Quantitative Modeling in Toxicology

Governments around the world are passing laws requiring industry to assess the toxicity of the chemicals and products they produce, but to do so while reducing, refining, or even replacing testing on animals. To meet these requirements, experimental toxicologists and risk assessors are adopting quantitative approaches and computer simulations to study the biological fate and effects of chemicals and drugs.
In Quantitative Modeling in Toxicology leading experts outline the current state of knowledge on the modeling of dose, tissue interactions and tissue responses. Each chapter describes the mathematical foundation, parameter estimation, challenges and perspectives for development, along with the presentation of a modeling template. Additionally, tools and approaches for conducting uncertainty, sensitivity and variability analyses in these models are described.  Topics covered include:
the quantitative models of pharmacokinetics of individual chemicals and mixturesmodels for toxicant–target tissue interaction. models for cellular, organ, and organism responses.approaches, tools and challenges for model application and evaluation
A website containing computer codes accompanies the book to help the reader reconstruct the models described and discussed in the various chapters.
Quantitative Modeling in Toxicology serves as an essential reference source and tool box for risk assessors and researchers and students in toxicology, public health, pharmacology, and human toxicology interested in developing quantitative models for a better understanding of dose–response relationships.

Spis treści:
About the Editors.
About the Book.
List of Contributors.
SECTION 1 INTRODUCTION.
1 Quantitative Modeling in Toxicology: An Introduction (Melvin E. Andersen and Kannan Krishnan).
SECTION 2 PHYSIOLOGICALLY–BASED TOXICOKINETIC AND PHARMACOKINETIC (PBPK) MODELING.
2 PBPK Modeling: A Primer (Kannan Krishnan, George D. Loizou, Martin Spendiff, John C. Lipscomb and Melvin E. Andersen).
3 Pharmacokinetic Modeling of Manganese – An Essential Element (Andy Nong, Michael D. Taylor, Miyoung Yoon, and Melvin E. Andersen).
4 Physiologically Based Modeling of Pharmacokinetic Interactions in Chemical Mixtures (Sami Haddad, Robert Tardif, Jonathan Boyd, and Kannan Krishnan).
5 Physiological Parameters and Databases for PBPK Modeling (Douglas O. Johns, Elizabeth Oesterling Owens, Chad M. Thompson, Babasaheb Sonawane, Dale Hattis and Kannan Krishnan).
SECTION 3 MODELING TOXICANT–TARGET INTERACTIONS.
6 Modeling Cholinesterase Inhibition (Charles Timchalk, Paul M. Hinderliter, and Torka S. Poet).
7 Modeling of Protein Induction and Dose–Dependent Hepatic Sequestration (Andy Nong and Melvin E. Andersen).
8 Bistable Signaling Motifs and Cell Fate Decisions (Sudin Bhattacharya, Qiang Zhang, and Melvin E. Andersen).
9 Ultrasensitive Response Motifs in Biochemical Networks (Qiang Zhang, Sudin Bhattacharya, Courtney G. Woods, and Melvin E. Andersen).
10 Gene and Protein Expression – Modeling Nested Motifs in Cellular and Tissue Response Networks (Melvin E. Andersen, Qiang Zhang, and Sudin Bhattacharya).
11 Modeling Liver and Kidney Cytotoxicity (Kai H. Liao, Yei M. Tan, Harvey J. Clewell III, and Melvin E. Andersen).
SECTION 4 MODELING TISSUE AND ORGANISM RESPONSES.
12 Computational Model for Iodide Economy and the HPT Axis in the Adult Rat (Jeffrey W. Fisher and Eva D. McLanahan).
13 Two–Stage Clonal Growth Modeling of Cancer (Rory B. Conolly and Melvin E. Andersen).
14 Statistical and Physiological Modeling of the Toxicity of Chemicals in Mixtures (Hisham A. El–Masri, Michael A. Lyons, and Raymond S.H. Yang).
15 (Q)SAR Mod els of Adverse Responses: Acute Systemic Toxicity (Mark T.D. Cronin, Yana K. Koleva, and Judith C. Madden).
SECTION 5 MODEL APPLICATION AND EVALUATION.
16 Modeling Exposures to Chemicals From Multiple Sources and Routes (Panos G. Georgopoulos, Sastry S. Isukapalli, and Kannan Krishnan).
17 Probabilistic Reverse Dosimetry Modeling for Interpreting Biomonitoring Data (Yu–Mei Tan and Harvey J. Clewell III).
18 Quantitative Modeling in Noncancer Risk Assessment (Q. Jay Zhao, Lynne Haber, Melissa Kohrman–Vincent, Patricia Nance, and Michael Dourson).
19 Application of Physiologically Based Pharmacokinetic Modeling in Health Risk Assessment (Harvey J. Clewell III).
20 Uncertainty, Variability, and Sensitivity Analyses in Simulation Models (Sastry S. Isukapalli, Martin Spendiff, Panos G. Georgopoulos and Kannan Krishnan).
21 Evaluation of Quantitative Models in Toxicology: Progress and Challenges (Kannan Krishnan and Melvin E. Andersen).
Index.

Okładka tylna:
Governments around the world are passing laws requiring industry to assess the toxicity of the chemicals and products they produce, but to do so while reducing, refining, or even replacing testing on animals. To meet these requirements, experimental toxicologists and risk assessors are adopting quantitative approaches and computer simulations to study the biological fate and effects of chemicals and drugs.
In Quantitative Modeling in Toxicology leading experts outline the current state of knowledge on the modeling of dose, tissue interactions and tissue responses. Each chapter describes the mathematical foundation, parameter estimation, challenges and perspectives for development, along with the presentation of a modeling template. Additionally, tools and approaches for conducting uncertainty, sensitivity and variability analyses in these models are described.  Topics covered in clude:
the quantitative models of pharmacokinetics of individual chemicals and mixturesmodels for toxicant–target tissue interaction. models for cellular, organ, and organism responses.approaches, tools and challenges for model application and evaluation
A website containing computer codes accompanies the book to help the reader reconstruct the models described and discussed in the various chapters.
Quantitative Modeling in Toxicology serves as an essential reference source and tool box for risk assessors and researchers and students in toxicology, public health, pharmacology, and human toxicology interested in developing quantitative models for a better understanding of dose–response relationships.