by using external prediction sets of compounds of different classes than those used in training. A quantitative model based on the current approach was developed to compute the diamagnetic susceptibility exaltation of aromatic compounds, which is exemplified by the study of polycyclic aromatic hydrocarbons. The rotatory power of organic compounds in a magnetic field was also described by the TOSS-MODE approach. Good linear correlations were obtained for this property in aliphatic and aromatic compounds. The predictive abilities of the models found were tested by external prediction sets for which good correlations between calculated and experimental values are found.     

A high-throughput digital imaging screen for the discovery and directed evolution of oxygenases.

BACKGROUND: Oxygenases catalyze the hydroxylation of a wide variety of organic substrates. An ability to alter oxygenase substrate specificities and improve their activities and stabilities using recombinant DNA techniques would expand their use in processes such as chemical synthesis and bioremediation. Discovery and directed evolution of oxygenases require efficient screens that are sensitive to the activities of interest and can be applied to large numbers of crude enzyme samples. RESULTS: Horseradish peroxidase (HRP) couples the phenolic products of hydroxylation of aromatic substrates to generate colored and/or fluorescent compounds that are easily detected spectroscopically in high-throughput screening. Coexpression of the coupling enzyme with a functional mono- or dioxygenase creates a pathway for the conversion of aromatic substrates into fluorescent compounds in vivo. We used this approach for detecting the products of the toluene-dioxygenase-catalyzed hydroxylation of chlorobenzene and to screen large mutant libraries of Pseudomonas putida cytochrome P450cam by fluorescence digital imaging. Colors generated by the HRP coupling reaction are sensitive to the site of oxygenase-catalyzed hydroxylation, allowing the screen to be used to identify catalysts with new or altered regiospecificities. CONCLUSIONS: The coupled oxygenase-peroxidase reaction system is well suited for screening oxygenase libraries to identify mutants with desired features, including higher activity or stability and altered reaction specificity. This approach should also be useful for screening expressed DNA libraries and combinatorial chemical libraries for hydroxylation catalysts and for optimizing oxygenase reaction conditions.     

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.     

IDSite: An accurate approach to predict P450-mediated drug metabolism

Accurate prediction of drug metabolism is crucial for drug design. Since a large majority of drugs metabolism involves P450 enzymes, we herein describe a computational approach, IDSite, to predict P450-mediated drug metabolism. To model induced-fit effects, IDSite samples the conformational space with flexible docking in Glide followed by two refinement stages using the Protein Local Optimization Program (PLOP). Sites of metabolism (SOMs) are predicted according to a physical-based score that evaluates the potential of atoms to react with the catalytic iron center. As a preliminary test, we present in this paper the prediction of hydroxylation and O-dealkylation sites mediated by CYP2D6 using two different models: a physical-based simulation model, and a modification of this model in which a small number of parameters are fit to a training set. Without fitting any parameters to experimental data, the Physical IDSite scoring recovers 83% of the experimental observations for 56 compounds with a very low false positive rate. With only 4 fitted parameters, the Fitted IDSite was trained with the subset of 36 compounds and successfully applied to the other 20 compounds, recovering 94% of the experimental observations with high sensitivity and specificity for both sets.     

SPORCalc: A development of a database analysis that provides putative metabolic enzyme reactions for ligand-based drug design

Understanding both the enzyme reactions that contribute to intermediate metabolism and the biochemical fate of candidate therapeutic and toxic agents are essential for drug design. Traditional metabolic databases indicate whether reactions have been observed but do not provide the likelihoods of reactions occurring, for example those of mixed function oxygenases and oxidases, during phase I metabolism. The desire for more quantitative predictions motivated the development of the recently introduced Substrate Product Occurrence Ratio Calculator (SPORCalc) that identifies metabolically labile atom positions in candidate compounds. This paper describes a further development and provides a clearer explanation of SPORCalc for the computational pharmacology, medicinal chemistry and drug design communities interested in metabolic prediction of xenobiotics using chemical databases of biotransformations.Examples of reaction centre detection in Metabolite? are described followed by a demonstration of almokalant, an anti-arrhythmic agent, undergoing phase I metabolism. In general, occurrence ratio (OR) values are calculated throughout a compound and its transformed metabolites to give propensity (p) values at each atom position. The OR values from substrates and products in the database are essential for addition and elimination reactions. For almokalant, the resulting p values ranged from 10^?1 to 10^?5 and their order of magnitude reflected the known and experimentally observed metabolites.SPORCalc depends entirely on the level of detail from isoform- or species-specific reaction classes in Metabolite?. Labile atom positions (sites of metabolism) are identified in both the candidate compound and its metabolites. In general, the likelihood of one enzyme isoform-dependent reaction occurring relative to another and the putative metabolic routes from different isoforms can be investigated. SPORCalc can be developed further to include suitable three-dimensional, structure–activity and physiochemical information.     

