Comparative molecular field analysis of polyhalogenated dibenzo-p-dioxins, dibenzofurans, and biphenyls

The aryl hydrocarbon receptor as a model PAS sensor

Proteins containing PER-ARNT-SIM (PAS) domains are commonly associated with environmental adaptation in a variety of organisms. The PAS domain is found in proteins throughout Archaea, Bacteria, and Eukarya and often binds small-molecules, supports protein-protein interactions, and transduces input signals to mediate an adaptive physiological response. Signaling events mediated by PAS sensors can occur through induced phosphorelays or genomic events that are often dependent upon PAS domain interactions. In this perspective, we briefly discuss the diversity of PAS domain containing proteins, with particular emphasis on the prototype member, the aryl hydrocarbon receptor (AHR). This ligand-activated transcription factor acts as a sensor of the chemical environment in humans and many chordates. We conclude with the idea that since mammalian PAS proteins often act through PAS-PAS dimers, undocumented interactions of this type may link biological processes that we currently think of as independent. To support this idea, we present a framework to guide future experiments aimed at fully elucidating the spectrum of PAS-PAS interactions with an eye towards understanding how they might influence environmental sensing in human and wildlife populations.

Pentachlorophenol molecule design with lower bioconcentration through 3D-QSAR associated with molecule docking

A three-dimensional quantitative structure activity relationship (3D-QSAR) model is built by using a comparative molecular similarity indices analysis (CoMSIA) technique with an experimentally determined logarithm of bioconcentration factors (logBCFs) for 36 phenols in fish. Meanwhile, with the pentachlorophenol (PCP) molecule as target molecules, contributions of the molecular fields indicate that the electrostatic fields are the main influences on the bioconcentration of the PCP molecule. Based on the analytical results of CoMSIA contour map of PCP and PCP molecular docking with SOD protease (PDB ID: 4A7T), the R₆ substituent positions of PCP were modified to give seven new modified PCP molecules with low bioconcentration in this paper. The energy barrier calculation of the new modified PCP molecular reaction pathways can infer the order of the substitution reaction s as -SCl > -CH₂Cl > -COCl > -CCl₃ > -CH=CH₂ > -NO₂ > -SH. These calculations, combined with anaerobic biodegradation, ecotoxic effect, and mobility of new modified PCP molecules, enable a new environmentally friendly compound when the Cl at the R₆ position of PCP was replaced with -COCl substituent with low bioconcentration (reduced by 32.89%), ecotoxic effect basically unchanged (increased by 1.37%), anaerobic biodegradation increased (increased by 24.81%), and mobility basically unchanged (reduced by 0.94%) to be designed.

Molecular Polarization Potential as a Molecular Descriptor to Predict Biological Activity of Dioxins and Dibenzofurans

Molecular interaction potential (MIP) maps are a powerful tool to develop structure-activity relationships of a series of compounds. In the present study we have studied the effect of molecular polarization on the description of the toxicity of a series of dioxins and benzofurans using their MIPs. Specifically, we have used principal component analysis in an exploratory way to understand the common structural features that describe the toxicity of these molecules through the analysis of their MIPs and each of their components, i.e.; molecular electrostatic and polarization potentials. Moreover, we have developed a predictive model using PLS that permits to evaluate the toxicity of compounds belonging to any of the families of compounds studied in the present work on the basis of their molecular electrostatic and interaction potentials.

Prediction of binding affinities of PCDDs, PCDFs and PCBs using docking-based Comparative Molecular Similarity Indices Analysis

Polychlorinated Dibenzodioxins (PCDDs), Dibenzofurans (PCDFs) and Biphenyls (PCBs) are industrial compounds or byproducts that can cause toxic effects after binding to aryl hydrocarbon receptor (AhR). But the mechanism about PCDDs, PCDFs and PCBs binding to AhR is unclear. To study the interaction and significant amino acid residues in binding of PCDDs, PCDFs and PCBs to AhR, a docking-based Comparative Molecular Similarity Indices Analysis (CoMSIA) was performed on a set of structurally diverse PCDDs, PCDFs and PCBs with known binding affinities. The docking-based CoMSIA model (non-cross-validated regression coefficient of 0.942 and cross-validated regression coefficient of 0.768) was developed and compared with previous report, the presented docking-based CoMSIA model showed good robustness and predictive performance. The obtained docking conformations and predictive CoMSIA model could provide clues to understand key residues and interactions between receptor and compounds of interest.

