Toxicity: 77 Must-Know Predictions of Organic Compounds. Including Ionic Liquids

Due to the advances of various methods for the prediction of toxicity of organic compounds and ionic liquids (ILs), it is necessary to review these methods for scientists and students. It is essential to compare the advantages and shortcomings of these methods. Since many organic compounds and ILs are synthesized each year, this book introduces suitable models for the assessment of their toxicities. This book reviews the best predictive methods for the prediction of toxicity of organic compounds and ILs, which were derived by *in vitro* or *in vivo* experiments. Different available quantitative structure‐toxicity relationship (QSTR) models based on various descriptors have been discussed to predict toxicity parameters such as *LD50* (50% lethal dose), *EC50* (the concentration of the desired IL that produces mortality of 50 percent of the bacterial population) and *log(IGC50-1)* (logarithm of 50% growth inhibitory concentration of *T. pyriformis*) of various classes of organic compounds and ILs. The reliability of these methods is compared and discussed. Each chapter contains some complimentary problems with their answers, which can improve the experience of students and researchers. The introduced subjects are suitable for advanced students in chemistry, biochemistry, medicinal chemistry, and chemical engineering. * Interesting for researchers including academics, national laboratories, and scientific agencies. * Suitable for advanced students in chemistry, biochemistry, medicinal chemistry, and chemical engineering. Cover Half Title Also of interest Toxicity: 77 Must-Know Predictions of Organic Compounds. Including Ionic Liquids Copyright Preface Contents 1. Toxicity Assessment 1.1 Toxicity Measurements and Predictions 1.1.1 Dose Descriptors and In Silico Tools 1.1.2 Aquatic Toxicity 1.2 Quantitative Structure–Activity/Property/Toxicity Relationships (QSAR QSPR/QSTR)/ 1.3 Statistical Parameters for Assessment of QSAR/QSPR/QSTR Models 1.4 Common Organic Solvents and Their Toxicities 1.4.1 Section of Solvents for Polymers with Less Toxicity 1.4.2 The Use of a Simple Approach for Selection Solvents of Polymers 1.4.3 General Comments on Using Solvents 1.5 Toxicities of Polycyclic Aromatic Hydrocarbons 1.5.1 Carcinogenicity of PAHs 1.5.2 Octanol/Water Partition Coefficient (Kow) 1.6 Organophosphate Pesticides and Their Toxicities 1.7 Prediction of Henry’s Law Constant of Pesticides, Solvents, Aromatic Hydrocarbons, and Persistent Pollutants 1.8 Common Process for Removal of Organic Compounds in the Environment 1.9 Summary Problems 2. Toxicity of Small Data Sets of Organic Compounds 2.1 Nitroaromatic Compounds 2.1.1 QSAR/QSTR Studies on Bacteria 2.1.1.1 Salmonella typhimurium TA100 Strain 2.1.1.2 Salmonella typhimurium (TA98) Bacterial Species with or Without Microsomal Activation (S9) 2.1.2 QSAR/QSTR Studies on Rodents 2.2 Aromatic Aldehydes 2.2.1 Two Descriptors log Kow and the Maximum Acceptor Superdelocalizability in a Molecule 2.2.2 Log Kow and Molecular Connectivity Index 2.2.3 Log Kow as well as Electronic and Topological Descriptors 2.3 Amino Compounds 2.3.1 Estimation of ISTP 2.3.2 Prediction of DSTP 2.3.3 Different Effects of –OH and –N=O 2.4 Halogenated Phenols 2.4.1 The DFT-B3LYP Method with the Basis Set 6-31G (d, p), and log Kow 2.4.2 Two-Dimensional (2D) and Two Three-Dimensional (3D) QSAR/QSTR Models 2.4.3 Wastewater-Derived Halogenated