The Mechanism of C-H Bond Oxidation by Aqueous Permanganate.

The permanganate ion (MnO4-) has been widely used as a reagent for water treatment for over a century. It is a strong enough oxidant to activate carbon-hydrogen bonds, one of the most important reactions in biological and chemical systems. Our current textbook understanding of the oxidation mechanism in aqueous solution involves an initial, rate-limiting hydride abstraction by permanganate followed by reaction of the carbocation with bulk water to form an alcohol. This mechanism fits well into the classic oxidation sequence of alkane → alcohol → aldehyde → carboxylate, the central paradigm for both abiotic and biotic alkane oxidation in aqueous environments. In this study, we provide three lines of evidence through (1) a broken-symmetry density functional theory approach, (2) isotope labeling experiments, and (3) kinetic network modeling to demonstrate that aqueous permanganate can circumvent prior alcohol formation and produce aldehydes directly via a reaction path that bifurcates after the initial transition state. In contrast to classic transition state theory, the rate-limiting step is found to not determine product distribution, bearing critical implications for pathway and rate predictions. This complex reaction network provides new insights into the oxidation mechanisms of organic compounds involving transition metal complexes as well as enzyme or metal oxide surface active sites.

DoseSim: a tool for pharmacokinetic/pharmacodynamic analysis and dose reconstruction.

UNLABELLED: Assessing and improving the safety of chemicals and the efficacy of drugs depends on an understanding of the biodistribution, clearance and biological effects of the chemical(s) of interest. A promising methodology for the prediction of these phenomena is physiologically based pharmacokinetic/pharmacodynamic modeling, which centers on the prediction of chemical absorption, distribution, metabolism and excretion (pharmacokinetics) and the biological effects (pharmacodynamics) of the chemical on the organism. Strengths of this methodology include modeling across multiple scales of biological organization and facilitate the extrapolation of results across routes of exposure, dosing levels and species. It is also useful as the foundation for tools to (i) predict biomarker levels (concentrations of chemical species found in the body that indicate exposure to a foreign chemical), given a chemical dose or exposure; (ii) reconstruct a dose, given the levels of relevant biomarkers; and (iii) estimate population variability. Despite the importance and promise of physiologically based pharmacokinetic /pharmacodynamics-based approaches to forward and reverse dosimetry, there is currently a lack of user-friendly, freely available implementations that are accessible and useful to a broad range of users. DoseSim was developed to begin to fill this gap. AVAILABILITY: The application is available under the GNU General Public License from http://scb.colostate.edu/dosesim.html.

What is computational toxicology?

Computational toxicology is a vibrant and rapidly developing discipline that integrates information and data from a variety of sources to develop mathematical and computer-based models to better understand and predict adverse health effects caused by chemicals, such as environmental pollutants and pharmaceuticals. Encompassing medicine, biology, biochemistry, chemistry, mathematics, computer science, engineering, and other fields, computational toxicology investigates the interactions of chemical agents and biological organisms across many scales (e.g., population, individual, cellular, and molecular). This multidisciplinary field has applications ranging from hazard and risk prioritization of chemicals to safety screening of drug metabolites, and has active participation and growth from many organizations, including government agencies, not-for-profit organizations, private industry, and universities.

Tools and techniques.

This chapter lists some of the software and tools that are used in computational toxicology, as presented in this volume.

Prediction of contaminant persistence in aqueous phase: a quantum chemical approach.

At contaminated field sites where active remediation measures are not feasible, monitored natural attenuation is sometimes the only alternative for surface water or groundwater decontamination. However, due to slow degradation rates of some contaminants under natural conditions, attenuation processes and their performance assessment can take several years to decades to complete. Here, we apply quantum chemical calculations to predict contaminant persistence in the aqueous phase. For the test compound hexamethylphosphoramide (HMPA), P-N bond hydrolysis is the only thermodynamically favorable reaction that may lead to its degradation under reducing conditions. Through calculation of aqueous Gibbs free energies of activation for all potential reaction mechanisms, it is predicted that HMPA hydrolyzes via an acid-catalyzed mechanism at pH < 8.2, and an uncatalyzed mechanism at pH 8.2-8.5. The estimated half-lives of thousands to hundreds of thousands of years over the groundwater-typical pH range of 6.0 to 8.5 indicate that HMPA will be persistent in the absence of suitable oxidants. At pH 0, where the hydrolysis reaction is rapid enough to enable measurement, the experimentally determined rate constant and half-life are in excellent agreement with the predicted values. Since the quantum chemical methodology described herein can be applied to virtually any contaminant or reaction of interest, it is especially valuable for the prediction of persistence when slow reaction rates impede experimental investigations and appropriate QSARs are unavailable.

