Health Risk Assessment of Environmental Chemicals: Pre-Emptive and Integrated Approaches 9819915597, 9789819915590

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Masami Ishido

Health Risk Assessment of Environmental Chemicals Pre-Emptive and Integrated Approaches

Health Risk Assessment of Environmental Chemicals

Masami Ishido

Health Risk Assessment of Environmental Chemicals Pre-Emptive and Integrated Approaches

Masami Ishido Center for Health and Environmental Risk Research National Institute for Environmental Studies Tsukuba, Ibaraki, Japan

ISBN 978-981-99-1559-0 ISBN 978-981-99-1560-6 https://doi.org/10.1007/978-981-99-1560-6

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© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Preface

There are as many as 80,000 or 100,000 chemicals in the surrounding environment which are awaiting health risk assessment, and the number of chemicals to be handled by researchers involved in organic synthesis and drug discovery is said to be on the scale of ten to the power of tens (~1010). Dr. Theo Colborn and colleagues discovered the structure of endocrine disruptors within this chemical space, extracting a common structure of “chemicals with estrogen activity” from an enormous amount of separately reported literature, which led to the classification of such chemicals as “endocrine disruptors.” As a result of research on the impact of these endocrine disruptors, we found ADHD-related chemicals among these chemicals. Also, this group of chemicals was found to include known carcinogens and nanomaterials, which are considered difficult in assessing health risks. In addition, it was discovered that the onset of toxicity for ADHD-related chemicals is impacted not only by dose but also by the timing of exposure. Furthermore, the final onset of central toxicity for ADHD-related chemicals is preceded by toxicity in the peripheral organs. This dynamic movement of toxicity is suggested to be a potential indicator for pre-emptive evaluation of toxicity. Finally, migrating toxicity outweighs the individual, and shows inheritance in toxicity. In the modern-day health risk assessment of chemicals, it is necessary to grasp toxicity which moves in a dynamic manner. An inclusive or integrated assessment of health risks associated with many and diverse chemicals is thought to be a possible approach to this matter. Tsukuba, Ibaraki, Japan

Masami Ishido

v

Acknowledgments

I would like to dedicate this book to all who supported me both spiritually and physically throughout my life. My research has been systemized by the chance of the Endocrine Disruptor Project, whose leader was Dr. Masatoshi Morita at the National Institute for Environmental Studies, Tsukuba, Japan. I sincerely thank him. The author would also like to thank Drs. Yoshinori Masuo and Hideki Imai for joining the Projects and for the comments from points of public health for achieving to write the book, respectively.

vii

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Part I 2

3

1 5

Current Health Risk Assessment of Chemicals

The Safe Dose in the Utilization of Chemicals . . . . . . . . . . . . . . . . . 2.1 Dose-Response Curve . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Health Risk Assessment of Rotenone and Simazine . . . . . . . . . . 2.2.1 Rotenone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.2 Simazine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Health Risk Assessment of Dioxin-Induced Malformation of Reproductive Organs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

9 9 18 18 19

Carcinogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Carcinogens . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Health Risk Assessment of Carcinogens . . . . . . . . . . . . . . . . . . 3.2.1 Long-Term Carcinogenicity Studies . . . . . . . . . . . . . . . 3.2.2 Carcinogenicity Studies with Surrogates . . . . . . . . . . . 3.3 Dose-Response Curve and Carcinogenic Mechanism of Benzo[a]Pyrene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Carcinogenic Mechanism of Benzo[a]Pyrene . . . . . . . . 3.4 Various Carcinogens and their Dose-Response Curves . . . . . . . . 3.5 Research on Carcinogens by Prof. Shoji Fukushima . . . . . . . . . 3.6 Dose-Response Curve of TPA (12-O-Tetradecanoylphorbol 13-Acetate) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.7 How to Interpret the Dose-Response Curve . . . . . . . . . . . . . . . . 3.8 Health Risk Assessment of Radiation . . . . . . . . . . . . . . . . . . . . 3.9 Genome Mutation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.9.1 Mutation as a Factor in the Evolution . . . . . . . . . . . . .

21 21 23 24 24

19 20

24 26 28 31 35 36 38 43 44

ix

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Contents

3.9.2 Mutation as a Factor of Carcinogenesis . . . . . . . . . . . . 3.9.3 Establishment of Mutagenicity Study Methods . . . . . . . 3.10 Characteristics of Cancer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.10.1 Carcinogenic Process in Colorectal Cancer . . . . . . . . . . 3.11 Ideas Relating to the Health Risk Assessment of Carcinogens . . 3.12 Concept of the Lifetime Risk of Carcinogenesis . . . . . . . . . . . . 3.12.1 Carcinogenic Risk of Benzo[a]Pyrene . . . . . . . . . . . . . 3.12.2 Carcinogenic Risk of Chlordane . . . . . . . . . . . . . . . . . 3.12.3 Benchmark Dose Method (BMDL: Benchmark Dose Lower Confidence Level) . . . . . . . . . . . . . . . . . . . . . . 3.13 Experiment on Cells with a Tumorigenic Probability of 10-6 . . . 3.14 The Signature of Cancer Genome Mutation . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

48 48 50 51 52 54 54 55

4

Endocrine Disruptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Dose Response of Endocrine Hormones . . . . . . . . . . . . . . . . . 4.2 Dose Response of Bisphenol A . . . . . . . . . . . . . . . . . . . . . . . 4.3 Carcinogenicity Evaluation of Bisphenol A . . . . . . . . . . . . . . . 4.4 Health Risk Assessment of Bisphenol A . . . . . . . . . . . . . . . . . 4.5 Strict Set Values for EU . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . . .

69 69 72 75 79 81 81

5

Nanomaterials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Physical Properties of Nanomaterials . . . . . . . . . . . . . . . . . . . . 5.2 Nanomaterials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Physicochemical Properties of Carbon Nanotubes . . . . . 5.3 Dose-Response Curve of Nanomaterials . . . . . . . . . . . . . . . . . . 5.4 Carcinogenicity Evaluation of Carbon Nanotube . . . . . . . . . . . . 5.4.1 Health Risk Assessment of MWNT-7 . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

83 83 84 85 87 89 90 90

6

Epigenetic Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Epigenetic Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 The Impact of Arsenic on the Epigenetic Mechanism . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . .

93 93 94 98

7

Toward the Integrated Health Risk Assessment of Chemicals . . . . 7.1 Toxicology, Epidemiology, and Health Risk Assessment . . . . . 7.2 Predominance of the Epidemiological Data . . . . . . . . . . . . . . . 7.3 Dichloromethane (DCM) and 1,2-Dichloropropane (1,2-DCP) . 7.3.1 Recent Cases of Cholangiocarcinoma . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . .

99 99 102 102 102 105

56 58 61 66

Contents

Part II

xi

New Concepts of Toxicological Mechanisms of Chemicals

8

Expanded Toxicological Mechanisms of Chemicals . . . . . . . . . . . . 8.1 A Strategy for New Aspects of Health Risk Assessment . . . . . 8.2 Three Exposure Routes of Chemicals . . . . . . . . . . . . . . . . . . . 8.2.1 Contact Toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.2.2 Main Pathways of Exposure to Chemicals . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . .

109 109 111 111 112 124

9

Fields and Exposure Periods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.1 Regulating Toxicological Onset by Fields . . . . . . . . . . . . . . . . . 9.2 Dynamic Toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9.2.1 Migrating Toxicity: α-Synuclein . . . . . . . . . . . . . . . . . 9.3 Critical Windows for Toxicity . . . . . . . . . . . . . . . . . . . . . . . . . 9.4 Development of the Central Nervous System . . . . . . . . . . . . . . 9.5 Neural Crest Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

125 125 128 129 130 131 134 136

10

Environmental Chemicals as Plasticity Disruptors . . . . . . . . . . . . . 10.1 Plasticity in Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.2 Brain Plasticity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3 Chemical-Induced Plasticity Disruption . . . . . . . . . . . . . . . . . . 10.3.1 Thalidomide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.3.2 Fetal Minamata Disease . . . . . . . . . . . . . . . . . . . . . . . 10.3.3 DES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10.4 Chemicals and Symbiosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

