348
Chem. Res. Toxicol. 2004, 17, 348-356
Structure-Activity Relationships for the Toxicity of Polychlorinated Dibenzofurans: Approach through Density Functional Theory-Based Descriptors Sundaram Arulmozhiraja* and Masatoshi Morita Environmental Chemistry Division, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan Received August 12, 2003
The applicability of various density functional theory (DFT)-based descriptorsschemical softness, electronegativity, and electrophilicity indexsfor quantitative structure-activity relationships (QSARs) was investigated for polychlorinated dibenzofurans (PCDFs). The DFT descriptors were obtained by using the three parameter hybrid density functional, B3LYP, with the 6-311G(d,p) basis set. QSARs were developed relating aryl hydrocarbon receptor (AhR) binding affinities, aryl hydrocarbon hydroxylase and ethoxyresorufin O-deethylase induction potencies of PCDFs with DFT descriptors, hydrophobicity, and steric parameters. These QSARs explain around 75% of variation in AhR binding affinities of PCDFs. Congeners with higher toxicity values had larger softness values. Studies also showed that the most toxic isomer of tetrachlorodibenzofurans (TCDFs) and dibenzo-p-dioxins (TCDDs), respectively, had the largest chemical softness value in its respective group. The results show that DFT descriptors could be used as useful electronic descriptors in QSARs for the prediction of toxicity of PCDFs. Overall, 85 congeners of PCDFs and TCDDs were considered in this study.
Introduction Several members of the broad class of HAHs1 including PCDDs, dibenzofurans (PCDFs), and biphenyls (PCBs) produce a characteristic toxicity syndrome. Unfortunately, the events leading to the toxicity of these compounds are highly complicated in nature, and furthermore, most of them are not understood clearly. Because of the extreme toxicity and the existence of many congeners of each halogenated aromatic, experimental studies on HAHs are rather difficult. In these circumstances, QSAR studies are useful and help to understand biological and toxicological activities of these toxins, to some extent. The prediction of toxicity using QSARs has been the goal of many workers who utilized a variety of models. This goal is alluring but has yet to be achieved satisfactorily. The selection of electronic properties is one of the important steps for QSAR studies (1). Most of the QSAR studies utilized MO energies. In some of the cases, MO parameters were found to be not as good as Hammett constants. However, one has to understand that almost all MO calculations in earlier QSAR works were made either with semiempirical or with lower level ab initio methodologies (1). In addition, many studies have con* To whom correspondence should be addressed. Fax: +81-29-8502574. E-mail:
[email protected]. 1 Abbreviations: HAH, halogenated aromatic hydrocarbon; PCDDs, polychlorinated dibenzo-p-dioxins; PCDFs, polychlorinated dibenzofurans; PCBs, polychlorinated biphenyls; TCDFs, tetrachlorinated dibenzofurans; TCDDs, tetrachlorinated dibenzo-p-dioxins; QSAR, quantitative structure-activity relationship; MO, molecular orbital; AhR, aryl hydrocarbon receptor; DFT, density functional theory; LUMO, lowest unoccupied molecular orbital; IP, ionization potential; EA, electron affinity; η, chemical hardness; S, softness; χ, electronegativity; µ, chemical potential; log P, logarithm of the octanol-water partition coefficient; ω, electrophilicity index.
