Electronic eigenvalue (EEVA): a new QSAR/QSPR descriptor for electronic substituent effects based on molecular orbital energies. A QSAR approach to the Ah receptor binding affinity of polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs)

In silico investigation of gas/particle partitioning equilibrium of polybrominated diphenyl ethers (PBDEs)

Polybrominated diphenyl ethers (PBDEs), a group of typical brominated flame retardants (BFRs), have drawn an increasing concern due to their widespread manufacture, usage and disposal around the world and the frequent detection in a variety of environmental media. In the present study, we investigated the molecular mechanism of the partitioning equilibrium of PBDEs between gas and atmospheric particles, and developed a new temperature-dependent predictive model for the gas/particle partition coefficient (K_P) of these chemicals. Quantum chemical computations were implemented at B3LYP/6-31G (d,p) level of theory based on the neutral electronic ground state of PBDE congeners by Gaussian 09 software package. The model performance was assessed by different validation strategies and the application domain was defined by Williams Plot. Mechanism analysis indicated that the interactions of dispersion, electrostatic and hydrogen bond play crucial roles in the partitioning of PBDEs between the two phases. The developed model can be used to estimate the K_P values of PBDEs for which experimental measurements are restricted. Therefore, this work provides an alternative method in a regulatory context of PBDEs.

Modeling adsorption of brominated, chlorinated and mixed bromo/chloro-dibenzo-p-dioxins on C60 fullerene using Nano-QSPR

Many technological implementations in the field of nanotechnology have involved carbon nanomaterials, including fullerenes such as the buckminsterfullerene, C60. The unprecedented properties of such organic nanomaterials (in particular their large surface area) gained extensive attention for their potential use as organic pollutant sorbents. Sorption interactions can be very hazardous and useful at the same time. This work investigates the influence of halogenation by bromine and/or chlorine in dibenzo-p-dioxins on their sorption ability on the C60 fullerene surface. Halogenated dibenzo-p-dioxins (PXDDs, where X = Br or Cl) are ever-present in the environment and accidently produced in many technological processes in only approximately known quantities. If all combinatorial Br and/or Cl dioxin substitution possibilities are present in the environment, the experimental characterization and investigation of sorbent effectiveness is more than difficult. In this work, we have developed a quantitative structure-property relationship (QSPR) model (R2 = 0.998), predicting the adsorption energy [kcal/mol] for 1,701 PXDDs adsorbed on C60 (PXDD@C60). Based on the QSPR model reported herein, we concluded that the lowest energy PXDD@C60 complexes are those that the World Health Organization (WHO) considers to be less dangerous with respect to the aryl hydrocarbon receptor (AhR) toxicity mechanism. Therefore, the effectiveness of fullerenes as sorbent agents may be underestimated as sorption could be less effective for toxic congeners than previously believed.

Estimation ofn-Octanol/Water Partition Coefficients (log KOW) of Polychlorinated Biphenyls by Using Quantum Chemical Descriptors and Partial Least Squares

Then-octanol/water partition coefficient (log KOW) is a useful parameter for the assessment of the environmental fate and impact of xenobiotic trace contaminants. Quantitative structure-activity relationship (QSAR) model for log KOWof polychlorinated biphenyls (PCBs) was analyzed by using the density functional theory at B3LYP/6-31G(d) level and the partial least squares (PLS) method with an optimizing procedure. A PLS model with reasonably good coefficient (R2=0.992) and cross-validation test (Q2cum=0.988) values was obtained. All the predicted values are within the range of±0.3log unit from the observed values. The log KOWvalues of 7 PCBs in the test set predicted by the model are very close to those observed, indicating that this model has high fitting precision and good predictability. The PLS analysis showed that PCBs with larger electronic spatial extent and lower molecular total energy values tend to be more hydrophobic and lipophilic.

A quantitative structure-property relationship analysis of soot-water partition coefficients for persistent organic pollutants

