Prediction of Metabolism by Cytochrome P450 2C9: Alignment and Docking Studies of a Validated Database of Substrates

Combined Structure- and Ligand-Based Approach for the Identification of Inhibitors of AcrAB-TolC in Escherichia coli

The inhibition of efflux pumps is a promising approach to combating multidrug-resistant bacteria. We have developed a combined structure- and ligand-based model, using OpenEye software, for the identification of inhibitors of AcrB, the inner membrane protein component of the AcrAB-TolC efflux pump in Escherichia coli. From a database of 1391 FDA-approved drugs, 23 compounds were selected to test for efflux inhibition in E. coli. Seven compounds, including ivacaftor (25), butenafine (19), naftifine (27), pimozide (30), thioridazine (35), trifluoperazine (37), and meloxicam (26), enhanced the activity of at least one antimicrobial substrate and inhibited the efflux pump-mediated removal of the substrate Nile Red from cells. Ivacaftor (25) inhibited efflux dose dependently, had no effect on an E. coli strain with genomic deletion of the gene encoding AcrB, and did not damage the bacterial outer membrane. In the presence of a sub-minimum inhibitory concentration (MIC) of the outer membrane permeabilizer colistin, ivacaftor at 1 μg/mL reduced the MICs of erythromycin and minocycline by 4- to 8-fold. The identification of seven potential AcrB inhibitors shows the merits of a combined structure- and ligand-based approach to virtual screening.

Biotransformation of sulfamethoxazole by microalgae: Removal efficiency, pathways, and mechanisms

Recently, the biotransformation of sulfamethoxazole (SMX) by microalgae has attracted increasing interest. In particular, cytochrome P450 (CYP450) has been suggested to be the main enzymatic contributor to this biodegradation. However, the molecular evidence of CYP450 enzymes being involved in SMX biodegradation remains relatively unclear, hindering its applicability. Herein, the biodegradation of SMX by Chlorella sorokiniana (C. sorokiniana) was investigated, and comprehensively elucidated the reaction mechanism underlying CYP450-mediated SMX metabolism. C. sorokiniana was able to efficiently remove over 80% of SMX mainly through biodegradation, in which CYP450 enzymes responded substantially to metabolize SMX in cells. Additionally, screening of transformation products (TPs) revealed that N⁴-hydroxylation-SMX (TP270) was the main TP in the SMX biodegradation pathway of microalgae. Molecular dynamics (MD) simulation suggested that the aniline of SMX was the most prone to undergo metabolism, while density functional theory (DFT) indicated that SMX was metabolized by CYP450 enzymes through H-abstraction-OH-rebound reaction. Collectively, this work reveals key details of the hydroxylamine group of SMX, elucidates the SMX biodegradation pathway involving CYP450 in microalgae in detail, and accelerates the development of using microalgae-mediated CYP450 to eliminate antibiotics.

Frequent occurrence of triclosan hydroxylation in mammals: A combined theoretical and experimental investigation

Triclosan (TCS) is a widespread antimicrobial agent with many adverse health risks. Its hepatoxicity invariably points to the activation of constitutive androstane receptor (CAR), which regulates cytochrome P450 (CYP) genes that are critical for oxidative metabolism. Here, we provide the theoretical and experimental evidences showing that metabolic activation of TCS frequently occurs through aromatic hydroxylation in mammals. CYP-mediated oxidation was predicted to take place at each aromatic C‒H bond. Molecular docking and in vitro approaches reveal oxidative reaction could be efficiently catalyzed by CAR-regulated CYP2B6 enzyme. Parallel reaction monitoring (PRM) high-resolution mass spectrometry was utilized to identify and profile TCS oxidative metabolites in paired mouse liver, bile, feces, plasma and urine. We found multiple hydroxylated isomers including the products generated via the NIH shift of chlorine, as well as their subsequent conjugates. These metabolites showed isomer-specific retention in mice. Glucuronide conjugates are more readily excreted than the sulfates. Moreover, for the first time, isomeric hydroxylated metabolites were detected in the urine and stool of human subjects used TCS-contained household and personal care products. Collectively, these findings suggest that hydroxylation is an important, yet often underestimated element that worth considering to fully evaluate the biological fates and health risks of TCS.

