Synergy between Experiments and Computations: A Green Channel for Revealing Metabolic Mechanism of Xenobiotics in Chemical Toxicology.

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ID: 101760
2020
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Abstract
Xenobiotic metabolism can lead to metabolites with altered physicochemical and biological properties, which may differ markedly from those of their parent compounds. Thus, xenobiotic metabolism has great implication for chemical safety evaluation, which has become one of the central research areas in chemical toxicology. A plethora of analytical and in vitro methods now are available for investigating the metabolic fate of xenobiotics especially by cytochrome P450 (CYP), at a high level of detail. However, the interpretations of metabolic reactions often face some mechanistic challenges, e.g. the mechanism of initial and rate-determining step is not easily distinguished due to the transient nature of active species of CYP, and some reactive intermediates are difficult to be identified. Alternatively, computational chemistry methodologies such as quantum chemical calculations have the capacity to calculate the electronic structures for enzymatic models with hundreds of atoms, thus to be able to characterize intermediates and transition states during whole metabolic reaction course from both structural and energetics aspects, which can confront some major limitations of experimental methods. In this perspective, I first introduce the state of the art experimental and computational approaches for investigating xenobiotic metabolism catalyzed by CYP, respectively. Then, the strategies to harvest the synergy between experiments and computations are highlighted, which can be conducted through comparison of their analytical, kinetic or isotope effect data at qualitative, semi-quantitative, or quantitative level to determine the metabolic mechanism. Two examples are chosen to demonstrate the synergy advantage in elucidation of metabolic mechanism of triphenyl phosphate and atrazine catalyzed by CYP, respectively, which shows the interplay between experiments and computations allows gaining greater insight than the isolated methods.
Reference Key
ji2020synergychemical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ji, Li;
Journal Chemical research in toxicology
Year 2020
DOI
10.1021/acs.chemrestox.9b00448
URL
Keywords
Antimicrobial activity quantum dots chemosensor cds cell image phosphate ion sensing

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