Modeling biophysical and biological properties from the characteristics of the molecular electron density, electron localization and delocalization matrices, and the electrostatic potential

The electron density and the electrostatic potential are fundamentally related to the molecular hamiltonian, and hence are the ultimate source of all properties in the ground- and excited-states. The advantages of using molecular descriptors derived from these fundamental scalar fields, both accessible from theory and from experiment, in the formulation of quantitative structure-to-activity and structure-to-property relationships, collectively abbreviated as QSAR, are discussed. A few such descriptors encode for a wide variety of properties including, for example, electronic transition energies, pK(a)'s, rates of ester hydrolysis, NMR chemical shifts, DNA dimers binding energies, π-stacking energies, toxicological indices, cytotoxicities, hepatotoxicities, carcinogenicities, partial molar volumes, partition coefficients (log P), hydrogen bond donor capacities, enzyme-substrate complementarities, bioisosterism, and regularities in the genetic code. Electronic fingerprinting from the topological analysis of the electron density is shown to be comparable and possibly superior to Hammett constants and can be used in conjunction with traditional bulk and liposolubility descriptors to accurately predict biological activities. A new class of descriptors obtained from the quantum theory of atoms in molecules' (QTAIM) localization and delocalization indices and bond properties, cast in matrix format, is shown to quantify transferability and molecular similarity meaningfully. Properties such as "interacting quantum atoms (IQA)" energies which are expressible into an interaction matrix of two body terms (and diagonal one body "self" terms, as IQA energies) can be used in the same manner. The proposed QSAR-type studies based on similarity distances derived from such matrix representatives of molecular structure necessitate extensive investigation before their utility is unequivocally established.

Docking-based three-dimensional quantitative structure-activity relationship (3D-QSAR) predicts binding affinities to aryl hydrocarbon receptor for polychlorinated dibenzodioxins, dibenzofurans, and biphenyls

Polychlorinated dibenzodioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and polychlorinated biphenyls (PCBs) cause toxic effects after binding to an intracellular cytosolic receptor called the aryl hydrocarbon receptor (AhR). Thymic atrophy, weight loss, immunotoxicity, acute lethality, and induction of cytochrome P4501A1 have all been correlated with the binding affinity to AhR. To study the key molecular features for determining binding affinity to AhR, a homology model of AhR ligand-binding domains was developed, a molecular docking approach was employed to obtain docking-based conformations of all molecules in the whole set, and 3-dimensional quantitative structure-activity relationship (3D-QSAR) methodology, namely, comparative molecular field analysis (CoMFA), was applied. A partial least square analysis was performed, and QSAR models were generated for a training set of 59 compounds. The generated QSAR model showed good internal and external statistical reliability, and in a comparison with other reported CoMFA models using different alignment methods, the docking-based CoMFA model showed some advantages.

Acute toxicity and n-octanol/water partition coefficients of substituted thiophenols: determination and QSAR analysis

The acute toxicity (-log EC(50)) to Photobacterium phosphoreum and the n-octanol/water partition coefficient (log K(ow)) of 31 kinds of substituted thiophenols were determined at 298.15K. The -log EC(50) values of studied chemicals are between 4.26 and 5.89. Their log K(ow) values are between 1.34 and 4.02. Comparative molecular field (CoMFA) and comparative molecular similarity index analysis (CoMSIA) models established were successful in predicting -log EC(50) and log K(ow) values of halogenated, methylic, amino and methoxy thiophenols. The size of molecule is the main factor influencing the properties. No correlation was found between the properties and their structural and thermodynamic descriptors from DFT calculation.

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.

QSAR models for predicting toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans using quantum chemical descriptors

By partial least square regression, simple quantitative structure-activity relationship (QSAR) models were developed for the toxicity of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs). Quantum chemical descriptors computed by semi-empirical PM3 method were used as predictor variables. Three optimal QSAR models are developed for 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together, respectively. The cross-validated Q (cum) (2) values for the three QSAR models of 25 PCDDs, 35 PCDFs, 25 PCDDs and 35 PCDFs together are 0.816, 0.629 and 0.603, respectively, indicating good predictive capabilities for the biological toxicity of these PCDD/Fs. The present study suggests that quantum chemical descriptors of POPs indeed govern the binding affinity of these chemicals for aryl hydrocarbon receptors. Moreover, different models contain different molecular descriptors to define respective equation, which suggests that the relationship between molecular structure and the binding affinity of these chemicals for aryl hydrocarbon receptors is complex.

