Development, validation and uncertainty analysis of quantitative structure and activity relationship models for Log P of disinfection by -products

Hydrophobicity as measured by Log P is an important molecular property related to toxicity and carcinogenicity. With increasing public health concerns for the effects of Disinfection By-Products (DBPs), there are considerable benefits in developing Quantitative Structure and Activity Relationship (QSAR) models capable of accurately predicting Log P. In this research, Log P values of 173 DBP compounds in 6 functional classes were used to develop QSAR models, by applying 3 molecular descriptors, namely, Energy of the Lowest Unoccupied Molecular Orbital (ELUMO), Number of Chlorine (NCl) and Number of Carbon (NC) by Multiple Linear Regression (MLR) analysis. The QSAR models developed were validated based on the Organization for Economic Co-operation and Development (OECD) principles. The model Applicability Domain (AD) and mechanistic interpretation were explored. Considering the very complex nature of DBPs, the established QSAR models performed very well with respect to goodness-of-fit, robustness and predictability. The predicted values of Log P of DBPs by the QSAR models were found to be significant with a correlation coefficient R2 from 81% to 98%. The Leverage Approach by Williams Plot was applied to detect and remove outliers, consequently increasing R 2 by approximately 2% to 13% for different DBP classes. The developed QSAR models were statistically validated for their predictive power by the Leave-One-Out (LOO) and Leave-Many-Out (LMO) cross validation methods. Finally, Monte Carlo simulation was used to assess the variations and inherent uncertainties in the QSAR models of Log P and determine the most influential parameters in connection with Log P prediction. The developed QSAR models in this dissertation will have a broad applicability domain because the research data set covered six out of eight common DBP classes, including halogenated alkane, halogenated alkene, halogenated aromatic, halogenated aldehyde, halogenated ketone, and halogenated carboxylic acid, which have been brought to the attention of regulatory agencies in recent years. Furthermore, the QSAR models are suitable to be used for prediction of similar DBP compounds within the same applicability domain. The selection and integration of various methodologies developed in this research may also benefit future research in similar fields.

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.

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.

The relationship of thymulin activity, immune parameters of HIV disease progression and substance use in a cohort of HIV seropositive alcohol and drug users

Zinc is essential for the activity of thymulin, a thymic hormone involved in T-lymphocyte differentiation and activation. Zinc deficiency is widespread in populations with HIV infection, and HIV+ drug users are particularly susceptible to zinc deficiency and immune suppression. This dissertation explored the relationship of zinc-bound active thymulin to plasma zinc, CD4+ and CD8+ cell count, the CD4+/CD8+ ratio, and drug use in HIV-infected drug users. Zinc-bound active thymulin was assessed in plasma of HIV+ drug users who were participating in a 30 month zinc supplementation trial. Plasma from 80 participants at the 12 month visit, and 40 of these same participants, randomly selected, at the baseline visit were assessed for zinc-bound active thymulin levels using a modification of the rosette inhibition assay. Thymulin activity was directly associated with CD4+ cell count (β = 0.127, p = 0.002) and inversely associated with cocaine use (β = −0.908, p = 0.026; R2 = 0.188, p = 0.019) independent of HIV viral load, age, gender and antiretroviral use. An increase in thymulin activity was 1.4 times more likely when CD4+ cell count increased (OR = 1.402, 95%CI: 1.006–1.956), independent of change in viral load, antiretroviral use, and age. Participants who used cocaine consistently, were 7.6 times less likely to have an increase in thymulin activity (OR = 0.133, 95%CI: 0.017–1.061). There was a direct correlation between change in plasma zinc and change in zinc-bound active thymulin (r = 0.243, p = 0.13). Analysis of CD4+ cell count decline in 222 participants in the zinc supplementation trial across the 30 months showed that both crack cocaine use and heavy alcohol use accelerated CD4+ cell count decline. Thymulin activity is directly associated with HIV disease progression, measured by CD4+ cell count, and is depressed with cocaine use independent of antiretroviral use and HIV viral load. Cocaine and heavy alcohol accelerate CD4+ cell count decline. The effect of cocaine on thymic output requires further evaluation as a mechanism for the association of cocaine use with faster HIV disease progression.

Effects of adolescent depressive symptoms, pubertal development, and interpersonal relationship satisfaction on sexual risk behaviors in adolescent romantic couples

This study examined links between adolescent depressive symptoms, actual pubertal development, perceived pubertal timing relative to one’s peers, adolescent-maternal relationship satisfaction, and couple sexual behavior. Assessments of these variables were made on each couple member separately and then these variables were used to predict the sexual activity of the couple. Participants were drawn from the National Longitudinal Study of Adolescent Health (Add Health; Bearman et al., 1997; Udry, 1997) data set (N = 20,088; aged 12–18 years). Dimensions of adolescent romantic experiences using the total sample were described and then a subsample of romantically paired adolescents ( n = 1,252) were used to test a risk and protective model for predicting couple sexual behavior using the factors noted above. Relevant measures from the Wave 1 Add Health measures were used. Most of the items used in Add Health to assess romantic relationship experiences, adolescent depressive symptoms, pubertal development (actual and perceived), adolescent-maternal relationship satisfaction, and couple sexual behavior were drawn from other national surveys or from scales with well documented psychometric properties. Results demonstrated that romantic relationships are part of most adolescents’ lives and that adolescents’ experiences with these relationships differ markedly by age, sex, and race/ethnicity. Further, each respective couple member’s pubertal development, perceived pubertal timing, and maternal relationship satisfaction were useful in predicting sexual risk-promoting and risk-reducing behaviors in adolescent romantic couples. Findings in this dissertation represent an initial step toward evaluating explanatory models of adolescent couple sexual behavior.

