Use of density functional theory in drug metabolism studies.

Review

Use of density functional theory in drug metabolism studies Patrik Rydberg, Flemming Steen Jørgensen & Lars Olsen† 1.

Introduction

University of Copenhagen, Department of Drug Design and Pharmacology, Denmark

2.

CYP-mediated reactions studied with DFT

3.

What role does the enzyme

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

Expert Opin. Drug Metab. Toxicol. Downloaded from informahealthcare.com by University of Laval on 07/07/14 For personal use only.

play? 4.

Inhibition of CYP enzymes

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Oxidation by other drug-metabolizing enzymes

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Expert opinion

Keywords: activation energies, CYP, DFT, docking, MD, prediction of site of metabolism, QM/MM Expert Opin. Drug Metab. Toxicol. (2014) 10(2):215-227

1.

Introduction

Cytochrome P450 enzymes (CYPs) is one of the most important enzyme families involved in the metabolism of xenobiotics and have therefore been studied extensively. Other enzymes like the aldehyde oxidases (AOXs) and flavin-containing monooxygenases (FMOs) contribute to the oxidative drug metabolism as well, but there has been less focus on these enzymes because they contribute to a smaller extent to the metabolism [1]. In the human genome there are 57 genes encoding different CYP isoforms, of which the 1A2, 2C9, 2C19, 2D6 and 3A4 isoforms contribute most to the metabolism of drugs and other xenobiotics [1]. All CYPs contain a heme group that carry out the reactions. These are almost always oxidation reactions, for example, aliphatic and aromatic oxidations, hetero-atom oxidations and N- and O-dealkylations that in most cases lead to more soluble compounds that are more easily excreted. However, these enzymes can also perform reduction reactions, for example, reduction of nitro groups to amines and of aldehydes to alcohols. Often, several CYPs are involved in the metabolism of a drug compound, catalyzing different reactions and thereby generating different metabolites. What metabolites that are generated therefore depend on which of the CYPs that have interacted with the drug, the orientation of the drug in the active site of each CYP, and how efficiently the subsequent reaction is performed. Some orientations can also lead to inhibition of the CYP, especially if the compound contains aromatic nitrogen atoms accessible to interact with the heme iron, which will result in so-called type II binding [2]. In addition to classic inhibition 10.1517/17425255.2014.864278 © 2014 Informa UK, Ltd. ISSN 1742-5255, e-ISSN 1744-7607 All rights reserved: reproduction in whole or in part not permitted

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Article highlights. . .

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Reliable estimates of the activation energy can be obtained using DFT calculations on model systems. These activation energies determined without the presence of the rest of the protein can be useful for determination of sites of metabolism for CYP 3A4. Knowledge about the binding is usually needed to do successful predictions for other CYP isoforms. The best ligand-based algorithms for prediction of CYP-mediated metabolism can correctly predict the experimental metabolite within the two most likely predicted metabolites for 80% of substrates. Inclusion of a dispersion correction to DFT improves predictions. QM/MM is useful to study the effect from the protein on the activation energies for smaller series of compounds.

This box summarizes key points contained in the article.

of CYPs, time-dependent inhibition (TDI, also known as mechanism-based inhibition) can also occur, in which the initial metabolism generates a metabolite that inhibits the enzyme through the formation of a covalent bond to the heme, to amino acids in the binding site, or through the formation of a semi-covalent bond to the heme iron (such a semi-covalent bond leads to quasi-irreversible inhibition, whereas the other types lead to irreversible inhibition) [3]. To rationalize the metabolism of a drug compound by a CYP enzyme with structure-based in silico methods, the orientation of the compound inside the CYP active site needs to be known. This is not a trivial task, since these enzymes are quite flexible. Large flexibility has, for example, been observed for CYP 3A4, where the size of the active site differ a lot depending on the substrate [4]. Additionally, for some substrates multiple binding modes may be possible, as has been observed in crystal structures of CYP 2D6 [5] and CYP 2A13 [6]. Next, the reaction profile for the subsequent reaction needs to be examined. Since the active heme group, denoted Compound I (cf. Figure 1), contains Fe and has a radical nature, a multi-configurational quantum mechanics approach for treating the reaction is in principle needed [7]. However, density functional theory (DFT) still seems to be the method of choice, probably due to considerations of the speed, simplicity and accuracy. The basis for DFT is the work by Hohenberg and Kohn, which states the ground state energy of a molecule is determined by the electron density [8]. Using the Kohn--Sham formulation, the challenge is to determine the exchange--correlation energy [9]. Many different exchange--correlation functionals exist, among others the widely used hybrid functional B3LYP [10-12]. However, there are still challenges to improve the performance of DFT, for example because an effect like dispersion is not well described [13,14]. 216