137 137 141 144 145 145 146 147 152

Part III 11

Integrated Health Risk Assessment of Chemicals

It Began with the Pharmacological Evaluation of Endocrine Disruptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 Availability of Animal Models of Neuronal Disorder . . . . . . . . . 11.2 Pioneer Works of Rat Hyperactivity Models by Dr. Shaywitz . . 11.3 Pharmacological Approaches for Endocrine Disruptors . . . . . . . 11.3.1 Bisphenol A (BPA) . . . . . . . . . . . . . . . . . . . . . . . . . . 11.3.2 ADHD-Related Chemicals . . . . . . . . . . . . . . . . . . . . . 11.3.3 Brain Chemicals and Central Detoxification Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.4 Pathological Features in the Hyperactive Rat Brains . . . . . . . . . 11.5 Results of Pharmacological Evaluation on Endocrine Disruptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6 Evaluation of Endocrine Disruptors Under the Exposure Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.1 Bisphenol A (BPA) . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.2 p-Nitrotoluene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.6.3 Atrazine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

157 160 162 166 166 173 178 183 184 188 188 190 190

xii

Contents

11.7

Epidemiological Evidence of the Relationship Between Endocrine Disruptors and ADHD . . . . . . . . . . . . . . . . . . . . . . . 192 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 12

13

Integration of Health Risk Assessment for Various Chemicals: Common Biomarkers in Different Exposure Routes . . . . . . . . . . . . 12.1 Hyperactivity-Related Chemicals . . . . . . . . . . . . . . . . . . . . . . . 12.1.1 Rotenone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.2 Silver Nanoparticles . . . . . . . . . . . . . . . . . . . . . . . . . . 12.1.3 Benzo[a]pyrene . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2 Hyperactivity-Related Air Pollutants . . . . . . . . . . . . . . . . . . . . 12.2.1 SOA (Secondary Organic Aerosol) . . . . . . . . . . . . . . . 12.2.2 PCB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.2.3 DEP (Diesel Exhaust Particles) . . . . . . . . . . . . . . . . . . 12.2.4 Adult ADHD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.3 Epidemiological Evidence of the Relationship between Air Pollution and ADHDAir Pollution and ADHD . . . . . . . . . . . . . 12.4 Voluntary Movement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5 Dopamine Nervous System: A Common Sensor for Chemical Toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12.5.1 Reward and Learning . . . . . . . . . . . . . . . . . . . . . . . . . 12.6 Development of the Dopamine Nervous System . . . . . . . . . . . . 12.6.1 The Birth of Mesencephalic DA Neurons . . . . . . . . . . . 12.6.2 Development of the Striatum . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Toward a Dimension-Free, Pre-Emptive, Integrated Health Risk Assessment of Chemicals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1 Peripheral Pathology as a Precursor for Central Nervous System Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.1.1 Relationship Between the Brain and the Intestines . . . . 13.2 Physiology of the Intestines . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.2.1 Development of the Intestines . . . . . . . . . . . . . . . . . . . 13.2.2 Enteric Nerves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3 Prion: Migrating Pathological Protein . . . . . . . . . . . . . . . . . . . . 13.3.1 Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . 13.3.2 Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4 Common Elements in Five Types of Psychiatric Disorders . . . . . 13.4.1 Autism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.2 Autism and the Intestines . . . . . . . . . . . . . . . . . . . . . . 13.4.3 ADHD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.4.4 Coexistence of ADHD and Autism . . . . . . . . . . . . . . . 13.5 Other Chemical-Induced NCD (Noncommunicable Disease) . . . 13.5.1 Obesogen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13.6 Alzheimer’s Disease and Diabetes Mellitus . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

201 201 201 204 206 208 208 209 209 213 215 216 219 222 223 224 228 229 231 231 232 233 234 235 236 237 241 248 248 250 252 253 254 255 256 259

Contents

Part IV

xiii

Transgenerational Inheritance of Chemical Toxicity

14

Inheritance of Neurological Disorders . . . . . . . . . . . . . . . . . . . . . . . 14.1 Single-Gene Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2 Multifactorial Disorders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.1 Parkinson’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.2 Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.3 ADHD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14.2.4 Autism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

265 265 266 266 268 268 270 274

15

Epigenetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.1 Epigenetics in Biology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.1.1 Transposon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.1.2 X-Chromosome Inactivation . . . . . . . . . . . . . . . . . . . . 15.1.3 Reprogramming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.2 De Novo Methylation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3 Epigenetics in Neurological Disorders . . . . . . . . . . . . . . . . . . . 15.3.1 Rett Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.2 Fragile X Syndrome . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.3 ADHD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15.3.4 Autism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

275 275 275 277 278 280 284 284 285 286 286 288

16

Environmental Epigenetics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.1 Food Intake and Epigenetics . . . . . . . . . . . . . . . . . . . . . . . . . . 16.2 Epigenetic Effects of Bisphenol A . . . . . . . . . . . . . . . . . . . . . . 16.3 Epidemiology of Environmental Epigenetics . . . . . . . . . . . . . . . 16.4 Epigenetic Transgenerational Inheritance of Chemical Toxicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.5 Research by Dr. Skinner . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16.6 Transgeneration of Rat Hyperactivity Disorder . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

289 289 289 290 291 293 295 298

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 299

Chapter 1

Introduction

Abstract In 2020, the outbreak of novel coronavirus (COVID-19) caused confusion not only in Japan but across the world. A similar confusion was experienced in Japan in the latter half of 1990s, not from the onset of mysterious diseases in a certain area or in the entire country, but due to the publication of certain literature. The book was “Our Stolen Future” by Dr. Theo Colborn (Fig. 1.1) and her colleagues, which stated that chemical substances that exist commonly in our surroundings have negative impacts on our health, especially those with actions similar to female hormones, causing concerns for the future well-being of the children who are born through reproduction. This issue is known as the health concerns relating to hormone-disrupting chemicals, and the chemicals with such effects came to be known under the academic name of “endocrine disruptors.” Keywords Theo Colborn · Endocrine disruptors · Estrogenic activity · Health risk assessment With consideration for this background, endocrine disruptors are regarded as a category of chemical substances, and phenols and phthalic acid are examples of endocrine disruptors of primary concern. More specific examples of such substances include bisphenol A and diethylhexylphthalate (DEHP); the former was previously used to coat food cans, and the latter was used as materials for toys and medical equipment. Dr. Theo Colborn (Fig. 1.1), born in the United States in 1927, graduated from the university with a degree in pharmacy (1947). She was said to be influenced by “Silent Spring” by Rachel Carson, published in 1962, which led her to study freshwater ecology for her master’s degree and specialized in zoology for her doctorate degree obtained at the age of 58 years (1985). Two years later, Dr. Theo Colborn participated in the Great Lakes environmental project and commenced research on a large number of relevant research reports. While marine biologist Rachel Carson raised an alarm on the harm of pesticides on wild animals, in particular, the role of the chemical DDT (dichlorodiphenyltrichloroethane) in driving the extinction of the cliff bird colonies, Dr. Theo Colborn pointed out not only the destruction of the ecological system but the abnormalities occurring in the © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Ishido, Health Risk Assessment of Environmental Chemicals, https://doi.org/10.1007/978-981-99-1560-6_1

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Introduction

Fig. 1.1 Dr. Theo Colborn. (Permitted from The Asahi Glass Foundation and remaked Colborn et al. 1997)

Fig. 1.2 Estrogenic activity of bisphenol A (BPA) was evaluated in comparison to E2 using the ER-CALUX® reporter gene assay. The maximum response of E2 was set to 100%. (Adapted from Herz et al. 2017)

endocrine system of animals, including human. As a result of this, important hormones related to development were found. Following this, scholars of various fields, including those specializing in endocrinology, toxicology, ecology, pharmacology, and anthropology, gathered to conduct a number of discussions on this matter. In the United States, the government agency was responsible for the risk assessment on the carcinogenicity of chemicals, and these scholars urged the risk assessment on endocrine disruption to be included in the risk assessment of chemicals, which was eventually realized in 1996. With due credit to the awareness campaigns conducted by Dr. Theo Colborn and her colleagues, these movements in the United States led to immediate responses in various countries, particularly in industrial nations. However, there was little initial interest in this issue from the researchers in Japan who specialized in endocrinology. This was because the potency of the endocrine disruptor was found to be extremely low, at 1/1000 to 1/10000 times lower than the effect of estrogen as a female hormone found in the natural function of the body. Figure 1.2 shows the comparison of the potency of estrogen and endocrine disruptors. Meanwhile, there was increased mental distress in the community, and the authorities strongly encouraged studies to be conducted on the environmental dynamics of endocrine disruptors and the state of human exposure, as well as studies on toxicity in animals. Environmental Agency (name as of 1998) in Japan conducted

