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Predicting Mammalian Metabolism and Toxicity of Pesticides in Silico

Overview
Journal Pest Manag Sci
Specialties Biology
Toxicology
Date 2018 May 16
PMID 29762898
Citations 12
Authors
Affiliations
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Abstract

Pesticides must be effective to be commercially viable but they must also be reasonably safe for those who manufacture them, apply them, or consume the food they are used to produce. Animal testing is key to ensuring safety, but it comes late in the agrochemical development process, is expensive, and requires relatively large amounts of material. Surrogate assays used as in vitro models require less material and shift identification of potential mammalian toxicity back to earlier stages in development. Modern in silico methods are cost-effective complements to such in vitro models that make it possible to predict mammalian metabolism, toxicity and exposure for a pesticide, crop residue or other metabolite before it has been synthesized. Their broader use could substantially reduce the amount of time and effort wasted in pesticide development. This contribution reviews the kind of in silico models that are currently available for vetting ideas about what to synthesize and how to focus development efforts; the limitations of those models; and the practical considerations that have slowed development in the area. Detailed discussions are provided of how bacterial mutagenicity, human cytochrome P450 (CYP) metabolism, and bioavailability in humans and rats can be predicted. © 2018 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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References
1.
Jones H, Rowland-Yeo K . Basic concepts in physiologically based pharmacokinetic modeling in drug discovery and development. CPT Pharmacometrics Syst Pharmacol. 2013; 2:e63. PMC: 3828005. DOI: 10.1038/psp.2013.41. View

2.
Salgado V, David M . Chance and design in proinsecticide discovery. Pest Manag Sci. 2016; 73(4):723-730. DOI: 10.1002/ps.4502. View

3.
Zang Q, Mansouri K, Williams A, Judson R, Allen D, Casey W . In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning. J Chem Inf Model. 2016; 57(1):36-49. PMC: 6131700. DOI: 10.1021/acs.jcim.6b00625. View

4.
Kirchmair J, Goller A, Lang D, Kunze J, Testa B, Wilson I . Predicting drug metabolism: experiment and/or computation?. Nat Rev Drug Discov. 2015; 14(6):387-404. DOI: 10.1038/nrd4581. View

5.
Coleman S, Liu S, Linderman R, Hodgson E, Rose R . In vitro metabolism of alachlor by human liver microsomes and human cytochrome P450 isoforms. Chem Biol Interact. 1999; 122(1):27-39. DOI: 10.1016/s0009-2797(99)00107-6. View