We will, in this review, go through the use of DFT in investigations of drug metabolism carried out by CYP enzymes. First, we will discuss the mechanistic investigations using model systems of the catalytic part of the enzymes, the heme group. Next, we will consider the effect of the protein environment on the prediction of metabolism by including MD and docking, as well as using QM/MM calculations. Finally, we also present DFT calculations on AOX and FMO reactions. 2.

CYP-mediated reactions studied with DFT

From semi-empirical methods to DFT Some of the first electronic structure studies on CYPmediated reactions were carried out by Jones and Korzekwa using the semi-empirical AM1 method [15]. This was before it was feasible to treat models containing a porphyrin ring with DFT methods. Instead, smaller surrogates, like a phenoxy radical, were used to compute activation energies and to develop models that could predict these activation energies consisting of the ground state properties of the substrate and product radical. These models were then applied to rationalize the sites of metabolism [16,17]. For example, using this approach it was possible to explain the regioselectivity on a small series of CH3 and CH2Cl-substituted aromatic compounds with energies computed both with AM1 and DFT [16]. Interestingly, the DFT results were in quantitative agreement with the measured differences in activation energies. The fact that DFT works nicely was also concluded by Park and Harris, who showed that the relative activation energy barriers when using a porphyrin model and a smaller surrogate model correlate at the DFT level, whereas AM1 calculations seem not to reproduce this [18]. Recently, Mayeno et al. benchmarked the ability of various semi-empirical methods for studying the reactions done by an iron--oxo-porphine. They showed not only that an R2 of 0.76 could be achieved when comparing with DFT-computed activation energies, but also that the absolute activation barriers at the semiempirical level were overestimated [19]. Although it is desirable to use semi-empirical methods for these purposes due to the speed of the calculations, the Fe-containing porphyrin systems are hard to describe with the semi-empirical approach. As a compromise between accuracy and speed, DFT has been the method of choice for these types of reactions for the past 15 years. Shaik and coworkers pioneered the studies of CYPs reacting with their substrates using DFT and porphyrin models (see Figure 1 for a typical model system used to investigate these types of reactions). Their results have been reviewed several times [20-22]. Initially, the hydroxylation of aliphatic carbon atoms was studied, showing that the initial hydrogen abstraction step has the same transition state (TS) energy for the doublet and quartet states, whereas the subsequent oxygen rebound step is barrierless on the doublet surface but has a small barrier for the quartet state [23]. 2.1

Expert Opin. Drug Metab. Toxicol. (2014) 10(2)

Use of density functional theory in drug metabolism studies


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B.

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Figure 1. A. Compound I (iron--oxo-porphyrine). B. Iron--oxo-porphine is typically used to model Compound I in the DFT calculations (R = H/CH3). C. The transition state structure of the initial and rate-limiting hydrogen abstraction step in the aliphatic hydroxylation of caffeine is shown. Adapted from [44].

In the following years, the reaction mechanisms of oxidation of sp2-hydridized carbon atoms, aromatic compounds, as well as N- and S-oxidation were studied (Figure 2) [20,24]. The first step in the oxidation of sp2-hybridized carbon atoms in ethene was studied by de Visser [25] and soon after, the competition with hydroxylation reactions [26]. On the doublet and quartet surfaces, the initial formations of the tetrahedral intermediates are quite similar. The aromatic oxidation reaction was initially studied by both Shaik [27] and Harvey [28]. It proceeds through an initial formation of a tetrahedral intermediate, which is the rate-limiting step, and is followed by several possible pathways. The N- and S-oxidation reactions proceed via a direct oxidation, whereas the one-electron oxidation is not favorable based on DFT calculations, because the ionization energies of the substrates are higher than the electron affinity of the heme model [29-33]. Recently, it has been shown that the relative energies of different types of reaction mechanisms are not always well reproduced. Especially, using the DFT functional B3LYP for comparing the barriers for epoxidation and hydroxylation of propene tend to artificially favor the hydroxylation [34]. The addition of an empirical dispersion correction (the so-called B3LYP-D functional) improves the comparisons to experimental data, both when using model systems and when using the full proteins with QM/MM methodology [34,35]. Activation energies of CYP reactions on smaller series of compounds