Geometrical optimization and electrostatic potential calculations have been performed at the HF/6-31G level of theory for investigated persistent organic pollutants (POPs). A number of statistically based parameters have been obtained. Relationship between soot-water partition coefficients (logK(SC)) of POPs and the structural descriptors has been established by the multiple linear regression method. The result shows that the quantities derived from electrostatic potential V(s)(-)¯ and V(s,max), together with molecular surface area (A(S)) and the energy of the highest occupied molecular orbital (E(HOMO)) can be well used to express the quantitative relationship between structure and logK(SC) (QSPR) of POPs. Predictive capability of the model has been demonstrated by leave-one-out cross-validation with the cross-validated correlation coefficient of 0.9797. Furthermore, the predictive power of this model was further examined for the external test set with the correlation coefficient of 0.9811 between observed and predicted logK(SC), validating the robustness and good predictive ability of our model. Furthermore, in order to further investigate the applicability of these parameters derived from electrostatic potential in prediction of soot-water partition coefficient for organic pollutants, eleven polycyclic aromatic hydrocarbons (PAHs), eleven polychlorinated biphenyls (PCBs) and nine phenyl urea herbicides (PUHs) from other source have also been studied. The QSPR models established may provide a new powerful method for predicting soot-water partition coefficients (logK(SC)) of organic pollutants.

Docking and 3D-QSAR studies on the Ah receptor binding affinities of polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs)

Polychlorinated biphenyls (PCBs), dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs) binding with the aryl hydrocarbon receptor (AhR) have been correlated with many toxic responses. Hence, it is very necessary to study the interactions between these ligands and AhR for further understanding of the mechanism of toxicity. In this study, an integrated molecular docking and 3D-QSAR approach was employed to investigate the binding interactions between PCBs, PCDDs, PCDFs and AhR. From molecular docking, hydrogen-bonding and hydrophobic interactions were observed to be characteristic interactions between compounds and AhR. Based on the mechanism of interactions, an optimum 3D-QSAR model with good robustness (Q(CUM)(2)=0.907) and predictability (Q(EXT)(2)=0.863) was developed by partial least squares. Additionally, the developed QSAR model indicated that the molecular size, shape profiles, polarizability and electropological states of compounds were related to the binding affinities to AhR.

QSPR/QSAR models for prediction of the physico-chemical properties and biological activity of polychlorinated diphenyl ethers (PCDEs)

Polychlorinated diphenyl ethers (PCDEs) are a group of important persistent organic pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed for all 209 PCDE congeners at the HF/6-31G(*) level of theory. A number of statistically-based parameters have been obtained. Linear relationships between gas-chromatographic relative retention time (RRT), n-octanol/water partition coefficient (log K(OW)), 298 K supercooled liquid vapour pressures (log p(L)), aqueous solubilities (logS(w,L)) and the immunotoxicity values (log ED(50)) of PCDEs and the structural descriptors have been established by multiple linear regression method. The result shows that the quantities derived from electrostatic potential V(s,min), SigmaV(s)(+),V(s,av)(-),Pi,sigma(tot)(2),sigma(+)(2),nu, and N(v)(+), together with the number of the chlorine atoms on the two phenyl rings (N(Cl)) can be well used to express the quantitative structure-property (activity) relationships of PCDEs. Good predictive capabilities have also been demonstrated by leave-group(1/5)-out cross-validation and external test set. Based on these equations, the predicted values have been presented for those PCDE congeners whose experimentally determined physico-chemical properties are unavailable.

Mechanism-based common reactivity pattern (COREPA) modelling of aryl hydrocarbon receptor binding affinity

The aryl hydrocarbon receptor is a ligand-activated transcription factor responsive to both natural and synthetic environmental compounds, with the most potent agonist being 2,3,7,8-tetrachlotrodibenzo-p-dioxin. The aim of this work was to develop a categorical COmmon REactivity PAttern (COREPA)-based structure-activity relationship model for predicting aryl hydrocarbon receptor ligands within different binding ranges. The COREPA analysis suggested two different binding mechanisms called dioxin- and biphenyl-like, respectively. The dioxin-like model predicts a mechanism that requires a favourable interaction with a receptor nucleophilic site in the central part of the ligand and with electrophilic sites at both sides of the principal molecular axis, whereas the biphenyl-like model predicted a stacking-type interaction with the aryl hydrocarbon receptor allowing electron charge transfer from the receptor to the ligand. The current model was also adjusted to predict agonistic/antagonistic properties of chemicals. The mechanism of antagonistic properties was related to the possibility that these chemicals have a localized negative charge at the molecule's axis and ultimately bind with the receptor surface through the electron-donating properties of electron-rich groups. The categorization of chemicals as agonists/antagonists was found to correlate with their gene expression. The highest increase in gene expression was elicited by strong agonists, followed by weak agonists producing lower increases in gene expression, whereas all antagonists (and non-aryl hydrocarbon receptor binders) were found to have no effect on gene expression. However, this relationship was found to be quantitative for the chemicals populating the areas with extreme gene expression values only, leaving a wide fuzzy area where the quantitative relationship was unclear. The total concordance of the derived aryl hydrocarbon receptor binding categorical structure-activity relationship model was 82% whereas the Pearson's coefficient was 0.88.