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Introduction

3

a measure called “speed98,” which involved the listing of 65 compounds suspected of causing endocrine disruption in order to narrow down the target of research. As a result of these measures, the investigation on human exposure has shown a wide and common exposure of bisphenol A, a substance with a large production quantity. Also, while the initial focus of toxicity was the estrogen effect of endocrine disruptors, further research showed these substances to have an impact not only on the reproductive system but also on the circulatory, respiratory, metabolic, immune, and cerebral/nervous systems. The results indicated that exposure to endocrine disruptors has led to disorders such as neurodevelopment disorders, neurodegenerative disorders, diabetes mellitus, cancer, and immune disorders. Furthermore, the timing of chemical exposure was observed to affect the magnitude of impact, with critical windows for chemical exposure discovered in the fetal stage and childhood. There is a long history of health risk assessment on chemicals. Assessments have been conducted based on the findings from animal toxicity studies and epidemiological surveys; however, difficulties in evaluating the carcinogenicity of chemicals as well as endocrine disruptors have been revealed. The risk assessments based on traditional toxicology were found to be incapable of evaluating the kinetics for the potency of these chemicals, and the limitation in the applicability of the previous assessment methods has been highlighted. Also, with consideration of the large number of chemicals in existence, for example, about 80,000 types in the United States and about 55,000 types in Japan, there are difficulties in the assessment of these chemicals from the practical perspective. For this reason, there is a demand for the development of simpler methods in health risk assessment. There are different pathways in which the chemicals enter the body; the primary pathways are oral exposure, inhalation exposure, and dermal exposure. In health risk assessment, particular attention is paid to whether the chemicals undergo metabolism when traveling through the liver. In addition, there are various barriers in the body, which require the chemicals to be evaluated individually. Therefore, it is desirable to establish a common indicator that enables the chemicals to be assessed inclusively, or at least in subgroups. This book is titled “Health Risk Assessment of Environmental Chemicals: Pre-Emptive and Integrated Approaches,” and primarily consists of the sections titled “Part I: Current Health Risk Assessment of Chemicals,” “Part II: New Concepts of Toxicological Mechanisms of Chemicals,” “Part III: Integrated Health Risk Assessment of Chemicals,” “Part IV: Transgenerational Inheritance of Chemical Toxicity.” “Health risk assessment” primarily focuses on the toxicological assessment of the chemicals, on whether the chemicals found in our surroundings have an impact on our health. While the number of chemicals in our surroundings is said to be as many as 55,000 or 80,000, the “integrated” approach aims to evaluate the toxicity of chemicals divided into groups to a certain extent, although it may not be possible to establish an assessment applicable to all chemicals. The indication of an “integrated” approach should be understood by common sense, considering the reality of having to assess not only the individual chemicals but by the route of exposure for each chemical.

4

1 Introduction

The word “pre-emptive” in the subtitle is used more in the sense of “pre-emptive medicine,” a topic commonly discussed in recent years. For example, the groups of chemicals discussed in this chapter all affect the central nervous system, and induce neuropsychiatric disorders. However, there are a number of reports that suggest the observation of precursor signs in the peripheral tissues prior to the onset of neuropsychiatric diseases. While “pre-emptive medicine” involves the application of treatment before the condition becomes untreatable, “pre-emptive health risk assessment” aims to evaluate the health risks in the peripheral tissues before the onset of disorders in the central nervous system. This involves the use of pre-emptive biomarkers and can be interpreted as a dimension-free assessment method. These ideas were conceived from the results of our previous research and the modern-day advancement in life science. The results of our research mentioned in this section originated in the research on endocrine disruptors. Our researches were the first in reporting the effect of endocrine disruptors in inducing hyperactivity in rats. Human disorders in the present day, which involve hyperactivity disorder, are attention-deficit hyperactivity disorder (ADHD) and autism, and these are classified as neuropsychiatric disorders. For this reason, our report, despite only showing the findings from animal experiments, has caused some stir at the time. After the publication of this report, there have been many epidemiological surveys in human with results supporting our findings. Hyperactivity disorder in rats is quantified with the locomotor activity of the rats as the indicator. This was found to enable the impact of chemicals to be evaluated regardless of the chemical structure and the route of chemical exposure. In addition to endocrine disruptors, carcinogens such as benzo[a] pyrene, nicotine, and PCB have been reported by other researchers to induce hyperactivity disorder, thereby resulting in a wider scope of subgroup classification. Furthermore, these chemicals have been observed to induce development disorder of the dopaminergic neurons, which are responsible for movement and rewards. A notable new finding in modern-day neuroscience is that ADHD, autism, Parkinson’s disease, and Alzheimer’s disease, which are classified the disorders of central nerves, have effects outside the brain. The core symptoms of autism in the central nerves include social communication disorder, repetitive behavior, hyperactivity, and attention deficiency. It has been reported that abnormalities of gastrointestinal function are observed before the onset of these central symptoms. Parkinson’s disease, which involves motor disorders such as tremors and akinesia, has been observed with the manifestation of olfactory disturbance and digestive tract dysfunction prior to the onset of motor disorders. These findings demonstrated that peripheral symptoms precede central symptoms. A report, which further reinforces these observations, has been published recently with regard to Alzheimer’s disease. It has been estimated that the pathology of Alzheimer’s disease involves the accumulation of a protein called β amyloid, which disturbs the function of the nerve cells and accelerates atrophy. This β amyloid is generated not only in the brain tissue but in peripheral tissues (platelets, blood vessels, and muscles), and the aforementioned report suggested that β amyloid

References

5

produced peripherally may infiltrate the brain to cause an impact on the healthy neural cells. These results of the latest research suggest that chemically induced central disorders can be seen as peripheral metabolic disorders and that health risk assessment can be integrated as the distinctions of dimensions no longer exist. The circumstances explained above are the reasons for the authorship of this book; hence, the title “Health Risk Assessment of Environmental Chemicals: Pre-Emptive and Integrated Approaches ” was given. Endocrine disruptors have created a new major field of research that is not only limited to the relationship with the aforementioned disorders; however, this may be still considered fragmental at this point. It would be fortunate if the readers are able to come to terms with the dawn of new academic studies and research to some extent through this book.

References Carson R (1962) Silent Spring. Houghton Mifflin Company, Boston, MA Colborn T, Dumanoski D, Myers JP (1997) Our stolen future. Plum Press, New York Herz C, Tran HTT, Schlotz N, Michels K, Lamy E (2017) Low-dose levels of bisphenol a inhibit telomerase via ER/GPR30-ERK signaling, impair DNA integrity and reduce cell proliferation in primary PBMC. Sci Rep 7:16631. https://doi.org/10.1038/s41598-017-15978-2

Part I

Current Health Risk Assessment of Chemicals

What is the health risk assessment of chemicals, and how is it implemented? The fact that we are surrounded by so many chemical substances reflects the usefulness of these chemicals in our daily lives. There are various types of chemicals, such as chemical products, food additives, and residual pesticides. Health risk assessment of chemicals is implemented to ensure that the chemicals being used never cause harm to our health. In order to utilize chemicals safely, it is necessary to identify the toxicity of the chemicals. Risk assessment of chemicals normally involves four steps, which are: (1) Toxicological assessment (identification), (2) Assessment of dose-response, (3) Exposure assessment, and (4) Risk determination. Of these, 1 and 2 are conducted to identify the toxicity of the chemicals and determine the safe dose, which primarily involves toxicity studies in experimental animals and epidemiological surveys assessing human exposure.

