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In Silico Approaches for the Prediction of In Vivo Biotransformation Rates

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Advances in QSAR Modeling

Part of the book series: Challenges and Advances in Computational Chemistry and Physics ((COCH,volume 24))

Abstract

The assessment of chemical bioaccumulation is a required procedure under several regulatory frameworks. However, since the experimental quantification of bioaccumulation and related metrics (such as the Bioconcentration Factor, BCF) is resource intensive (money, animals) and time consuming, several computational approaches have been proposed as an alternative. Most bioaccumulation model estimates based on the octanol water partition coefficient (KOW) alone can be inaccurate, if they do not take into account additional processes that influence chemical partitioning, chemical uptake and elimination rates. In particular, the biotransformation rate constant (k B) can play a significant role in mitigating the bioaccumulation potential of hydrophobic chemicals. Bioaccumulation model (e.g., BCF) estimates can be refined when experimental or predicted k Bvalues are available. The aim of this chapter is to illustrate the development and the application of in silico models for in vivo biotransformation rates, for the cost-effective estimation of k Bfor screening assessment. The chapter includes several examples of quantitative structure-activity relationships (QSARs), which predict k B or the associated half-life from the chemical structure. Furthermore, the chapter describes the complementary role of in vitro biotransformation rate estimation and the subsequent in vitro-to-in vivo extrapolation (IVIVE) calculations for refining bioaccumulation model predictions.

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Authors and Affiliations

  1. Department of Theoretical and Applied Sciences, QSAR Research Unit in Environmental Chemistry and Ecotoxicology, University of Insubria, Varese, Italy

    Ester Papa, Alessandro Sangion & Paola Gramatica

  2. ARC Arnot Research & Consulting, Toronto, ON, Canada

    Jon A. Arnot

  3. Department of Physical and Environmental Science, University of Toronto Scarborough, Toronto, ON, Canada

    Jon A. Arnot

  4. Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada

    Jon A. Arnot

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  1. Ester Papa
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  2. Jon A. Arnot
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  3. Alessandro Sangion
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  4. Paola Gramatica
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Correspondence to Ester Papa .

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Editors and Affiliations

  1. Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India

    Kunal Roy

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Papa, E., Arnot, J.A., Sangion, A., Gramatica, P. (2017). In Silico Approaches for the Prediction of In Vivo Biotransformation Rates. In: Roy, K. (eds) Advances in QSAR Modeling. Challenges and Advances in Computational Chemistry and Physics, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-56850-8_11

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