Role of Electrostatic Potential in the in Silico Prediction of Molecular

Article pubs.acs.org/molecularpharmaceutics

Role of Electrostatic Potential in the in Silico Prediction of Molecular Bioactivation and Mutagenesis Kevin A. Ford* Safety Assessment, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States S Supporting Information *

ABSTRACT: Electrostatic potential (ESP) is a useful physicochemical property of a molecule that provides insights into inter- and intramolecular associations, as well as prediction of likely sites of electrophilic and nucleophilic metabolic attack. Knowledge of sites of metabolic attack is of paramount importance in DMPK research since drugs frequently fail in clinical trials due to the formation of bioactivated metabolites which are often difficult to measure experimentally due to their reactive nature and relatively short half-lives. Computational chemistry methods have proven invaluable in recent years as a means to predict and study bioactivated metabolites without the need for chemical syntheses, or testing on experimental animals. Additional molecular properties (heat of formation, heat of solvation and ELUMO − EHOMO) are discussed in this paper as complementary indicators of the behavior of metabolites in vivo. Five diverse examples are presented (acetaminophen, aniline/phenylamines, imidacloprid, nefazodone and vinyl chloride) which illustrate the utility of this multidimensional approach in predicting bioactivation, and in each case the predicted data agreed with experimental data described in the scientific literature. A further example of the usefulness of calculating ESP, in combination with the molecular properties mentioned above, is provided by an examination of the use of these parameters in providing an explanation for the sites of nucleophilic attack of the nucleic acid cytosine. Exploration of sites of nucleophilic attack of nucleic acids is important as adducts of DNA have the potential to result in mutagenesis. KEYWORDS: acetaminophen, aniline, electrostatic potential, imidacloprid, in silico prediction, metabolism, mutagen, nefazodone, phenylamine, vinyl chloride

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INTRODUCTION In silico methods for elucidation of metabolic pathways, bioactivation and prediction of mutagenic potential have become popular in recent years. The advantage of using in silico procedures is that they are quick and inexpensive, significantly reduce the use of animals for experimentation (e.g., in vivo mutagenicity testing) and avoid the need for synthesis of compounds for testing. Numerous studies have shown that in silico approaches are reliable for the prediction of several important toxicological end points including carcinogenicity (caused by mutagenic carcinogens),1,2 hERG alerts3,4 and phospholipidosis.5,6 The importance of in silico methods is demonstrated in the fact that several regulatory agencies, including the U.S. FDA7 and the EMA,8 consider a candidate genotoxic impurity that is predicted to be negative for mutagenicity when screened through validated (Q)SAR methods as being equivalent to being negative in the Ames assay. In light of this, many pharmaceutical research organizations have moved physicochemical property screens much earlier in drug discovery to try to anticipate toxicological end points.9−11 Examples of such screens include attempts to elucidate the physiochemical properties involved in timedependent inhibition of cytochrome P450 enzymes, binding to hERG or PPAR inhibition. © 2013 American Chemical Society

Predictive metabolism platforms are becoming increasingly more popular due to the availability of software from established vendors, such as Meteor (Lhasa Ltd.; Leeds, U.K.)12 and Metasite (Molecular Discovery; Perugia, Italy).13 Biotransformations can greatly impact compound bioavailability, efficacy, chronic toxicity, excretion rate and route. Both the parent molecule and metabolites may also interfere with endogenous metabolism or the metabolism of other coadministered compounds. For example, the inhibition of metabolizing enzymes, such as cytochrome P450s and flavincontaining monooxygenases, can be associated with drug−drug interactions, which can have potentially fatal consequences for patients. In light of these issues, a detailed knowledge of metabolism is a crucial component during the early stages of drug discovery.14 Special Issue: Predictive DMPK: In Silico ADME Predictions in Drug Discovery Received: Revised: Accepted: Published: 1171

August 8, 2012 December 18, 2012 January 16, 2013 January 16, 2013 dx.doi.org/10.1021/mp3004385 | Mol. Pharmaceutics 2013, 10, 1171−1182

Molecular Pharmaceutics

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principles of electrostatics, i.e., that like charges repel one another (e.g., 2 positive charges), whereas unlike charges (i.e., a positive and a negative charge) will attract one another. The significance of these electrostatic principles to drug research is that unlike charges lead to negative, more stabilizing interactions and consequently an increased probability for the formation of a more stable inhibitor−target complex, whereas the interaction energy between like charges is positive and is destabilizing.21 Rewriting the Poisson equation in terms of Coulomb’s law gives the following (eq 3): qi Φ(r ) = ∑ (r − ri) (3)

