Louisiana State University LSU Digital Commons LSU Master's Theses Graduate School 2005 Identification of 'structural alerts' and associated mechanisms of action of mammary gland carcinogens in female rodents Shanna Tommika Moss Louisiana State University and Agricultural and Mechanical College Follow this and additional works at: https://digitalcommons.lsu.edu/gradschool_theses Part of the Environmental Sciences Commons Recommended Citation Moss, Shanna Tommika, "Identification of 'structural alerts' and associated mechanisms of action of mammary gland carcinogens in female rodents" (2005). LSU Master's Theses. 76. https://digitalcommons.lsu.edu/gradschool_theses/76 This Thesis is brought to you for free and open access by the Graduate School at LSU Digital Commons. It has been accepted for inclusion in LSU Master's Theses by an authorized graduate school editor of LSU Digital Commons. For more information, please contact [email protected]. IDENTIFICATION OF ‘STRUCTURAL ALERTS’ AND ASSOCIATED MECHANISMS OF ACTION OF MAMMARY GLAND CARCINOGENS IN FEMALE RODENTS A Thesis Submitted to the Graduate Faculty of the Louisiana State University and Agricultural and Mechanical College in partial fulfillment of the requirements for the degree of Master of Science in The Department of Environmental Studies by Shanna T. Moss B.S., University of Nebraska, 2002 May 2005 ACKNOWLEDGEMENTS I gratefully acknowledge support for this work from the Congressionally Directed Medical Research Program for Breast Cancer Idea Award DAMD17-01-0376. Moreover, several persons provided guidance and support for the successful completion of this project from the very beginning. My most sincere gratitude goes out to my very considerate research advisor, Dr. Albert R. Cunningham, whose impressive research work sparked my interest in computational toxicology. I am very grateful for the time and patience he allotted to help get this massive, and I think we both can agree on, sometimes tedious research completed in a timely fashion. Thanks for the well-equipped lab Dr. Cunningham. Furthermore, I would like to thank Suzanne L. Cunningham for her valuable technical contribution to this project. I want to thank my committee members Dr. Vincent L. Wilson and Dr. Ralph J. Portier for serving on my committee and also for their scientific input and willingness to assist whenever they can. I am also grateful for their editorial assistance along the way. I am cordially thankful to my colleague Daniel M. Consoer for allowing me to use his ESCREEN models for statistical evaluation. I truly enjoyed our conversations at the lab. I would like to thank my parents, for their continuous moral and financial support throughout the years. Special thanks to my brother and sister, Duran and Kay, for their encouragement, and other supportive family members and friends I failed to mention. I want to thank my very caring boyfriend, B.J. Alexander, for his constant support and love through all of this. Thanks for keeping me up with the coffee B.J. I would also like to extend my most heartfelt thanks and appreciation to Ms. Charlotte St. Romain who has been there for me since day one. Thanks for keeping me on track and for your guidance Charlotte. Finally, I want to thank all the faculty and friends I have ii encountered in the Department of Environmental Studies who have enlightened me with their knowledge and long-lasting friendship. iii TABLE OF CONTENTS ACKNOWLEDGMENTS..………………………………………………………………..….ii LIST OF TABLES.…………..……………………………………………………………….vi LIST OF FIGURES.…………………………………..…………………………………….viii LIST OF ABBREVIATIONS...…………………………………………………………..…..ix ABSTRACT…………………………………………………….……………………….…...xi CHAPTER 1. INTRODUCTION……..………………………………………………………1 CHAPTER 2. LITERATURE REVIEW……………………………………………………...6 2.1 Breast Cancer Development.……………..……………………………………….6 2.2 Steroidal, Non-steroidal, and Synthetic Estrogens……...…..…………………….8 2.3 Probable Modes of Action……………..…….………….……………….………10 2.4 Rodent Cancer Bioassay…………………………………………………………12 2.5 Comparison of SAR and QSAR Models…………….…………….………….....13 2.6 Expert Systems………………………..………………….…………………...….14 2.7 SAR Models as Classifiers...………..…………………………...…………..…...17 2.8 Influence of Structural Diversity of Datasets on Model Prediction….…………..18 CHAPTER 3. MATERIALS AND METHODS………………....………….....………...….20 3.1 Cat-SAR Study: Introduction.……………..………………………………….….20 3.2 Methodology……………..………..…………………………………………......21 3.2.1 Carcinogenic Potency Database (CPDB)………………..……………..21 3.2.1.1 Selection of Mammary Carcinogens..…………..……………24 3.2.1.2 Selection of Mammary Noncarcinogens………..……………24 3.2.1.3 General Rat and Mouse Learning Sets...……………..………26 3.2.2 Database Construction…..………………………………....………..…26 3.2.2.1 Significance of Balanced cat-SAR Datasets…………..