Stereoselective environmental behavior and biological effects of the chiral bitertanol

Bitertanol is a widely used chiral triazole fungicide. The stereoselective environmental behavior and biological effects of bitertanol are not clear. The present study evaluated the stereoselectivity of bitertanol, including its degradation in five typical soils (under laboratory controlled aerobic, anaerobic and sterilization conditions), metabolism in rat liver microsomes (RLM; in vitro), and the endocrine disruption effects on the estrogen receptor (ER) and thyroid hormone receptor (TR) using reporter gene assays. The results indicated that (1S,2R)-bitertanol and (1R,2S)-bitertanol had faster degradation rates in soil than the other stereoisomers. The half-lives of four bitertanol stereoisomers ranged from 9.1 d to 86.6 d in different soils under different conditions. (1S,2R)-bitertanol was preferentially metabolized in RLM. The molecular docking results confirmed the in vitro experiments that (1S,2R)-bitertanol had shortest binding distances and lowest energies with cytochrome P450 enzymes (CYPs). Four bitertanol stereoisomers showed stereoselective antagonistic effects on ER. Additionally, (1S,2R)-bitertanol and (1R,2S)-bitertanol exhibited antagonistic effects on TR. These results suggest that the use of pure (1S,2R)-bitertanol instead of the commercial stereoisomer mix, may help reduce environmental pollution and biological toxicity.

Enantioselective bioactivity, toxicity, and degradation in different environmental mediums of chiral fungicide epoxiconazole

To clarify the environmental behaviour and bioactivity of epoxiconazole enantiomers, an integrated assessment has been done. The degradation in soil, water, and river-sediments were studied. The toxicity to Chlorella vulgaris and Daphnia magna was also examined. The bioactivity to plant-pathogens and molecular docking to CYP51 were investigated. The obtained results showed that the half-lives of R,S-(+)- and S,R-(-)-epoxiconazole were 38.8 and 21.2 days in Jiangsu soil, 43.2 and 22.7 days in Jiangxi soil, 29.1 and 21.3 days in Jilin soil, 43.5 and 32.7 days in anaerobic Jilin soil, 12.3 and 10.1 days in river sediments, and 33.2 and 9.3 days in river water, respectively. Maximum EF was 0.36 in Yangzi-river water. No enantioselective degradation was found in sterilized conditions. The EC₅₀ to C. vulgaris after 48 h was 27.78 mg L^-1, and 18.93 mg L^-1 for R,S-(+)-, and S,R-(-)-epoxiconazole, respectively. The LC₅₀ to D. magna was 4.16 mg L^-1, and 8.49 mg L^-1 for R,S-(+)-, and S,R-(-)-epoxiconazole, respectively. R,S-(+)-epoxiconazole bioactivity was 1.3-7.25 times higher than S,R-(-)-epoxiconazole. In conclusion, R,S-(+)- has higher bioactivity and higher environmental toxicity. In opposite, S,R-(-)- has lower environmental toxicity and lower bioactivity. R,S-(+)-epoxiconazole use is recommended with lower concentrations, which is appropriate for environment safety.

Stereoselective bioactivity of the chiral triazole fungicide prothioconazole and its metabolite