Anticancer activity and quantitative-structure activity relationship (QSAR) studies of a series of antioxidant/anti-inflammatory aryl-acetic and hydroxamic acids

A series of aryl-acetic acids and hydroxamic acids possessing antioxidant/anti-inflammatory activities were tested for anticancer activity using different cancer cell lines. The compounds have low antitumor activity considering the 1/IC(50) values attained for the cell lines. Compound 5iv presents the best anticancer activity. Moreover, they depict the same activity pattern, suggesting similar mechanisms of action correlated to their antioxidant activities. The obtained results subjected in a QSAR analysis. It seems reasonable to conclude that the same molecular structural features are responsible for the compounds biological activity, these being the electron accepting/donating ability and the molar volume. For all cellular lines (HT-29, A-549 and OAW-42) log 1/IC(50) exhibits a reasonable correlation with a two parameters relationship in which the Esp-min and D term are present. Apart from Esp-min the other descriptor found important for anticancer activity is the molar volume (MgVol). The QSAR analyses did not indicate any role for lipophilicity Electrostatic potential, dipole moment and the bulk, primarily affect the biological response.

QSARs for the toxicity of polychlorinated dibenzofurans through DFT-calculated descriptors of polarizabilities, hyperpolarizabilities and hyper-order electric moments

DFT-B3LYP method with 6-31G(**) basis set was employed to fully optimize the electronic structures of 135 polychlorinated dibenzofurans and parent compound, namely dibenzofuran. It was demonstrated that polarizability anisotropy and mean polarizability could change sensitively and systematically with chlorine number and substitution pattern. And new quantitative structure-activity relationships (QSARs) focused on the binding affinities of aryl hydrocarbon receptor (AhR), aryl hydrocarbon hydroxylase (AHH) and 7-ethoxyresorufin O-deethylase (EROD) induction potencies of PCDFs were developed. It was concluded that polarizability anisotropy in conjunction with hyperpolarizabilties and hyper-order electric moments, e.g. octupole moments could well interpret the variation of toxicity of different congeners and dispersion interaction should be the leading form among various interactions. Although the terms of hyperpolarizabilities and hyper-order electric moments were not the same significant ones as polarizability anisotropy, the long-range interactions characterized by them should not be ignored in explaining the toxicity.

Semi-empirical topological method for prediction of the gas chromatographic relative retention times of Polybrominated Diphenyl Ethers (PBDEs)

Quantitative structure-retention relationship (QSRR) studies have proved to be a valuable approach in the prediction of the gas chromatographic relative retention time (GC-RRT) of organic chemicals. Polybrominated diphenyl ether (PBDE) congeners are now ubiquitous environmental pollutants. Of the 209 possible PBDE congeners, 126 have been synthesized and their retention-time data on seven different stationary phases has been determined [Korytár et al.:J Chromatography A 1065:239-249, (2005)]. To estimate and predict the GC-RRT values of all 209 PBDEs on different stationary phases, 17 molecular descriptors from the semi-experience algorithm in MOPAC program and the topological structures of PBDE molecules were calculated. By means of the VSMP (variable selection and modeling based on prediction) program [Liu et al.:J Chem Inf Comput Sci 43:964-969, (2003)], six optimal descriptors were selected to develop a QSRR model for the prediction of GC-RRT of PBDE. The descriptors contain some energy information (such as the energy of the lowest unoccupied molecular orbital and highest occupied molecular orbital) and topological information (the number of ortho-, meta-, and para- substituted bromine atoms) as well as the molecular weight (lnM (W)). All the models developed were cross-validated using leave-one-out (LOO). For seven GC stationary phases, the estimated correlation coefficients (r(2)) are all more than 0.985 but for the column CP-Sil 19 (r(2) = 0.9392) and LOO-validated correlation coefficients (q(2)) all more than 0.985 but for the column CP-Sil 19 (q(2) = 0.9345).