One of the main problems with commercially available metabolism prediction software is that a prediction of basic physicochemical properties of the metabolites (which are frequently the principal determinants of chemical toxicity and bioactivation),15 such as water solubility, stability or reactivity, is not always provided. Lack of such data leaves drug development teams with few options but to experimentally determine these properties, which may significantly delay project timelines. In an attempt to resolve this issue, 4 physicochemical parameters are discussed in this paper which when examined in unison may help to predict certain in vivo behaviors of metabolites: electrostatic potential (a measure of potential energy per unit charge), heat of formation (a measure of molecular stability), energy of solvation (a measure of water solubility) and ELUMO − EHOMO (a measure of molecular reactivity). These parameters have been used for decades by physical chemists to gain insight into the behaviors of molecules in solution, but their application in the fields of DMPK, investigative toxicology and pharmacology has been rarely reported in the scientific literature. It is the goal of this paper to acquaint investigative toxicologists and drug metabolism scientists with these useful tools and to demonstrate that they can serve as reliable indicators of compound reactivity, stability and solubility. The fundamental principles on which these 4 parameters are based are described in detail below. Electrostatic Potential. Any alteration in the electrical charge of a molecule (e.g., due to variation in the pH of the solution in which a molecule resides, or a change in electric field)16,17 changes the electrostatic energy (or potential) in the surrounding space to create a more positively or negatively charged local environment.18 Electrostatic potential (ESP) is an important property that plays a crucial role in the interaction of molecules, and it can be defined simply as the difference in electrical charge between any two points. The most fundamental equation of electrostatics is the Poisson equation19 (eq 1): ∇2 Φ(r ) = −4πρ(r )

where ri is the position and qi the magnitude of the ith point charge. Essentially all electrostatic models used in studying macromolecules, such as DNA, are based on the Poisson equation. If a region of a molecule responds in a uniformly distributed way to an electric field, then the relationship between polarization density (χ) and induced dipole moment over the volume of the region (P) is given by eq 4: P = χE

(4)

where E is the average electric field in that region. Since the region responds in a uniform manner, a permittivity constant, ε, can be applied to the Poisson and Coulomb equations. However if the dielectric varies through space, then Coulomb’s law becomes invalid, while the Poisson equation becomes (eq 5) ∇·ε(r )∇Φ(r ) = −4πρ(r )

(5)

where Φ is now a function of the position r. ESP is well established as an effective tool for interpreting and predicting molecular reactive behavior.22−24 Two important applications of ESP that will be discussed in this paper are the prediction of regions of a molecule that are susceptible to electrophilic or nucleophilic metabolic attack (serves an valuable tool in drug metabolism research) and prediction of mutagenicity (important in investigational toxicology assessments). Electrophiles25 (electron-deficient, positively charged species) tend to be attracted to regions of a molecule in which the ESP attains its most negative values (the local minima, Vmin) since these are where the effects of the molecule’s electrons are most dominant, whereas nucleophiles25 (an electron-rich, negatively charged species) are especially attracted to areas where the ESP is the most positive (the local maxima, Vmax). The ESP due to a set of nuclei {ZA} and the electronic density ρ(r) of the molecule is described in eq 626

(1)

which relates spatial variation of the potential, Φ, with position r to the charge density distribution ρ, where the permittivity of free space is unity. When the charge distribution is described in terms of a set of point charges (q), the Poisson equation becomes Coulomb’s law, which calculates the force of attraction between point charges of molecules (e.g., such as a drug inhibitor and an amino acid at the active site of a target enzyme). Coulomb’s law20 states that the magnitude of the electrostatic force between two point charges (q1 and q2) is directly proportional to the product of the magnitudes of charges and inversely proportional to the square of the distances between them (r2), eq 2: qq F ∝ 1 22 (2) r

ESP =

∑

ZA − |RA − r |

∫

ρ(r′) dr′ |r ′ − r |

(6) 27−29

where ZA is the charge on nucleus A, located at RA. The first term on the right of eq 6 represents the contribution of the nuclei (which is positive), and the second term describes the contribution of the electrons (which is negative). The electronic density is obtained from ab initio (or semiempirical) calculations and accordingly is approximate, and consequently the measure of the ESP of a molecule is also an approximation. Previous studies have shown that Hartree−Fock wave functions give good results for properties that are calculated from ρ(r), such as ESP.30−32 Furthermore, investigations have shown that a reliable measure of ESP can be obtained even with self-

The inverse-square nature of this law signifies that the closer the proximity of the charges, the greater the electrostatic force of attraction between the 2 charges. This is an important consideration in the design of novel drug inhibitors, during which every effort must be made to maximize interactions at the active site of the enzyme by ensuring that the candidate inhibitor does not possess highly repulsive charged properties which would likely produce a nonpotent compound. The direction of the force between charges is dictated by the 1172

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ELUMO − EHOMO. The lowest unoccupied orbital (LUMO) and the highest occupied molecular orbital (HOMO) are the so-called frontier orbitals, and they play a critical role in chemical reactivity.69 The difference in energies between the energy of the LUMO (ELUMO) and the HOMO (EHOMO) is called the band gap (i.e., ELUMO − EHOMO). The smaller the band gap of a molecule, the more likely it is to be a reactive compound. A decrease in the ELUMO, or more correctly the band gap, going from a parent molecule to a metabolite due to biotransformation signifies that the resulting metabolite represents an increase in energy, and therefore it is likely to be more reactive. Likewise, an increase in the band gap from parent to metabolite signifies that a less energetic metabolite is formed.

