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Our motivation for bringing about a book on systems computational pharmacology and toxicology was a natural development from teaching courses on these subjects, first at the University of California in Berkeley and later at the University of Michigan in Ann Arbor. Our courses and this book address a critical need to modernize pharmacology and toxicology—to transform these fields from descriptive disciplines to predictive sciences. This transformation is necessary, because classic descriptive approaches are far too inefficient and expensive to assess the medical efficacy or toxicity of the many thousands of synthetic chemicals or natural products to which humans and other species are or will be exposed.

Not long ago, the approaches set forth in this book were either not possible or performed by specialists using such tools as quantum mechanics and mainframe computers. Now, because of rapid advances in technology, software, and theory, coupled with the public availability of large chemical and biomedical data sets through the internet, it is possible for non-specialist bench scientists to undertake sophisticated molecular modeling, bioinformatics, cheminformatics, and systems biology procedures on desktop computers as well as mobile devices, including to some extent electronic tablets and smart phones. Thus, powerful computational tools have become highly accessible, but knowing how and when to use the right tools in the right way can be a daunting task. This book seeks to make the job easier to understand and implement.

Recognizing that we now have the capability to understand pharmacological and toxicological effects at multiple biological levels, our book highlights the process of integrating the elements of complex phenomena into a systems approach. Thus, whereas inverse docking and pharmacophore mapping can identify molecular targets of candidate drugs or toxicants, intelligent mining of databases can identify networks of genes and proteins involved in the system-wide biological responses to chemicals. A pharmacological or toxicological effect may begin with atomic-level binding, but ultimately the intact organism responds in a holistic manner. It is necessary to continually readjust our focus by many orders of magnitude to encompass the spectrum from molecular orbitals to human populations.

The tools and models discussed in the book hold tremendous promise for advancing applied and basic science, streamlining drug efficacy and safety testing, and increasing the efficiency and effectiveness of risk assessment for environmental chemicals. The content of chapters is designed to provide readers with an understanding of the basic principles and current methods of computational pharmacology and toxicology. These principles and approaches are discussed in several chapters in order to show how to connect chemicals with diseases and associated genes, and how to create pharmacology/toxicology connectivity maps or networks.

Vital to these expositions of principles and methods are illustrations of modeling and/or predicting potential pharmacological or toxicological effects from multiple properties. These characteristics include chemical structure, inference from similar compounds, in silico target identification, exposure, bioaccumulation, environmental persistence, biomarkers, and networks of biological pathways affected by a chemical.

Systems toxicology approaches used in the safer design of chemicals and identification of safer alternatives, which are major parts of global green chemistry initiatives, are also discussed, along with the concept of the adverse outcome pathway and modeling approaches for hazard identification and risk assessments for large numbers of environmental chemicals for which supporting data are sparse.

The book also expands the conventional boundaries of research and development of pharmaceutical agents. Thus, traditional Chinese medicines that include recipes containing several pharmacologically active phytochemicals are becoming role models of polypharmacy research.

The final chapter describes an inquiry-based computational toxicology course. Students work in small cooperative groups and are given tools, data, and basic concepts to solve toxicity-related environmental, public health, and/or disease-oriented problems in novel ways. Several case studies serve both to educate the reader and to provide material for teaching.

As co-editors, we are each involved in research and education on the topics covered in the book. We have authored or co-authored several of the chapters ourselves, and the other chapters have been written by experts recruited from around the world.

Dale E. Johnson and Rudy J. Richardson

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