Predictive Toxicology: From Vision to Reality -- Contents -- List of Contributors -- Preface -- A Personal Foreword -- 1 Introduction to Predictive Toxicology Tools and Methods -- 1.1 Computational Tools and Bioinformatics -- 1.1.1 In Silico Prediction Tools -- 1.1.2 Bioinformatics -- 1.2 Omics Technologies -- 1.2.1 Toxicogenomics (Transcriptomics) -- 1.2.2 Proteomics -- 1.2.3 Metabolomics -- 1.3 Data Interpretation and Knowledge Management -- 1.4 Biomarker Development -- 1.5 Advanced In Vitro Systems and Stem Cell Research -- 1.5.1 Advanced In Vitro Testing -- 1.5.2 Stem Cell Research -- 1.6 Immunogenicity -- 1.7 Integration and Validation -- 1.7.1 Use of Omics for Toxicology Testing -- 1.7.2 Integration of "New" Technologies into Risk Assessment -- 1.7.3 Use of Human-Derived Cellular Systems -- 1.7.4 "General" Acceptance - Translation into Guidelines -- 1.8 Research Initiative/Collaborations -- 1.9 Concluding Remarks -- References -- 2 In Silico Toxicology - Current Approaches and Future Perspectives to Predict Toxic Effects with Computational Tools -- 2.1 Introduction -- 2.2 Prediction of Hazard -- 2.2.1 Definition of Hazard and Its Use -- 2.2.2 Prediction of Mutagenicity -- 2.2.3 Prediction of Phospholipidosis -- 2.2.4 Prediction of Carcinogenicity -- 2.2.5 Prediction of Skin Sensitization -- 2.2.6 Prediction of Skin and Eye Irritation -- 2.2.7 Approaches to Systemic Toxicity Prediction -- 2.2.7.1 The Cramer Classes -- 2.2.7.2 Predicting Toxic Doses of Drugs -- 2.2.7.3 Predicting Organ Toxicity -- 2.2.7.4 Adverse Outcome Pathways and Potential for Prediction -- 2.3 Prediction of Risk -- 2.3.1 Risk Definition and Some Basic Considerations -- 2.3.2 Data Availability -- 2.3.3 Database Structure and Data Curation -- 2.3.4 Approaches to Model and Predict Risk -- 2.4 Thoughts on Validation -- 2.5 Conclusions and Outlook -- References.

3 In Silico Approaches: Data Management - Bioinformatics -- 3.1 Introduction -- 3.2 Experimental Setup and Statistical Power -- 3.3 Properties of Different Omics Data -- 3.3.1 Next-Generation Sequencing Data -- 3.3.2 DNA Methylation Data -- 3.3.3 miRNA Data -- 3.3.4 CNV and SNP Data -- 3.3.5 ChIP-seq Data -- 3.3.6 Gene Expression Microarray Data (Affymetrix) -- 3.3.7 Mass Spectrometry Data -- 3.3.8 Missing Values and Zero Values -- 3.3.9 Data Normalization -- 3.4 Statistical Methods -- 3.4.1 Data Overviews -- 3.4.2 Null Hypothesis/Type I and Type II Errors -- 3.4.3 Multiple Testing Methods -- 3.4.4 Statistical Tests -- 3.4.5 Linear Models and Linear Mixed Models -- 3.5 Prediction and Classification -- 3.5.1 Overview -- 3.5.2 Generating a Reference Compendium of Compounds -- 3.5.3 Cross-Validation -- 3.5.4 Selection Bias -- 3.6 Combining Different Omics Data and Biological Interpretations -- 3.7 Data Management -- References -- 4 Role of Modeling and Simulation in Toxicology Prediction -- 4.1 Introduction -- 4.2 The Need to Bring PK and PD in Predictive Models Together -- 4.2.1 Physiologically Based Pharmacokinetic Modeling -- 4.2.2 Mathematical (PBPK, PK/PD) Modeling -- 4.2.3 Predictive Tools -- 4.3 Methodological Aspects and Concepts -- 4.3.1 "Cascading" Drug Effects -- 4.3.2 Linking Exposure and Effect -- 4.3.3 Receptor Occupancy/Enzyme Inhibition -- 4.3.4 Transduction into In Vivo Response -- 4.3.4.1 Indirect Response Models -- 4.3.4.2 Transit Compartment Models -- 4.3.5 Disease Modeling -- 4.4 Application During Lead Optimization -- 4.4.1 Example 1: PK/PD Modeling for Identifying the Therapeutic Window between an Efficacy and a Safety Response -- 4.5 Application During Clinical Candidate Selection -- 4.5.1 Example 2: Translational PK/PD Modeling to Support Go/No Go Decisions -- 4.6 Entry-into-Human Preparation and Translational PK/PD Modeling.