consistent-field (SCF) wave functions that are not near Hartree−Fock quality.33−35 ESP may also be determined experimentally by diffraction methods,36−38 but at present derivations based on quantum methods remain the more accurate approach. ESP plays an important role in maintaining the structural properties of nucleic acids and proteins, including enzymes and transporters.39−44 For example, interactions such as salt bridges, van der Waals interactions and hydrogen bonds, which are all primarily electrostatic in nature,45−47 are critical in maintaining and stabilizing the structure of proteins.48−50 Therefore, it is essential to understand the role played by electrostatic forces of biomolecules and their ligands in order to improve the structure activity relationship (SAR) efforts used in the design of less toxic candidate pharmaceutical molecules. As demonstrated in this paper ESP maps are a quick and convenient method to visualize metabolic “hot-spots” and elucidate mutagenic potential of molecules. Since the early work of Politzer and colleagues,22,24,50−52 ESP has been routinely used as a tool for assisting medicinal chemists in the synthesis of potent drug candidates for numerous indications including cancer,53−55 HIV,56−58 depression,59,60 malaria,61,62 bacterial infections63,64 and epileptic seizures65,66 to name a few. However, as mentioned previously, its use as a driving force to aid in decision making in the fields of DMPK, investigative toxicology and pharmacology is seldom reported in the literature. Heat of Formation. Heat of formation (ΔHfθ) is the change of enthalpy that accompanies the formation of 1 mol of a pure substance from its elements, with all substances in their standard states (i.e., T = 298 K and P = 1 atm). ΔHfθ can be calculated from Hess’s law (also known as the law of constant heat summation), which proves that the heat change (ΔH) for a single reaction can be calculated from the difference between the ΔHfθ of the products and the ΔHfθ of the reactants67 (eq 7): ΔHf θ reaction = ∑ΔHf θ products − ∑ΔHf θ reactants

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EXPERIMENTAL SECTION Geometries of the compounds utilized in this study were fully optimized by using density functional theory (DFT) with Becke’s three-parameter hybrid exchange function and the Lee−Yang−Parr correlation function (B3LYP) in combination with the 6-31+G(d) basis set using Gaussian ’09 (Gaussian, Wallingford, CT).70 Energies of the lowest unoccupied molecular orbital (ELUMO) and highest occupied molecular orbitals (EHOMO) were subsequently calculated using these settings. Standard heats of formation in the gas phase (ΔHfθ) and solvation energies were calculated using the PM3 semiempirical method in Spartan ’10 (Wavefunction, Irvine, CA), and all values were verified with MOPAC 2012 (CAChe Research, Beaverton, OR) using the same settings and level of theory. Electrostatic potential maps of the 5 small compounds, and their selected metabolites discussed in this paper, were constructed using Spartan ’10. Spartan ’10 calculates the electrostatic potential at selected points on the 0.002 isodensity surface and maps the surface by color, where different colors are used to identify different potentials. The electrostatic potential varies from most negative (red) to most positive (blue) as follows: red < orange < yellow < green < blue.71 Electrostatic potential maps of A-, B- and Z-DNA confirmations were constructed using GAMESS72 and Avogadro open-source software, version 1.0.3, using the MMFF94 force field and minimization of DNA, according to manufacturer’s instructions.73 The same color scale as Spartan ’10 was used for the GAMESS and Avogadro analysis. Chemical structures were constructed using ChemBioDraw Ultra, version 12.0.2.1076 (CambridgeSoft, Cambridge, MA).

(7)

ΔHfθ plays an important role in the thermodynamic stability of compounds because the more negative the ΔHfθ, the more stable the compound.68 Prediction of molecular stability has the potential to be an important indicator of compound lability because presumably the more stable a metabolite, the less likely it is to be labile. Energy of Solvation. Solvation is the process of attraction of molecules of a solvent (e.g., water) with molecules of a solute. The energy of solvation is the Gibbs free energy required for solvation to occur, and it is required in order to first break bonds within the solute and within the solvent and then to form new bonds between the solvent and solute. Knowledge of the energy of solvation of a compound is important as part of distribution, metabolism, and excretion studies because it influences whether or not a compound is likely to be distributed in water or stored in lipid; if a metabolite is likely to require phase II conjugation in order to be excreted; and whether a compound is more or less watersoluble than the parent molecule and therefore whether it is likely to be excreted in urine or bile. Furthermore the energy of solvation has an important role in distinguishing between more and less water-soluble metabolites, since not every metabolic process leads to increases in water solubility (e.g., methylation and acetylation).

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RESULTS A. Predicting Bioactivation, Stability and Water Solubility of Metabolites. Five diverse demonstrations of the application of electrostatic potential, heat of formation, energy of solvation and ELUMO − EHOMO in the prediction of bioactivation, stability and water solubility for compounds of pharmaceutical, agrochemical and toxicological importance are discussed in this paper: phenylamine (an important functional group present in numerous medications), acetaminophen (an important analgesic), nefazodone (a hepatotoxic antidepressant), vinyl chloride (a known human carcinogen) and imidacloprid (an extensively used insecticide). Example 1: Phenylamine-Containing Drugs. The phenylamine (aniline) group is a common structural component of many pharmaceutical compounds, including several antibiotics (e.g., sulfanilamide and dapsone), anesthetics (e.g., benzocaine) 1173