Rapid estimation of activation enthalpies for cytochrome-P450-mediated hydroxylations.

Cytochrome P450 (CYP) enzymes play a critical role in detoxication and bioactivation of xenobiotics; thus, the ability to predict the biotransformation rates and regioselectivity of CYP enzymes toward substrates is an important goal in toxicology and pharmacology. Here, we present the use of the semiempirical quantum chemistry method SAM1 to rapidly estimate relative activation enthalpies (ΔH(‡)) for the hydroxylation of aliphatic carbon centers of various substrates. The ΔH(‡) were determined via a reaction path calculation, in the reverse direction (RRP), using the iron-hydroxo-porphine intermediate and the substrate radical. The SAM1 ΔH(‡) were calculated via unrestricted Hartree-Fock (UHF) and configuration interaction (CI) formalisms for both the doublet and quartet spin states. The SAM1 RRP ΔH(‡), after subtracting a correction factor, were compared with density functional theory (DFT) B3LYP activation energies for two sets of substrates and showed R(2) ranging from 0.69 to 0.89, and mean absolute differences ranging from 1.2 ± 1.0 to 1.7 ± 1.5 kcal/mol. SAM1 UHF and CI RRP calculation times were, on average, more than 200 times faster than those for the corresponding forward reaction path DFT calculations. Certain key transition-state (TS) geometry measurements, such as the forming O···H bond length, showed good correlation with the DFT values. These results suggest that the SAM1 RRP approach can be used to rapidly estimate the DFT activation energy and some key TS geometry measurements and can potentially be applied to estimate substrate hydroxylation rates and regioselectivity by CYP enzymes.

Quantum chemical prediction of redox reactivity and degradation pathways for aqueous phase contaminants: an example with HMPA.

Models used to predict the fate of aqueous phase contaminants are often limited by their inability to address the widely varying redox conditions in natural and engineered systems. Here, we present a novel approach based on quantum chemical calculations that identifies the thermodynamic conditions necessary for redox-promoted degradation and predicts potential degradation pathways. Hexamethylphosphoramide (HMPA), a widely used solvent and potential groundwater contaminant, is used as a test case. Its oxidation is estimated to require at least iron-reducing conditions at low to neutral pH and nitrate-reducing conditions at high pH. Furthermore, the transformation of HMPA by permanganate is predicted to proceed through sequential N-demethylation. Experimental validation based on LC/TOF-MS analysis confirms the predicted pathways of HMPA oxidation by permanganate to phosphoramide via the formation of less methylated as well as singly and multiply oxygenated reaction intermediates. Pathways predicted to be thermodynamically or kinetically unfavorable are similarly absent in the experimental studies. Our newly developed methodology will enable scientists and engineers to estimate the favorability of contaminant degradation at a specific field site, suitable approaches to enhance degradation, and the persistence of a contaminant and its reaction intermediates.

Predicting activation enthalpies of cytochrome-P450-mediated hydrogen abstractions. 2. Comparison of semiempirical PM3, SAM1, and AM1 with a density functional theory method.

Predicting the biotransformation of xenobiotics is important in the chemical and pharmaceutical industries, as well as in toxicology. Here, we extend and evaluate the rapid methodology of Korzekwa, Jones, and Gillette (J. Am. Chem. Soc. 1990, 112, 7042-7046 ) to estimate the activation enthalpy (DeltaH) of hydrogen-abstraction by cytochrome P450 (CYP) enzymes, using the p-nitrosophenoxy radical (PNPO) as a simple surrogate for the CYP active oxygen species. The DeltaH is estimated with a linear regression model using the reaction enthalpy and ionization energy (of the substrate radical) as predictor variables, calculated by semiempirical (SE) methods. While Korzekwa et al. used the SE method AM1, we applied PM3 and SAM1 and compared the results of the three methods. For 24 substrates, the AM1-, PM3-, and SAM1-derived regression models showed R(2) values of 0.89, 0.90, and 0.93, respectively, for the correlation between calculated and predicted DeltaH. Furthermore, we compared the DeltaH() calculated semiempirically using PNPO radical with density functional theory (DFT) B3LYP activation energies calculated by Olsen et al. (J. Med. Chem. 2006, 49, 6489-6499 ) using a more realistic iron-oxo-porphine model, and the results revealed limitations of the PNPO radical model. Thus, predictive models developed using SE predictors provide rapid and generally internally consistent results, but they should be interpreted and used cautiously.