Chapter 2

The Safe Dose in the Utilization of Chemicals

Abstract The dose response of chemicals is fundamental in estimating the safe dose in their usage. In this chapter, it is shown that their relationship formulates the sigmoid curve, which has several characteristics to be explained. Furthermore, it is shown how to implement the health risk assessment of chemicals such as rotenone (insecticide) and simazine (herbicide), as well as dioxin. Keywords Dose response · Safe dose · NOAEL · LD50 · ADI · TDI · Thresholds

2.1

Dose-Response Curve

In estimating the safe dose of chemicals, it is important to obtain the dose-response relationship of these chemicals. While there are various indicators used to assess the response to the chemicals, including pharmacological changes, molecular biological changes, and pathological changes, the ultimate indicator of toxicity is death. The result obtained from the actual laboratory is shown in Fig. 2.1. The rats used in the investigation have been purchased from a breeder who is a supplier of experimental animals. In this investigation, the rats were divided into groups of 8 rats, and rotenone, an agricultural chemical that has not demonstrated carcinogenicity in rats, was administered orally and then the mortality for each group was calculated. No deaths were observed for any rats administered 0, 0.1, and 1 mg/kg of rotenone, and 50% of the rats died after 10 mg/kg of rotenone was administered. Only one rat survived after receiving 50 mg/kg of rotenone. Figure 2.1A is a direct (discrete) representation of these results in a graph. Although the numbers of animals are discontinuous integer values in nature, these can be thought of as continuous values under the assumption that links mathematics and biology to generate sigmoid lines found commonly in textbooks (Figure 2.1B, Yamaguchi 2010; R Development Core Team 2016). In general textbooks of Toxicology, the figure below shows a general schematic diagram of the dose response of chemicals. One of the characteristics in this figure is that due to the formation of the sigmoid curve, the central curve can be perceived as being almost linear, and this is said to reflect the individual differences in the test organisms. The result of the investigation © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Ishido, Health Risk Assessment of Environmental Chemicals, https://doi.org/10.1007/978-981-99-1560-6_2

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The Safe Dose in the Utilization of Chemicals

Fig. 2.1 Dose-response curve of rotenone. (a) Discrete bars (plots) (b) Continuous plots 100

% responding

80

60 50% 40

20

0 10

100

LD50 1000 dose (log scale)

10000

Fig. 2.2 A schematic diagram of the dose-response curve

on the control groups explained that while some of the organisms showed high sensitivity to the chemicals investigated, some others showed resistance, with a large distribution of those with responses in between. Also, the presence of maximum and minimum values is another characteristic of this figure. When mortality (%) was shown on the vertical axis, the concentration of the chemicals, which results in death for 50% of the group, can be expressed as LD50. It is not difficult to assume that a certain population consists of some individuals who are highly sensitive to certain chemicals and some with resistance, with a majority of the population showing intermediate results. Figures 2.2 and 2.3 show the representation of the attributed dose-response curve, resulting in a normal distribution curve with LD50 in the center.

2.1

Dose-Response Curve

11

Fig. 2.3 Attributed dose-response curve

Again, we will go back to Fig. 2.1, and let’s think about the attributed doseresponse curve. If 50 mg of rotenone is weighed and dissolved in 3 mL of solvent and if 30 μL of the rotenone solution was given to eight of 10 g rats, death occurs in 7 animals. As general textbooks say, Fig. 2.4 also showed the frequency of response at a particular concentration: Frequency for 50 mg/kg of rotenone after subtracting the effect of rotenone at 10 mg/kg is 37.5%. 10 mg/kg with 50% frequency is equivalent to LD50 and thus corresponds to Fig. 2.1, which is expressed in the death rate. Actually, Dr. Abdelmawla et al. (1996) reported that there is no difference between cumulative and noncumulative dose-response curves to the drug on the human dorsal hand vein under the washing methods they used (Fig. 2.5). What specifically is the individual difference in the toxicity of chemicals? Genetic dispositions such as CNV (copy number variant) and SNP (single nucleotide polymorphism) and the differences in ADME (Absorption, Distribution, Metabolism, Excretion) are thought to have an impact on the individual differences in the response to chemical substances; however, the results above were obtained from experimental animals, which are purchased from the same breeder and have almost identical genetic backgrounds. It is not possible to provide an answer as to why the

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The Safe Dose in the Utilization of Chemicals

Fig. 2.4 Frequency of rat death by rotenone

sigmoid curve is generated; however, we will start the discussion from the perspective of a statistical model without consideration for biological significance. Under the assumption that individual differences are distributed normally, the graph forms a symmetrical bell curve (standard type) as shown in Fig. 2.6 (blue line). This is considered to be a probability density function expressed as 1/{1 + exp (x)}. The integral of the probability density function for continuous values results in probability. This graph is shown in Fig. 2.6 (red line). Therefore, the derivative of the sigmoid function results in a probability density function (1/(1 + exp (-x))) × (1 (1/(1 + exp (-x))): first-order derivative of the sigmoid function is expressed as the sigmoid function itself. This is a probability density function for a distribution referred to as exponential distribution. As explained here, the probability function may be expressed as functions in the continuous probability space, and the probability density function and the cumulative distribution function, which is the sum of the function, are linked by the relationship of integration and differentiation. As a result, the sigmoid curve reflects not only the individual differences but also other factors with normal distributions. The logistic function is included in the sigmoid function (sigmoid function as a generic function that includes logistic function (standard sigmoid function in

2.1

Dose-Response Curve

Fig. 2.5 Dose-response curves for the vasoconstrictor effect of noradrenaline during local infusion into the superficial dorsal hand vein (occlusion pressure 45 mmHg), (open circle) cumulative, and ( filled circle) noncumulative application of noradrenaline

Fig. 2.6 Sigmoid function and its derivative

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2 The Safe Dose in the Utilization of Chemicals

Fig. 2.7 Dose-response curves with different LD50

particular)). The functional type here is expressed by the following formula, with variable Zi as the linear predictor Zi = a + bxi.

q = 1= 1 þ 1=ezi When b is fixed at b = 1 and a is changed, the following graph is obtained, where a = 0 is shown as a blue curve. See Fig. 2.7 When the horizontal axis is interpreted as the concentration of the chemicals and the vertical axis is interpreted as the response, this figure can be thought of as doseresponse curves with different LD50. Therefore, a in the formula above can be said to represent the sensitivity of the individuals and the individual differences. Also, in statistical modeling, the generalized linear model consists of the probability distribution, linear predictor, and link function, while logistic regression uses binomial distribution and logit link function. We will consider the individual differences again here; when the survival and death of individuals due to chemical substances (response variable) is assumed to follow a binomial distribution, and individual differences have parts that are common to the majority and parts that are completely different between individuals, with the latter represented as r with normal distribution. When r is added to the formula above as an item with normal distribution, the result is as follows: Logit ðqÞ = a þ b × x þ r The probability of the event occurring y times can be calculated when the probability q of the event with binomial distribution is obtained. The procedure for

Dose-Response Curve

b

0.5

logit(q) = 0.5 + 0.5* x + r, r ~ N(0,1)

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Fig. 2.8 The generalized linear model

calculation is as follows: Binomial distribution and normal distribution are multiplied together and integrated with r to obtain the likelihood. Since the product of the probability is too small in values, a and b are estimated to obtain the maximum log-likelihood. Figure 2.8 shows the case with a = -0.3, b = 0.3. Following this, various values are fitted to x, and the maximum distribution of y is estimated for each case, then the relationship with the explanatory variable was plotted. The result of this plotting is the white circles in Fig. 2.8b. The solid blue line was obtained by fitting the logistic curve to this plot (with a maximum set as 1). Furthermore, the result of fitting the curve according to the logarithmic logit method to the same values is shown in Fig. 2.8c. We have discussed the significance of the sigmoid curve expressing the toxicity of the chemicals from the perspective of statistical modeling without considering the biological significance involved. From here, we would like to look at how genetic dispositions such as CNV and SNP and the differences in ADME result in individual differences in the response to chemicals.

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The Safe Dose in the Utilization of Chemicals

Fig. 2.9 Pharmacogenetics of Nortriptyline. (a) Mean plasma concentrations of nortriptyline after a single 25-mg oral dose are shown in subjects with 0, 1, 2, 3, or 13 functional CYP2D6 genes. (Modified from Dalen et al. (1998) with the permission of the publisher) (b) Relationship between the number of CYP2D6 allele and half time of plasma concentration of nortriptyline by transforming (a) Table 2.1 LD50 of various animal species in dioxin toxicity

Animal species Hamster Mouse (C57/BL) Rat (Wistar) Monkey Guinea pig

LD50 (μg/kg) 1157 to 5051 182a 110 50 0.6

Geyer et at. Ecotoxico. Environ. Safty (1997) 36:213-230 a; Chapman & Schiller, Toxicol. Appl. Pharmacol.( 1985) 78:147157

Figure 2.9 shows the data from the research paper by Dr. Dalen and colleagues, which investigates the number of gene copies (CNV) and metabolic activity involved in the drug metabolism of CYP2D6 (the data appear to be obtained from humans). The data shows the result of plasma concentration measurement following the administration of nortriptyline (antidepressant) in the subjects with CYP2D6 gene deficiency and those with 1, 2 (normal), 3, or 13 of this gene. The results suggest that humans with a greater number of copies have increased metabolic activity due to the high level of expression. The graph seems to suggest that the maximum plasma concentration differs according to the number of copies, while the ratio of the attenuation pattern (slope) appears to be similar. Therefore, the half-life of the plasma concentration was freshly plotted against the number of copies, resulting in Fig. 2.9B. The resulting graph also showed a smooth, decreasing curve. It is possible to compare the toxicity of a chemical relative to other chemicals with the use of LD50 values. However, since LD50 varies according to the animal species, sex, age, and other factors, care must be taken when using this for the actual comparison. A further difficulty is faced when extrapolating LD50 from animal experiments to humans. For example, with regards to the LD50 of dioxin for various