2.2

The knowledge of what is the rate-limiting step gained from detailed reaction mechanism studies has been used extensively in subsequent studies on series of compounds. The focus of these studies has often been to understand trends in substitution patterns on specific fragments and to propose faster means to determine the activation energy.

Shaik and de Visser studied trends for the barrier of aliphatic hydroxylation and managed to correlate the bond dissociation energies to the activation energies for a set of 10 compounds [36]. Olsen et al. determined the activation energies for the hydrogen abstraction reaction for a set of 24 small organic compounds, in which a span from 29 to 87 kJ/mol was observed [37]. The lowest activation energies were observed for the dealkylation of amines, whereas the highest was observed for the hydroxylation of methane. A number of schemes for prediction of the activation energies were developed, among others the use of smaller surrogates of heme, inspired by the work of Jones and Korzekwa, and bond dissociation energies. Generally, these models can be used to determine the activation energies. However, the most simple and fastest approach was the use of five simple fragment rules for determining the activation energy. These rules gave activation energies of 74, 61, 53, 47 and 30 kJ/mol for hydrogen abstractions from primary carbons, secondary/tertiary carbons, carbons with adjacent sp2 or aromatic groups, ethers/ thioethers, and amines, respectively. Thus, the initial hydrogen abstraction reaction on amines (leading to N-dealkylation) would be faster than that on the primary carbon atoms. Bathelt and coworkers studied the rate-limiting step of the oxidation of aromatic carbon atoms for a series of parasubstituted benzenes containing F, Cl, CH3, OCH3, NO2 and CN [28]. They found a significant span in activation barriers from 49 to 73 kJ/mol (NMe2 had the lowest and unsubstituted benzene, the highest activation energy). Based on an analysis of the charge and spin distribution along the reaction coordinate, where the complexes in the TS showed mixed radical and cationic properties, it was shown that a simple relation between radical and cationic Hammett substitution parameters with the activation energies could be developed. This study was later extended to 16 substituted compounds,


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A.

seems to have lower activation energy than the direct oxidation of the N-atom. Still, the N-oxidation is sometimes observed experimentally. It was proposed that compounds with a reasonable low N-inversion barrier (of about 30 kJ/mol) would be more likely to undergo N-dealkylation than to form the corresponding N-oxide, in agreement with the experimental findings.

B.

DFT for prediction of the site of metabolism Most of the DFT-calculated activation energies are related to whether the site of metabolism is also observed experimentally. Some studies also relate the calculated activation energies directly to the experimental rate constants [38,40], but this is usually not trivial, for example, because the binding to the enzyme may play a role and is challenging to model accurately. The calculation time is still an obstacle when using DFT to study CYP-mediated reactions on large series of compounds. However, there are several studies that show that more simple properties of the compounds derived from DFT, that are faster to compute, can be used to model the reactivity (like bond dissociation energies [36] and spin distribution [37]). These properties are then more easily used on larger series of compounds. For example, Afzelius et al. used DFT-calculated bond orders and spin distributions to determine sites of metabolism for a large set of CYP 2C9 and 3A4 compounds [43]. In addition to applying DFT to make fast estimates of the reactivity, the knowledge gained from DFT studies about the activation energies for the rate-limiting steps has been used extensively as outlined in the following paragraphs. Inspired by the five rules developed for prediction of hydrogen abstraction [37] and five rules developed for prediction of oxidation of sp2 carbon atoms [44], Rydberg did DFT calculations of activation energies on 139 compounds and developed 41 rules that were applied to predict the site of metabolism for a large set of CYP 3A4 compounds (the SMARTCyp model) [45]. Using these pre-computed activation energies on a set of CYP 3A4 substrates, the experimental sites of metabolism were identified among the two most reactive sites for 76% of the substrates. The fact that the predicted reactive sites are often also seen experimentally as sites of metabolism for CYP 3A4 could be due to CYP 3A4’s large and flexible active site (Figure 3) [4]. The number of DFT calculations and rules in the SMARTCyp model has been extended and its predictive power toward other human isoforms tested. Surprisingly, most CYP isoforms seem to metabolize the most reactive sites, and thus it may seem that the binding plays a less important role than what one might expect [46]. However, for the CYP 2D6 and 2C9 enzymes, the predictive power increases significantly when introducing a penalty for sites that are close to positively and negatively charged chemical groups, respectively. This fits well with observations from the crystal structures of these enzymes [5,47], which show that the Glu216 and Asp301 (Figure 3) residues in CYP 2D6 interact favorably with