Mammary carcinogen-protein binding potentials: novel and biologically relevant structure-activity relationship model descriptors

Previously, SAR models for carcinogenesis used descriptors that are essentially chemical descriptors. Herein we report the development of models with the cat-SAR expert system using biological descriptors (i.e., ligand-receptor interactions) rat mammary carcinogens. These new descriptors are derived from the virtual screening for ligand-receptor interactions of carcinogens, non-carcinogens, and mammary carcinogens to a set of 5494 target proteins. Leave-one-out validations of the ligand mammary carcinogen-non-carcinogen model had a concordance between experimental and predicted results of 71%, and the mammary carcinogen-non-mammary carcinogen model was 72% concordant. The development of a hybrid fragment-ligand model improved the concordances to 85 and 83%, respectively. In a separate external validation exercise, hybrid fragment-ligand models had concordances of 81 and 76%. Analyses of example rat mammary carcinogens including the food mutagen and oestrogenic compound PhIP, the herbicide atrazine, and the drug indomethacin; the ligand model identified a number of proteins associated with each compound that had previously been referenced in Medline in conjunction with the test chemical and separately with association to breast cancer. This new modelling approach can enhance model predictivity and help bridge the gap between chemical structure and carcinogenic activity by descriptors that are related to biological targets.

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 congener-specific toxicity of polyhalogenated dibenzo-p-dioxins with DFT and WHIM theory

Polyhalogenated dibenzo-p-dioxins (PHDDs) have become the most notorious pollutants in the environment. However, the origin of their congener-specific toxicity is not well understood. For explaining the difference in toxicity between PHDDs as well as their potencies of aryl hydrocarbon hydroxylase (AHH) and 7-ethoxyresorufin O-deethylase (EROD) induction, quantitative structure-activity relationships (QSARs) were constructed through the combined application of DFT (density functional theory) and WHIM (weighted holistic invariant molecular) theory. Results from the QSAR analyses suggest that dispersion interaction along the lateral sites of PHDDs should interpret the vast majority of variance of binding affinities as well as the consequent toxicity. Although electrostatic interaction is comparatively less influential, it should not be negligible. Long-range dispersion interaction is also described in QSARs with minute influence. Quadrupole moment tensor perpendicular to the ring plane, i.e., Q(zz) and its implicated electrostatic interaction plays an important role in the contribution to induction potencies.

Inhibition and induction of human cytochrome P450 enzymes: current status

Variability of drug metabolism, especially that of the most important phase I enzymes or cytochrome P450 (CYP) enzymes, is an important complicating factor in many areas of pharmacology and toxicology, in drug development, preclinical toxicity studies, clinical trials, drug therapy, environmental exposures and risk assessment. These frequently enormous consequences in mind, predictive and pre-emptying measures have been a top priority in both pharmacology and toxicology. This means the development of predictive in vitro approaches. The sound prediction is always based on the firm background of basic research on the phenomena of inhibition and induction and their underlying mechanisms; consequently the description of these aspects is the purpose of this review. We cover both inhibition and induction of CYP enzymes, always keeping in mind the basic mechanisms on which to build predictive and preventive in vitro approaches. Just because validation is an essential part of any in vitro-in vivo extrapolation scenario, we cover also necessary in vivo research and findings in order to provide a proper view to justify in vitro approaches and observations.

DFT study on the structure-toxicity relationship of dioxin compounds using PLS analysis

Density functional theory (DFT) at B3LYP/6-311G** level was employed to optimise the dioxin compounds, i.e., 25 polychlorinated or brominated dibenzo-p-dioxins (PCDDs or PBDDs) and 34 polychlorinated dibenzofurans (PCDFs) involved in this investigation. Three groups of descriptors mainly related to chemical reactivity, molecular overall charge distribution and thermochemical property were calculated. With partial least squares (PLS) analysis and variable importance in the projection (VIP), the least significant descriptors were removed from the quantitative structure-activity relationship (QSAR), which was focused on exploring the influential factors responsible for the variance of binding affinities of dioxins to aryl hydrocarbon receptor (AhR). With better-improved and predictive QSAR (Q(2)(cum) = 0.827), further understanding of the nature of toxicity was available. Both dispersion interaction and electrostatic interaction were considered to be important and together capable of accounting for the most part of the total binding affinities, though the former could make more contribution than the latter. Comparatively, the long-range dispersion interaction should be very small.