2.1

Dose-Response Curve

17

Fig. 2.10 Extrapolation of dose-response data from animal-based toxicological studies to sensitive humans. (Adapted from Galli, et al., 2008)

animal species in the table below, extrapolation should be performed with reference to 50 μg/kg in monkeys, which is about 100-fold higher than 0.6 μg/kg in Guinea pigs (Table 2.1). In addition, a number of parameters for estimating the safe dose of the chemicals can be obtained from this dose-response relationship. Examples of these are NOAEL (no observable adverse effective level), ADI (acceptable daily intake), TDI (Tolerance Daily Intake), and VSD (virtually safe dose). The risk of food additives or persistent pesticides is acceptable, while chemicals such as dioxin are not. Thus, a safe dose of acceptable chemicals is represented as ADI and avoidable chemicals are as TDI. Substantially, ADI is deduced as below: ADI = NOAEL=safety coefficient safety coefficient = 100 to 1000 VSD is defined as exposure that results in an extremely low probability of risk, equivalent to the rare risks that people encounter in their daily lives. It is often used as the dose at which carcinogenesis is induced at the probability of 1 in 100,000 (10-5) to 1 in 1000,000 (10-6) as a result of lifelong exposure in humans (Fig. 2.10). It has been empirically demonstrated that the distribution of the uncertainty factor for each element follows the log-normal distribution. For this reason, a proposal has been made to use the median value and geometric standard deviation in obtaining the uncertainty factor, which covers 95% of the distribution. Moreover, the proposal has been made to integrate the uncertainty factors for multiple elements by the integration of log-normal distribution.

18

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The Safe Dose in the Utilization of Chemicals

As explained here, the most significant rationale in estimating the safe dose of a chemical is the presence of thresholds, and this method is referred to as the NOAEL approach. In the next section, we will discuss the health risk assessment of agricultural chemicals rotenone (insecticide) and simazine (herbicide), as well as dioxin.

2.2 2.2.1

Health Risk Assessment of Rotenone and Simazine Rotenone

Rotenone is found naturally in the seeds and stems of several plants, such as derris (Fig. 2.11). According to the IRIS database of the United States Environmental Protection Agency (EPA; Table 2.2), the results of the 2-year chronic toxicity study on rats have indicated the NOAEL of rotenone = 0.373 mg/kg/day. If, for example, the pond is saturated with 250 μg/L and if the daily water intake is 2 L, the consumption of a mouthful (50 mL) is 0.178 μg/kg/day (mouthful). Thus, it is acceptable.

Fig. 2.11 Rotenone (Taken from Wikipedia) Table 2.2 Rotenone Fact Sheet EPA pesticide fact sheet Rotenone 1 Annual usage: 50,000 to 120,000 lb 2 Predominant usage: Agriculture (potatoes, tomatoes, apples) 3 Toxicological characteristics: Acute oral LD50 (rat): 39.5 ± 2.21 mg/kg (for female) 2 year feeding (rat): NOAEL = 0.375 mg/kg/day ADI = NOAEL/ uncertainty factor (=1000): 0.375 μg/kg/day 4 Environmental characteristics: Rotenone is rapidly degraded in soil and water with a half-life of 1-3 days for both aerobic aquatic and anaerobic aquatic soils.

2.3

Health Risk Assessment of Dioxin-Induced Malformation of Reproductive Organs

19

Table 2.3 Summary of the risk of simazine Method of calculating the risk of simazine (herbicide) – Result of 2-year chronic toxicity study in rats: NOAEL = 0.52 mg/kg/day (based on the IRIS data) – Indeterminate coefficient: 100 (10: Interspecies extrapolation, 10: Individual differences) NOAEL=uncertaincoefficient = 0:52 mg=kg=day=100 – Acceptable intake: = 5 μg=kg=day – Concentration of simazine in tap water: 50 ppt (50 ng/L) – Exposure (daily intake if 2 L): 50 (ng/L) x 2 L = 2 ng/kg/day Exposure=acceptable intake = 2 ðng=kg=dayÞ=5 ðμg=kg=dayÞ – Hazard ratio: = 0:4 × 10 - 3 < 1

2.2.2

Simazine

It has introduced the health risks of herbicide simazine as shown below (Gamou 2017; Theory on Environmental Risk Assessment, 2009). According to the IRIS database of the EPA, the results of the 2-year chronic toxicity study on rats have indicated the NOAEL of simazine = 0.52 mg/kg/day. When the uncertainty factor is presumed to be 100, the acceptable intake is calculated as 5 μg/kg/day. If, for example, a concentration of 50 ng/L was detected in the tap water and the daily water intake is 2 L, the intake is calculated to be 2 ng/kg/day. Since Intake ð2 ng=kg=dayÞ < Acceptable intake ð5 μg=kg=dayÞ, the health risk of simazine is considered negligible (hazard ratio = intake (2 ng/kg/ day)/acceptable intake (5 μg/kg/day) = 0.4 × 10-3). The explanation above can be summarized in Table 2.3. The exposure here refers to the intake and dose. It is strictly necessary to make an estimate using body burden with consideration for pharmacokinetics, which takes into account the biological half-life and biological absorption of the chemicals. With this point in focus, we will now look at the health risk assessment of dioxin with the malformation of reproductive organs as the endpoint (Ide and Watanabe 2001).

2.3

Health Risk Assessment of Dioxin-Induced Malformation of Reproductive Organs

As an example, there is an animal study where dioxin (TCDD) was administered to pregnant rats, which led to the observation of reproductive organ malformation in the offspring rats. In this study, the body burden of dioxin (TCDD) in the maternal rats was estimated to be 86 ng/kg. The biological half-life of dioxin was determined to be 7.5 years and the biological absorption was determined to be 0.5, and these were substituted in the following formula:

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The Safe Dose in the Utilization of Chemicals

Daily intake = ðelimination rate coefficientÞ × ðminimal body burdenÞ × ðabsorption rateÞ‐1 × ðuncertainty factorÞ‐1 =

0:693 1 1 ðdayÞ - 1 × 86 ðng=kgÞ × × 7:5 × 365 0:5 10 = 0:004354 ðng=kg=dayÞ = 4:3 ðpg=kg=dayÞ

References Abdelmawala AH, Langley RW, Szabadi E, Bradshaw CM (1996) Cumulative and noncumulative dose-response curves to noradrenaline on the dorsal hand vein. J Pharmacol Toxicol Methods 36:77–80 Dalen P, Dahl ML, Bernal Ruiz ML et al (1998) 10-hydroxylation of nortriptyline in white persons with 0, 1, 2, 3, and 13 functional CYP2D6 genes. Clin Pharmacol Ther 63:444–452 Galli CL, Marinovich M, Lotti M (2008) Is the acceptable daily intake as presently used an axiom or a dogma? Toxicol Lett 180:93–99 Gamou M (2017) Ecology, chemicals, and risk assessment. Kyoritsu smart selection 18. Kyouritsu Publication, Tokyo Ide S, Watanabe K (2001) Estimation of body burden of dioxin-like compounds in Japan. Environmental Syst Res 29:299–304 R Development Core Team (2016) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. https://www.R-project.org/ Yamaguchi M (2010) Chaos and fractal. Chikuma Shobo, Tokyo

Chapter 3

Carcinogens

Abstract In the graphs shown in Chap. 2 (graphs with thresholds), the safety of the chemical substance can be deduced from the estimated toxic level of the chemicals. This chapter will first discuss the following research paper, which reported the doseresponse curve of carcinogens and how to develop the health risk assessment of carcinogens. Furthermore, the health risk assessment of radiation is also shown both of which are based on the same principle. Keywords Carcinogen · Radiation · DNA adducts · DNA mutation · Thresholds