2.3

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D.

Figure 2. A. Mechanism for hydroxylation of aliphatic carbon atoms. The initial hydrogen abstraction is followed by an oxygen rebound step. For amines and ethers, this results in a dealkylation. B. Mechanism for the formation of epoxides. C. Mechanism for oxidation of aromatic carbon atoms. This can lead to the formation of ketones, hydroxylated species and epoxides. D. Mechanism for the oxidation of heteroatoms (e.g., of sulfides or amines)

meta-substitutions, and a model for determining the activation energy was developed that included bond dissociation energies for addition of a hydroxyl radical and cation to substituted benzenes [38]. The importance of this approach is that the model is independent on whether the Hammett parameters exist for the fragment of interest, and it is, thus, more generally applicable. Other studies on DFT-calculated activation energies on series of compounds have been reported, in which simplified models of the activation energies and correlations to experimental data have been done. For example, Lui et al. investigated the oxidation of 30 aldehydes to carboxylic acids [39]. Rydberg et al. studied a number of compounds containing aromatic fragments [40], and Hsiao et al. addressed oxidation of aromatic substrates [41]. It was recently suggested that other factors than the activation energy play a role considering the N-oxidation of tertiary amines [42]. For such compounds, the competing reaction is the N-dealkylation reaction that in DFT-based studies always 218




A. CYP 1A2

B. CYP 2C9

C. CYP 2C19

D. CYP 2D6

E. CYP 3A4

Figure 3. A. CYP 1A2 with alpha-naphthoflavone bound (PDB ID: 2HI4) [90]. B. CYP 2C9 with flurbiprofen bound (1R9O) [49]. C. CYP 2C19 with an inhibitor (2-methyl-1-benzofuran-3-yl)-(4-hydroxy-3,5 dimethylphenyl)methanone bound [91]. D. CYP 2D6 (2F9Q). E. CYP 3A4 with two ketoconazole ligands bound (2V0M) [4].

positively charged groups in substrates (often amines) [48] and the Arg108 residue in CYP 2C9 interact favorably with negatively charged groups in substrates [49]. This specific CYP 2D6 pharmacophore was used by Rydberg and Olsen to improve the SMARTCyp model by adding a correction to the activation energy based on the distance between the potential site of metabolism and a positively charged amine [48]. Similarly, both Lui and Rydberg used a correction based on the distance between the negatively charged site and the potential site of metabolism to improve models for the CYP 2C9 isoform [50,51]. The work of Lui and a later study by Rydberg have confirmed that corrections to the precomputed activation energies can be used to obtain good site of metabolism predictions for other CYP isoforms as well [46,50]. In general, the prediction rates for all isoforms are around 80% for the 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1 and 3A4 isoforms. Although corrections to the reactivity have been included in some of these isoform-specific models (2C8, 2C9, 2C19, 2D6), the most important contribution to the site of metabolism models arise from the reactivity for all isoforms [46].

3.

What role does the enzyme play?

Combining docking with pre-computed DFT activation energies

3.1

The fact that binding and orientation of the substrates play a significant role in the 2D6 and 2C enzymes illustrates that the protein environment is important to include when