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.

3D QSAR studies of dioxins and dioxin-like compounds using CoMFA and CoMSIA

In the present study we have performed comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) on structurally diverse ligands of Ah (dioxin) receptor to explore the physico-chemical requirements for binding. All CoMFA and CoMSIA models have given q(2) value of more than 0.5 and r(2) value of more than 0.84. The predictive ability of the models was validated by an external test set, which gave satisfactory predictive r(2) values. Best predictions were obtained with CoMFA model of combined modified training set (q(2) = 0.631, r(2) = 0.900), giving predictive residual value = 0.02 log unit for the test compound. Addition of CoMSIA study has elucidated the role of hydrophobicity and hydrogen bonding along with the effect of steric and electrostatic properties revealed by CoMFA. We have suggested a model comprises of four structurally different compounds, which offers a good predictability for various ligands. Our QSAR model is consistent with all previously established QSAR models with less structurally diverse ligands.

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.

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.

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).

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.

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.

An alignment-independent versatile structure descriptor for QSAR and QSPR based on the distribution of molecular features

A molecular descriptor based upon a count statistic of the topological distance matrix is described and evaluated for use in QSAR studies. Encoding a molecule is done by computing many selective count statistics (histograms) reflecting the distribution of different atom types and bond types in the molecule. The descriptor was also extended to incorporate geometric features of molecules by weighting the topological distance counts with the geometric distance. It is invariant to both translation and rotation. As a result, it does not require the alignment of the structures under study. The method was applied to several QSAR data sets and performed equally well or better than CoMFA and the EVA descriptor. Compared to the latter two methods, it is computationally easier.

Polarizability fields for use in three-dimensional quantitative structure-activity relationship (3D-QSAR)

Comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) technique, has proven to be a valuable tool in the field of rational drug design. In its native form, CoMFA utilizes a pseudoreceptor, in the form of a regularly spaced lattice of probe atoms, to characterize the steric and electrostatic properties of a series of mutually superimposed molecules. Statistical analyses are performed in an attempt to correlate changes in these shape and charge related fields to observed differences in biological activities at a given target. Graphical analyses of the resulting "negative receptor images" have been demonstrated to provide insight into the physicochemical requirements of novel ligands. Several groups have previously demonstrated the benefits of additional or alternative fields for these types of analyses. In this report, a novel molecular potential field derived from atomistic contributions to molecular polarizability is presented. Comparison studies will be presented using literature data sets and CoMFA models derived from steric, electrostatic, and polarizability fields. The overall conclusion is that molecular polarizability fields derived from semiempirically determined atomic polarizabilities are highly predictive and graphically descriptive supplements to, and perhaps surrogates for, the standard CoMFA steric and electrostatic fields.

Three-dimensional moments of molecular property fields

Descriptors that capture certain three-dimensional molecular features and do not require molecular superposition or alignment for the assignment of molecular similarity have recently been proposed and investigated. Among these, moments of molecular features have been utilized in the QSAR of several molecular series. The present work examines certain formal aspects of the use of moment expansions for which the zero-order moment of a property field is nonvanishing. The first-order term of such moment expansion is then dependent upon the origin of expansion, and it is pointed out that expansion about the molecular centroid is descriptive of first-order differences about the property-field mean. For a hydrophobic property field, this first-order term is just the Eisenberg hydrophobic moment. Second-order moments about the centroid can be written as components of the WHIM covariant matrix. Moment expansions are also performed about the property-field center in analogy with expansions about the center of mass. For such expansion, a set of descriptors consisting of moments of the molecular density and of a molecular hydrophobic property field as well as of related quantities are used in a QSAR of the binding of 74 polyhalogenated aromatic molecules to the Ah cystolic receptor. This QSAR has been named CoMMA2 to distinguish it from CoMMA, for which the zero-order moment of the expansion vanishes.