cluded that much more physical and chemical data are needed in order to maximize our understanding of both toxicological and biological mechanisms via QSARs. Earlier, QSARs have been established for HAHs to model toxicities and different physicochemical properties (2-4). Most of the toxic effects of HAHs are thought to be mediated through a specific protein known as the AhR. After entering the body, halogenated aromatics bind with the AhRs leading to a complex sequence of events that alternate the biological and toxicological effects of HAHs. Thus, the initial binding to the AhR is the key step in the toxic behavior of HAHs (2, 5). Several studies have demonstrated a correlation between the toxicities of halogenated aromatics and their binding affinities (BA) for the AhRs (2, 6, 7). To study the toxicities of HAHs, structure-activity studies have been performed mostly by relating the structure and structure-related parameters of HAHs with their BA for the AhRs (8, 9). The results of previous experimental and theoretical studies have also indicated that the interaction between the HAHs and the AhRs is a charge transfer type and that toxins appear to act as electron acceptors in the charge transfer complex (10-12); our recent investigations have confirmed this fact (13-15). Hence, electrophilicity (EA in the real sense) becomes one of the important electronic properties to quantify the BA of HAHs and it governs the toxicities of HAHs (16-18). EA measures a molecule’s potential to accept precisely one electron from the surrounding donor, while electrophilicity accounts for the electrophilic power of a molecule. The former one is a well-defined quantity while the latter is poorly described. In most QSAR studies on HAHs in the past, the energy of the LUMO (ELUMO), negative of Koopmans’ EA, was considered to be a measure of electrophilicity, but studies
10.1021/tx0300380 CCC: $27.50 © 2004 American Chemical Society Published on Web 02/13/2004
SARs for the Toxicity of Polychlorinated Dibenzofurans
have also shown that other quantities such as the maximum acceptor superdelocalizability, Amax, are superior descriptors of electrophilicity in comparison to ELUMO (19). Furthermore, semiempirical or other lower level theories have been utilized to obtain the ELUMO values. Semiempirical methods approximate certain properties up to a certain level, and it is obvious that variation in ELUMO values of various congeners is very small. The foregoing limitations reveal the need to explore and determine various electronic descriptors, with better accuracy, to make the necessary improvement in the QSAR models. DFT-based reactivity descriptors (20), which play an important role2 in many areas of research, could be considered for this purpose. Chemical hardness (η) and chemical potential (µ) are a few examples of these reactivity descriptors. Chemical potential refers to the measure of escaping tendency of electrons from equilibrium, and it has been identified with the negative of electronegativity (χ ) -µ), while the chemical hardness acts as a resistance to change in electron density. These descriptors were considered in a few studies to study the toxicity of molecules: Schuurmann (21) has used η and χ as descriptors along with many other descriptors in his QSAR analysis for the toxicity of 10 phosphorothionates; Kobayashi et al. (22) showed the correlation between the hardness values of the selected dioxins with their potency of biological activities. Both η and χ were used along with the number of parameters in multivariate physicochemical characterization and QSAR modeling of PCBs, polybrominated diphenyl ethers, and hydroxylated PCBs (23-26). In all of the above studies, semiempirically calculated HOMO (highest occupied molecular orbital) and LUMO energies were used to obtain the η and χ values. However, it has been suggested by Chermette (20) to use what Parr and Yang (27) called “calculational DFT” in place of Hartree-Fock or semiempirical calculations for the computation of various reactivity descriptors introduced by “conceptional DFT”. Hence, a complete study exclusively focusing on the applicability of DFT descriptors, calculated using higher level theory, in quantifying the toxicities of HAHs would be very useful and timely. Therefore, we have undertaken a complete investigation of the usefulness of DFT-based descriptors obtained through exact descriptions (for IP and EA) using calculational DFT to quantify the biological and toxicological effects of PCDFs, as a first step. Various DFT-based descriptors have been obtained for PCDFs, and the calculated values were used in structure-activity relationships to estimate their toxicological and biological activities. By considering the importance of electrophilicity, we have also utilized a newly derived DFT descriptor, electrophilicity index (ω), for this purpose. This investigation would enable us to assess the applicability of various DFT-based reactivity descriptors to toxicological QSARs. A large number of congeners of PCDFs were considered for this study.