determining the site of metabolism. Several groups have used docking to position the substrate and applied different approaches to correct the docking scores using a reactivity scheme, taking into account that after binding of the substrate the reaction should be able to occur. Thus, it is only the steric and polarizing effect from the CYP isoform that is taken into account. Other effects that potentially could affect the drug metabolism, such as the influence from binding of co-enzymes, for example, are not included since there is not much experimentally determined structural data available. Jung et al. [52] combined docking with AM1-estimated activation energies (according to the method developed by Jones et al. [53]) to rationalize the sites of metabolism by CYP1A2 on 12 compounds (correctly predicting 8 of the 12 metabolites). Rydberg et al. used the same set of compounds and additional 60 compounds in their study, showing that a combination of docking (with Chemscore) and rules for the activation energies (the so-called neighboring atom model, NAT, which later was further developed to the SMARTCyp model) could improve the prediction rates beyond using reactivity alone (72% combining docking with the NAT model versus 65% using just the NAT model) [44]. In this case, the Chemscore values were simply added to the activation energies to make the prediction. A combination of docking and reactivity has also been applied to CYP 2D6 [54]. In this study, the substrates were docked into an ensemble of structures, and various schemes were tested, differing in what distance from the heme iron atom to the site of metabolism and what type of reactivity


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Figure 4. General reaction mechanism for aldehyde oxidases and flavin monooxygenases. Adapted from [82,89].

that should be used. It was found that combining ensemble docking with reactivities generally improved the site of metabolism prediction. However, computationally it is not trivial to use ensemble docking. Danielson et al. also used a structure-based approach, combining pre-calculated DFT activation energies (using the NAT model described above) with docking scores (with the AutoDock Vina program) [55]. Both static docking (into the crystal structure) and ensemble docking (in 10 structures selected from an MD simulation) were used. The latter had the best overall predictivity, with 61% of the metabolites found among the top two ranked atoms. This was significantly better than using either the docking score or the reactivity by themselves. The drawback of just using a distance criterion from Fe to the potential site of metabolism is that the TS still can be strained although the Fe ion is close to the site of metabolism. Tyzack developed an approach to select reasonable binding poses using the GOLD docking program. They docked the substrate in multiple orientations ensuring that each site is accessible to the heme by applying a tethered docking approach [56]. The docking was combined with a DFT-based reactivity measure (based on the ionization energy) to predict 220

the site of metabolism for CYP 2C9, 2D6 and 3A4, achieving top two prediction rates of 78, 80 and 75%, respectively. Another way to select the binding modes that resemble the TS is to use the gas phase TSs to parameterize specialized TS force fields [57,58]. Recently, the IMPACTS procedure [59] was developed, which combines docking, TS modeling and pre-computed reactivities. Thus, it takes into account the binding energy of the substrate, in addition to including the restraints in the TS. This method was applied to metabolism by CYP 1A2, 2C9, 2D6 and 3A4 isoforms with accuracies of 70 -- 80% (lowest for CYP 3A4). In particular, IMPACTS performs better for CYP 1A2 and CYP 2C9 than CYP 3A4, probably because the binding is hard to predict accurately for this flexible enzyme. QM/MM calculations Instead of performing DFT calculations in the gas phase for small model systems or correcting the poses obtained from MD simulations or docking studies with various types of reactivity schemes, QM/MM can be used. Here a part of the system is treated with DFT, typically the same number of atoms as in the gas phase calculations, and the rest of the system handled with cheaper force field methods. In 3.2




