Modeling cyanide release from nitriles: prediction of cytochrome P 450-mediated acute nitrile toxicity

Grogan, James; DeVito, Stephen C.; Pearlman, Robert S.; Korzekwa, Kenneth R.

doi:10.1021/tx00028a014

Cited by 45 publications

(28 citation statements)

References 14 publications

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“…The required diversity is accomplished by families and subfamilies of enzymes with generally broad substrate specificities, a very reactive oxygenating species and a broad regioselectivity. Thus, for many reactions, the electronic features of the substrate are all that are required to predict regioselectivity (Grogan et al, 1992;Harris et al, 1992;de Groot et al, 1995de Groot et al, , 1999Yin et al, 1995).…”

mentioning

confidence: 99%

Computational Models for Cytochrome P450: A Predictive Electronic Model for Aromatic Oxidation and Hydrogen Atom Abstraction

Jones¹,

Mysinger²,

Korzekwa³

2002

Drug Metab Dispos

120

132

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This paper is available online at http://dmd.aspetjournals.org ABSTRACT:Experimental observations suggest that electronic characteristics play a role in the rates of substrate oxidation for cytochrome P450 enzymes. For example, the tendency for oxidation of a certain functional group generally follows the relative stability of the radicals that are formed (e.g., N-dealkylation > O-dealkylation > 2°c arbon oxidation > 1°carbon oxidation). In addition, results show that useful correlations between the rates of product formation can be developed using electronic models. In this article, we attempt to determine whether a combined computational model for aromatic and aliphatic hydroxylation can be developed. Toward this goal, we used a combination of experimental data and semiempirical molecular orbital calculations to predicted activation energies for aromatic and aliphatic hydroxylation. The resulting model extends the predictive capacity of our previous aliphatic hydroxylation model to include the second most important group of oxidations, aromatic hydroxylation. The combined model can account for about 83% of the variance in the data for the 20 compounds in the training set and has an error of about 0.7 kcal/mol. The P4501 enzymes are a superfamily of monooxygenases involved in the metabolism of both exogenous and endogenous compounds. Ironically, these enzymes play a central role in both the prevention and induction of chemical toxicities and carcinogenicity. Although most P450 oxidations of xenobiotics result in detoxification, occasionally a more toxic intermediate is formed. In fact, many ultimate toxins and carcinogens are formed by the bioactivation of less reactive compounds, and many bioactivation reactions are mediated by the P450 enzymes. Often bioactivation reactions are in competition with detoxification pathways for the same substrate. Since these enzymes play such a central role in both detoxification and bioactivation, predictive models for cytochrome P450 catalysis will be useful tools for evaluating of the potential risks of environmental exposures. One of the most pertinent but difficult problems in risk assessment is translating bench results and mechanistic information into a form that can be used. This article outlines steps toward the development of computational models from laboratory data into a form that can be used for risk assessments that accurately reflect experimental results. For example, these models now more completely describe all positions of metabolism for nitriles and should be more complete in predicting the toxicity related to nitrile metabolism. These semiempirical computational models blend experimental data and computational chemistry in such a way as to provide a consistent prediction of the bioactivation rates for a broad spectrum of compounds. In particular, these models can be used to predict xenobiotic metabolism by the P450 enzyme family, including the bioactivation of compounds to toxins and carcinogens.Models such as those presented here for P450-mediated reaction...

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mentioning

confidence: 99%

Computational Models for Cytochrome P450: A Predictive Electronic Model for Aromatic Oxidation and Hydrogen Atom Abstraction

Jones¹,

Mysinger²,

Korzekwa³

2002

Drug Metab Dispos

120

132

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“…Thus, this model depends only on ground-state energies to calculate activation energies. Previous studies on the relationship of activation energies for hydrogen-atom abstraction, calculated with this model, to the LD50 values of 26 nitriles (10) and to the amount of urinary trifluoroacetic acid formed from four H(C)FCs in rats (11) indicated that the PNR model may have the potential to predict rates of biotransformation by CYP. The present study was undertaken to test the capacity of the PNR model to predict rates of halogenated alkane biotransformation.…”

mentioning

confidence: 99%

“…For compounds that are directly biotransformed to toxic products-e.g., nitriles being converted to cyanide-biotransformation and toxicity are well correlated (10) neoantigen formation (23). For the inhalation anesthetics presented in this study, a correlation between rate of metabolism to give fluoride and renal toxicity also exists (20).…”

mentioning

confidence: 99%

Designing safer chemicals: predicting the rates of metabolism of halogenated alkanes.

Yin

Anders

Korzekwa

et al. 1995

Proc. Natl. Acad. Sci. U.S.A.

