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O G Mekenyan

Publications and source records attributed to O G Mekenyan.

9 recordsLinked to original sources
PubMed2002

Non-linear modeling of bioconcentration using partition coefficients for narcotic chemicals.

Bioconcentration factors (BCFs) have traditionally been used to describe the tendency of chemicals to concentrate in aquatic organisms. A reexamination of the log-log QSAR between the BCF and Kow for non-congener narcotic chemicals is presented on the basis of recommended data for fish. The model is extended to give a simple correlation between BCF and the toxicity of highly, moderately and weakly hydrophilic chemicals. For the first time, in this study an equation for calculating BCF was applied in a QSAR model for predicting the acute toxicity of chemicals to aquatic organisms.

Animals↗
PubMed2002

Charged partial surface area (CPSA) descriptors QSAR applications.

The charged partial surface area, or CPSA descriptors were originally designed for use in structure-physical relationship studies to capture information about the features of molecules responsible for polar intermolecular interactions. Since their development, they have found applications in a broad variety of both structure-property and structure-activity relationship studies. In the present work, the CPSA descriptors are examined in more detail, evaluating their characteristics with regard to conformational dependence, sources of partial atomic charges, utility of whole molecule and substructure varieties, and the inclusion or exclusion of explicit hydrogens. Additionally, an examination of the physical interpretation that can be derived from structure-activity relationships that incorporate the CPSA descriptors is made. Most recently, the CPSA descriptors have been found to be practically useful in the study of acute aquatic toxicity where they appear to provide an alternative to LUMO energy level measures for describing global and local electrophilicity in cases of non-covalent molecular interactions. A second example illustrates the ability of the CPSA descriptors to discriminate agonists and antagonists among compounds that bind strongly at the estrogen receptor. While measures of global and local nucleophilicity and interatomic distances are required to explain receptor binding, volumetric parameters, such as CPSAs, were found to be necessary to provide separation between reactivity patterns for agonists and antagonists, all having high binding affinity to estrogen receptor.

Electricity↗
PubMed2002

Reactivity profiles of ligands of mammalian retinoic acid receptors: a preliminary COREPA analysis.

Retinoic acid and associated derivatives comprise a class of endogenous hormones that bind to and activate different families of retinoic acid receptors (RARs, RXRs), and control many aspects of vertebrate development. Identification of potential RAR and RXR ligands is of interest both from a pharmaceutical and toxicological perspective. The recently developed COREPA (COmmon REactivity PAttern) algorithm was used to establish reactivity profiles for a limited data set of retinoid receptor ligands in terms of activation of three RARs (alpha, beta, gamma) and an RXR (alpha). Conformational analysis of a training set of retinoids and related analogues in terms of thermodynamic stability of conformers and rotational barriers showed that these chemicals tend to be quite flexible. This flexibility, and the observation that relatively small energy differences between conformers can result in significant variations in electronic structure, highlighted the necessity of considering all energetically reasonable conformers in defining common reactivity profiles. The derived reactivity patterns for three different subclasses of the RAR (alpha, beta, gamma) were similar in terms of their global electrophilicity (nucleophilicity) and steric parameters. However, the profile of active chemicals with respect to interaction with the RXR-alpha differed qualitatively from that of the RARs. Variations in reactivity profiles for the RAR versus RXR families would be consistent with established differences in their affinity for endogenous retinoids, likely reflecting functional differences in the receptors.

Algorithms↗
PubMed2000

A computationally based identification algorithm for estrogen receptor ligands: part 2. Evaluation of a hERalpha binding affinity model.

The objective of this study was to evaluate the capability of an expert system described in the previous paper (S. Bradbury et al., Toxicol. Sci. 58, 253-269) to identify the potential for chemicals to act as ligands of mammalian estrogen receptors (ERs). The basis of the expert system was a structure activity relationship (SAR) model, based on relative binding affinity (RBA) values for steroidal and nonsteroidal chemicals derived from human ERalpha (hERalpha) competitive binding assays. The expert system enables categorization of chemicals into (RBA ranges of < 0.1, 0.1 to 1, 1 to 10, 10 to 100, and >150% relative to 17ss-estradiol. In the current analysis, the algorithm was evaluated with respect to predicting RBAs of chemicals assayed with ERs from MCF7 cells, and mouse and rat uterine preparations. The best correspondence between predicted and observed RBA ranges was obtained with MCF7 cells. The agreement between predictions from the expert system and data from binding assays with mouse and rat ER(s) were less reliable, especially for chemicals with RBAs less than 10%. Prediction errors often were false positives, i.e., predictions of greater than observed RBA values. While discrepancies were likely due, in part, to species-specific variations in ER structure and ligand binding affinity, a systematic bias in structural characteristics of chemicals in the hERalpha training set, compared to the rodent evaluation data sets, also contributed to prediction errors. False-positive predictions were typically associated with ligands that had shielded electronegative sites. Ligands with these structural characteristics were not well represented in the training set used to derive the expert system. Inclusion of a shielding criterion into the original expert system significantly increased the accuracy of RBA predictions. With this additional structural requirement, 38 of 46 compounds with measured RBA values greater than 10% in hERalpha, MCF7, and rodent uterine preparations were correctly categorized. Of the remaining 129 compounds in the combined data sets, RBA values for 65 compounds were correctly predicted, with 47 of the incorrect predictions being false positives. Based upon this exploratory analysis, the modeling approach, combined with a high-quality training set of RBA values derived from a diverse set of chemical structures, could provide a credible tool for prioritizing chemicals with moderate to high ER binding affinity for subsequent in vitro or in vivo assessments.