3.1

Carcinogens

The carcinogenicity of chemicals has been studied for many years. In 1775, an English surgeon by the name of Dr. Percival Pott reported the occurrence of scrotal cancer in chimney cleaners, which was the first report on occupational cancer. At the time of the report, the irritation theory by a German pathologist Dr. Virchow was widely believed with regards to the origin of cancer, which stated that stimulations from external factors are involved in the onset of cancer. Dr. Katsusaburo Yamagiwa (1863–1930, Department of Pathology, the University of Tokyo) traveled abroad to study with Dr. Virchow. In 1915, which was 140 years after the report by Dr. Pott, Dr. Katsusaburo Yamagiwa (Fig. 3.1) and colleagues successfully generated artificial cancer in rabbits through the application of coal tar. In studies using 101 animals, 7 animals were observed to have developed skin cancer over the period of about 1 year (660 days, nearly 2 years). Since a long period is required for the development of cancer, rabbits were selected over rodents, which only live for 2 years. In addition, the observation of cancer in 7 of 101 animals (6.9% of cancer incidence) suggested that the cancer is induced by the chemicals in the coal tar. The achievement of Dr. Yamagiwa and colleagues was worthy of receiving the Nobel prize; however, “theory on the cancer development due to parasite” by Dr. Fibiger and colleagues was nominated. Although the results of the research by Dr. Fibiger were clear, these were found to be only applicable to limited strains of mice and cannot be generalized by any means. The research of Dr. Yamagiwa on the

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 M. Ishido, Health Risk Assessment of Environmental Chemicals, https://doi.org/10.1007/978-981-99-1560-6_3

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Carcinogens

Fig. 3.1 Dr. Katsuzaburo Yamagiwa

Fig. 3.2 Sir Richard Doll

development of cancer due to tar remains an immortal achievement, and he became one of the three Japanese researchers who have missed out on the Nobel prize. Coal tar contains a mixture of various chemical substances, and dibenzanthracene was isolated as a carcinogen in 1930, which was followed by the isolation of benzo [a]pyrene (benzopyrene, benzpyrene) 2 years later. Both of these substances have 5 benzene rings. The carcinogenic mechanism of these substances was only elucidated in 1970, 40 years after the isolation of these substances. In 1954, Sir Richard Doll (Fig. 3.2), an epidemiologist, and his colleagues published data that are considered important for the research on cancer. Since around 1950, many researchers have drawn attention to the fact that epithelial cancer in humans (such as esophageal, skin, lung, gastric, and colon cancer) increases steadily with age, resulting in a linear log-log plot of the age-corrected mortality against age, and that this tendency is observed for both male and female patients (Fig. 3.3, Armitage and Doll 1954, 1957). These results suggested that cancer is produced in multiple stages; if the onset is a single process, this should produce a linear graph with an ordinal scale without logarithmic transformation. These findings suggest that cancer develops over time, although pediatric cancer seems to be an exception. This is also thought to explain the reason that Dr. Katsusaburo Yamagiwa and his colleagues used rabbits, which have a mean lifespan of 8 years under bred conditions, as experimental animals over rats and mice with a lifespan of up to 2 years.

3.2

Health Risk Assessment of Carcinogens

23

Fig. 3.3 Relationship between age and death rate in cancer

With consideration for these factors, the study methods for carcinogen in the present days were established as shown in the following section.

3.2

Health Risk Assessment of Carcinogens

These studies are conducted under the idea that all human carcinogens are also carcinogenic to other animals.

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3.2.1

3

Carcinogens

Long-Term Carcinogenicity Studies

At least 50 animals (groups of rats and mice) were used, with 3–4 treatment groups. The administration method was set as oral (mixed with feed or as gavage), and the test substance was administered for at least 24 months in rats and at least 18 months in mice. Examinations conducted included necropsy and histopathology on all tissues for all groups, and the increase in primary occurrence in the treated group was examined in comparison with the control group.

3.2.2

Carcinogenicity Studies with Surrogates

The establishment of a test method with a shorter duration and improved efficiency was proposed (in the 1990s). A guideline was established so that one of the shortterm carcinogenicity study models using transgenic animals, initiation-promotion models, or neonatal animal model is selected in addition to the long-term carcinogenicity study on one rodent species, and carcinogenicity is evaluated based on the results of these studies (D’Arcy and Harron 1998).

3.3

Dose-Response Curve and Carcinogenic Mechanism of Benzo[a]Pyrene

What are the characteristics of the dose-response curve of carcinogen benzo[a] pyrene? Graphs were prepared from the values in the research papers published on the results of animal experiments by Drs. Thyssen et al. (1981), Neal and Rigdon (1967), and Levin et al. (1977). In the preparation of these graphs (Fig. 3.4), the logistic regression, which uses binomial distribution as the probability distribution, was obtained by statistical software R (R Development Core Team 2016), and these graphs are shown in the right column. Similarly, the graphs obtained by the logarithmic logit method are shown in the left column, in the corresponding row (Fig. 3.4). In the logarithmic logit method, zero is handled as a control, and the axis is disconnected. The symbols show the reported values, and the line is fitted. The results here are shown by the route of exposure: The top row shows the incidence of tumor from exposure to benzo[a]pyrene in the feed, followed by inhaled exposure (middle row) and dermal exposure (bottom row). The values reported by Dr. Neal and colleagues are used for exposure through feed, the values reported by Dr. Thyssen and colleagues are used for inhaled exposure, and the values reported by Dr. Levin and colleagues are used for dermal exposure. These results were summarized in a review by Dr. Collins et al. (1991).

3.3

Dose-Response Curve and Carcinogenic Mechanism of Benzo[a]Pyrene

Fig. 3.4 Dose-response curve of benzopyrene

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Carcinogens

The details of the experiment by Dr. Neal and colleagues could not be obtained; however, an investigation was conducted on the incidence of gastrointestinal cancer after exposure to the indicated dose of benzo[a]pyrene (1–250 ppm) through mice feed. No tumors were observed in 289 control mice. Dr. Thyssen and colleagues conducted an experiment in adult male hamsters, and inhaled exposure was performed with 2.2, 9.5, and 46.5 mg of benzo[a]pyrene/m3 for 4.5 h daily, 7 days a week, with observation conducted on respiratory tract tumor. The control group without exposure did not develop tumors over the period of 2 years, which is considered to be the lifespan of hamsters. The study was conducted on a scale of about 25 animals in each group. Dr. Levin and colleagues applied benzo[a]pyrene to adult mice twice weekly for 60 weeks at the concentrations shown in the graph. The treated group contained 30 animals in each group. Since the lifespan of mice is about 2 years, the age of 60 weeks or older was considered to be old age, and the number of mice (probably those with tumor) was counted at this point. The control mice applied the vehicle only, and no skin tumor was observed. The graph passed the origin, and the tumorgenicity of benzo[a]pyrene at 0.02 μmol, which was the lowest concentration tested, was said to be different depending on the type of vehicle used. Although some of these cases included a small number of measured values, both the logistic regression analysis and logarithmic logit method were found to generate sigmoid curves. We will focus on the experiment on feeding as relatively frequent data have been provided on the threshold for the estimate of a safe dose. In this experiment, the logarithmic logit method has provided adequate fitting with the measured values in the range with a low concentration of benzo[a]pyrene. The values reported have suggested that gastrointestinal tumor does not occur up to 10 ppm of benzo[a]pyrene. Meanwhile, the graphs on the threshold seem to be difficult to interpret for inhaled and dermal exposure due to the insufficiency in measured values.

3.3.1

Carcinogenic Mechanism of Benzo[a]Pyrene

Benzo[a]pyrene undergoes metabolic activation by an enzyme called P450 (Fig. 3.5), and this was a major discovery in the 1970s. P450(CYP) initially induces the addition of an oxygen atom to form an epoxide and the epoxide changes to transdiol form due to the action of epoxide hydrase. The main component in the two diol forms is 17β form, which activates the double bonds at the ninth and tenth positions to generate 7,8-diol-9,10-epoxide. Benzo[a]pyrene is thought to react in this manner with DNA to cause carcinogenicity (Fig. 3.6). A covalent bond is formed between the carbon atom at the tenth position of epoxide and the extracyclic nitrogen atom of guanine in the DNA. Following this, benzo[a]pyrene binds with the guanine base in the DNA to form a DNA adduct conjugate. Although the DNA adduct is repaired by the repair enzyme,

3.3

Dose-Response Curve and Carcinogenic Mechanism of Benzo[a]Pyrene

27

Fig. 3.5 Carcinogenicity of benzo[a]pyrene

Fig. 3.6 Reaction between the metabolites of benzo[a]pyrene and the amino group in guanine

however, the failure to do so results in inaccurate DNA replication (in most cases, guanine is replaced with thymine). Carcinogenicity of benzo[a]pyrene occurs due to a more complex mechanism, including the involvement of reactive oxygen (Fig. 3.7). Benzo[a]pyrene was previously thought to be metabolized by diol epoxide before forming a DNA adduct, which causes damage to DNA. However, it has been shown that benzo[a]pyrene7,8-dione, which is another metabolite of benzo[a]pyrene, causes oxidative DNA damage, including the generation of 8-oxodG. This oxidative DNA damage was thought to play an important role in the carcinogenic mechanism of benzo[a]pyrene in addition to the formation of adducts.