……..27 3.2.2.2 Model Parameters………….…………………..……...……..27 3.2.3 In silico Fragmentation of Test Compounds..……...……..…………....28 3.2.4 Set Criteria: cat-SAR Rules………………………………..…………..29 3.2.4.1 Selection of Significant Fragments……………….……...…..29 3.2.5 Model Predictions……………………..……………………………….33 3.2.6 Model Validations...……………………..………………...…………...33 3.2.6.1 Statistical Evaluation: Sensitivity, Specificity, and OCP..…..34 3.2.7 Mechanistic Analysis: Chemical Diversity Approach…………..……..35 3.2.8 Overview of cat-SAR Method……………………..……………..……37 CHAPTER 4. RESULTS AND DISCUSSION……………...………..…………………......38 4.1 Study Results…………………………..………………………………………...38 iv 4.1.1 Predictability of cat-SAR Mammary Carcinogen Models………..…....38 4.1.2 Analysis of Mammary Carcinogen Models.……..…...……………......41 4.1.2.1 ABC and ABCH 75% and 90% Models………………..……42 4.1.3 Predictive Performance of cat-SAR CPDB Rodent Models…...………57 4.1.4 Analysis of the General Rodent Models…....…..……………………...60 4.1.5 Training/Test Set: Respiratory Sensitization cat-SAR Study.…..…......62 4.1.6 Identifying Structural Alerts…………………..……………………….64 4.1.6.1 1-Phenyl-3,3-dimethyltriazene (PDMT)…………………..…65 4.1.6.2 Nithiazide………………..…………………………………...67 4.1.6.3 Fenaminosulf…………………………..……………………..71 4.1.6.4 Atrazine………………………………..……………………..72 4.1.6.5 Diazepam…………………………..………………………...74 4.1.6.6 Calciferol……………………………..………………………76 4.1.6.7 1’-Hydroxysafrole……………………..……………………..78 4.1.6.8 Diethylstilbestrol………..……………………………………81 4.1.6.9 Capsaicin………………………………..……………………83 4.1.6.10 1-Trans-delta-9-tetrahydrocannabinol (∆9-THC)………..…85 4.1.6.11 Acetohexamide…………………..…………………………88 4.1.6.12 Bemitradine……………………………………………...….91 4.1.7 Mechanistic Analysis……….……..………………………….………..93 4.1.7.1 Similarities in Mechanistic Relatedness: cat-SAR and MCASE…..…………………………………………………………..98 4.2 Discussion……………………………………………..…………………………99 4.2.1 Data Interpretation.……………………………..……………………...99 4.2.2 Strengths and Weaknesses of cat-SAR…………………………….....101 CHAPTER 5. SUMMARY AND CONCLUSIONS………..……………………………...104 5.1 Summary...……………..………………………………………………….……104 5.2 Conclusions……………………………………………………………………..105 5.3 Suggestions for Future Research Work……………………..………………….107 REFERENCES..……..……………………………………………………………………..109 VITA………………..…………………………………………………………………...….123 v LIST OF TABLES 4.1 Predictive Performance Summary for the Rat Mammary-Non-mammary Carcinogen (MC-NMC) cat-SAR Model..………………………………………………………..38 4.2 Predictive Performance Summary for the Rat Mammary carcinogen-Noncarcinogen (MC-NC) cat-SAR Model …………………………………………………………...39 4.3 Predictive Performance Summary for the Mouse Mammary Carcinogen-Non- mammary Carcinogen (MC-NMC) cat-SAR Model with 3 to 7 Heavy Atoms……………………………………………..…………………..……………...40 4.4 Predictive Performance Summary for the Mouse Mammary Carcinogen-Rodent Noncarcinogen cat-SAR Model with 3 to 7 Heavy Atoms …...…………..………....41 4.5 Model Validation for Rat Mammary Gland Carcinogens and Non-mammary Gland Carcinogens (MC-NMC)…………..………..…………………….…………...…….44 4.6 Model Validation for Rat Mammary Gland Carcinogens and Noncarcinogens (MC- NC)……..……………….……………..……………………...……………………...50 4.7 Model Validation for Mouse Mammary Gland Carcinogens and Non-mammary gland Carcinogens (MC-NMC).…….……….……..……………...…………………….…55 4.8 Model Validation for Mouse Mammary Gland Carcinogens and Rodent Noncarcinogens (MC-NC)………..……………………..……….………...….……..56 4.9 Predictive Performance of the General CPDB Rat Carcinogen cat-SAR Model with 3 to 7 Heavy Atoms….……………………………………………………….………..58 4.10 Predictive Performance of the General CPDB Mouse Carcinogen cat-SAR Model with 3 to 7 Heavy Atoms…………..…………..………………..…...………..…….58 4.11 Predictive Performance of the CPDB Female Rat Carcinogen Model with 3 to 7 Heavy Atoms…………………………………………………..……….…………...59 4.12 Predictive Performance of the CPDB Female Mouse Carcinogen Model with 3 to 7 Heavy Atoms…………...………………………………….………………………..59 4.13 Predictive performance of ABC and ABCH respiratory sensitization models……..63 4.14 Fragments from the ABC model leave-one-out validation analysis used to predict the inactivity of the rat non-mammary gland carcinogen 1-Phenyl-3,3- dimethyltriazene...………………………………………………………………..….66 vi 4.15 Fragments from the ABC model leave-one-out validation analysis used to predict the activity of the rat mammary gland carcinogen nithiazide.……………. ………..69 4.16 Fragments from the ABC 3/0.90 model leave-one-out validation analysis used to predict the inactivity of the rat noncarcinogen fenaminosulf.…………...……..…...71 4.17 Fragments from the ABC 3/0.90 model leave-one-out validation analysis used to predict the activity of the rat mammary gland carcinogen atrazine.……...…….…...73 4.18 Fragments from the ABC 3/0.75 model leave-one-out validation analysis used to predict the