4.6.1 Selection of Safe and Pharmacologically Active Dose for Anticancer Drugs -- 4.6.1.1 Example 3 -- 4.6.1.2 Example 4 -- 4.6.2 PK/PD for Toxicology Study Design and Evaluation -- 4.6.2.1 Example 5 -- 4.6.2.2 Example 6 -- 4.6.2.3 Example 7 -- 4.7 Justification of Starting Dose, Calculation of Safety Margins, and Support of Phase I Clinical Trial Design -- 4.8 Outlook and Conclusions -- References -- 5 Genomic Applications for Assessing Toxicities of Liver and Kidney Injury -- 5.1 Introduction -- 5.1.1 Toxicogenomics in Drug Development -- 5.2 Toxicogenomic Approaches -- 5.2.1 High-Throughput Expression Profiles and DNA Microarrays -- 5.2.2 Data Analysis -- 5.3 Specific Applications of Toxicogenomics -- 5.3.1 Mechanistic Toxicogenomics and Risk Assessment -- 5.3.2 Toxicogenomic Profiling of Hepatotoxicity -- 5.3.2.1 Hepatotoxicity in Drug Development -- 5.3.3 Functional and Structural Properties of the Liver -- 5.3.4 Liver Morphology -- 5.3.5 Cell Types -- 5.3.6 Functional Gradients -- 5.4 Toxicogenomic Applications for the Better Understanding of Hepatotoxicity -- 5.4.1 Mechanistic Toxicology -- 5.4.2 Class Identification -- 5.4.3 Predictive Toxicology -- 5.4.4 In Vitro Classifiers of Hepatotoxicity -- 5.4.5 Biomarker Identification -- 5.5 Toxicogenomic Profiling of Nephrotoxicity -- 5.5.1 Toxicogenomic Approaches in Nephrotoxicity -- 5.5.2 Finding Genes that Matter in AKI -- 5.5.3 Searching for New Biomarkers of Kidney Injury -- 5.6 Limitations of Toxicogenomics -- 5.6.1 Idiosyncrasies -- 5.6.2 Epigenetics -- 5.7 Conclusions -- References -- 6 Use of Toxicogenomics for Mechanistic Characterization of Hepatocarcinogens in Shorter Term Studies -- 6.1 Introduction -- 6.1.1 Rodent Carcinogenicity Testing -- 6.1.2 Classes of Carcinogens -- 6.2 Toxicogenomics.

