Chlorine contribution to quantitative structure and activity relationship models of disinfection by -products' quantum chemical descriptors and toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: (1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (E LUMO) via QSAR modelling and analysis; (2) to validate the models by using internal and external cross-validation techniques; (3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl ) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: (1) Linear or Multi-linear Regression (MLR); (2) Partial Least Squares (PLS); and (3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: (1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; (2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; (3) E LUMO are shown to correlate highly with the NCl for several classes of DBPs; and (4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.

Contribution of lipophilic secondary metabolites to the toxicity of strains of freshwater cyanobacterial harmful algal blooms, identified using the zebrafish (Danio rerio) embryo as a model for vertebrate development

Cyanobacteria ("blue-green algae") are known to produce a diverse repertoire of biologically active secondary metabolites. When associated with so-called "harmful algal blooms", particularly in freshwater systems, a number of these metabolites have been associated—as "toxins", or commonly "cyanotoxins"—with human and animal health concerns. In addition to the known water-soluble toxins from these genera (i.e. microcystins, cylindrospermopsin, and saxitoxins), our studies have shown that there are metabolites within the lipophilic extracts of these strains that inhibit vertebrate development in zebrafish embryos. Following these studies, the zebrafish embryo model was implemented in the bioassay-guided purification of four isolates of cyanobacterial harmful algal blooms, namely Aphanizomenon, two isolates of Cylindrospermopsis, and Microcystis, in order to identify and chemically characterize the bioactive lipophilic metabolites in these isolates. ^ We have recently isolated a group of polymethoxy-1-alkenes (PMAs), as potential toxins, based on the bioactivity observed in the zebrafish embryos. Although PMAs have been previously isolated from diverse cyanobacteria, they have not previously been associated with relevant toxicity. These compounds seem to be widespread across the different genera of cyanobacteria, and, according to our studies, suggested to be derived from the polyketide biosynthetic pathway which is a common synthetic route for cyanobacterial and other algal toxins. Thus, it can be argued that these metabolites are perhaps important contributors to the toxicity of cyanobacterial blooms. In addition to the PMAs, a set of bioactive glycosidic carotenoids were also isolated because of their inhibition of zebrafish embryonic development. These pigmented organic molecules are found in many photosynthetic organisms, including cyanobacteria, and they have been largely associated with the prevention of photooxidative damage. This is the first indication of these compounds as toxic metabolites and the hypothesized mode of action is via their biotransformation to retinoids, some of which are known to be teratogenic. Additional fractions within all four isolates have been shown to contain other uncharacterized lipophilic toxic metabolites. This apparent repertoire of lipophilic compounds may contribute to the toxicity of these cyanobacterial harmful algal blooms, which were previously attributed primarily to the presence of the known water-soluble toxins.^

Chlorine Contribution to Quantitative Structure and Activity Relationship Models of Disinfection By-Products' Quantum Chemical Descriptors and Toxicities

Quantitative Structure-Activity Relationship (QSAR) has been applied extensively in predicting toxicity of Disinfection By-Products (DBPs) in drinking water. Among many toxicological properties, acute and chronic toxicities of DBPs have been widely used in health risk assessment of DBPs. These toxicities are correlated with molecular properties, which are usually correlated with molecular descriptors. The primary goals of this thesis are: 1) to investigate the effects of molecular descriptors (e.g., chlorine number) on molecular properties such as energy of the lowest unoccupied molecular orbital (ELUMO) via QSAR modelling and analysis; 2) to validate the models by using internal and external cross-validation techniques; 3) to quantify the model uncertainties through Taylor and Monte Carlo Simulation. One of the very important ways to predict molecular properties such as ELUMO is using QSAR analysis. In this study, number of chlorine (NCl) and number of carbon (NC) as well as energy of the highest occupied molecular orbital (EHOMO) are used as molecular descriptors. There are typically three approaches used in QSAR model development: 1) Linear or Multi-linear Regression (MLR); 2) Partial Least Squares (PLS); and 3) Principle Component Regression (PCR). In QSAR analysis, a very critical step is model validation after QSAR models are established and before applying them to toxicity prediction. The DBPs to be studied include five chemical classes: chlorinated alkanes, alkenes, and aromatics. In addition, validated QSARs are developed to describe the toxicity of selected groups (i.e., chloro-alkane and aromatic compounds with a nitro- or cyano group) of DBP chemicals to three types of organisms (e.g., Fish, T. pyriformis, and P.pyosphoreum) based on experimental toxicity data from the literature. The results show that: 1) QSAR models to predict molecular property built by MLR, PLS or PCR can be used either to select valid data points or to eliminate outliers; 2) The Leave-One-Out Cross-Validation procedure by itself is not enough to give a reliable representation of the predictive ability of the QSAR models, however, Leave-Many-Out/K-fold cross-validation and external validation can be applied together to achieve more reliable results; 3) ELUMO are shown to correlate highly with the NCl for several classes of DBPs; and 4) According to uncertainty analysis using Taylor method, the uncertainty of QSAR models is contributed mostly from NCl for all DBP classes.