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amine group. In the planar geometry, on the other hand, nitrogen is sp2-hybridized, and the electron pair is delocalized between a p orbital of nitrogen and the π system of the ring. Various groups have shown that aniline adopts a nonplanar configuration due to the more energetically favorable sp3hybridized configuration,74,75 and consequently the nonplanar ESP map could be considered to be the more energetically favorable representation. This example demonstrates the importance of not simply relying on a “plug-and-play” software approach in the construction of ESP maps and instead conveys the necessity of employing optimized geometry and appropriate minimization in order to produce accurate and meaningful ESP maps. The nonplanar configuration of aniline creates sites of negative potential (red areas) above and below the aromatic ring (Vmin is −118.202 kJ/mol) and the amine (Vmin is −92.527 kJ/mol) which in part may help to provide a mechanistic basis for the observation of several N-conjugated phase II metabolites (derived from the conjugation of electrophiles, such as the activated acetyl group, with the amine; (Supporting Information; Figure S1) in several mammalian species treated with, or exposed to, aniline,76 including humans.77 The solvation energy of aniline (−21.68 kJ/mol) suggests that it is moderately soluble in water, as supported by experimental data (i.e., 0.04 g/mL).78 Furthermore as can be deduced from the differences in the heats of formation (ΔHfθ) (−107.34 vs 87.03 kJ/mol) and energies of solvation (−27.06 vs −21.68 kJ/mol) for N-phenylacetamide and aniline respectively (Table 1), the N-acetylated metabolite is more stable and more water-soluble than aniline, which may help to explain why N-acetylated metabolites are the major urinary metabolites of aniline observed in humans.77 The N-phenylacetamide is slightly less reactive than aniline (ELUMO − EHOMO: 5.68 eV vs 5.64 eV respectively), suggesting that aniline is

and antiarrhythmic agents (e.g., procainamide), as shown in Figure 1A. Figure 3A maps the ESP for aniline in its nonplanar

Figure 1. Structures of (A) aniline and phenylamine-containing drugs (highlighted in blue), (B) acetaminophen, (C) vinyl chloride, (D) nefazodone, (E) imidacloprid and (F) cytosine.

and planar configurations, computed from density functional theory (DFT) methods. The values of the contours are described in kJ/mol, and the color scale is the same for both models. Importantly the ESP maps for aniline differ depending on the 3-dimensional configuration of the amine group. In the nonplanar geometry, the unshared pair of electrons occupies an sp3 hybrid orbital of nitrogen, and consequently the region of highest electron density is associated with the nitrogen of the

Figure 2. Metabolic pathways for acetaminophen, vinyl chloride (adapted from Whysner et al., 1996),137 nefazodone (adapted from Peterman et al., 2006)138 and imidacloprid (adapted from Ford and Casida, 2007).123 The identities of the metabolites are described in Table 1. 1174



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Table 1. Predicted Heats of Formation, Solvation Energies and ELUMO − EHOMO Values for Acetaminophen, Aniline, Imidacloprid, Nefazodone and Vinyl Chloride, and Their Principal Metabolites compound acetaminophen (1) p-aminophenol (2) p-quinoneimine (3) 4-acetamidophenol sulfate (4) 4-acetamidophenol glucuronide (5) N-acetyl-p-benzoquinoneimine (6) 3-(glutathionyl)acetaminophen (7) 3-(cysteinyl)acetaminophen (8) 3-acetaminophen mercapturic acid (9)

ΔHfθgas (kJ/mol) −276.67 −74.16 52.28 −744.79 −1317.66 −227.48 −1339.17 −614.72 −802.00

aniline N-phenylacetamide

87.03 −107.34

imidacloprid (1) imidacloprid-olefin (2) 6-chloronicotinic acid (3) imidacloprid-NNO (4) imidacloprid-NNH2 (5) imidacloprid-NH (6)

222.37 489.32 −275.33 280.35 333.89 372.27

nefazodone (1) nefazodone-hydroxy (2) nefazodone-quinoneimine (3) chlorobenzoquinone (4)

145.94 −31.37 831.42 −279.56

vinyl chloride (1) chloroethylene oxide (2) chloroacetaldehyde (3) chloroacetic acid (4) S-formylmethylglutathione (5) S-carboxymethylglutathione (6) S-formylmethylcysteine (7) S-(2-hydroxyethyl)cysteine (8) S-carboxymethylcysteine (9) N-acetyl-S-(2-hydroxyethyl)cysteine (10) thioglycolic acid (11)

29.00 −58.14 −174.68 −431.23 −955.68 −1489.69 −501.58 −631.40 −776.69 −782.35 −381.18

solvation energy (kJ/mol) Acetaminophen −43.49 −43.16 −29.69 −79.76 −76.68 −28.88 −224.26 −74.19 −70.32 Aniline −21.68 −27.06 Imidacloprid −51.98 −72.41 −62.14 −86.20 −59.04 −55.34 Nefazodone −3.15 −49.00 −207.85 −17.25 Vinyl Chloride 1.62 −26.37 −13.85 −24.46 −217.24 −94.78 −37.62 −63.27 −53.20 −63.07 −28.28

