Predicting mammalian metabolism and toxicity of pesticides in silico

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.

Identification of Novel Src Inhibitors: Pharmacophore-Based Virtual Screening, Molecular Docking and Molecular Dynamics Simulations

“…The lead compounds in drug development were found to have favorable absorption, distribution, metabolism, elimination and toxicity (ADMET) properties [ 26 ]. Pharmacokinetic properties predict the drug-likeness of ligand molecules.…”

Design and tests of prospective property predictions for novel antimalarial 2-aminopropylaminoquinolones

“…The results for CYP2C9 and CYP2C19 in Table 3 also illustrate why it is important to make regression models for enzyme activity contingent on classification models that distinguish substrates from nonsubstrates [ 6 , 31 ]. Nonsubstrates are necessarily absent from the data used to construct the regression models, and the classification models confidently identify them [ 32 ] as being out-of-scope for the corresponding regression models.…”

“…That is not relevant to the blood phase of the Plasmodium life cycle, but would affect the ability of the compound to attack liver stages. The fact that they can offset potential first-pass metabolic liabilities is one good reason to examine pharmacokinetic simulation results for compounds of interest that raise piece-wise risk flags [ 31 ].…”

Editorial

“…38 A mini-review covered prediction of mammalian metabolism and toxicity of pesticide with in silico methods. 39 This year (2019) promises to be an interesting year. We will have an In Focus section with papers from the 11th European Vertebrate Pest Management Conference and another from a Rockefeller/OECD Conference on Natural Products in Pest Management.…”

“…In the last issue of 2018, a perspective on the potential use of gene drive technology in the light of weed population biology was provided . A mini‐review covered prediction of mammalian metabolism and toxicity of pesticide with in silico methods …”