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)

A new descriptor of molecular structure for use in the derivation of predictive QSAR and QSPR models, electronic eigenvalue (EEVA), is described. This is a modification of the recently proposed EVA approach, but is based on computationally-derived molecular orbital energies instead of vibrational frequencies. Like EVA, it is also invariant as to the alignment of the structures concerned. Its performance has been tested with respect to the Ah receptor binding of PCBs, PCDDs and PCDFs, and its predictive ability has been clearly demonstrated. In particular, it seems to be suitable for 'pure' electronic substituent effects. i.e., for cases in which both hydrophobic and steric factors are of minor importance.

Molecular characteristics of carbaryl, a CYP1A1 gene inducer

Carbaryl belongs to a series of compounds that activate the CYP1A1 gene. This study demonstrates the inability of carbaryl to compete with 2,3,7,8-tetrachlorodibenzo-p-dioxin for binding to the rat aryl hydrocarbon (dioxin) receptor. Structural and physicochemical properties of this insecticide, in relation to the requirements for binding to the aryl hydrocarbon receptor, are described. The crystal structure was determined experimentally using X-ray diffraction. A conformational search using molecular mechanics was performed by means of a Monte-Carlo-type method and a stochastic dynamics simulation. Lipophilicity calculations, log P, and molecular lipophilicity potential are also presented. Common and discriminating properties of carbaryl and aryl hydrocarbon receptor ligands are discussed.

Structural requirements of para-alkylphenols to bind to estrogen receptor

Octyl- and nonylphenols in the environment have been proposed to function as estrogens. To gain insight into their structural essentials in binding to the estrogen receptor, a series of phenols with saturated alkyl groups at the para position, HO-C6H4-CnH2n+1 (n = 0-12), were examined for their ability to displace [3H]17beta-estradiol in the recombinant human estrogen receptor, which was expressed in Sf9 cells using the vaculovirus expression system. All tested para-alkylphenols were found to bind fully to the estrogen receptors in a dose-dependent manner. The interaction of alkylphenols with the receptor became stronger with increase in the number of the alkyl carbons and the activity was maximized with n = 9 of nonylphenol. Phenol (n = 0) exhibited weak but full binding to the receptor, whereas anisole with a protected phenolic hydroxyl group was completely inactive. Also, alkanes such as n-octane, 2,2, 4-trimethylpentane corresponding to tert-octane, and n-nonane exhibited no binding. The results indicate that the binding of para-alkylphenols to the estrogen receptor is due to the effect of covalent bonding of two constituents of the phenol and alkyl groups, which correspond to the A-ring and hydrophobic moiety of the steroid structure, respectively. When alkylphenols were examined for their receptor binding conformation by 1H-NMR measurements and ab initio molecular orbital calculations, it was suggested that nonbranched alkyl groups are in an extended conformation, while branched alkyl groups are in a folded conformation. These results suggest that branched and nonbranched alkyl moieties of alkylphenols interact differently with the lipophilic ligand binding cavity of the estrogen receptor when compared to the binding of 17beta-estradiol.

Three-dimensional quantitative structure-activity relationship of interleukin 1-beta converting enzyme inhibitors: A comparative molecular field analysis study

A three-dimensional quantitative structure-activity relationship (QSAR) study using the comparative molecular field analysis (CoMFA) method was performed on a series of interleukin 1-beta converting enzyme (ICE) inhibitors. The compounds studied have been reported to be time-dependent inhibitors of ICE. This study was performed using 49 compounds, in which the CoMFA models were developed using a training set of 39 compounds. All the compounds were modeled using the X-ray crystal structure of tetrapeptide aldehyde inhibitor/ICE complex. The inhibitor compounds were considered both as neutral species and as P1 carboxylate ionized species. Superimpositions were performed using two alignment rules, namely, an alignment of the structures based on RMS fitting of the backbone heavy atoms of each structure to compound 2 and an alignment based on SYBYL QSAR rigid body field fit of the steric and electrostatic fields of the molecules to the fields of compound 2. Use of LUMO energies or ClogP as additional descriptors in the QSAR table did not improve the significance of the CoMFA models. Steric and electrostatic fields of the inhibitors were found to be the relevant descriptors for structure-activity relationships. The predictive ability of the CoMFA model was evaluated by using a test set of 10 compounds (r2pred as high as 0.859). Further comparison of the coefficient contour maps with the steric and electrostatic properties of the receptor show a high level of compatibility.