Chiral triazole fungicides have played a significant role in plant pathogen control. Although their enantiomers often exhibit different bioactivity, the mechanism of the stereoselectivity has not been well studied. The stereoselective bioactivity and mechanisms of prothioconazole and its chiral metabolite against plant pathogenic fungi were investigated. The results indicated that the metabolite exerted more fungicidal activities than the activities of the parent compound. R-Prothioconazole and R-prothioconazole-desthio were 6-262 and 19-954 times more potent against pathogenic fungi than the S-enantiomers, respectively. The R-enantiomers were more effective than in inhibiting the biosynthesis of ergosterol and deoxynivalenol the S-enantiomer. Homology modeling and molecular docking suggested that the R-enantiomers of prothioconazole and prothioconazole-desthio possessed better binding modes than S-enantiomers to CYP51B. Moreover, exposure to prothioconazole and its metabolite enantiomers significantly changed the transcription levels of the CYP51 (CYP 51A, CYP51B, CYP 51C) and Tri (Tri5, Tri6, Tri12) genes. The results showed that application of the R-prothioconazole could require a smaller application amount to eliminate the carcinogenic mycotoxins and any environmental risks.

Enantioselective metabolism of four chiral triazole fungicides in rat liver microsomes

Triazole fungicides with one or two chiral centers are widely used worldwide. The liver microsomes plays a major role in the metabolism and systemic elimination of chiral pesticides after exposure. In this present work, enantioselective metabolism of four representative chiral triazole fungicides (prothioconazole, flutriafol, triticonazole, and epoxiconazole) in rat liver microsomes (RLM) was investigated using LC-MS/MS. Baseline separation of the four chiral fungicides and prothioconazole-desthio was achieved on Lux-cellulose-1. The results demonstrated that the R-enantiomers of flutriafol and triticonazole were preferentially metabolized with half-life ranged from 17.33 min to 99.00 min. The R,S-epoxiconazole accumulated with a half-life of 173.25 min. There was no stereoselectivity for prothioconazole. However, remarkable stereoselective metabolism was observed for prothioconazole-desthio. The results of enzyme kinetic revealed different affinities between the enantiomers and metabolic enzymes. In addition, homologous modeling and molecular docking results indicated that enantioselectivity were partially to enantiospecific binding affinities with CYP enzymes. This study highlights a new quantitative approach for stereoselective metabolism of chiral agrochemicals and provides more accurate data on risk assessment of triazole fungicides.

Roles of maize cytochrome P450 (CYP) enzymes in stereo-selective metabolism of hexabromocyclododecanes (HBCDs) as evidenced by in vitro degradation, biological response and in silico studies

In vitro biotransformation of HBCDs by maize cytochrome P450 (CYP) enzymes, responses of CYPs to HBCDs at protein and transcription levels, and in silico simulation of interactions between CYPs and HBCDs were investigated in order to elucidate the roles of CYPs in the metabolism of HBCDs in maize. The results showed that degradation reactions of HBCDs by maize microsomal CYPs followed the first-order kinetics and were stereo-selective, with the metabolic rates following the order (-)γ- > (+)γ- > (+)α- > (-)α-HBCD. The hydroxylated metabolites OH-HBCDs, OH-PBCDs and OH-TBCDs were detected. (+)/(-)-α-HBCDs significantly decreased maize CYP protein content and inhibited CYP enzyme activity, whereas (+)/(-)-γ-HBCDs had obvious effects on the induction of CYPs. HBCDs selectively mediated the gene expression of maize CYPs, including the isoforms of CYP71C3v2, CYP71C1, CYP81A1, CYP92A1 and CYP97A16. Molecular docking results suggested that HBCDs could bind with these five CYPs, with binding affinity following the order CYP71C3v2 < CYP81A1 < CYP97A16 < CYP92A1 < CYP71C1. The shortest distances between the Br-unsubstituted C atom of HBCD isomers and the iron atom of heme in CYPs were 4.18-11.7 Å, with only the distances for CYP71C3v2 being shorter than 6 Å (4.61-5.38 Å). These results suggested that CYP71C3v2 was an efficient catalyst for degradation of HBCDs. For (+)α- and (-)γ-HBCDs, their binding affinities to CYPs were lower and the distances to the iron atom of heme in CYPs were shorter than their corresponding antipodes, consistent with the in vitro experimental results showing that they had shorter half-lives and were more easily hydroxylated. This study provides solid evidence for the roles of maize CYPs in the metabolism of HBCDs, and gives insight into the molecular mechanisms of the enantio-selective metabolism of HBCDs by plant CYPs.