rendered less reactive by N-acetylation.79 In a similar way halogenated anilines are conjugated by nucleophilic attack by glutathione, as was discussed by the author and colleagues in a previous study.80 Example 2: Acetaminophen. Acetaminophen (paracetamol; N-acetyl-p-aminophenol; Figure 1B) is a widely used analgesic and antipyretic drug, which upon overdosing may cause centrilolobular hepatic necrosis.81,82 The metabolism of acetaminophen has been studied extensively in experimental animals and humans (Figure 2).83,84 The primary metabolites of acetaminophen in humans are phase II metabolites formed by conjugation with sulfate and glucuronic acid to produce 4acetamidophenol sulfate and 4-acetamidophenol glucuronide (metabolites 4 and 5 respectively).85 N-Acetyl-p-benzoquinoneimine (NAPQI; metabolite 6) is a bioactivated phase I metabolite of acetaminophen and has been the subject of numerous toxicity studies because it causes hepatoxicity following acetaminophen overdose.86−90 Another bioactivated phase I acetaminophen metabolite is p-quinoneimine (metabolite 3), which has been shown to be more reactive but less stable than NAPQI in vivo.91,92 Additioanlly several glutathione-

EHOMO (eV)

ELUMO (eV)

ELUMO − EHOMO (eV)

−5.55 −4.99 −6.07 −6.59 −6.14 −7.04 −5.82 −5.91 −6.02

−0.36 0.12 −2.80 −1.09 −0.61 −3.43 −0.74 −0.83 −0.95

5.19 5.11 3.27 5.5 5.53 3.61 5.08 5.08 5.07

−5.39 −5.95

0.25 −0.27

5.64 5.68

−6.97 −6.29 −7.46 −5.46 −5.37 −6.00

−1.48 −1.27 −1.93 −1.36 −0.89 −0.84

5.49 5.02 5.53 4.1 4.48 5.16

−5.28 −5.01 −9.51 −7.67

−0.11 −0.24 −4.13 −3.79

5.17 4.97 4.18 3.88

−7.14 −7.96 −7.56 −7.84 −6.81 −6.62 −6.50 −6.24 −6.34 −6.37 −6.74

−0.04 0.56 −1.40 −0.91 −1.29 −0.82 −0.72 0.04 −0.45 −0.40 −0.34

7.10 8.52 6.16 6.93 5.52 5.80 5.78 6.28 5.89 5.97 6.40

derived metabolites are formed in vivo including a glutathione conjugate (metabolite 7), a cysteine conjugate (metabolite 8) and a mercapturic acid conjugate (metabolite 9). The ΔHfθ, solvation energies and ELUMO − EHOMO values (Table 1) agree with experimental data which demonstrate that NAPQI and p-quinoneimine are bioactivation metabolites of acetaminophen.93,94 The ΔHfθ and solvation energies of acetaminophen (−276.67 and −43.49 kJ/mol respectively) both increase, due to metabolic processes, in going from paminophenol (−74.16 and −43.16 kJ/mol) to p-quinoneimine (52.28 and −29.69 kJ/mol), indicating the larger thermodynamic instability and decreased water solubility of the two quinoneimines. Decreased water solubility suggests the possibility the two quinoneimines are unlikely to be excreted unchanged in urine (unlike acetaminophen, which can be excreted unchanged up to 9% of therapeutic dose),95 and consequently they are predicted to require phase II conjugation (such as with glutathione) in order to be excreted; this prediction is in agreement with experimental data. Metabolism of acetaminophen to these quinoneimines in excess of an adequate store of glutathione is associated with hepatic 1175



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Figure 3. Electrostatic potential maps of (A) aniline (nonplanar (i) and planar (ii) conformations), (B) (i) acetaminophen and (ii) NAPQI, (C) (i) vinyl chloride and (ii) chloroacetaldehyde, (D) (i) nefazodone and (ii) nefazodone-quinoneimine, (E) (i) imidacloprid and (ii) imidacloprid-NH and (F) cytosine (ESP contours are color-coded from red (negative) to blue (positive), and potentials are provided in kJ/mol).

failure.96 The solvation energy of acetaminophen suggests that it is a moderately water-soluble compound, which is supported by experimental data (i.e., 12.78 mg/mL at 20 °C).97 The ESP maps for acetaminophen and NAPQI (Figure 3B) clearly show the presence of numerous electrophilic sites in NAPQI (as indicated by the blue regions; Vmax is 119.945 kJ/ mol) which are prone to nucleophilic attack by glutathione. E LUMO − E HOMO values decrease from 5.19 eV (for acetaminophen) to 3.27 eV (for p-quinoneimine) and 3.61 eV (for NAPQI), indicating that the quinoneimines are more reactive than acetaminophen. As expected, the sulfate, glucuronide, cysteine and mercapturic acid metabolites all have high solvation energies, and therefore they would be predicted to be very water-soluble and found in urine. These predictions are in agreement with their presence as acetaminophen metabolites in urine derived from experimental animal data.98−100 Example 3: Vinyl Chloride. Vinyl chloride (chloroethene) (Figure 1C) is an organochlorine compound that is used extensively in the plastics industry during the synthesis of polyvinyl chloride (PVC). Vinyl chloride can cause angiosarcoma in humans and experimental animals, and thus it is classified by IARC as a class 1 compound, which signifies that there are sufficient data to confirm that it is carcinogenic to humans.101 Vinyl chloride is metabolized primarily in the liver by CYP2E1 to the electrophilic phase I metabolites chloroethylene oxide and chloroacetaldehyde (Figure 2, metabolites 2 and 3 respectively), which can react with the nitrogenous bases of DNA to form mutagenic adducts, such as 1,N6-ethenoade-