this manner, the orientation of the substrate inside the protein is taken into account, as well as the polarization from the protein environment. QM/MM has been used to understand the properties of Compound I (cf. Figure 1) and the reactions in the presence of the surrounding protein. The method, and its application to CYPs, has been reviewed in several other publications [21,60,61]. Lonsdale studied the electronic structure of Compound I and concluded that it is very similar for the different human enzymes. This is an important finding because it is the assumption behind many metabolism prediction methods using the reactivity of Compound I [60,62]. Most QM/MM calculations have been done on CYP101 (P450cam) reacting with small model compounds such as propene, cyclohexene [63,64] and camphor [65-67]. Several groups have studied camphor hydroxylation, showing that the setup of the QM/ MM calculations is critical for getting the correct activation energies and that the smaller model systems treated with DFT are technically easier to handle with respect to optimizing the geometries [68]. Only few activation energy calculations with QM/MM have been done on the human CYP enzymes, probably due to the inherent challenge of determining how the substrate binds in the first place. Bathelt et al. investigated hydroxylation of benzene in CYP 2C9 and found that two different pathways are possible with the aromatic ring oriented either face-on or side-on toward the heme group, and that there are small differences in the alpha and beta spin distributions, compared to the gas-phase DFT results [69]. All the activation barriers are close in energy, comparing the QM/MM and QM in the gas phase. The fact that even such a simple fragment as benzene can have differences in the binding, and thus also in activation energies, shows how challenging it is to include the effect of the protein environment. However, it also shows that the protein ideally should be included when studying these enzymatic reactions. It is difficult to determine the correct binding mode of the substrate to the CYP enzyme, and therefore, the subsequent activation energy calculations may not be directly comparable due the differences in the free energy of binding. This difficulty is eliminated when considering competing reactions for the same binding mode, like done by Ola´h et al. [70]. Ola´h investigated why CYP 2D6 more easily makes the Odemethylation reaction than the competing aromatic hydroxylation when interacting with dextromethorphan. Both these metabolic types of reactions are often observed in other species for aromatic ethers, but the human 2D6 enzyme only makes the demethylation. In the gas phase, there is a preference for O-demethylation (41 kJ/mol for the hydrogen abstraction leading to O-demethylation versus 59 kJ/mol for the aromatic hydroxylation). In the protein, the activation energy for O-demethylation is higher than in the gas phase (76 kJ/mol) but even higher for the aromatic hydroxylation (138 kJ/mol). The reason why the activation energies are higher in the protein is mostly due to the steric hindrance

that distorts the TS from that in the gas phase. For example, is not sufficient to probe the intrinsic reactivity or consider the proximity, derived from docking or MD, of the different sites in the substrate to the heme. In relation to the latter point, the authors propose to use knowledge from gas-phase TS calculations to access whether the docking pose is a relevant one for the subsequent reaction. In a recent study, this approach was used to select structures from MD simulations that resemble the TS for subsequent QM/MM investigations of CYP 2C9 reacting with three substrates, (S)-ibuprofen, diclofenac and warfarin [71]. The activation energies determined are in agreement with the experimental sites of metabolism for (S)-ibuprofen and warfarin. However, for diclofenac, the calculated activation energy for the formation of 5-hydroxydiclofenac (which experimentally is produced by CYP 3A4) is lower activation barrier than one leading to 4¢-hydroxydiclofenac (which experimentally is produced by CYP 2C9). The reason for the discrepancy between the calculated and the experimental data is that the free energy of binding is not taken into account when determining the activation energies. Shyman also considered two competing sites in the substrate in the same binding orientation, and thus, eliminated the problem of predicting the binding mode as well [72]. In a study on how tyramine is hydroxylated to yield dopamine by CYP 2D6, it was shown that the usual aromatic hydroxylation ortho to the hydroxyl group was unfavorable compared with the alternative reaction path in which the H atom is abstracted from the OH group and the subsequent rebound occurs on the ortho C atom to form the dihydroxy group. Interestingly, this H-abstraction from the OH group is endothermic in the gas phase but the charges in the protein stabilized the intermediate and make this reaction possible. It is attributed to the presence of an electric field generated by two charged amino acids in the active site of CYP 2D6 (Asp301 and Glu216). QM/MM methods have also been used to study CYP 2A6 oxidation of (S)-nicotine [73,74]. Here, the competition between hydroxylation of the 5¢ or the N-methyl position was studied. Typically, both reactions would be rather favorable in vacuum because they are next to an amine N atom [37], provided that the H atom to be abstracted point in the opposite direction of the N lone pair [42]. Both QM/ MM studies come to the conclusion that the activation barrier for hydroxylation in the 5¢ position is lower, in agreement with experimental findings. A subsequent crystal structure of the CYP 2A6-nicotine complex also revealed that this position is closer to Fe than the N-methyl group [6]. 4.

Inhibition of CYP enzymes

While prediction of enzyme inhibition is generally not a problem for which DFT tends to be used, CYP inhibition is a special case. Ligands inhibiting CYPs are categorized into two groups, type I and type II binders, which can be characterized