Metabolic enhancement of 2,3',4,4',5-pentachlorobiphenyl (CB118) using cytochrome P450 monooxygenase isolated from soil bacterium under the presence of perfluorocarboxylic acids (PFCAs) and the structural basis of its metabolism

2,3',4,4',5-Pentachlorobiphenyl (CB118) is one of the most abundant polychlorinated biphenyl (PCB) congeners in the environment, and perfluoroalkyl acids, including perfluorocarboxylic acids (PFCAs), are widely distributed in the environment. Although CB118 and perfluoroalkyl acids are present in all humans and biota, effects in the metabolic fate of CB118 leading to toxicity change are unclear. P450BM3, which is isolated from the soil bacterium Bacillus megaterium, metabolized CB118 to three different hydroxylated pentachlorobiphenyls (M1-M3). M2 was identified as 4'-OH-2,3',4,5,5'-pentachlorobiphenyl. These reactions were promoted by the presence of PFCAs, and perfluorooctanoic acid (PFCA-C8) was the most effective for accelerating these reactions among PFCAs with different carbon chain length. The production rate of M2 was accelerated by 25-times using PFCA-C8. Furthermore, the docking models of P450BM3 with CB118 and PFCAs revealed that the conformational changes of the substrate-binding cavity of P450BM3 after binding of PFCAs to P450BM3 were important for selective production of CB118 metabolites. This study leads to the clarification of the different metabolic fates of PCBs under complex contamination with PFCAs.

Accumulation properties of polychlorinated biphenyl congeners in Yusho patients and prediction of their cytochrome P450-dependent metabolism by in silico analysis

In what has become known as the Yusho incident, thousands of people in western Japan were poisoned by the accidental ingestion of rice bran oil contaminated with polychlorinated biphenyls (PCBs) and various dioxins and dioxin-like compounds. In this study, we investigated the accumulation patterns of 69 PCB congeners in the blood of Yusho patients in comparison with those of non-exposed controls. The blood samples were collected at medical check-ups in 2004 and 2005. To compare the patterns of PCB congeners, we calculated the concentration ratio of each congener relative to the 2,2',4,4',5,5'-hexaCB (CB153) concentration. The concentration ratios of tetra- and penta-chlorinated congeners in the blood of Yusho patients were significantly lower than those of controls. To examine the cytochrome P450 (CYP)-dependent metabolic potential of the 2,3',4,4'5-pentaCB (CB118), CB153, and 2,3,3',4,4'5-hexaCB (CB156) congeners, we conducted PCB-CYP (CYP1A1, CYP1A2, CYP2A6, and CYP2B6) docking simulation by in silico analysis. The docking models showed that human CYP1A1, CYP2A6, and CYP2B6 isozymes have the potential to metabolize CB118 and CB153. On the other hand, it was inferred that CB156 is difficult to be metabolized by these four CYP isozymes. These results indicate that CYP1 and CYP2 isozymes may be involved in the characteristic accumulation patterns of PCB congeners in the blood of Yusho patients.

Pharmacogenomics of CYP2C9: Functional and Clinical Considerations

CYP2C9 is the most abundant CYP2C subfamily enzyme in human liver and the most important contributor from this subfamily to drug metabolism. Polymorphisms resulting in decreased enzyme activity are common in the CYP2C9 gene and this, combined with narrow therapeutic indices for several key drug substrates, results in some important issues relating to drug safety and efficacy. CYP2C9 substrate selectivity is detailed and, based on crystal structures for the enzyme, we describe how CYP2C9 catalyzes these reactions. Factors relevant to clinical response to CYP2C9 substrates including inhibition, induction and genetic polymorphism are discussed in detail. In particular, we consider the issue of ethnic variation in pattern and frequency of genetic polymorphisms and clinical implications. Warfarin is the most well studied CYP2C9 substrate; recent work on use of dosing algorithms that include CYP2C9 genotype to improve patient safety during initiation of warfarin dosing are reviewed and prospects for their clinical implementation considered. Finally, we discuss a novel approach to cataloging the functional capabilities of rare 'variants of uncertain significance', which are increasingly detected as more exome and genome sequencing of diverse populations is conducted.