Molecular determinants of hormone mimicry: halogenated aromatic hydrocarbon environmental agents

The potential of ostensibly structurally diverse environmental chemicals to modulate endocrine processes in biological systems has been recognized. Difficulty in classifying endocrine system modulators by chemical structure may in large part be due to lack of understanding of mechanisms of action. New developments in understanding nuclear receptor mechanisms of hormone action support a more complex mechanism, possibly involving dimerization/aggregation events leading to multimeric receptor complexes in agonist action. Because of the requirement for high structural specificity in agonist action, it is suggested that most environmental chemicals of concern are likely to function as imperfect hormones with partial agonist-antagonist properties, especially at environmentally realistic concentrations. In the absence of having appropriately placed molecular recognition domains to affect agonist action, partial agonism-antagonism may be associated with favorable low-energy conformational flexibility and complementary receptor protein flexibility. The halogenated aromatic hydrocarbons are of particular concern as hormone mimics since they often have (1) similar molecular recognition factors but in many cases relatively more flexible structures, (2) similar bulk physico-chemical properties controlling uptake and distribution in biological systems, and (3) are relatively more resistant to metabolism and elimination. Some important molecular reactivity properties underlying thyromimetic and estrogenic actions of some of these chemicals are identified and described in terms of structure-activity relationships (SARs). It is proposed that specificity of hormone action in the nucleus could be associated with differential interaction of ligand-bound receptor dimeric forms with other transcription factors specific to the target cell. The small-molecule ligand can be viewed as playing a central, multifunctional role in nuclear receptor action as an organic unmasking and reclustering agent for critical macromolecules. Evidence is discussed in support of a nuclear heterodimerization model for dioxin and related compound action involving a structural transition mechanism. These models with some molecular detail also have utility in understanding the different structural properties of agonists and antagonists. There would appear to be ample opportunities for environmental chemicals to act as antagonists for multiple receptor systems with little more than anchor-ring similarities in structure. The application of three-dimensional quantitative structure-activity (3D QSAR) models incorporating such structural information should be a useful adjunct for identifying endocrine system modulating chemicals. This data has implications for (1) improved drug design, (2) understanding of chemical interaction toxicity, (3) removing undesirable chemicals from our environment, and (4) reducing their chemical release.

Three-dimensional quantitative structure-activity relationships from molecular similarity matrices and genetic neural networks. 2. Applications

Validation of a method that uses a genetic neural network with electrostatic and steric similarity matrices (SM/GNN) to obtain quantitative structure-activity relationships (QSARs) is performed with eight data sets. Biological and physicochemical properties from a broad range of chemical classes are correlated and predicted using this technique. Quantitatively the results compare favorably with the benchmarks obtained by a number of well-established QSAR methods; qualitatively the models are consistent with the published descriptions on the relative contribution of steric and electrostatic factors. The results demonstrate the general utility of this method in deriving QSARs. The implication of the importance of molecular alignment and possible methodological improvements are discussed.

Evaluation of a novel infrared range vibration-based descriptor (EVA) for QSAR studies. 1. General application

A novel molecular descriptor (EVA) based upon calculated infrared range vibrational frequencies is evaluated for use in QSAR studies. The descriptor is invariant to both translation and rotation of the structures concerned. The method was applied to 11 QSAR datasets exhibiting both a range of biological endpoints and various degrees of structural diversity. This study demonstrates that robust QSAR models can be obtained using the EVA descriptor and examines the effect of EVA parameter changes on these models; recommendations are made as to the appropriate choice of parameters. The performance of EVA was found to be comparable in statistical terms to that of CoMFA, despite the fact that EVA does not require the generation of a structural alignment. Models derived using semiempirical (MOPAC AM1 and PM3) and AMBER mechanics calculated normal mode frequencies are compared, with the overall conclusion that the semiempirical methods perform equally well and both outperform the AMBER-based models.

Ligand-based identification of environmental estrogens

Comparative molecular field analysis (CoMFA), a three-dimensional quantitative structure-activity relationship (3D-QSAR) paradigm, was used to examine the estrogen receptor (ER) binding affinities of a series of structurally diverse natural, synthetic, and environmental chemicals of interest. The CoMFA/3D-QSAR model is statistically robust and internally consistent, and successfully illustrates that the overall steric and electrostatic properties of structurally diverse ligands for the estrogen receptor are both necessary and sufficient to describe the binding affinity. The ability of the model to accurately predict the ER binding affinity of an external test set of molecules suggests that structure-based 3D-QSAR models may be used to supplement the process of endocrine disruptor identification through prioritization of novel compounds for bioassay. The general application of this 3D-QSAR model within a toxicological framework is, at present, limited only by the quantity and quality of biological data for relevant biomarkers of toxicity and hormonal responsiveness.