nine.102 Thiodiglycolic acid (metabolite 11) is the major urinary metabolite for humans exposed to vinyl chloride.103 The solvation energies and heats of formation (both in kJ/ mol) for vinyl chloride and its metabolites are shown in Table 1. The solvation energies predict that although vinyl chloride is fairly insoluble in water (1.62 kJ/mol), as verified by experimental data (i.e., 2.7 g/L),104 all of its primary metabolites are soluble, including chloroacetaldehyde (−13.85 kJ/mol (predicted), ≥ 100 mg/mL (experimentally derived);105,106 thioglycolic acid (−28.28 kJ/mol (predicted), ≥ 100 mg/mL (experimentally derived)107) and a series of glutathione-derived metabolites, such as S-formylmethylglutathione (−217.24 kJ/mol). The heats of formation for chloroethylene oxide (−58.14 kJ/mol) vs chloroacetaldehyde (−174.68 kJ/mol) suggest that the latter metabolite is much more stable than the former. This observation is in agreement with experimental data which have shown that chloroethylene oxide can spontaneously rearrange to form chloroacetaldehyde. The larger ELUMO − EHOMO differences for vinyl chloride (7.1 eV) and chloroethylene oxide (8.52 eV) suggest that these compounds are less reactive than the other metabolites and that they require metabolic conversion in order to become bioactivated. The smaller ELUMO − EHOMO difference for chloroacetaldehyde (6.16 eV) vs chloroethylene oxide indicates that chloroacetaldehyde is more reactive than chloroethylene oxide so that the former can form adducts with DNA more easily, in agreement with experimental data. In the case of chloroacetaldehyde, the position of most negative ESP is located on the oxygen atom (Vmin is −128.528 1176



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to rapid death.119 The >500-fold selectivity of imidacloprid for the insect (IC50: 4.6 nM) vs the α4β2 mammalian nAChR (IC50: 2600 nM) is based, to a large extent, on the ESP of the molecule: an overall negative ESP at the “tip” of imidacloprid, as provided by the presence of the nitro group, is required in order for binding to the insect nAChR to occur. The negative ESP of the imidacloprid tip (red area) can be seen in Figure 3D. The selectivity in binding is due to key differences in amino acids at the active sites of the nAChRs: the insect nAChR contains numerous key cationic amino acids (to which the negative tip is attracted) whereas the active site of the mammalian nAChR contains numerous key anionic amino acids (which repel the negative tip).120 However, when imidacloprid is metabolized to its guanidine metabolite (imidacloprid-NH; Figure 2), the ESP of the tip changes from negative to positive, as confirmed by the positive ESP (blue color) in Figure 3D. The result is that the guanidine metabolite is selective for the mammalian α4β2 nAChR (IC50: 8.2 nM; i.e., this metabolite is a potent mammalian neurotoxicant in contrast to the parent molecule) instead of the insect nAChR (IC50: 1530 nM; i.e., it is not selective for the insect nAChR). Thus, although the 3-dimensional structures of imidacloprid and its guanidine metabolite are very similar, this example clearly demonstrates how ESP can directly influence pharmacology and can play a role in determining selective toxicity between organisms. This ESP assessment is in full agreement with electrostatic calculations performed by other research groups.121 The metabolism, toxicology and pharmacokinetics of imidacloprid in plants and mice have been described by the author and colleagues previously.122−126 Briefly, upon absorption, imidacloprid is metabolized via dehydration across the ethano-bridge of the imidazaolidine ring to form an olefin compound (metabolite 2). Reduction of the nitro group yields a nitroso metabolite (metabolite 4), which is further reduced to aminoguanidine and guanidine metabolites (metabolites 5 and 6 respectively). N-Methylene hydroxylation leads to the formation of 6-chloronicotinic acid (metabolite 3). The solvation energy of imidacloprid was calculated to be −51.98 kJ/mol, which suggests that it is a water-soluble compound, in support of experimental data (0.61 g/L at 20 °C).127 The solvation energies of the metabolites of imidacloprid (Table 1) are all predicted to be more watersoluble than the parent; this prediction is in agreement with experimental data which demonstrate that these metabolites are found to a greater extent in the urine of imidacloprid-treated mice and rats than the parent compound.123,128 The ELUMO − EHOMO value for imidacloprid (5.49 eV) is greater than for the other compounds, with the exception of metabolite 3 (5.53 eV). Not surprisingly the nitrosamine metabolite (metabolite 4) had the lowest ELUMO − EHOMO value (4.1 eV), implying that this metabolite would be expected to be a more reactive compound than imidacloprid (indicating bioactivation). In addition metabolite 3 had the lowest ΔHfθ (−275.33 kJ/mol), suggesting that it is likely to be the most stable and least labile of the metabolites, in full agreement with experimental data.127 B. Using ESP To Predict Mutagenic Potential of Molecules. As illustrated by the previous 5 diverse examples, an important characteristic of ESP is that it is a discrete and measurable physicochemical property of a molecule, as demonstrated by the fact that it can be determined experimentally.129,130 ESP, as defined by eq 6, has an important physical significance: it describes the overall electrostatic effect