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by spectroscopy. Whereas type I binders behave as normal protein ligands and do not cause any unexpected changes to the measured spectra, type II binders bind directly to the iron ion in the heme group and change the observed spectra [75]. The typical interaction between the ligand and the heme iron is a semi-covalent bond between the iron and an aromatic nitrogen atom. Such an interaction is not easily described by methods not considering the electronic properties, like force field methods, which is why type II ligands have been studied with DFT methods [76-79]. The first DFT study on type II binding was performed by Balding et al., who investigated how Fluconazole, Econazole and Metapyrone displace the water molecule that binds to the heme iron in the resting state and form the inhibition complex [76]. They studied the FeIII porphyrin complex in doublet, quartet and sextet spin states for all three ligands, and did two-dimensional (2D) scans of the iron--oxygen (water) and iron--nitrogen (ligand) distances. Their work was followed by a comprehensive study by Leach and Kidley [77], who used DFT, statistics and the experimental data in AstraZeneca’s databases of CYP inhibition assays to investigate likelihood for a ligand to be among the most potent compounds. They used DFT on 15 small fragments containing aromatic nitrogen atoms to represent the data in their databases. Together these two studies showed that the most likely interaction complex is a type II ligand interacting with an FeIII heme in the doublet spin state. A set of 16 drug molecules that had all been investigated in the same experimental study where investigated by Lee et al. [78] and Rydberg [79] who calculated the interaction energies between the drug molecules and an iron--porphyrin complex in the FeIII oxidation state in the doublet spin state. Since the rest of the enzyme is not taken into account, the DFTdetermined binding energies will only correlate with those determined experimentally, if this semi-covalent bond strength is the dominating factor for binding. Lee et al. showed that the calculated free energy of binding correlated well to the experimental data (r2 = 0.91). Rydberg found that for the 13 molecules that did not have any substituents in ortho position to the aromatic nitrogen atom, the electronic interaction energy correlated very well to the experimental free energy of binding (r2 = 0.90), and this correlation could be improved even further by adding a correction using calculated logP values (r2 = 0.96, using XlogP3), a result that was also found by Leach and Kidley [77] using clogP, and Lee et al. using solvation energies calculated with DFT [78].

Oxidation by other drug-metabolizing enzymes 5.

DFT calculations have also been used to explore oxidation reactions catalyzed by other drug-metabolizing enzymes than the CYPs, although to a lesser extent. 222

Aldehyde oxidase Aldehyde oxidase (AOX) is an enzyme oxidizing structurally different compounds ranging from simple aldehydes and small bicyclic compounds to complicated structures containing multiple ring systems [80]. The enzyme has become increasingly important in drug metabolism, because structural modifications introduced in order to avoid CYP-mediated metabolism, for example, replacing an aromatic carbon atom with a nitrogen atom, often makes the compounds more susceptible for AOX-mediated metabolism. Torres et al. reported in 2007 a DFT-based study of the AOX-mediated metabolism of eight drug molecules [81]. The study was based on the assumption that the reaction involves a tetrahedral intermediate, and that the formation of the tetrahedral intermediate represents the rate-determining step and accordingly determines the metabolite, the site of metabolism. The DFT calculations yielded the correct prediction for 25 out of 27 major metabolites (93% correct prediction). The authors concluded that the surprisingly good agreement with experimental data probably indicated that the AOXmediated reaction resembled the CYP 3A4-mediated reactions and involved a relatively large active site with the possibility for different binding modes, and that the reaction accordingly proceeded through the pathway with the lowest electronic energy. Recently, the 3D structure of the first mammalian AOX was determined by X-ray crystallography [82]. This structure confirmed that the active site of AOX indeed is larger than the active site of the related enzyme xanthine oxidase (XO). In a subsequent publication Alfaro and Jones study the AOX- and XO-mediated reaction of 6-substituted 4-quinazolinones in more details, including the effect of various substitutions [83]. The DFT calculations indicate that a concerted mechanism is preferred, and that the reaction proceeds in an anti-Hammond way with a TS being very product-like for the faster reactions. In the TS, the C--O bond is 90% formed and the C--H bond is 80% broken relative to the product geometries yielding a tetrahedral TS, which explains why the predictions based on tetrahedral intermediates were successful. The authors do not exclude a stepwise mechanism, but conclude that the DFT calculations together with experimental data are more consistent with a concerted mechanism (cf. Figure 4). In a recent paper, Jones and Korzekwa [84] report on the development of a computational method for prediction of in vitro and in vivo human intrinsic clearance for eight drug compounds based on just two descriptors quantifying the electronic and steric features of the AOX-mediated metabolism. The electronic descriptor was calculated by the DFT method as the energy difference between the drug plus a water molecule and the tetrahedral intermediate. The steric descriptor was supposed to reflect the nucleophilic attack of water to an aromatic system. The actual calculation involved the creation of a simplified model of the shape of the AOX active site. 5.1