Predicting biotransformation potential from molecular structure

The program PASS-BioTransfo is presented, which is capable of predicting many classes of biotransformation for chemical compounds. A particular class of biotransformation is defined by the chemical transformation type and may additionally include the name of the enzyme involved in a transformation. An evaluation of the approach is presented, using biotransformations taken from the databases Metabolite (MDL) and Metabolism (Accelrys), respectively. When trained with biotransformations from Metabolite, PASS-BioTransfo predicts 1927 classes of biotransformation; the average accuracy estimated in LOO cross-validation is about 88%. After training with the biotransformations from the Metabolism database, 178 classes of biotransformation are predicted with an average accuracy of about 85%. The results of cross-prediction with several training and evaluation sets are presented and discussed.     

Comparison of metabolic soft spot predictions of CYP3A4, CYP2C9 and CYP2D6 substrates using MetaSite and StarDrop

Metabolite identification study plays an important role in determining the sites of metabolic liability of new chemical entities (NCEs) in drug discovery for lead optimization. Here we compare the two predictive software, MetaSite and StarDrop, available for this purpose. They work very differently but are used to predict the site of oxidation by major human cytochrome P450 (CYP) isoforms. Neither software can predict non-CYP catalyzed metabolism nor the rates of metabolism. For the purpose of comparing the two software packages, we tested known probe substrate for these enzymes, which included 12 substrates of CYP3A4 and 18 substrates of CYP2C9 and CYP2D6 were analyzed by each software and the results were compared. It is possible that these known substrates were part of the training set but we are not aware of it. To assess the performance of each software we assigned a point system for each correct prediction. The total points assigned for each CYP isoform experimentally were compared as a percentage of the total points assigned theoretically for the first choice prediction for all substrates for each isoform. Our results show that MetaSite and StarDrop are similar in predicting the correct site of metabolism by CYP3A4 (78% vs 83%, respectively). StarDrop appears to do slightly better in predicting the correct site of metabolism by CYP2C9 and CYP2D6 metabolism (89% and 93%, respectively) compared to MetaSite (63% and 70%, respectively). The sites of metabolism (SOM) from 34 in-house NCEs incubated in human liver microsomes or human hepatocytes were also evaluated using two prediction software packages and the results showed comparable SOM predictions. What makes this comparison challenging is that the contribution of each isoform to the intrinsic clearance (Clint) is not known. Overall the software were comparable except for MetaSite performing better for CYP2D6 and that MetaSite has a liver model that is absent in StarDrop that predicted with 82% accuracy.     

Tentative identification of new metabolites of epimedin C by liquid chromatography-mass spectrometry

Epimedin C is one of the major bioactive constituents of Herba Epimedii. The aim of this study is to characterize and elucidate the structure of metabolites in the rat after administration of epimedin C. Metabolite identification was performed using a predictive multiple reaction monitoring-information dependent acquisition-enhanced product ion (pMRM-IDA-EPI) scan in positive ion mode on a hybrid triple quadrupole-linear ion trap mass spectrometer. A total of 18 metabolites were characterized by the changes in their protonated molecular masses, their MS/MS spectrum and their retention times compared with those of the parent drug. The results reveal possible metabolite profiles of epimedin C in rats; the metabolic pathways including hydrolysis, hydroxylation, dehydrogenation, demethylation and conjugation with glucuronic acid and different sugars were observed. This study provides a practical approach for rapidly identifying complicated metabolites, a methodology that could be widely applied for the structural characterization of metabolites of other compounds.     