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A computational model is presented that can be used as a tool in the design of safer chemicals. This model predicts the rate of hydrogen-atom abstraction by cytochrome P450 enzymes. Excellent correlations between biotransformation rates and the calculated activation energies (AHact) of the cytochrome P450-mediated hydrogen-atom abstractions were obtained for the in vitro biotransformation of six halogenated alkanes (1-fluoro-1,1,2,2-tetrachloroethane, 1,1-difluoro-1,2,2-trichloroethane, 1,1,1-trifluoro-2,2-dichloroethane, 1,1,1,2-tetrafluoro-2-chloroethane, 1,1,1,2,2-pentafluoroethane, and 2-bromo-2-chloro-1,1,1-trifluoroethane) with both rat and human enzyme preparations: ln(rate, rat liver microsomes) = The design of chemicals with lowest possible toxicity would decrease the damage to the environment; decrease the costs of production, health care, and site remediation; and increase the safety in the workplace (1). Although many factors are involved in the design of safer chemicals, one significant aspect is the prediction of rates of bioactivation of protoxins and procarcinogens to toxic metabolites (2). Bioactivation plays a major role in the toxicity of many chemicals, and the most important enzymes involved are the cytochromes P450 (CYPs) (2). The CYP enzymes catalyze the activation of molecular oxygen to a reactive monooxygen species (3), which can oxidize a variety of compounds (4). The transition state for the hydrogen-atom abstraction reaction is not, however, stabilized by the enzyme (3). Furthermore, the energetics of the active oxygen species is conserved among the several CYP enzymes studied (5). These factors make the CYP enzymes ideal candidates for predictive computational methods.We describe herein the application of a computational method for predicting the CYP-mediated oxidation rates of halogenated alkanes. Developing strategies for the design of safer halogenated alkanes is a particularly relevant objective, since hydro (chloro) Chemicals. 1-Fluoro-1,1,2,2-tetrachloroethane (HCFC-121), 1,1-difluoro-1,2,2-trichloroethane (HCFC-122), 1,1,1-trifluoro-2,2-dichloroethane (HCFC-123), 1,1,1,2-tetrafluoro-2-chloroethane (HCFC-124), pentafluoroethane (HFC-125), dichlorofluoroacetic acid, and chlorodifluoroacetic acid were obtained from PCR Research Chemicals (Gainesville, FL) and were used as received. 2-Bromo-2-chloro-1,1,1-trifluoroeth-

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“…Another example is the study on the neurotoxic effects of nitriles (compounds with a cyano, R-CN, group) on the sensory and central nervous systems. Many nitriles are known to cause acute toxicity through cyanide release by xenobiotic metabolism enzymes, a toxic endpoint already addressed by some modeling studies (Grogan et al, 1992). The work by several groups, including ours, has demonstrated that several nitriles cause a variety of neurotoxic effects, such as degeneration of specific populations of neurons in the central nervous system (Boadas-Vaello et al, 2005;Seoane et al, 2005) and degeneration of sensory systems including the olfactory (Genter et al, 1992), auditory (Gagnaire et al, 2001) and vestibular systems (Llorens et al, 1993;Llorens, 2001, 2003).…”

Section: Regulatory Neurotoxicity Testing For Risk Assessment Purposementioning

confidence: 99%

Strategies and tools for preventing neurotoxicity: To test, to predict and how to do it

Llorens

Ceccatelli

et al. 2012

NeuroToxicology

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A change in paradigm is needed in the prevention of toxic effects on the nervous system, moving from its the present reliance solely on data from animal testing to a prediction model mostly based on in vitro toxicity testing and in silico modelling.According to the report published by the National Research Council (NRC) of the US National Academies of Science, high-throughput in vitro tests will provide evidence for alterations in "toxicity pathways" as the best possible method of large scale toxicity prediction. The challenges to implement this proposal are enormous, and provide much room for debate. While many efforts address the technical aspects of implementing the vision, many questions around it need also to be addressed. Is the overall strategy the only one to be pursued? How can we move from current to future paradigms? Will we ever be able to reliably model for chronic and developmental neurotoxicity in vitro?.

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Modeling cyanide release from nitriles: prediction of cytochrome P 450-mediated acute nitrile toxicity

Cited by 45 publications

References 14 publications

Computational Models for Cytochrome P450: A Predictive Electronic Model for Aromatic Oxidation and Hydrogen Atom Abstraction

Computational Models for Cytochrome P450: A Predictive Electronic Model for Aromatic Oxidation and Hydrogen Atom Abstraction

Designing safer chemicals: predicting the rates of metabolism of halogenated alkanes.

Strategies and tools for preventing neurotoxicity: To test, to predict and how to do it

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