QSPR/QSAR models for prediction of the physicochemical properties and biological activity of polybrominated diphenyl ethers

Polybrominated diphenyl ethers (PBDEs) are a group of important persistent organic pollutants. In the present study, geometrical optimization and electrostatic potential calculations have been performed for all 209 PBDE congeners at the HF/6-31G level of theory. A number of statistically-based parameters have been obtained. Linear relationships between gas-chromatographic relative retention time (RRT), n-octanol/air partition coefficient (lgK(OA)), 298 K supercooled liquid vapour pressures (lgp(L)), Henry's law constant (lgH) and Ah receptor binding affinity (-lgRBA) of PBDEs and the structural descriptors have been established by multiple regression method. The result shows that the quantities derived from electrostatic potential V(s,max),V(s,min),Pi, Sigma V+(S), V-(S) , nu, sigma 2(tot), and N-(v), together with the molecular volume (Vmc) can be well used to express the quantitative structure-property relationships of PBDEs, which proves the general applicability of this parameter set to a great extent. Good predictive capabilities have also been demonstrated. Based on these equations, the predicted values have been presented for those PBDE congeners whose experimentally determined physicochemical properties are unavailable. The QSAR model for the Ah receptor binding affinity is relatively poor, which can be ascribed to the complexity of factors which affect biological activity and the limitations of the present parameter set in describing steric characters of the molecule.

Formation of trichlorinated dibenzo-p-dioxins from 2,4-dichlorophenol and 2,4,5-trichlorophenolate: a theoretical study

The reaction of the 2,4,5-trichlorophenolate anion with 2,4-dichlorophenol to afford trichlorinated dibenzo-p-dioxins (T3CDDs) is investigated at the B3LYP/6-31+G(d) and B3LYP/6-311+G(3df,2p)//B3LYP/6-31+G(d)+ZPVE(B3LYP/6-31+G(d)) levels of theory. The first stage of the process corresponds to the formation of a predioxin, which can evolve through four different routes. Two of them lead directly to the products 2,3,7-T3CDD and 1,3,8-T3CDD, and the other two afford different predioxin-type intermediates, which in turn can evolve through all or some of the four routes to give new predioxins or T3CDD. Consequently, the theoretical results obtained show plainly the complex chemistry implied in the formation of dioxins from chlorophenols via anionic mechanisms by disclosing all the critical structures through which the system evolves, thus allowing assessment of the viability of the different mechanistic routes and the accessible products. The statistical thermodynamics treatment at 1 atm and 298.15, 600, 900, and 1200 K indicates that at higher temperatures, the Gibbs energy barrier for the formation of the initial predioxin is clearly the rate-determining step for the whole process, but at lower temperatures the Gibbs energy barrier for this step is similar to those for its evolution into 2,3,7-T3CDD. This result is in contrast with previous proposals that the closure of the central ring is the rate-limiting step. Finally, according to our results the rate constant for the formation of polychlorinated dibenzo-p-dioxins increases with the temperature, in agreement with the experimental observation that the conversion of trichlorophenols increases when going from 600 to 900 K in the gas phase in the absence of catalysts, and with DFT molecular dynamics results.

SAR and QSAR modeling of endocrine disruptors

A number of xenobiotics by mimicking natural hormones can disrupt crucial functions in wildlife and humans. These chemicals termed endocrine disruptors are able to exert adverse effects through a variety of mechanisms. Fortunately, there is a growing interest in the study of these structurally diverse chemicals mainly through research programs based on in vitro and in vivo experimentations but also by means of SAR and QSAR models. The goal of our study was to retrieve from the literature all the papers dealing with structure-activity models on endocrine disruptor xenobiotics. A critical analysis of these models was made focusing our attention on the quality of the biological data, the significance of the molecular descriptors and the validity of the statistical tools used for deriving the models. The predictive power and domain of application of these models were also discussed.

Quantitative structure-activity relationship for prediction of the toxicity of polybrominated diphenyl ether (PBDE) congeners

Levels of Polybrominated diphenyl ether (PBDEs) are increasing in the environment due to their use as flame retardants. The similarities of structure to polychlorinated biphenyl (PCB) congeners suggest that they may share similar toxicological properties, such as hepatic enzyme induction. In this work, quantitative structure-activity relationship (QSAR) models were constructed based on 406 descriptors for the logarithm of toxicology index (aryl hydrocarbon receptor relative binding affinities, AhR, I) of 18 PBDE congeners. The method used for building model is the Heuristic method, which is included in comprehensive descriptors for structural and statistical analysis (CODESSA) software. The best regression model involved four descriptors, which were related to the conformational changes, atomic reactivity, molecular electrostatic field, and non-uniformity of mass distribution in a molecule of PBDEs, etc. The high square of the correlation coefficient R(2)(0.903) showed the model was satisfactory.