Use of moment of inertia in comparative molecular field analysis to model chromatographic retention of nonpolar solutes

A quantitative structure-retention relationship (QSRR) was developed from chromatographic data on 31 unsubstituted 3-6 ring polycyclic aromatic hydrocarbons (PAHs) using the 3D-QSAR method known as comparative molecular field analysis (CoMFA). The resulting CoMFA model gave an excellent correlation to high-performance liquid chromatography retention data for these PAHs yielding r2 values of 0.947 (conventional) and 0.865 (cross-validated). The steric and electrostatic contributions to the CoMFA model were 100% and 0%, respectively. A unique feature of this study was the use of moment of inertia, I, as a basis for CoMFA alignment of the PAH molecules. The moment of inertia also provided an alternative method for calculating the solute length-to-breadth ratio (L/B), which has been applied in previous QSRR studies as a molecular descriptor for PAH retention. By virtue of its mathematical simplicity and lack of ambiguity, the present derivation of L/B from I offers several advantages over other geometry-based schemes. Finally, Ix was evaluated for use as a molecular descriptor in QSRR regression analysis to predict the log of the retention index (log I) for these PAHs. The correlation with PAH retention was weak when the moment of inertia was considered alone but improved dramatically (r2 = 0.928) when the moment of inertia and connectivity index chi were used in combination as descriptors.

Inhibition of microsomal and mitochondrial Ca2+-sequestration in rat cerebellum by polychlorinated biphenyl mixtures and congeners. Structure-activity relationships

Recent studies from our laboratory indicate that polychlorinated biphenyl (PCB) congeners in vitro perturbed signal transduction mechanisms including cellular Ca2+-homeostasis and protein kinase C translocation. We have now investigated the structure-activity relationship (SAR) of three PCB mixtures, 24 PCB congeners and one dibenzofuran for their effects on microsomal and mitochondrial Ca2+-sequestration in rat cerebellum. Ca2+-sequestration by these intracellular organelles was determined using radioactive 45CaCl2. All three mixtures studied, Aroclor 1016, Aroclor 1254 and Aroclor 1260, were equally potent in inhibiting microsomal and mitochondrial Ca2+-sequestration with IC50 values of 6-8 microM. 1,2,3,7,8-Pentachlorodibenzofuran had no effect on Ca2+-sequestration by these organelles. The SAR among the congeners revealed: (1) congeners with ortho-/meta- or ortho-, para-chlorine substitutions were the most potent in inhibiting microsomal and mitochondrial Ca2+-sequestration (IC50 = 2.4-22.3 microM); (2) congeners with only para- but without ortho-substitutions were not effective in inhibiting Ca2+-sequestration by microsomes and mitochondria; (3) increased chlorination was not related to the effectiveness of these congeners. The present SAR studies indicate that the effects of most PCB congeners in vitro may be related to an interaction at specific sites having preference for low lateral substitution or lateral content (meta- or para) in the presence of ortho-substitution.

Conformational analysis, molecular modeling, and quantitative structure-activity relationship studies of agents for the inhibition of astrocytic chloride transport

Molecular modeling studies were carried out on a series of 1-oxoisoindolines which are pharmacologically active as inhibitors of astrocytic chloride transport. Conformational analysis revealed that the halogen substituent exerted a pronounced steric directing effect on the acid side chain. The 4-substituted analogs apparently provided for the best spatial arrangement of pharamacophoric elements of the molecules. Conventional quantitative structure-activity relationship (QSAR) studies using lipophilic and dipole moment characteristics of the molecules as physical descriptor variables in the regression equation yielded a statistically significant model. Comparative molecular field analysis (CoMFA) was utilized as a three-dimensional QSAR technique to explore changes in the steric and electrostatic fields of the molecules that can account for differences in biological activity values. A highly predictive model was attained which supported the results from the qualitative and conventional quantitative structure-activity relationship analyses. These modeling techniques represent the evolutionary process by which structure-activity methods were employed to aid in the development of novel more potent inhibitors of astrocytic chloride transport.