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).

Predicting the binding modes and sites of metabolism of xenobiotics

Metabolism studies are an essential integral part of ADMET profiling of drug candidates to evaluate their safety and efficacy. Cytochrome P-450 (CYP) metabolizes a wide variety of xenobiotics/drugs. The binding modes of these compounds with CYP and their intrinsic reactivities decide the metabolic products. We report here a novel computational protocol, which comprises docking of ligands to heme-containing CYPs and prediction of binding energies through a newly developed scoring function, followed by analyses of the docked structures and molecular orbitals of the ligand molecules, for predicting the sites of metabolism (SOM) of ligands. The calculated binding free energies of 121 heme-containing protein-ligand docked complexes yielded a correlation coefficient of 0.84 against experiment. Molecular orbital analyses of the resultant top three unique poses of the docked complexes provided a success rate of 87% in identifying the experimentally known sites of metabolism of the xenobiotics. The SOM prediction methodology is freely accessible at .

In Vitro and in Silico Analyses for Predicting Hepatic Cytochrome P450-Dependent Metabolic Potencies of Polychlorinated Biphenyls in the Baikal Seal

The aim of this study was to understand the cytochrome P450 (CYP)-dependent metabolic pathway and potency of polychlorinated biphenyls (PCBs) in the Baikal seal (Pusa sibirica). In vitro metabolism of 62 PCB congener mixtures was investigated by using liver microsomes of this species. A decreased ratio of over 20% was observed for CB3, CB4, CB8, CB15, CB19, CB22, CB37, CB54, CB77, and CB105, suggesting the preferential metabolism of low-chlorinated PCBs by CYPs. The highly activated metabolic pathways in Baikal seals that were predicted from the decreased PCBs and detected hydroxylated PCBs (OH-PCBs) were CB22 to 4'OH-CB20 and CB77 to 4'OH-CB79. The total amount of OH-PCBs detected as identified and unidentified congeners accounted for only a 3.8 ± 1.7 mol % of loaded PCBs, indicating many unknown PCB metabolic pathways. To explore factors involved in CYP-dependent PCB metabolism, we examined the relationships among the structural and physicochemical properties of PCBs, the in silico PCB-CYP docking parameters, and the in vitro PCB decreased ratios by principal component analysis. Statistical analysis showed that the decreased PCB ratio was at least partly accounted for by the substituted chlorine number of PCBs and the distance from the Cl-unsubstituted carbon of docked PCBs to the heme Fe in CYP2A and 2B.

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.

Combining structure- and ligand-based approaches to improve site of metabolism prediction in CYP2C9 substrates

PURPOSE

Predicting atoms in a potential drug compound that are susceptible to oxidation by cytochrome P450 (CYP) enzymes is of great interest to the pharmaceutical community. We aimed to develop a computational approach combining ligand- and structure-based design principles to accurately predict sites of metabolism (SoMs) in a series of CYP2C9 substrates.

METHODS

We employed the reactivity model, SMARTCyp, ensemble docking, and pseudo-receptor modeling based on quantitative structure-activity relationships (QSAR) to account for influences of both the inherent reactivity of each atom and the physical structure of the CYP2C9 binding site.

RESULTS

We tested ligand-based prediction alone (i.e. SMARTCyp), structure-based prediction alone (i.e. AutoDock Vina docking), the linear combination of the SMARTCYP and docking scores, and finally a pseudo-receptor QSAR model based on the docked compounds in combination with SMARTCyp. We found that by using the latter combined approach we were able to accurately predict 88% and 96% of the true SoMs, within the top-1 and top-2 predictions, respectively.

CONCLUSIONS

We have outlined a novel combination approach for accurately predicting SoMs in CYP2C9 ligands. We believe that this method may be applied to other CYP2C9 ligands as well as to other CYP systems.