Determination of in vitro relative potency (REP) values for mono-ortho polychlorinated biphenyls after purification with active charcoal

The TEF system for dioxin-like compounds has included assignment of TEF values for mono-ortho polychlorinated biphenyls (MO-PCBs). Small traces of aryl hydrocarbon receptor (AhR)-active impurities could result in artifactually higher relative potency (REP) values. MO-PCBs -105, -118, -156, and -167 were purified on an active charcoal column to remove AhR agonists that could be present as impurities. Activation or inhibition of AhR-dependent gene expression by purified MO-PCBs was studied in stably transfected cell lines (H1G1.1c3 mouse, H4G1.1c2 rat hepatoma), containing an AhR-responsive (AhR-EGFP) reporter gene. In addition, EROD activity was used as marker for CYP1A1 activity in these cell lines. MO-PCBs -105, -118, -156 induced AhR-EGFP expression in both rodent cell lines, with PCB-156 (10microM) being most effectively; inducing gene expression to approximately 27% of TCDD (mouse cells) and 62.5+/-3.4% (rat cells) of TCDD. This concurred with increased EROD activity in both cell lines to maxima of 20.5+/-1.5% and 68+/-3.2% of TCDD, respectively. No induction was observed for PCB-167. In the H1G1.1c3 mouse cells, PCB-105, -118 and -156 (10microM) significantly reduced TCDD-induced AhR-EGFP expression to 50.9+/-2.9%, 58.3+/-2.2% and 70.8+/-1.3% of TCDD. Reduced EROD activity was also observed, of 39.3+/-2.8%, 67+/-5% and 48.3+/-4% compared to TCDD. PCB-167 did not result in significant reduction. In rat cells, only PCB-156 resulted in significant decrease in TCDD-induced AhR-EGFP expression of 35%, suggesting species differences play a role. Our results suggest that purification of MO-PCBs is an essential step in determining accurate REP values, and could very likely lead to lower TEF values than those presently assigned by the WHO.

Interaction of diuron and related substituted phenylureas with the Ah receptor pathway

The aryl hydrocarbon receptor (AhR) is a ligand-dependent transcription factor that mediates many of the biological and toxicological actions of structurally diverse chemicals, including the ubiquitous environmental contaminant 2,3,7,8-tetrachlorodibenzo-p-dioxin. Here, we have examined the ability of diuron, a widely used herbicide, and several structurally related substituted phenylureas to bind to and activate/inhibit the AhR and AhR signal transduction. Diuron induced CYP1A1 mRNA levels in mouse hepatoma (Hepa1c1c7) cells and AhR-dependent luciferase reporter gene expression in stably transfected mouse, rat, guinea pig, and human cell lines. In addition, ligand binding and gel retardation analysis demonstrated the ability of diuron to competitively bind to and stimulate AhR transformation and DNA binding in vitro and in intact cells. Several structurally related substituted phenylureas competitively bound to the guinea pig hepatic cytosolic AhR, inhibited 2,3,7,8-tetrachlorodibenzo-p-dioxin-induced AhR-dependent luciferase reporter gene expression in a species-specific manner and stimulated AhR transformation and DNA binding, consistent with their role as partial AhR agonists. These results demonstrate not only that diuron and related substituted phenylureas are AhR ligands but also that exposure to these chemicals could induce/inhibit AhR-dependent biological effects.

Alternative QSAR models for selected estradiol and cytochrome P450 ligands: comparison between classical, spectroscopic, CoMFA and GRID/GOLPE methods

The performance of the spectroscopic EVA (eigenvalue) and EEVA (electronic eigenvalue) methods was tested with data sets applying coumarin 7-hydroxylation inhibitors (28 compounds) for cytochrome P450 mouse CYP2A5 and human CYP2A6 enzymes and 11ss-, 16a-, and 17a-substituted estradiol derivatives (30 compounds) for the lamb uterine estrogen receptor, and compared with the performance of the classical Hansch-type, CoMFA and GRID/GOLPE methods. Besides the internal predictability, the external predictability of the models was tested with several randomized training and test sets to ensure the validity and reliability of the models. Partial least squares (PLS) regression was employed as a general statistical tool with the EVA and EEVA methods. Some supplementary models were also built using only one PLS component with McGowan's volumes (MgVol and MgVol(2)) as additional descriptors and employing multiple linear regression (MLR) as the modelling tool. In general, both the internal and external performance of the EVA model, and more especially the EEVA model, with one PLS component and MgVol parameters was satisfactory, being either as good as or clearly better than that of the Hansch-type, CoMFA and GRID/GOLPE models.