6.2.1 Mechanistic Toxicogenomic Analysis after Short-Term Treatment with Rodent Hepatocarcinogens -- 6.2.2 Approaches for Prediction of Potential Hepatocarcinogens Based on Gene Expression Profiling -- 6.2.3 Recent Developments: Transcriptional Benchmark Dose Modeling Based on Functional Analyses -- 6.2.4 Recent Opportunities: Publicly Available Data -- 6.3 Conclusions and Outlook -- References -- 7 Discovery and Application of Novel Biomarkers -- 7.1 Introduction -- 7.1.1 New Technologies Give Rise to Novel Opportunities for Biomarker Discovery -- 7.2 Novel RNA Biomarkers -- 7.2.1 The Complex RNA Biomarker in Cancer -- 7.2.2 The Complex RNA Biomarker in Toxicology -- 7.2.3 Connectivity Mapping with the Complex RNA Biomarker for Hazard Identification -- 7.2.4 miRNA Biomarkers -- 7.3 DNA as a Biomarker -- 7.3.1 DNA Polymorphisms as Future Biomarkers of Disease and Xenobiotic Susceptibility -- 7.3.2 DNA and Protein Adduct Biomarkers -- 7.3.3 Epigenetic Biomarkers -- 7.4 Novel Biomarkers: Beyond Nucleotide-Based Discovery -- 7.5 Summary and Outlook -- References -- 8 Predictive Toxicology: Genetics, Genomics, Epigenetics, and Next-Generation Sequencing in Toxicology -- 8.1 Introduction -- 8.2 Technological Advances -- 8.3 Applications in Toxicology -- 8.3.1 Genome Sequencing and Sequence Level Comparisons -- 8.3.2 Genotype and Metabolism -- 8.3.3 Mechanistic Toxicology and Toxicogenomics -- 8.3.4 Epigenetic Changes and miRNAs -- 8.4 Summary and Outlook -- References -- 9 Biomarkers as Tools for Predictive Safety Assessment: Novel Markers of Drug-Induced Kidney Injury -- 9.1 Need and Search for Novel Biomarkers of Kidney Injury -- 9.2 Urinary Biomarkers of Drug-Induced Kidney Injury -- 9.2.1 Structure and Function of Novel Urinary Biomarkers -- 9.2.1.1 Kidney Injury Molecule-1 -- 9.2.1.2 Clusterin -- 9.2.1.3 Cystatin C -- 9.2.1.4 β2-Microglobulin.

9.2.1.5 Liver-Type Fatty Acid Binding Protein -- 9.2.1.6 Neutrophil Gelatinase-Associated Lipocalin -- 9.2.1.7 Others -- 9.2.2 Experimental and Clinical Support for the Use of Novel Urinary Biomarkers for the Detection and Prediction of Acute Kidney Injury -- 9.2.2.1 Performance of Novel Urinary Biomarkers in Preclinical Models of Renal Injury -- 9.2.2.2 Clinical Support for Novel Urinary Kidney Injury Biomarkers -- 9.3 Genomic Biomarkers -- 9.3.1 Individual Genes -- 9.3.2 Biomarker Panels and Gene Signatures -- 9.3.3 MicroRNAs -- 9.4 Qualification and Use of Novel Kidney Injury Biomarkers in Preclinical Safety Assessment -- 9.4.1 Biomarker Qualification and Regulatory Acceptance -- 9.4.2 Application of Novel Renal Safety Markers to Preclinical Decision Making -- 9.4.3 Technological Aspects -- 9.5 Summary and Perspectives -- References -- 10 The Use of Renal Cell Culture for Nephrotoxicity Investigations -- 10.1 Introduction -- 10.2 In Vitro Renal Models -- 10.2.1 Characterization -- 10.2.2 Immortalization of Primary Cells -- 10.2.3 Available Podocyte and Proximal Tubule Cell Lines -- 10.3 Stem Cells -- 10.4 Optimal Cell Culture Conditions -- 10.5 In Vitro Nephrotoxicity Assessment -- 10.6 Outlook -- References -- 11 The Zebrafish Model in Toxicology -- 11.1 The Need for a Physiologically Relevant Organ Model in Drug Toxicity Testing -- 11.2 Extensive Knowledge about Genetics, Development, and Physiology of D. rerio -- 11.3 Studies of Specific Organ Toxicities in Zebrafish Embryos and Larvae -- 11.3.1 Cardiotoxicity -- 11.3.2 Neurotoxicity -- 11.3.3 Hepatotoxicity -- 11.3.4 Teratogenicity -- 11.3.5 Future Directions: ADME Studies and Future Explorative Research -- 11.3.5.1 Absorption and Distribution -- 11.3.5.2 Metabolism -- 11.3.5.3 Harmonization and Validation -- 11.3.5.4 Future Explorative Research -- References.

12 Predictive Method Development: Challenges for Cosmetics and Genotoxicity as a Case Study.