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Fig. 3.7 Mechanism of oxidative DNA damage. (1) BaP; (2,3) benzo[a]pyrene-diones; (4) benzo [a]pyrene-7,8-trans-dihydrodiol; (5) catechol analog; (6) semiquinone anion radicals; (7) 7β,8α-dihydroxy-9α,10α-epoxy-7,8,9,10-tetrahydrobenzo[a]pyrene (BPDE-I); (8) benzo[a]pyrene-7,8dione. https://www.mdpi.com/1420-3049/27/4/1379/htm

Table 3.1 Classification of carcinogens based on the binding to DNA Carcinogens that bind to DNA 1. Benzo[a]pyrene 2. Aflatoxin 3. Dimethyl nitrosamine (DMN)

Carcinogens that do not bind to DNA 1. DDT 2. Chloroform, dieldrin 3. DES, TCDD, DEHP 4. TPA

Carcinogens with unclear mechanisms of action Asbestos, arsenic

3.4

Various Carcinogens and their Dose-Response Curves

As explained in the previous section, carcinogen benzo[a]pyrene binds with DNA to form DNA adducts. The observations on benzo[a]pyrene have shown that the behaviors of carcinogens vary depending on whether they bind to DNA or not. Table 3.1 is a summary of the different types of carcinogens. What are the characteristics of the dose-response curves for carcinogens other than benzo[a]pyrene? Dr. Takashi Yanagawa analyzed the dose-response curves of

3.4

Various Carcinogens and their Dose-Response Curves

29

Fig. 3.8 Dose-response curves of 4 kinds of carcinogens such as aflatoxin, dieldrin, and dimethylnitrosamine (DMN), and DDT

carcinogens other than benzo[a]pyrene using the values reported by the United States Food Safety Council (2002). The carcinogens analyzed were aflatoxin, dieldrin, dimethyl nitrosoamine (DMN), and DDT. The detailed experimental methods are not available as the original paper could not be accessed; however, these chemicals are known to cause hepatic tumors. The models for logistic regression analysis and logarithmic logit method were applied using the statistical software R in a similar manner as benzo[a]pyrene, and the measured values reported were shown in symbols, with the fitting results shown as a curve (Fig. 3.8). As expected, both cases of fitting showed tendencies for large sigmoid curves, and the results obtained indicated that the analysis by logarithmic logit method was

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Fig. 3.8 (continued)

preferred for fitting compared with logistic regression analysis as observed earlier. Although the number of measured values available was not considered sufficient for establishing threshold values, it appeared that threshold values may exist for chemicals other than dieldrin (aflatoxin, DMN, and DDT). Dr. Takashi Yanagawa reported the dose-response curve estimated from these data using the logistic model (2002), and these were largely consistent with the results we obtained by logistic regression analysis. He also conducted a further investigation on the probit model and reported the results to be almost identical, and added that marked deviation occurs in the dose-response curve within the range of very low concentration. The results of their analysis are shown in Fig. 3.9.

3.5

Research on Carcinogens by Prof. Shoji Fukushima

31

Fig. 3.9 Dose-response curves of 4 kinds of carcinogens such as aflatoxin, dieldrin, DMN, and DDT. (Adapted from Yanagawa 2002)

3.5

Research on Carcinogens by Prof. Shoji Fukushima

At this point, we would like to introduce the research on the carcinogenicity of chemicals in animal experiments by Prof. Fukushima, which was conducted with consideration for the issues of threshold in the carcinogenicity of chemicals and shows a number of characteristics in the research designs (Fukushima et al. 2016). One such characteristic is the use of preneoplastic lesions as the indicator for the determination of carcinogenicity (medium-term carcinogenicity study method). This is based on the pathological perspective where, for example, there is a clearly established sequence of healthy hepatocytes → hyperplastic foci of stem cells as preneoplastic lesion → stem cell adenoma → stem cell carcinoma in the case of hepatic cancer in rats, and glutathione S-transferase placental form (GST-P) positive foci are used as the indicator of preneoplastic lesions. The second characteristic is the investigation of the formation of adducts and oxidative stress marker 8-OHdG (8-hydroxy-2′-deoxyguanosine) as the indicator of the chemical impact. The third characteristic is the investigation of multiple carcinogens (MeIQx: 2-amino-3,8dimethylimidazo[4,5-f]quinoxaline, IQ: 2-Amino-3-methylimidazo[4,5-f]quinoline, DEN: N-nitrosodiethylamine, DMN, PhIP). We will first focus on MeIQx. MeIQx is a substance found in the charred parts of fish and meat, with chemical structures shown in Fig. 3.10. It is a carcinogen that has a direct impact on the DNA and causes hepatic tumors in rats.

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Fig. 3.10 Characteristics of MeIQx (Top) and an experimental design (Bottom) (adapted from Fukushima et al. 2016)

MeIQx was administered in the feed of 1180 21-day-old male F344 rats at the low-dose range of 0, 0.001, 0.01, 0.1, 1, and 10 mg/kg and the dose of 100 mg/kg for up to 32 weeks (about 8 months) consecutively (Fig. 3.10, bottom). The results showed that for the 16-week administration group, there was no difference in the occurrence of GST-P positive foci as the preneoplastic lesion in the liver for the MeIQx 0.001–1 mg/kg groups compared with the control group while increasing tendency was observed for 10 mg/kg and a clearly significant increase in occurrence was observed for the high dose of 100 mg/kg. In addition, the similar curve was obtained when the administration of MeIQx was extended to 32 weeks (Fig. 3.11). MeIQx-induced hepatic cancer in rats occurred due to various carcinogenic reactions in the formation process at various doses (Fig. 3.12): Firstly, the formation of DNA adducts is observed from an extremely low dose, followed by a certain range of NOAEL, then an increased level of 8-OHdG formation, H-ras cancer gene mutation, lacI gene mutation, and increase in the initiation activity, followed by a certain range of NOAEL, followed by an increased generation of GST-P positive foci as preneoplastic lesions. These results strongly suggest that this leads to an even wider NOAEL range before an increase in hepatic cancer is observed. As a result of a two-year carcinogenicity study of MeIQx in male rats, the onset of hepatic cancer was only observed at the dose of 100 mg/kg. Similar methods have been used in the suggestion of NOAEL of genotoxic carcinogen DEN and DMN for carcinogenicity in rat liver, the practical threshold of PhIP for carcinogenicity in rat large intestine (Fig. 3.13). As shown here, clear thresholds exist for carcinogens that cause oxidative stress.

3.5

Research on Carcinogens by Prof. Shoji Fukushima

33

Fig. 3.11 Dose-response relationships with MeIQx-DNA adduct (a), 8-OHdG (b), and GST-P positive foci (c) (adapted from Fukushima et al. 2016)

Fig. 3.12 Quantitative approaches to assess key carcinogenic events of genotoxic carcinogens (adapted from Fukushima et al. 2016)

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Fig. 3.13 Dose-response relationships with DEN, DMN, and PhIP (adapted from Fukushima et al. 2016)

3.6

Dose-Response Curve of TPA (12-O-Tetradecanoylphorbol 13-Acetate)

35

Fig. 3.13 (continued)

3.6

Dose-Response Curve of TPA (12-O-Tetradecanoylphorbol 13-Acetate)

In this section, we will look at the dose response of TPA, which is a nongenomic carcinogen that has been found to act on promoters, rather than initiators, in multistage carcinogenesis. TPA was discovered as the active ingredient of croton oil. Since the increased availability of TPA in the latter half of the 1970s, rapid advancement was observed in the research of promoters such as TPA, which was also helped by the completion of the Ames test as well as the identification of the final product of benzo[a]pyrene.