Improving the performance of SOMFA by use of standard multivariate methods

Self-Organizing Molecular Field Analysis (SOMFA) comes with a built-in regression methodology, the Self-Organizing Regression (SOR), instead of relying on external methods such as PLS. In this article we present a proof of the equivalence between SOR and SIMPLS with one principal component. Thus, the modest performance of SOMFA on complex datasets can be primarily attributed to the low performance of the SOMFA regression methodology. A multi-component extension of the original SOR methodology (MCSOR) is introduced, and the performances of SOR, MCSOR and SIMPLS are compared using several datasets. The results indicate that in general the performance of SOMFA models is greatly improved if SOR is replaced with a more sophisticated regression method. The results obtained for the Cramer (CBG) dataset further underline the fact that it is a very poor benchmark dataset and should not be used to evaluate the performance of QSAR techniques.

Comparing the Performance of FLUFF-BALL to SEAL-CoMFA with a Large Diverse Estrogen Data Set: From Relevant Superpositions to Solid Predictions

In this work a template-based molecular mechanistic superposition algorithm FLUFF (Flexible Ligand Unified Force Field) and an accompanying local coordinate QSAR method BALL (Boundless Adaptive Localized Ligand) are validated against the benchmark techniques SEAL (Steric and Electrostatic Alignment) and CoMFA (Comparative Molecular Field Analysis) using a large diverse set of 245 xenoestrogens extracted from the EDKB (Endocrine Disruptor Knowledge Base) maintained by NCTR (National Centre for Toxicological Research). The results indicate that FLUFF is capable of generating relevant superpositions not only for BALL but also for CoMFA, as both techniques give predictive QSAR models. When the BALL and CoMFA methods are compared, it is clear that the BALL algorithm met or even exceeded the results of the standard 3D-QSAR method CoMFA using alignments either from the tailor-made superposition technique FLUFF or the reference method SEAL. The FLUFF-BALL method can be easily automated, and it is computationally light, providing thus a good computational "sieve" capable of fast screening of large molecule libraries.

QSAR study on the Ah receptor-binding affinities of polyhalogenated dibenzo-p-dioxins using net atomic-charge descriptors and a radial basis neural network

A radial basis function neural network (RBFN) has been used to correlate Ah receptor-binding affinities of polychlorinated, polybrominated, and polychlorinated-brominated dibenzo-p-dioxins with molecular weight and eight net atomic charge descriptors. Support vector machine (SVM) and partial least square (PLS) regression models based on the same data set have also been built. Leave-one-out cross-validation was used to train the RBFN, SVM, and PLS models. For predicting Ah receptor-binding affinities, the RBFN model with a squared cross-validation correlation coefficient (q2) of 0.8818 outperforms the SVM and PLS models and also compares favorably with any other reported quantitative structure-activity relationship model based on the same activity data set. The significance of the RBFN model with net atomic charges as descriptors suggests that electrostatic and dispersion-type interactions play important roles in governing the Ah receptor binding of polychlorinated, polybrominated, and polychlorinated-brominated dibenzo-p-dioxins.

Quantitative structure-activity relationship models for prediction of the toxicity of polybrominated diphenyl ether congeners

Levels of polybrominated diphenyl ethers (PBDEs) are increasing in the environment and may cause long-term health problems in humans. The similarity in the chemical structures of PBDEs and other halogenated aromatic pollutants hints on the possibility that they might share similar toxicological effects. In this work, three-dimensional quantitative structure activity relationships (3-D-QSAR) models, using comparative molecular field analysis (CoMFA) and comparative similarity indices analysis (CoMSIA), were built based on calculated structural indices and a reported experimental toxicology index (aryl hydrocarbon receptor relative binding affinities, RBA) of 18 PBDEs congeners, to determine the factors required for the RBA of these PBDEs. After performing leave-one-out cross-validation, satisfactory results were obtained with cross-validation O2 and R2 values of 0.580 and 0.995 by the CoMFA model and 0.680 and 0.982 by the CoMSIA model, respectively. The results showed clearly that the nonplanar conformations of PBDEs result in the lowest energy level and that the electrostatic index was the main factor reflecting the RBA of PBDEs. The two QSAR models were then used to predict the RBA value of 46 PBDEs for which experimental values are unavailable at present.