kJ/mol), meaning that this area is subject to electrophilic attack (Figure 3C). On the other hand the carbon to which the chlorine is attached is the most positive ESP region of the molecule (Vmax is 145.814 kJ/mol) and is the site that is most prone to nucleophilic attack. The predicted nucleophilic attack of chloroacetaldehyde at the carbon with the most positive ESP is in agreement with experimental data which confirm that a glutathione-derived metabolite, thiodiglycolic acid, is the primary urinary metabolite of chloroacetaldehyde and vinyl chloride in rats and occupational workers.105−109 Example 4: Nefazodone. Nefazodone (Serzone; Nefadar; 1(3-[4-(3-chlorophenyl)piperazin-1-yl]propyl)-3-ethyl-4-(2-phenoxyethyl)-1H-1,2,4-triazol-5(4H)-one; Figure 1D) is an antidepressant first marketed by Bristol-Myers Squibb in 1994. Its antidepressant properties are due primarily to its role as a potent antagonist at the 5-HT2A receptors (Kd: 26 nM).110 Nefazodone was withdrawn from the market in 2004 due to reports of adverse hepatic events, including jaundice, hepatitis and hepatocellular necrosis.111 While the precise mechanism for the hepatotoxicity of nefazodone remains uncertain, it is possible that a bioactivated, electrophilic quinoneimine metabolite that is formed in vitro and in vivo could play a role (metabolite 3; Figure 2).112 The metabolism of nefazodone has been described previously.113 Briefly aromatic hydroxylation occurs para to the piperazinyl nitrogen to produce p-hydroxynefazodone (metabolite 2; Figure 2) by CYP2D6.114 Rearrangement of metabolite 2 leads to the formation of the reactive quinoneimine (metabolite 3), and N-dearylation forms 2chlorocyclohexa-2,5-diene-1,4-dione (metabolite 4). The solvation energy of nefazodone was calculated to be −3.15 kJ/mol, which suggests that it has low water solubility, in agreement with experimental data (6.41 mg/L at pH 7).115 The solvation energies of the metabolites of nefazodone (Table 1) are all predicted to be more water-soluble than the parent. The ELUMO − EHOMO value for nefazodone (5.17 eV) is greater than for the other compounds, signifying that the compound gives rise to metabolites that are more reactive than the parent compound during its biotransformation. Not surprisingly the two quinone metabolites (metabolites 3 and 4) have the lowest ELUMO − EHOMO value (4.18 and 3.88 eV respectively), indicating that they are expected to be more reactive compounds than nefazodone. Metabolite 4 had the lowest ΔHfθ (−279.56 kJ/mol), signifying that it is likely to be stable (in agreement with reported data)116 and the least labile of the metabolites. In contrast, metabolite 3 has the highest ΔHfθ (831.42 kJ/mol), indicating that it is relatively unstable and likely prone to nucleophilic attack (e.g., by GSH). This is further supported by the ESP map for metabolite 3, which shows a large area of positive ESP (blue color) near and above the charged nitrogen of the piperazine ring (N+), with a large Vmax of 533.831 kJ/mol, indicating that this region is particularly prone to nucleophilic attack (Figure 3). Glutathione conjugates of metabolite 3 have been reported in the literature in support of these ESP-based predictions.117 Example 5: Imidacloprid. Imidacloprid (N-[1-[(6-chloro-3pyridyl)methyl]-4,5-dihydroimidazol-2-yl]nitramide; Figure 1E), the world’s best-selling pesticide,118 is a systemic insecticide that is used to control insect populations in crops and for flea control in cats and dogs. It belongs to a family of insecticides called the neonicotinoids which act as potent agonists for the insect nicotinic acetylcholine receptor (nAChR); blockage of ACh transmission in the insect leads 1177



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attack cytosine at the N3 and O8 positions, which is what is found to occur experimentally. N3 is the preferred site for alkylation reactions by electrophiles.135 When N3 is not accessible, as in DNA (in which it is involved in hydrogen bonding), some electrophiles have been observed to react instead with O8.136 Thus, cytosine, chosen here as an example, is observed experimentally to behave toward electrophiles in exactly the manner that would be predicted from its ESP map.

of the electrons and nuclei of a molecule in their surrounding space. By defining the electrostatic signatures of molecules ESP offers enormous potential in studying and improving interactions of small molecules, including those of medicinal interest, with biological systems of importance. As an example of its utility in improving genotoxicity screening of candidate drug molecules, the role played by ESP in predicting the mutagenic potential and chemical carcinogenesis of molecules is described in this section. Electrostatic effects in DNA can be quite different from those in proteins due to the negative charges of the phosphate backbone of DNA which contributes to an overall negative ESP, as shown for A-, B- and Z-configurations of DNA (red color in Figure 4). The negative charge of DNA attracts