Mass spectrometry based identification of geometric isomers during metabolic stability study of a new cytotoxic sulfonamide derivatives supported by quantitative structure-retention relationships

A set of 15 new sulphonamide derivatives, presenting antitumor activity have been subjected to a metabolic stability study. The results showed that besides products of biotransformation, some additional peaks occurred in chromatograms. Tandem mass spectrometry revealed the same mass and fragmentation pathway, suggesting that geometric isomerization occurred. Thus, to support this hypothesis, quantitative structure-retention relationships were applied. Human liver microsomes were used as an in vitro model of metabolism. The biotransformation reactions were tracked by liquid chromatography assay and additionally, fragmentation mass spectra were recorded. In silico molecular modeling at a semi-empirical level was conducted as a starting point for molecular descriptor calculations. A quantitative structure-retention relationship model was built applying multiple linear regression based on selected three-dimensional descriptors. The studied compounds revealed high metabolic stability, with a tendency to form hydroxylated biotransformation products. However, significant chemical instability in conditions simulating human body fluids was noticed. According to literature and MS data geometrical isomerization was suggested. The developed in sillico model was able to describe the relationship between the geometry of isomer pairs and their chromatographic retention properties, thus it supported the hypothesis that the observed pairs of peaks are most likely geometric isomers. However, extensive structural investigations are needed to fully identify isomers' geometry. An effort to describe MS fragmentation pathways of novel chemical structures is often not enough to propose structures of potent metabolites and products of other chemical reactions that can be observed in compound solutions at early drug discovery studies. The results indicate that the relatively non-expensive and not time- and labor-consuming in sillico approach could be a good supportive tool assisting the identification of cis-trans isomers based on retention data. This methodology can be helpful during the structural identification of biotransformation and degradation products of new chemical entities--potential new drugs.

Structural and energetic analysis to provide insight residues of CYP2C9, 2C11 and 2E1 involved in valproic acid dehydrogenation selectivity

Docking and molecular dynamics (MD) simulation have been two computational techniques used to gain insight about the substrate orientation within protein active sites, allowing to identify potential residues involved in the binding and catalytic mechanisms. In this study, both methods were combined to predict the regioselectivity in the binding mode of valproic acid (VPA) on three cytochrome P-450 (CYP) isoforms CYP2C9, CYP2C11, and CYP2E1, which are involved in the biotransformation of VPA yielding reactive hepatotoxic intermediate 2-n-propyl-4-pentenoic acid (4nVPA). There are experimental data about hydrogen atom abstraction of the C4-position of VPA to yield 4nVPA, however, there are not structural evidence about the binding mode of VPA and 4nVPA on CYPs. Therefore, the complexes between these CYP isoforms and VPA or 4nVPA were studied to explore their differences in binding and energetic stabilization. Docking results showed that VPA and 4nVPA are coupled into CYPs binding site in a similar conformation, but it does not explain the VPA hydrogen atom abstraction. On the other hand, MD simulations showed a set of energetic states that reorient VPA at the first ns, then making it susceptible to a dehydrogenation reaction. For 4nVPA, multiple binding modes were observed in which the different states could favor either undergo other reaction mechanism or ligand expulsion from the binding site. Otherwise, the energetic and entropic contribution point out a similar behavior for the three CYP complexes, showing as expected a more energetically favorable binding free energy for the complexes between CYPs and VPA than with 4nVPA.

Structure-based ligand design to overcome CYP inhibition in drug discovery projects

Cytochrome P450 (CYP) enzymes are key players in xenobiotic metabolism, and inhibition of CYPs can therefore result in unwanted drug-drug interactions. Within drug discovery, CYP inhibition can cause delays in the progression of candidate drugs, or even premature closure of projects. During the past decade, a massive effort in the pharmaceutical industry and academic research has produced a wealth of structural information in the CYP field. In this short review, we will describe how structure-based approaches can be used in the pharmaceutical industry to work away from CYP inhibition, with a focus on the opportunities and challenges. We will show two examples from our own work where structural information on CYP2C9 and CYP3A4 inhibitor complexes have been successfully exploited in ongoing drug discovery projects.

Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm

A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6.

DR-predictor: incorporating flexible docking with specialized electronic reactivity and machine learning techniques to predict CYP-mediated sites of metabolism

Computational methods that can identify CYP-mediated sites of metabolism (SOMs) of drug-like compounds have become required tools for early stage lead optimization. In recent years, methods that combine CYP binding site features with CYP/ligand binding information have been sought in order to increase the prediction accuracy of such hybrid models over those that use only one representation. Two challenges that any hybrid ligand/structure-based method must overcome are (1) identification of the best binding pose for a specific ligand with a given CYP and (2) appropriately incorporating the results of docking with ligand reactivity. To address these challenges we have created Docking-Regioselectivity-Predictor (DR-Predictor)--a method that incorporates flexible docking-derived information with specialized electronic reactivity and multiple-instance-learning methods to predict CYP-mediated SOMs. In this study, the hybrid ligand-structure-based DR-Predictor method was tested on substrate sets for CYP 1A2 and CYP 2A6. For these data, the DR-Predictor model was found to identify the experimentally observed SOM within the top two predicted rank-positions for 86% of the 261 1A2 substrates and 83% of the 100 2A6 substrates. Given the accuracy and extendibility of the DR-Predictor method, we anticipate that it will further facilitate the prediction of CYP metabolism liabilities and aid in in-silico ADMET assessment of novel structures.

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.

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.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Prediction of sites of metabolism in a substrate molecule, instanced by carbamazepine oxidation by CYP3A4

In drug discovery process, improvement of ADME/Tox properties of lead compounds including metabolic stability is critically important. Cytochrome P450 (CYP) is one of the major metabolizing enzymes and the prediction of sites of metabolism (SOM) on the given lead compounds is key information to modify the compounds to be more stable against metabolism. There are two factors essentially important in SOM prediction. First is accessibility of each substrate atom to the oxygenated Fe atom of heme in a CYP protein, and the other is the oxidative reactivity of each substrate atom. To predict accessibility of substrate atoms to the heme iron, conventional protein-rigid docking simulations have been applied. However, the docking simulations without consideration of protein flexibility often lead to incorrect answers in the case of very flexible proteins such as CYP3A4. In this study, we demonstrated an approach utilizing molecular dynamics (MD) simulation for SOM prediction in which multiple MD runs were executed using different initial structures. We applied this strategy to CYP3A4 and carbamazepine (CBZ) complex. Through 10 ns MD simulations started from five different CYP3A4-CBZ complex models, our approach correctly predicted SOM observed in experiments. The experimentally known epoxidized sites of CBZ by CYP3A4 were successfully predicted as the most accessible sites to the heme iron that was judged from a numerical analysis of calculated ΔG(binding) and the frequency of appearance. In contrast, the predictions using protein-rigid docking methods hardly provided the correct SOM due to protein flexibility or inaccuracy of the scoring functions. Our strategy using MD simulation with multiple initial structures will be one of the reliable methods for SOM prediction.

Potentially increasing the metabolic stability of drug candidates via computational site of metabolism prediction by CYP2C9: The utility of incorporating protein flexibility via an ensemble of structures