The longitudinal influence of physical activity on adolescent alcohol use

The purpose of this research was to explore the influence of physical activity on depressive symptomatology and adolescent alcohol use during an underexplored transition from middle school to high school. The study initiative is supported by the fact that research has shown a unique and simultaneous decrease in physical activity (CDC, 2010), increase in depressive symptomatology (SAMHSA, 2010) and increase in alcohol use (USDHHS, 2011) during middle adolescence. A risk and resilience framework was used in efforts to conceptualize how these variables may be inter-related. Data from waves I and II of the National Longitudinal Study of Adolescent Health (Add Health, Bearman et al., 1997; Udry, 1997) was used (N = 2,054; aged 13–15 years). The sample was ethnically and racially diverse (58.2% White, 24% African American, 11.7% Hispanic, and 6.1% other). Structural equation models were developed to test the potential influence physical activity has on adolescent alcohol use (e.g., frequency of alcohol use and binge alcohol use) and whether any of the relationship was mediated by depressive symptomatology or varied as a function of gender. Results demonstrated that there was a significant influence of structured physical activity (e.g., sports) on adolescent alcohol use. However, contrary to the proposed hypothesis, engaging in structured physical activity appeared to contribute to greater binge drinking among adolescents. Instead of demonstrating a protective feature, the findings suggest that engaging in structured physical activity places adolescents at risk for binge drinking. Furthermore, no significant relationships, positive or negative, were found for the influence of physical activity (structured and unstructured) on frequency of alcohol use. The findings regarding mediation revealed binge drinking as a mediator between physical activity (structured) and depressive symptomatology. These findings provide support for research, practice, and policy initiatives focused on developing a more comprehensive understanding of alcohol use drinking behaviors, physical activity involvement, and depressive symptomatology among adolescents, which this study demonstrates are all associated with one another. Results represent an initial step toward evaluating these relationships at a much younger age.

Effects of Adolescent Depressive Symptoms, Pubertal Development, and Interpersonal Relationship Satisfaction on Sexual Risk Behaviors in Adolescent Romantic Couples

This study examined links between adolescent depressive symptoms, actual pubertal development, perceived pubertal timing relative to one’s peers, adolescent-maternal relationship satisfaction, and couple sexual behavior. Assessments of these variables were made on each couple member separately and then these variables were used to predict the sexual activity of the couple. Participants were drawn from the National Longitudinal Study of Adolescent Health (Add Health; Bearman et al., 1997; Udry, 1997) data set (N = 20,088; aged 12-18 years). Dimensions of adolescent romantic experiences using the total sample were described and then a subsample of romantically paired adolescents (n = 1,252) were used to test a risk and protective model for predicting couple sexual behavior using the factors noted above. Relevant measures from the Wave 1 Add Health measures were used. Most of the items used in Add Health to assess romantic relationship experiences, adolescent depressive symptoms, pubertal development (actual and perceived), adolescent-maternal relationship satisfaction, and couple sexual behavior were drawn from other national surveys or from scales with well documented psychometric properties. Results demonstrated that romantic relationships are part of most adolescents’ lives and that adolescents’ experiences with these relationships differ markedly by age, sex, and race/ethnicity. Further, each respective couple member’s pubertal development, perceived pubertal timing, and maternal relationship satisfaction were useful in predicting sexual risk-promoting and risk-reducing behaviors in adolescent romantic couples. Findings in this dissertation represent an initial step toward evaluating explanatory models of adolescent couple sexual behavior.

Further evidence for the relationship between eating frequency, body mass index, and physical activity

The relationship between the frequency of eating, physical activity and Body Mass Index (BMI) was investigated. Seventy five women, aged 24 to 55, were recruited from Florida International University. Via interview, subjects provided information regarding demographics and habitual eating frequency over 24-hours, and completed both the Baecke Questionnaire of Habitual Physical Activity and the Health Insurance Plan of New York Questionnaire on Physical Activity. Pearson correlations and partial correlation coefficients were used to assess the relationship between eating frequency, physical activity, age, and BMI. Results revealed significant positive correlations between eating frequency and total physical activity scores, and leisure time physical activity scores, but not between eating frequency and physical activity on the job. Partial correlations suggest that there may be an effect of eating frequency on BMI both through an effect on physical activity and through another mechanism. These results suggest that more frequent eaters tend to be more physically active, which may partially explain why lower body weights is associated with more frequent eating.