Ligand intramolecular motions in ligand-protein interaction: ALPHA, a novel dynamic descriptor and a QSAR study with extended steroid benchmark dataset

The role of intramolecular motions in ligand-macromolecule interactions has been explored by developing and validating ALPHA, a novel QSAR (quantitative structure-activity relationship) descriptor. It is based on the spectral exponents (alpha), which measure the degree of 1/f alpha noise of coordinate fluctuations in molecular dynamics (MD) simulations. ALPHA is the first truly 'dynamic' QSAR descriptor, i.e., it can be derived directly from an MD trajectory. The performance of ALPHA was tested in detail employing the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids, supplemented with 11 steroids as an external test set. The only fair (42-50%) correlations of ALPHA with static 3D and electronic descriptors mean that ALPHA forms an independent molecular property. Furthermore, inclusion of ALPHA in the SOMFA/ESP model improves the correlation coefficient from 0.86 to 0.91, and /delta/ave from 0.46 to 0.36 for the benchmark dataset. The predictive ability of ALPHA can be interpreted as indirect evidence of the dynamic contribution to ligand-macromolecule interactions. The physical background of ALPHA is discussed and the importance of molecular motions for biological activity is anticipated.

Synthesis and QSAR study of the anticancer activity of some novel indane carbocyclic nucleosides

A set of 14 indane carbocyclic nucleosides were synthesized and experimentally assayed for their inhibitory effects in the proliferation of murine leukemia (L1210/0) and human T-lymphocyte (Molt4/C8, CEM/0) cells. The compounds have promising inhibitory activity judging from the IC(50) values obtained for all these cellular lines. Multiple linear regression analysis was then applied to build up consistent QSAR models based on quantum mechanics-derived molecular descriptors. The derived models reproduce well the experimental data of both three cells (r(2) >/=0.90), display a good predictive power and are, above all, easily interpretable. They show that frontier-orbital energies and hydrophobicity are mainly responsible for the activity of the synthesized compounds and also, suggest similar mechanisms of action. The final QSAR-models involve only two descriptors: the lowest unoccupied molecular orbital energy and the solvent accessible-hydrophobic surface area, but describe a sound correlation between predicted and experimental activity data (r(2)=0.931, r(2)=0.936 and r(2)=0.931 for the cells L1210/0, Molt4/C8 and CEM/0, respectively).

Spectroscopic QSAR Methods and Self-Organizing Molecular Field Analysis for Relating Molecular Structure and Estrogenic Activity

The performance of three "spectroscopic" quantitative structure-activity relationship (QSAR) methods (eigenvalue (EVA), electronic eigenvalue (EEVA), and comparative spectra analysis (CoSA)) for relating molecular structure and estrogenic activity are critically evaluated. The methods were tested with respect to the relative binding affinities (RBA) of a diverse set of 36 estrogens previously examined in detail by the comparative molecular field analysis method. The CoSA method with (13)C chemical shifts appears to provide a predictive QSAR model for this data set. EEVA (i.e., molecular orbital energy in this context) is a borderline case, whereas the performances of EVA (i.e., vibrational normal mode) and CoSA with (1)H shifts are substandard and only semiquantitative. The CoSA method with (13)C chemical shifts provides an alternative and supplement to conventional 3D QSAR methods for rationalizing and predicting the estrogenic activity of molecules. If CoSA is to be applied to large data sets, however, it is desirable that the chemical shifts are available from common databases or, alternatively, that they can be estimated with sufficient accuracy using fast prediction schemes. Calculations of NMR chemical shifts by quantum mechanical methods, as in this case study, seem to be too time-consuming at this moment, but the situation is changing rapidly. An inherent shortcoming common to all spectroscopic QSAR methods is that they cannot take the chirality of molecules into account, at least as formulated at present. Moreover, the symmetry of molecules may cause additional problems. There are three pairs of enantiomers and nine symmetric (C(2) or C(2)(v)) molecules present in the data set, so that the predictive ability of full 3D QSAR methods is expected to be better than that of spectroscopic methods. This is demonstrated with SOMFA (self-organizing molecular field analysis). In general, the use of external test sets with randomized data is encouraged as a validation tool in QSAR studies.

Activation of the aryl hydrocarbon receptor by structurally diverse exogenous and endogenous chemicals

The induction of expression of genes for xenobiotic metabolizing enzymes in response to chemical insult is an adaptive response found in most organisms. In vertebrates, the AhR is one of several chemical/ligand-dependent intracellular receptors that can stimulate gene transcription in response to xenobiotics. The ability of the AhR to bind and be activated by a range of structurally divergent chemicals suggests that the AhR contains a rather promiscuous ligand binding site. In addition to synthetic and environmental chemicals, numerous naturally occurring dietary and endogenous AhR ligands have also been identified. In this review, we describe evidence for the structural promiscuity of AhR ligand binding and discuss the current state of knowledge with regards to the activation of the AhR signaling pathway by naturally occurring exogenous and endogenous ligands.