Cytochrome P450 enzymes are responsible for metabolizing many endogenous and xenobiotic molecules encountered by the human body. It has been estimated that 75% of all drugs are metabolized by cytochrome P450 enzymes. Thus, predicting a compound's potential sites of metabolism (SOM) is highly advantageous early in the drug development process. We have combined molecular dynamics, AutoDock Vina docking, the neighboring atom type (NAT) reactivity model, and a solvent-accessible surface-area term to form a reactivity-accessibility model capable of predicting SOM for cytochrome P450 2C9 substrates. To investigate the importance of protein flexibility during the ligand-binding process, the results of SOM prediction using a static protein structure for docking were compared to SOM prediction using multiple protein structures in ensemble docking. The results reported here indicate that ensemble docking increases the number of ligands that can be docked in a bioactive conformation (ensemble: 96%, static: 85%) but only leads to a slight improvement (49% vs. 44%) in predicting an experimentally known SOM in the top-1 position for a ligand library of 75 CYP2C9 substrates. Using ensemble docking, the reactivity-accessibility model accurately predicts SOM in the top-1 ranked position for 49% of the ligand library and considering the top-3 predicted sites increases the prediction success rate to approximately 70% of the ligand library. Further classifying the substrate library according to K(m) values leads to an improvement in SOM prediction for substrates with low K(m) values (57% at top-1). While the current predictive power of the reactivity-accessibility model still leaves significant room for improvement, the results illustrate the usefulness of this method to identify key protein-ligand interactions and guide structural modifications of the ligand to increase its metabolic stability.

Toluidinesulfonamide hypoxia-induced factor 1 inhibitors: alleviating drug-drug interactions through use of PubChem data and comparative molecular field analysis guided synthesis

Inhibitors of hypoxia-inducible factor 1 (HIF-1) represent promising anticancer therapeutics. We have identified a series of potent toluidinesulfonamide HIF-1 inhibitors. However, the series was threatened by a potential liability to inhibit CYP2C9 which could cause dangerous drug-drug interactions when being coadministered with other drugs. We used structure-activity data from the PubChem database to develop a topomer CoMFA model that guided the design of novel sulfonamides with high selectivity for HIF-1 over CYP2C9 inhibition.

Site of metabolism prediction on cytochrome P450 2C9: a knowledge-based docking approach

A novel structure-based approach for site of metabolism prediction has been developed. This knowledge-based method consists of three steps: (1) generation of possible metabolites, (2) docking the predicted metabolites to the CYP binding site and (3) selection of the most probable metabolites based on their complementarity to the binding site. As a proof of concept we evaluated our method by using MetabolExpert for metabolite generation and Glide for docking into the binding site of the CYP2C9 crystal structure. Our method could identify the correct metabolite among the three best-ranked compounds in 69% of the cases. The predictive power of our knowledge-based method was compared to that achieved by substrate docking and two alternative literature approaches.

EaMEAD: Activation energy prediction of cytochrome P450 mediated metabolism with effective atomic descriptors

In an effort to improve drug design and predictions for pharmacokinetics (PK), an empirical model was developed to predict the activation energies (Ea) of cytochrome P450 (CYP450) mediated metabolism. The model, EaMEAD (Activation energy of Metabolism reactions with Effective Atomic Descriptors), predicts the Ea of four major metabolic reactions of the CYP450 enzyme: aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation. To build and validate the empirical model, the E(a) values of the substrates with diverse chemical structures (394 metabolic sites for aliphatic hydroxylation, 27 metabolic sites for N-dealkylation, 9 metabolic sites for O-dealkylation, and 85 metabolic sites for aromatic hydroxylation) were calculated by AM1 molecular orbital (MO). Empirical equations, Quantitative Structure Activity Relationship (QSAR) models, were derived using effective atomic charge, effective atomic polarizability, and bond dipole moments of the substrates as descriptors. EaMEAD is shown to accurately predict Ea with a correlation coefficient (R) of 0.94 and root-mean-square error (RMSE, unit is kcal/mol) of 0.70 for aliphatic hydroxylation, N-dealkylation, and O-dealkylation, and R of 0.83 and RMSE of 0.80 for aromatic hydroxylation, respectively. Physical origin and the role of the effective atomic descriptors of the models are presented in detail. With this model, the Ea of the metabolism can be rapidly predicted without any experimental parameters or time-consuming QM calculation. Regioselectivity prediction with our model is presented in the case of CYP3A4 metabolism. The reliability and ease of use of this model will greatly facilitate early stage PK predictions and rational drug design. Moreover, the model can be applied to develop the Ea prediction model of various types of chemical reactions.