Trainable structure–activity relationship model for virtual screening of CYP3A4 inhibition

A new structure–activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defining the model applicability domain. For compounds within this domain the statistical results of the final model approach the data consistency between experimental data from literature and PubChem datasets with the overall accuracy of 89%. However, the original model is applicable only for less than a half of PubChem database. Since the similarity correction procedure of GALAS modeling method allows straightforward model training, the possibility to expand the applicability domain has been investigated. Experimental data from PubChem dataset served as an example of in-house high-throughput screening data. The model successfully adapted itself to both data classified using the same and different IC₅₀ threshold compared with the training set. In addition, adjustment of the CYP3A4 inhibition model to compounds with a novel chemical scaffold has been demonstrated. The reported GALAS model is proposed as a useful tool for virtual screening of compounds for possible drug-drug interactions even prior to the actual synthesis.     

Geographical origin of durum wheat studied by 1H-NMR profiling

(1)H-Nuclear magnetic resonance (NMR) spectroscopy was used to detect metabolic profiles of wheat flour samples of different geographical and botanical origin. The NMR profiles were analyzed by multivariate statistical techniques in order to establish the origin of the samples. A linear model, able to discriminate among three different locations, was built achieving a prediction level of about 80% of correctly assigned samples. The principal classes of compounds responsible for the geographic origin discrimination were individuated in aromatic compounds and amino acids. The statistical modeling also indicated that botanical origin information is very poor in the NMR profiles of the analyzed wheat samples.     

A pragmatic approach using first-principle methods to address site of metabolism with implications for reactive metabolite formation

A majority of xenobiotics are metabolized by cytochrome P450 (CYP) enzymes. The discovery of drug candidates with low propensity to form reactive metabolites and low clearance can be facilitated by understanding CYP-mediated xenobiotic metabolism. Being able to predict the sites where reactive metabolites form is beneficial in drug design to produce drug candidates free of reactive metabolite issues. Herein, we report a pragmatic protocol using first-principle density functional theory (DFT) calculations for predicting sites of epoxidation and hydroxylation of aromatic substrates mediated by CYP. The method is based on the relative stabilities of the CYP-substrate intermediates or the substrate epoxides. Consequently, it concerns mainly the electronic reactivity of the substrates. Comparing to the experimental findings, the presented protocol gave excellent first-ranked epoxidation site predictions of 83%, and when the test was extended to CYP-mediated sites of aromatic hydroxylation, satisfactory results were also obtained (73%). This indicates that our assumptions are valid and also implies that the intrinsic reactivities of the substrates are in general more important than their binding poses in proteins, although the protocol may benefit from the addition of docking information.     

A metabonomic approach applied to predict patients with cerebral infarction

Cerebral infarction is always of sudden onset, and usually leading to serious consequence. It is of therapeutic significance to develop fast and accurate diagnosis methods for cerebral infarction so that patients can be treated timely and properly. A metabonomic approach was then proposed to investigate the potential biomarkers and metabolic pathways associated with cerebral infarction and also establish a prediction model of cerebral infarction for the fast diagnosis. Serum metabolic profiling of sixty-seven cerebral infarction patients and sixty-two controls was obtained using UPLC-TOF MS. The resulting data were then processed by multivariate statistical analysis to graphically demonstrate metabolic variations. The PLS-DA model was validated with cross validation and permutation tests to assure the model's reliability, and significant difference was obtained between the original and hypothetical models (p < 0.0001). A series of endogenous metabolites in the one-carbon cycle, such as folic acid, cysteine, S-adenosyl homocysteine and oxidized glutathione, were determined as potential biomarkers of cerebral infarction. A prediction model developed using PLS-KNN algorithm was established to differentiate cerebral infarction patients from controls, and an average accuracy of 100% was obtained. In conclusion, metabonomic approach is a powerful tool to investigate the pathogenesis of stroke and is expected to be developed as a useful method for the fast diagnosis.