Phenolic Disinfection By-Products 2.5 Organophosphate Compounds 2.6 Polychlorinated Naphthalenes 2.7 Assessment of the Agonistic Activity of Dibenzazepine Derivatives 2.8 Summary Problems 3. Toxicity of Medium-Sized Data Sets 3.1 Polycyclic Aromatic Hydrocarbons (PAHs) 3.2 Benzene Derivatives 3.2.1 3D-QSAR/QSTPR Studies Using CoMFA, CoMSIA, and VolSurf Approaches 3.2.2 Atom-Based Nonstochastic and Stochastic Linear Indices 3.2.3 Semiempirical Descriptors 3.3 Phenol Derivatives 3.4 Benzoic Acid Derivatives 3.4.1 Predicting Toxicity Through Mouse via Oral LD50 3.4.1.1 Modified Molecular Connectivity Index 3.4.1.2 Elemental Composition and Molecular Fragments 3.4.2 Estimating Toxicity Through Rats via Oral LD50 3.4.2.1 The Effect of Quantum Chemistry Parameters 3.4.2.2 Desk Calculation of Toxicity of Benzoic Acid Derivatives in Rats via Oral LD50 3.5 Assessment of Antitrypanosomal Activity of Sesquiterpene Lactones 3.6 Assessment of Activities of Thrombin Inhibitors 3.7 Assessing the Psychotomimetic Activity of the Substituted Phenethylamines 3.8 Summary Problems 4. Toxicity of Large Data Sets 4.1 Aromatic Compounds 4.1.1 Regression-Based QSTR and Read-Across Algorithm 4.1.1.1 Descriptors with Negative Contributions 4.1.1.2 Descriptors with Positive Contributions 4.1.2 Acute Toxicity of Aromatic Chemicals in Tadpoles of the Japanese Brown Frog (Rana japonica) Using Correlation Weights 4.1.3 Chemometric Modeling of Acute Toxicity of Diverse Aromatic Compounds Against Rana japonica 4.1.4 Toxicity of Different Substituted Aromatic Compounds to the Aquatic Ciliate Tetrahymena pyriformis 4.1.5 Toxicity of Aromatic Pollutants and Photooxidative Intermediates in Water 4.1.6 Risk Assessment of Aromatic Compounds to Tetrahymena pyriformis by a Simple QSAR/QSTR Model 4.1.7 Toxicity Toward Chlorella vulgaris of Organic Aromatic Compounds in Environmental Protection 4.2 Organic Compounds 4.2.1 Chemical Toxicity to Tetrahymena pyriformis with Four Descriptor Models 4.2.2 Ecotoxicological QSAR/QSTR Modeling of Organic Compounds Against Fish 4.2.2.1 The QSAR/QSTR Modeling of the Local Data Sets 4.2.2.1.1 The QSAR/QSTR Model for Aldehydes 4.2.2.1.2 The QSAR/QSTR Model for Aliphatic Amines 4.2.2.1.3 The QSAR/QSTR Model for Amides 4.2.2.1.4 The QSAR/QSTR Model for Anilines 4.2.2.1.5 The QSAR/QSTR Model for Esters 4.2.2.1.6 The QSAR/QSTR Model for Neutral Organics 4.2.2.1.7 The QSAR/QSTR Model for Phenols 4.2.2.1.8 The QSAR/QSTR Model for Vinyl/Allyl/Propargyl (V/A/P) Moiety Containing Chemicals 4.2.2.1.9 The QSAR/QSTR Model for Miscellaneous Chemicals 4.2.2.2 The QSAR/QSTR Modeling of the Global Data Set 4.2.2.2.1 Using Only Dragon and PaDEL Descriptor Software 4.2.2.2.2 Using SiRMS Descriptors (2D Fragmental Descriptors) 4.2.2.2.3 Some Features of Descriptors in Equations (4.39✶) and (4.40✶) 4.2.2.2.4 Overall Interpretation and Application of Equations (4.30✶)–(4.40✶) 4.3 Summary Problems 5. Toxicity of Ionic Liquids 5.1 Toxicity of ILs Based on Vibrio fischeri Through the Structure of Cations with Specific Anions 5.2 Relationships of the Toxicity with the Structure and the 1-Octanol–Water Partition Coefficient of ILs 5.3 Using a Simple Group Contribution Method for Some ILs 5.4 Using Atomic Electrostatic Potential Descriptors for Predicting the Ecotoxicity of ILs Toward Leukemia Rat Cell Line (ICP-81) 5.5 Summary Problems List of Symbols Answers to Problems Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 References About the Author Index