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In addition to the previously mentioned benzo[a]pyrene, substances such as 7,12dimethylbenznthracene (DMBA) are known to be highly carcinogenic. When DMBA was applied as a patch to a section of mouse skin every day, skin cancer was found to occur a few months later. Skin cancer was not observed when treatment with DMBA was performed once, or within a period of up to 3 months. Meanwhile, the following experimental results have provided further intriguing insights: When DMBA was initially applied once, and treatment was performed with the second substance TPA (12-O-tetradecanoylphorbol 13-acetate) once a week, the onset of papilloma was observed in 4–8 weeks in case of mice. However, when the initial treatment with DMBA was not performed, neither papilloma nor carcinoma occurred following the repeated application of TPA. This was explained by a single application of DMBA acting as an initiator in leaving long-term traces on a group of cells, and TPA acting as a promoter on these groups of cells with traces to eventually cause papilloma. In this case, TPA does not act on the promoter area of the gene; in fact, papilloma was resolved after the application of TPA is discontinued. Therefore, the effect of TPA is thought to be reversible and does not directly impact the genes of the papilloma cells. After 40 years after the discovery of this phenomenon, the genes and proteins, which play primary roles in skin tumorigenesis, were identified. The initiator DMBA randomly creates a variety of mutations; however, this always includes an H-ras cancer gene of point mutation type. Following this, repeated treatment with a promoter TPA accelerates the growth of cells with the cancer gene through a synergic effect with the active H-ras cancer gene, which leads to the onset of papilloma. This process not only involves the active H-ras cancer gene but also the mutant p53 gene. TPA acts on protein kinase C, which is a phosphorylated protein discovered by Prof. Yasutomi Nishizuka in the latter half of the 1970s and has been found to play an important role in the system of biological information transduction (Inoue et al. 1977; Nishizuka 1984). The downstream effector of protein kinase C acts in combination with the H-ras cancer gene by some mechanism to promote the growth of keratinocytes in its process, and the descendant cells of these cells form papilloma, which eventually leads to the onset of advanced carcinoma. What does the dose response of tumorigenesis by TPA, which is not thought to have a direct impact on the gene, look like? We have prepared a figure from the data published by Dr. Duuren and colleagues in Cancer Research in 1972 (Fig. 3.14).

3.7

How to Interpret the Dose-Response Curve

Figure 3.15 shows a schematic representation of the dose-response curve for many carcinogens discussed in the previous sections. The response at a low dose is shown as range A, and the greater response is shown as range B. The focus of risk

3.7

How to Interpret the Dose-Response Curve

37

Fig. 3.14 Dose-response curves of DMBA plus TPA or TPA alone. (Adapted from Duuren et al. 1973)

Fig. 3.15 A schematic representation of the dose-response curve

determination is whether carcinogenicity is found in the dose range shown as range A and whether this is a safe dose for the handling of the chemicals. The interpretation of these points is presented in Fig. 3.16.

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Fig. 3.16 Threshold approaches of doseresponse curve

In the dose-response curve of the figure above, the doses included in the bioassay study are shown as points f and g. The statistical significance is shown with an asterisk (*). NOAEL (no observed adverse effect level) corresponds to point f, and has the least statistical significance among the studied doses. Lines a, b, c, and d are possible extrapolations from point e which indicates ED10 (POD: the point of departure) to the lower concentrations. Also, point g is the LOAEL (lowest observed adverse effect levels) in this assay. We will refer again to the previously quoted data on the carcinogenicity of benzo [a]pyrene, more specifically the data by Dr. Neal and colleagues with relatively frequent data available at low doses. This time, the Weibull model included in the statistical software R was used for fitting (Fig. 3.17). Weibull model uses Cancer incidence = 1 - exp (-xm) where x is the dose. Since the data for benzo[a]pyrene at the dose of 1 mg/kg and lower are not available, this can be estimated using the Weibull model. When the parameter is m = 7, a downward curve is observed, and m = 0.5 results in an upward curve, while the result with m = 1 is almost linear. Also, m = 1 indicates a one-hit model.

3.8

Health Risk Assessment of Radiation

Meanwhile, the scientific rationale was sought for the concept that the methods of health risk assessment of chemicals must follow the methods of health risk assessment of radiation. For the health risk assessment of radiation, in particular that of the low-dose radiation, the risk of cancer was found to be clearly increased for the dose of about 100 mSv or higher based on the epidemiological research on atomic bomb survivors in Hiroshima and Nagasaki, however, the effect at lower dose have been

Health Risk Assessment of Radiation

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3.8

39

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Fig. 3.17 Weibull functional analysis of dose-response relationship

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Carcinogens

0.5 0.4 0.3 0.2 0.1

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Fig. 3.18 A schematic diagram of dose-response relationship on the risk of cancer due to radiation

unclear. For this reason, suggestions have been made to establish scenarios for the extrapolation of risks associated with low-dose exposure. In any case, the health risk assessment is based on the idea that radiation causes damage to DNA, then changes the gene (mutation), and the accumulation of mutations leads to the onset of cancer. The following explanation is provided in Fig. 3.18a. 1. Linear dose-response relationship (line a) 2. Nonlinear dose-response relationship (curve b, c, d, e) (a) Scenario in which the risk is underestimated by linear assumption: Curve upward (b) Scenario in which low dose exposure is associated with no risk or even some benefit (curve downward) Figure 3.18b was derived from the report by Dr. Pierce et al. (1996). In reality, slight risks associated with low-dose radiation cannot be detected epidemiologically or by experiments. LNT (Linear Nonthreshold) can only be observed clearly from the epidemiological perspective for the total of all cancers and at the dose of 100 mSv and higher. The dose-effect relationship is not necessarily linear for the individual types of cancer (Fig. 3.19, Nakanishi 1995, 2004a, b). For example, skin cancer has been found to have thresholds, and breast cancer shows a dose-effect relationship deviated from the line, with an upward curve (Preston et al. 2007). For this reason, the relationship cannot generally be concluded as linear for all types of cancer. This is thought to be due to the slight differences in the mechanism of carcinogenesis in different tissues. The reason for using LNT is that it is considered to be a tool for risk prediction in protection against radiation. In the risk assessment for radiological protection, assessment is conducted for the risks of all cancers with correction of each type of

3.8

Health Risk Assessment of Radiation

41

Fig. 3.19 Solid cancer incidence in atomic bomb survivors: 1958–1998. (top) Nonmelanoma skin cancer dose-response function. The thick solid line is the fitted linear-spline gender-averaged excess relative risk (ERR) dose response at age 70 after exposure at age 30 based on data in the 0- to 2-Gy dose range. The light thin dashed line is the simple linear fit to this dose range. The points are nonparametric estimates of the ERR in dose categories. The thick dashed is a nonparametric smooth of the category-specific estimates and the dark dashed lines are one standard error above and below this smooth. (bottom) Female breast cancer dose-response function. The thick solid line is the fitted excess relative risk (ERR) dose response at the age 70 after exposure at the 30 based on data in the 0- to 2-Gy dose range. The solid line is a nonparametric smooth of the category-specific estimates and the dotted lines are standard error above and below this smooth. (Adapted from Preston, et al., 2007)

cancer by mortality ratio, rather than for specific types of cancer. Therefore, even if a threshold is available for the onset of a particular type of cancer, this threshold cannot be used for protection unless it is common to all types of cancer. As a result, there is no choice but to use LNT for protection against radiation (ICRP 2005). Figure 3.20a shows the distribution of the background radiation dose in the United States. Meanwhile, Fig. 3.20b shows the mortality due to cancer, where greater distribution is observed along the Mississippi river. What are the substances that cause damage to the genes? The left side on Table 3.2 is a frequently quoted figure based on the results of a cancer epidemiology study by Sir Doll and colleagues in 1981. This report states that about 60% of cancer is caused by tobacco and food. Tobacco not only contains the aforementioned benzo [a]pyrene but also acrylamide, which does not have a benzene ring. Acrylamide is also generated during the processing of food products. Substances such as food additives and mold toxin (aflatoxin B1) are carcinogens found in food products, and radiation is not considered to be a major contributor (Fig. 3.21).

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Fig. 3.20 U.S. radiation dose rates from the natural background (a) and cancer mortality rates (b). (Adapted from Brooks 2019)

The right side of Table 3.2 shows an estimate based on epidemiological research by Harvard University in the United States in 1996. No marked changes were shown in the general trends 15 years after the publication by Sir Doll, with food, obesity, and tobacco as the primary contributors. In addition to these factors, infection is a major cause of cancer in developing countries.

3.9

Genome Mutation

Table 3.2 The cause of cancer

3.9

43

Tobacco Diet/obesity Infections Reproductive organ/hormone Occupation Alcohol Environmental pollution Food additives Pharmaceutical Industrial consumables Genetics Insufficiency in exercise

Doll et al. 30% 35% 10% 7% 4% 3% 2%