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From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

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Computational Toxicology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 930))

Abstract

Quantitative structure activity relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to quantitative structure in vitro–in vivo relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment.

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Acknowledgments

The work was supported by the Society of Toxicology (grant number 11-0897). The author want to thank Dr. Alexander Tropsha of University of North Carolina at Chapel Hill for his help in the past five years.

Most of the research projects that were reviewed in this chapter were finished with his help and guidance.

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Authors and Affiliations

  1. Department of Chemistry, The Rutgers Center for Computational and Integrative Biology, Rutgers University, 315 Penn St., Camden, NJ, 08102, USA

    Hao Zhu

  2. University of North Carolina, Chapel Hill, NC, USA

    Hao Zhu

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  1. Hao Zhu
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Correspondence to Hao Zhu .

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Editors and Affiliations

  1. School of Biomedical Engineering, Chemical & Biological Engineering, Colorado State University, Campus Delivery 1370, Fort Collins, 80523-1370, Colorado, USA

    Brad Reisfeld

  2. , Chemical & Biological Engineering, Colorado State University, Campus Delivery 1370, Fort Collins, 80523-1370, Colorado, USA

    Arthur N. Mayeno

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Zhu, H. (2013). From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models. In: Reisfeld, B., Mayeno, A. (eds) Computational Toxicology. Methods in Molecular Biology, vol 930. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-059-5_3

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  • DOI: https://doi.org/10.1007/978-1-62703-059-5_3

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-058-8

  • Online ISBN: 978-1-62703-059-5

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