5.2

Flavin-containing monooxygenases

Flavin-containing monooxygenases (FMOs) can perform a variety of oxygen-transfer reactions and are involved in the metabolism of many xenobiotics [85]. They oxidize soft nucleophiles, such as nitrogen and sulfur atoms, through a mechanism involving a hydroperoxyflavin (cf. Figure 4). Model systems of this hydroperoxyflavin and various possible reaction mechanisms have been studied by DFT since 2002 using different model substrates such as trimethylamine, trimethylphosphine, dimethylsulfide and dimethylselenide [86-88]. However, it was not until 2011 that a more drug-like substrate was investigated when Taxak et al. studied the S-oxidation of thiazolidinedione, a chemical group that is part of all the glitazone class drugs (Troglitazone, Rosiglitazone and Pioglitazone) [89]. Some of the major reasons for the relatively small interest in these enzymes could be that their reactions are reasonably easy to predict without theoretical calculations, and the actual oxidation reaction is not the rate-limiting step. Instead, the release of water is slower than the oxidation reaction, leading to the observation that Vmax is virtually the same for all substrates [87]. 6.

Expert opinion

The DFT calculations have over the past decades proven to be a valuable tool for studying the reaction mechanisms of enzymes involved in drug metabolism. With relatively small model systems, that for CYPs consist of a model of Compound I and the substrate, these types of calculations are not too time-consuming and detailed reaction profiles can be obtained. This is useful for understanding mechanistic issues and predicting which metabolites that will be formed by the different CYP isoforms for which the binding to the enzyme is not the major determinant. Many CYP isoforms are structurally flexible and it may be argued that exactly this feature is the reason why the site predicted to be most reactive by DFT gas-phase calculations often also is observed experimentally as the metabolic site. The crystal structures of the CYP 3A4 enzymes have shown that the binding site for one isoform can differ in size, and also a presumed more specific enzyme like the 2D6 enzyme has shown a remarkably flexibility. One major challenge in predicting the site of metabolism is the determination of the free energy of binding for these flexible enzymes. This could be the reason why many of the in silico methods that actually takes the protein environment

into account, using for example, docking or MD simulations, are not always more predictive than ligand-based methods. Many studies have been reported where predicted binding modes of substrates have been combined with computed activation energies. However, an inherent feature of combining the binding and reactivity is what scheme to use for that purpose. Using the combined methods, it is critical to use the relevant structure(s) for the predictions. There has been a tendency to use the distance from the Fe ion to the heavy atom in the substrate that is involved in the reaction as a cutoff (5 -- 6 A˚). This criterion seems to be a requirement, but it may not be sufficient and as pointed out by others, TS-like structures should preferably be used. This means in practice that the relevant angles in the TS would indicate whether the subsequent reaction is likely to take place. Having sampled the possible binding modes, the use of QM/MM methods are a logic next step for studying the reaction mechanism, possibly on an ensemble of structures. In that manner, both the steric and electronic polarization effects from the surrounding protein are included. These methods are still not high-throughput methods, but are currently applicable for smaller sets of compounds. In particular, competing sites in the substrate being in near proximity of the heme can be studied, because they share the same binding mode, and thus the problem of determining the free energy of binding is eliminated. A lot of effort has been invested in understanding the function of the CYP enzymes both experimentally and theoretically over the past decades. However, for the community to move forward, a thorough benchmark of the performance of the DFT methods in this scientific area would be useful. For example, it would be valuable to know not only the experimental sites of metabolism but also the associated rate constants to benchmark whether the calculated activation barriers are reasonable. Importantly, the experimental free energies of binding would also be extremely useful to judge whether we can reliably calculate absolute values of the free energy of binding for the CYPs.

Declaration of interest The authors have no competing interests to declare. The work has been supported by an unrestricted grant from Lhasa Limited.


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Affiliation


Patrik Rydberg, Flemming Steen Jørgensen & Lars Olsen† † Author for correspondence University of Copenhagen, Department of Drug Design and Pharmacology, Denmark E-mail: [email protected]


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