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DISCUSSION

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ASSOCIATED CONTENT

This paper has demonstrated the use of electrostatic potential in predicting the bioactivation and reactivity of molecules in certain biochemical systems, including reactivity with the nucleic acid cytosine. Additionally the complementary employment of important thermodynamic parameters (heat of formation, heat of solvation and ELUMO − EHOMO) in the prediction of reactivity and behavior of metabolites has been discussed. It is worth mentioning that, although compounds that are more energetic in nature frequently lead to the formation of more toxic compounds in vivo (e.g., NAPQI metabolite of acetaminophen), this is not always the case. For example, while the value of the band gap (as a measure of compound reactivity) for the aminoguanidine metabolite of imidacloprid (imidacloprid-NNH2) vs imidacloprid (4.48 and 5.49 eV respectively) would suggest that imidacloprid-NNH2 would be more toxic (or reactive) than the parent molecule, it is important that a holistic “weight of evidence” examination of the heats of formation (as a measure of compound stability; 333.89 and 222.37 kJ/mol respectively), heats of solvation (as a measure of water solubility; −59.04 and −51.98 kJ/mol respectively) and electrostatic potentials (as a means to identify metabolic “hot-spots”) is performed in order to provide a more comprehensive toxicokinetic prediction of the metabolite’s behavior in vivo. In the case of imidacloprid-NNH2 and imidacloprid the heats of formation and solvation indicate that despite the value of the band gap being lower for the metabolite vs the parent, the metabolite is predicted to be less stable, more water-soluble and more prone to nucleophilic attack (e.g., by glutathione) than the parent compound, suggesting that it is likely to be excreted more rapidly in the urine and be further metabolized to other metabolites, including phase II detoxification metabolites. Electrostatic potential is increasingly becoming a routinely used tool in the basic research of molecular behavior in the design and synthesis of potent and safer molecules of medicinal interest, and its application will likely continue to grow with implementation of more sophisticated computational algorithms and improvements in the ease-of-use of computational modeling software. Numerous upgrades have recently been implemented by several of the main software suppliers, including enhanced prediction of NMR and improved chirality assessments, both of which will help to produce more accurate, and thus more meaningful, ESP maps for molecules of interest. Thus, the importance of electrostatic potential in the research of molecular reactivity is anticipated to grow.

Figure 4. Structures and electrostatic potential maps of several conformers of DNA. A−D: 16 base-pair B-DNA duplex shown in longitudinal and side view (PDB: 3BSE). E−H: Left-handed Z-DNA double helix in longitudinal and side view (PDB: 2DCG). I and J: ADNA decamer (PDB: 213D). K and L: A-DNA tetramer (PDB: 1ANA). For the ESP maps the electrostatic potentials are represented by red (negative), green (neutral) and blue (positive).

counterions which help stabilize the tertiary structure of the polymer;131 however positively charged electrophiles are also attracted by the negative ESP which can lead to the formation of highly mutagenic adducts.132−134 The ESP of cytosine is discussed as follows in order to illustrate the application of ESP in the prediction of chemical mutagenicity. Of the four nitrogenous bases of DNA, cytosine was selected for discussion since an abundance of mutagenesis data concerning this base are available in the scientific literature135,136 and thus the predictions can be compared to experimental results. Further applications of ESP, which will include an examination of the 3 remaining DNA bases, in predicting mutagenicity, will be discussed by the author in a future publication. Cytosine (4-aminopyrimidin-2(1H)-one; Figure 1F) is one of the four main bases found in DNA and RNA. In Watson− Crick base pairing, cytosine interacts with guanine via 3 Hbonds. The ESP map for cytosine shows a region of negative potential near both N3 and O8 which provides two Vmin (i.e., regions to which an electrophile is predicted to be most strongly attracted) (Figure 3F): one of these is near N3, where the potential reaches a value of −115.3 kJ/mol, and the other is near O8, with a potential of −148.9 kJ/mol. There is also a much weaker region of negative potential near the amine nitrogen, N7, with a Vmin of −67.1 kJ/mol. From the ESP map, it would be predicted that an electrophile would preferentially

S Supporting Information *

Figure depicting reaction for the N-acetylation of aniline by acetyltransferase to yield N-phenylacetamide (acetanilide). This material is available free of charge via the Internet at http:// pubs.acs.org. 1178



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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Tel: 650-225-7595. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Stephen Gomez, Theresa Reynolds, Donna Dambach, Dolo Diaz, Jane Kenny and Melisa Masuda are thanked for their advice and suggestions in the preparation of this manuscript.

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ABBREVIATIONS USED

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REFERENCES

ACh, acetylcholine; Cys, cysteine; DMPK, drug metabolism and pharmacokinetics; EMA, European Medicines Agency; eV, electronvolts; FDA, U.S. Food and Drug Administration; gluc, glucuronide; GSH, glutathione; hERG, human Ether-à-go-goRelated Gene; HOMO, highest occupied molecular orbital; IC50, half maximal inhibitory concentration; Kd, dissociation constant; LUMO, lowest unoccupied molecular orbital; mercap, mercapturic acid; nAChR, nicotinic acetylcholine receptor; NAPQI, N-acetyl-p-benzoquinoneimine; P, pressure; PDB, Protein Data Bank; (Q)SAR, (quantitative) structure activity relationship; SCF, self-consistent field; T, temperature; ΔHfθ, heat of formation (standard state)

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