Chem. Res. Toxicol., Vol. 17, No. 3, 2004 349
to N, respectively (28, 29):
µ ) (∂E/∂N)v(r) ) -χ
(1)
η ) 1/2(∂2E/∂N2)v(r) ) 1/2(∂µ/∂N)v(r)
(2)
and
Considering the variation in energy when one electron is added or removed from the system (i.e., using a finite difference approximation), one gets
η ) (IP - EA)/2
(3)
µ ) - (IP + EA)/2
(4)
where IP and EA are the vertical IP and EA of the molecules, respectively, in line with the constant v(r) requested by relations 1 and 2. The chemical softness is defined as the reciprocal of hardness:
S ) 1/(2η) ) 1/(IP - EA)
(5)
The newly derived electrophilicity index takes the following equation (30):
ω ) µ2/(2η)
(6)
Computational Details All computations were performed by using Gaussian 98 programs (31). The three parameter hybrid density functional, B3LYP, which includes a mixture of HartreeFock exchange and DFT exchange correlation, was used (32, 33). The geometries of PCDFs were optimized first at the B3LYP/6-31G(d) level of theory followed by frequency calculations, which showed that all of the optimized structures were minima on the potential energy surface. Next, the geometries were optimized by using a triple-ζ type basis set, 6-311G(d,p), which also included polarization functions for all atoms. Finally, single point calculations were made for both cations and anions of all of the selected PCDFs at their respective optimized neutral geometries to calculate the vertical IP and EA values at the B3LYP/6-311G(d,p) level. Then, DFT-based descriptors, S, χ, and ω, were obtained using the relations 1-6. In addition to the 33 congeners of PCDFs whose experimental toxicity parameters are available, all of the TCDFs were also considered for this study. The calculation at the B3LYP/6-311G(d,p) level of theory takes into account electron correlation and d orbitals of chlorine atoms, which are very essential to produce qualitative results for molecules such as these considered in the present study. Additionally, EA values obtained in the present study should be more qualitative than the ones taken as the negative energies of LUMOs, since relaxation effects were absent in the latter approach. DFT descriptors were obtained as the definitions 1-6 requested and were not approximated through ELUMO and EHOMO values.
Theory According to DFT, the chemical potential and chemical hardness for the N-electron molecular system with total energy E and external potential v(r) are defined as the first and second derivatives of the energy with respect 2
For example, see the references in 14 and 20.
Statistical Analyses At first, direct correlations were made between the calculated descriptors with the experimental toxicity parameters: BA for AhRs and aryl hydrocarbon hydroxylase (AHH) and ethoxyresorufin O-deethylase (EROD) induction potencies. Then, the hydrophobicity values of
350
Chem. Res. Toxicol., Vol. 17, No. 3, 2004
Arulmozhiraja and Morita
Results and Discussion
Figure 1. Schematic structure of a PCDF model. Table 1. Global Softness (S in au-1), Electronegativity (χ in eV), and Electrophilicity Index (ω in eV) for Various Congeners of PCDFs along with Their Experimental BA for AhRs, AHH, and EROD Induction Activities [log(1/EC50)] log(1/EC50)a PCDFs
S
χ
2-MCDF 3-MCDF 4-MCDF 2,3-DCDF 2,6-DCDF 2,8-DCDF 1,3,6-TrCDF 1,3,8-TrCDF 2,3,4-TrCDF 2,3,8-TrCDF 2,6,7-TrCDF 1,2,3,6-TCDF 1,2,3,7-TCDF 1,2,4,8-TCDF 2,3,4,6-TCDF 2,3,4,7-TCDF 2,3,4,8-TCDF 2,3,6,8-TCDF 2,3,7,8-TCDF 1,2,3,4,8-PeCDF 1,2,3,7,8-PeCDF 1,2,3,7,9-PeCDF 1,2,4,6,7-PeCDF 1,2,4,6,8-PeCDF 1,2,4,7,8-PeCDF 1,2,4,7,9-PeCDF 1,3,4,7,8-PeCDF 2,3,4,7,8-PeCDF 2,3,4,7,9-PeCDF 1,2,3,4,7,8-HCDF 1,2,3,6,7,8-HCDF 1,2,4,6,7,8-HCDF 2,3,4,6,7,8-HCDF
3.319 3.347 3.289 3.413 3.398 3.398 3.427 3.479 3.442 3.473 3.475 3.497 3.555 3.547 3.505 3.555 3.523 3.522 3.581 3.599 3.617 3.617 3.540 3.607 3.600 3.576 3.628 3.614 3.599 3.671 3.643 3.642 3.641
4.012 3.955 3.996 4.149 4.183 4.233 4.343 4.329 4.310 4.378 4.316 4.461 4.419 4.464 4.467 4.443 4.504 4.554 4.476 4.569 4.580 4.587 4.618 4.617 4.612 4.645 4.558 4.610 4.586 4.687 4.715 4.725 4.736
a
ω
BA
AHH
EROD
1.963 3.553 1.924 4.377 1.930 >3.000 5.000 4.767 2.159 5.326 5.600 5.315 2.185 3.609 4.210 4.200 2.238 3.590 4.403 4.398 2.375 5.357 5.597 5.472 2.396 4.071 4.712 4.520 2.350 4.721 6.821 6.606 2.447 6.000 5.604 5.807 2.379 6.347 5.553 5.504 2.557 6.451