Algorithms↗
PubMed1996

A QSAR evaluation of Ah receptor binding of halogenated aromatic xenobiotics.

Because of their widespread occurrence and substantial biological activity, halogenated aromatic hydrocarbons such as polychlorinated biphenyls (PCBs), polychlorinated dibenzofurans (PCDFs), and polychlorinated dibenzo-p-dioxins (PCDDs) comprise one of the more important classes of contaminants in the environment. Some chemicals in this class cause adverse biological effects after binding to an intracellular cytosolic protein called the aryl hydrocarbon receptor (AhR). Toxic responses such as thymic atrophy, weight loss, immunotoxicity, and acute lethality, as well as induction of cytochrome P4501A1, have been correlated with the relative affinity of PCBs, PCDFs, and PCDDs for the AhR. Therefore, an important step in predicting the effects of these chemicals is the estimation of their binding to the receptor. To date, however, the use of quantitative structure activity relationship (QSAR) models to estimate binding affinity across multiple chemical classes has shown only modest success possibly due, in part, to a focus on minimum energy chemical structures as the active molecules. In this study, we evaluated the use of structural conformations other than those of minimum energy for the purpose of developing a model for AhR binding affinity that encompasses more of the halogenated aromatic chemicals known to interact with the receptor. Resultant QSAR models were robust, showing good utility across multiple classes of halogenated aromatic compounds.

Benzofurans↗
PubMed1994

The electronic factor in QSAR: MO-parameters, competing interactions, reactivity and toxicity.

Reactive chemicals pose unique problems in the development of SAR and QSAR in environmental chemistry and toxicology. Models of the stereoelectronic interactions of reactive toxicants with biological systems require formulation of parameters that quantify the electronic structure of the chemicals. A review of early approaches to modeling reactivity is presented in this work, with emphasis on the generalized polyelectronic perturbation theory. Applications of GPPT are demonstrated with QSARs for predicting toxicity of soft electrophiles and proelectrophiles using superdelocalizability and the charges on frontier orbitals. Prediction of toxicity for hard electrophiles such as organophosphates require atomic charges and bond orders in the QSAR. Special considerations for the orthogonality of factors and for the classification of reactive chemicals are reviewed.

Analysis of Variance↗
PubMed1993

Relationships between descriptors for hydrophobicity and soft electrophilicity in predicting toxicity.

The toxicity of chemicals is orthogonal with individual molecular descriptors used to quantify hydrophobicity and soft electrophilicity when considering large data sets. Estimating the toxicity of reactive chemicals requires descriptors of both passive transport and the stereoelectronic interaction, which are largely independent processes. QSARs using either log P or an electronic parameter alone are only significant for sets of chemicals that represent special, albeit some important, cases in QSAR. Chemicals were clustered according to their reactivity as soft electrophiles by defining isoelectrophilic windows along the toxicity response surface. Within these narrow windows of reactivity, the variation of toxicity was explained by the variation of log P. We observed that the dependence of toxicity on log P in different isoelectrophilic windows decreased as reactivity increased. The data are consistent with toxicity models where competing nucleophilic interaction sites are distributed along the transport route of the chemicals.

Animals↗
PubMedDate not supplied

'Dynamic' QSAR for semicarbazide-induced mortality in frog embryos.

The conventional quantitative-structure-activity relationship (QSAR) provides only a single three-dimensional (3D) model for a given two-dimensional (2D) item in the modeling process. However, in complex reaction environments with solvents of different polarity, especially biological systems, the molecules can take the form of different conformers depending on the particular interaction. Therefore, chemical behavior, e.g. toxicity, may be considered the integral effect of a set of conformers rather than the property of a single 3D isomer. The 'dynamic' QSAR method is unique in that it provided for the calculation of a set of conformers for 2D representation of each chemical of the series under investigation. Moreover, these conformers can be selected interactively according to the hypothesized mechanism of toxic action. The acute lethality of 36 semicarbazides and thiosemicarbazides, evaluated using the Frog Embryo Teratogenesis Assay: Xenopus (FETAX), was modeled by using the 'dynamic' QSAR method. The assumed mode of action, osteolathyrism, was defined by the failure of connective tissue to polymerize properly due to interference with lysyl oxidase. Conformer screenings were based on parameter distribution according to the frontier orbital energies and volume polarizability, conditioning their reactivity and hydrophobicity, respectively. The best results were obtained by the selection of conformers providing prevailing values of electron acceptor properties. Moreover, the best two-parameter QSARs encompassing all the evaluated compounds incorporate a geometric parameter, the geometric analog of the Wiener topological index, and the local electronic characteristics of the C=O or C=S group, superdelocalizabilities and charges.

Animals↗