Modeling The Bioconcentration Factors and Bioaccumulation Factors of Polychlorinated Biphenyls with Posetic Quantitative Super Structure/Activity Relationship

Summary During bioconcentration, chemical pollutants from water are absorbed by aquatic animals via the skin or a respiratory surface, while the entry routes of chemicals during bioaccumulation are both directly from the environment (skin or a respiratory surface) and indirectly from food. The bioconcentration factor (BCF) and the bioaccumulation factor (BAF) for a particular chemical compound are defined as the ratio of the concentration of a chemical inside an organism to the concentration in the surrounding environment. Because the experimental determination of BAF and BCF is time-consuming and expensive, it is efficacious to develop models to provide reliable activity predictions for a large number of chemical compounds. Polychlorinated biphenyls (PCBs) released from industrial activities are persistent pollutants of the environment thereby producing widespread contamination of water and soil. PCBs can bioaccumulate in the food chain, constituting a potential source of exposure for the general population. To predict the bioconcentration and bioaccumulation factors for PCBs we make use of the biphenyl substitution-reaction network for the sequential substitution of H-atoms by Cl-atoms. Each PCB structure then occurs as a node of this reaction network, which is some sort of super-structure, turning out mathematically to be a partially ordered set (poset). Rather than dealing with the molecular structure via ordinary QSAR we use only this poset, making different quantitative super-structure/activity relationships (QSSAR). Thence we developed cluster expansion and splinoid QSSAR for PCB bioconcentration and bioaccumulation factors. The predictive ability of the BAF and BCF models generated for 20 data sets (representing different conditions and fish species) was evaluated with the leave-one-out cross-validation, which shows that the splinoid QSSAR (r between 0.903 and 0.935) are better than models computed with the cluster expansion (r between 0.745 and 0.887). The splinoid QSSAR models for BAF and BCF yield predictions for the missing PCBs in the investigated data sets

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Due to their widespread use in bactericides, insecticides, herbicides, andfungicides, chlorophenols represent an important source of soil contaminants. Theenvironmental fate of these chemicals depends on their physico-chemical properties. In theabsence of experimental values for these physico-chemical properties, one can use predictedvalues computed with quantitative structure-property relationships (QSPR). As analternative to correlations to molecular structure we have studied the super-structure of areaction network, thereby developing three new QSSPR models (poset-average, cluster-expansion, and splinoid poset) that can be applied to chemical compounds which can behierarchically ordered into a reaction network. In the present work we illustrate these posetQSSPR models for the correlation of the octanol/water partition coefficient (log Kow) and thesoil sorption coefficient (log KOC) of chlorophenols. Excellent results are obtained for allQSSPR poset models to yield: log Kow, r = 0.991, s = 0.107, with the cluster-expansionQSSPR; and log KOC, r = 0.938, s = 0.259, with the spline QSSPR. Thus, the poset QSSPRmodels predict environmentally important properties of chlorophenols

Role of hydrogen sulfide in paramyxovirus infections

Hydrogen sulfide (H2S) is an endogenous gaseous mediator that has gained increasing recognition as an important player in modulating acute and chronic inflammatory diseases. However, its role in virus-induced lung inflammation is currently unknown. Respiratory syncytial virus (RSV) is a major cause of upper and lower respiratory tract infections in children for which no vaccine or effective treatment is available. Using the slow-releasing H2S donor GYY4137 and propargylglycin (PAG), an inhibitor of cystathionine-γ-lyase (CSE), a key enzyme that produces intracellular H2S, we found that RSV infection led to a reduced ability to generate and maintain intracellular H2S levels in airway epithelial cells (AECs). Inhibition of CSE with PAG resulted in increased viral replication and chemokine secretion. On the other hand, treatment of AECs with the H2S donor GYY4137 reduced proinflammatory mediator production and significantly reduced viral replication, even when administered several hours after viral absorption. GYY4137 also significantly reduced replication and inflammatory chemokine production induced by human metapneumovirus (hMPV) and Nipah virus (NiV), suggesting a broad inhibitory effect of H2S on paramyxovirus infections. GYY4137 treatment had no effect on RSV genome replication or viral mRNA and protein synthesis, but it inhibited syncytium formation and virus assembly/release. GYY4137 inhibition of proinflammatory gene expression occurred by modulation of the activation of the key transcription factors nuclear factor κB (NF-κB) and interfero

Prediction of Environmental Properties for Chlorophenols with Posetic Quantitative Super-Structure/Property Relationships (QSSPR)

Due to their widespread use in bactericides, insecticides, herbicides, andfungicides, chlorophenols represent an important source of soil contaminants. Theenvironmental fate of these chemicals depends on their physico-chemical properties. In theabsence of experimental values for these physico-chemical properties, one can use predictedvalues computed with quantitative structure-property relationships (QSPR). As analternative to correlations to molecular structure we have studied the super-structure of areaction network, thereby developing three new QSSPR models (poset-average, cluster-expansion, and splinoid poset) that can be applied to chemical compounds which can behierarchically ordered into a reaction network. In the present work we illustrate these posetQSSPR models for the correlation of the octanol/water partition coefficient (log Kow) and thesoil sorption coefficient (log KOC) of chlorophenols. Excellent results are obtained for allQSSPR poset models to yield: log Kow, r = 0.991, s = 0.107, with the cluster-expansionQSSPR; and log KOC, r = 0.938, s = 0.259, with the spline QSSPR. Thus, the poset QSSPRmodels predict environmentally important properties of chlorophenols

INTRINSIC GRAPH DISTANCES COMPARED TO EUCLIDEAN DISTANCES FOR CORRESPONDENT GRAPH EMBEDDING #

Abstract. Chemical structures of organic compounds are characterized numerically by a variety of structural descriptors computed either from the molecular graph or from the three-dimensional (3D) molecular geometry. Extensive use of such structural descriptors or topological indices has been made in drug design, screening of chemical databases, similarity and diversity assessment, and quantitative structure-activity relationships. In recent years a large variety of topological indices were derived from different sorts of graph distance functions which have been considered to characterize the molecular shape and structure. These include not only the shortest-path distance but also the resistance distance and the quasi-Euclidean distance. A comparison is made between five intrinsic graph distance functions and the geometric distance for a set of benzenoid hydrocarbons. Overall, a very good correlation is obtained for all graph distances, indicating that the graph descriptors derived from them capture some part of the 3D information of the molecular structure

Wiener Index Extension by Counting Even/Odd Graph Distances

Chemical structures of organic compounds are characterized numerically by a variety of structural descriptors, one of the earliest and most widely used being the Wiener index W, derived from the interatomic distances in a molecular graph. Extensive use of such structural descriptors or topological indices has been made in drug design, screening of chemical databases, and similarity and diversity assessment. A new set of topological indices is introduced representing a partitioning of the Wiener index based on counts of even and odd molecular graph distances. These new indices are further generalized by weighting exponents which can be optimized during the quantitative structure-activity/-property relationship (QSAR/QSPR) modeling process. These novel topological indices are tested in QSPR models for the boiling temperature, molar heat capacity, standard Gibbs energy of formation, vaporization enthalpy, refractive index, and density of alkanes. In many cases, the even/odd distance indices proposed here give notably improved correlations