FLUFF-BALL, A Template-Based Grid-Independent Superposition and QSAR Technique: Validation Using a Benchmark Steroid Data Set

The Flexible Ligand Unified Force Field (FLUFF) is a molecular mechanistic superposition algorithm utilizing a template structure, on top of which the ligand(s) are superimposed. FLUFF enables a flexible semiautomatic superimposition in which the ligand and the template are allowed to seek the best common conformation, which can then be used to predict the biological activity by Boundless Adaptive Localized Ligand (BALL). In BALL, the similarity of the electrostatic and van der Waals volumes of the template and ligand is evaluated using the template-based coordinate system which makes the FLUFF-BALL invariant as to the rotations and translations of the global coordinate system. When tested using the CBG (corticosteroid binding globulin) affinities of 31 benchmark steroids, the FLUFF-BALL technique produced results comparable to standard 3D-QSAR methods. Supplementary test calculations were performed with five additional data sets. Due to its high level of automation and high throughput, the FLUFF-BALL is highly suitable for use in drug design and in scanning of large molecular libraries.

Molecular hologram derived quantitative structure-property relationships to predict physico-chemical properties of polychlorinated biphenyls

Polychlorinated biphenyls (PCBs) congeners with various degrees of chlorination and substitution patterns are among the most widespread and persistent man-made organic pollutants. They are toxic, lipophilic and tend to be bioaccumulated. The knowledge of the physico-chemical properties is very useful to explain the environmental behavior of PCBs and to perform an exposure assessment. In this paper, we have used a new molecular representation, the molecular hologram, to generate quantitative structure-property relationship models to predict the physico-chemical properties of biphenyl and all of its chlorinated congeners. The investigated properties include 1-octanol/water partition coefficient (logK(ow)), aqueous solubility (-logS(w)), aqueous activity coefficient (-logY(w)), Total molecular surface area, Henry's law constant (logH). The results show that this new quantitative structure-activity relationship approach presents highly predictive models for important physico-chemical properties of PCBs.

Ligand binding and activation of the Ah receptor

The Ah receptor (AhR) is a ligand-dependent transcription factor that can be activated by structurally diverse synthetic and naturally-occurring chemicals. Although a significant amount of information is available with respect to the planar aromatic hydrocarbon AhR ligands, the actual spectrum of chemicals that can bind to and activate the AhR is only now being elucidated. In addition, the lack of information regarding the actual three-dimensional structure of the AhR ligand binding domain (LBD) has hindered detailed analysis of the molecular mechanisms by which these ligands bind to and active AhR signal transduction. In this review we describe the current state of knowledge with respect to naturally occurring AhR ligands and present and discuss the first theoretical model of the AhR LBD based on crystal structures of homologous PAS family members.

Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 2. Application of the novel 3D molecular descriptors to QSAR/QSPR studies

In a previous paper the theory of the new molecular descriptors called GETAWAY (GEometry, Topology, and Atom-Weights AssemblY) was explained. These descriptors have been proposed with the aim of matching 3D-molecular geometry, atom relatedness, and chemical information. In this paper prediction ability in structure-property correlations of GETAWAY descriptors has been tested extensively by analyzing the regressions of these descriptors for selected properties of some reference compound classes. Moreover, the general performance of the new descriptors in QSAR/QSPR has been evaluated with respect to other well-known sets of molecular descriptors.

Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set

A novel electronic eigenvalue (EEVA) descriptor of molecular structure for use in the derivation of predictive QSAR/QSPR models is described. Like other spectroscopic QSAR/QSPR descriptors, EEVA is also invariant as to the alignment of the structures concerned. Its performance was tested with respect to the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids. It appeared that the electronic structure of the steroids, i.e., the "spectra" derived from molecular orbital energies, is directly related to the CBG binding affinities. The predictive ability of EEVA is compared to other QSAR approaches, and its performance is discussed in the context of the Hammett equation. The good performance of EEVA is an indication of the essential quantum mechanical nature of QSAR. The EEVA method is a supplement to conventional 3D QSAR methods, which employ fields or surface properties derived from Coulombic and van der Waals interactions.