Computational toxicology of chloroform: reverse dosimetry using Bayesian inference, Markov chain Monte Carlo simulation, and human biomonitoring data.

BACKGROUND: One problem of interpreting population-based biomonitoring data is the reconstruction of corresponding external exposure in cases where no such data are available. OBJECTIVES: We demonstrate the use of a computational framework that integrates physiologically based pharmacokinetic (PBPK) modeling, Bayesian inference, and Markov chain Monte Carlo simulation to obtain a population estimate of environmental chloroform source concentrations consistent with human biomonitoring data. The biomonitoring data consist of chloroform blood concentrations measured as part of the Third National Health and Nutrition Examination Survey (NHANES III), and for which no corresponding exposure data were collected. METHODS: We used a combined PBPK and shower exposure model to consider several routes and sources of exposure: ingestion of tap water, inhalation of ambient household air, and inhalation and dermal absorption while showering. We determined posterior distributions for chloroform concentration in tap water and ambient household air using U.S. Environmental Protection Agency Total Exposure Assessment Methodology (TEAM) data as prior distributions for the Bayesian analysis. RESULTS: Posterior distributions for exposure indicate that 95% of the population represented by the NHANES III data had likely chloroform exposures < or = 67 microg/L [corrected] in tap water and < or = 0.02 microg/L in ambient household air. CONCLUSIONS: Our results demonstrate the application of computer simulation to aid in the interpretation of human biomonitoring data in the context of the exposure-health evaluation-risk assessment continuum. These results should be considered as a demonstration of the method and can be improved with the addition of more detailed data.

Cytochromes P450: a structure-based summary of biotransformations using representative substrates.

Cytochromes P450 (CYPs) are a superfamily of enzymes that metabolize the majority of xenobiotics in humans. This review presents a structure-based outline of CYP-catalyzed biotransformations of selected substrates, representing diverse structural classes of chemicals, ranging from drugs to toxicants. Data are presented in a tabular format for easy reference, with visual representations of all substrates and sites-of-attack. The major metabolites, isozymes responsible, chemical classification, and other information related to the biotransformation are provided. Pharmacophores proposed for the major CYP isozymes are discussed. This visual combination of substrates and biotransformation sites can serve as a useful reference for researchers.

Biochemical reaction network modeling: predicting metabolism of organic chemical mixtures.

All organisms are exposed to multiple xenobiotics, through food, environmental contamination, and drugs. These xenobiotics often undergo biotransformation, a complex process that plays a critical role in xenobiotic elimination or bioactivation to toxic metabolites. Here we describe the results of a new computer-based simulation tool that predicts metabolites from exposure to multiple chemicals and interconnects their metabolic pathways, using four common drinking water pollutants (trichloroethylene, perchloroethylene, methylchloroform, and chloroform) as a test case. The simulation tool interconnected the metabolic pathways for these compounds, predicted reactive intermediates, such as epoxides and acid chlorides, and uncovered points in the metabolic pathways where typical endogenous compounds, such as glutathione or carbon dioxide, are consumed or generated. Moreover, novel metabolites, not previously reported, were predicted via this methodology. Metabolite prediction is based on a reaction-mechanism-based methodology, which applies fundamental organic and enzyme chemistry. The tool can be used to (a) complement experimental studies of chemical mixtures, (b) aid in risk assessment, and (c) help understand the effects of complex chemical mixtures. Our results indicate that this tool is useful for predictive xenobiotic metabolomics, providing new and important insights into metabolites and the interrelationship between diverse chemicals that hitherto may have remained unnoticed.

A novel, sensitive method for determining benzo[a]pyrene-diones using high-performance liquid chromatography with post-column zinc reduction.

A novel and sensitive high-performance liquid chromatography (HPLC) method was developed to analyze dione metabolites of benzo[a]pyrene (BaP). Because BaP-diones do not fluoresce, detection of low concentrations is difficult to achieve when analyzing these chemicals with a simple HPLC system. We developed a method to increase the detection sensitivities for BaP-diones using reduction by zinc after the chromatographic separation. A post-column zinc reducer was used to convert BaP-diones, in-line, to their corresponding fluorescent BaP-hydroquinones, which can be measured by fluorescence detection with high sensitivity. With 20-muL injections, the limits of detection for the BaP-diones tested (BaP-1,6-dione, BaP-3,6-dione, and BaP-6,12-dione) were all below 1.0 nM. In addition to the high detection sensitivity, this HPLC method provides a wide linear dynamic range for BaP-dione detection (1.0-220 nM). We also studied the extraction recovery of BaP-diones from recombinant human cytochrome P450 and epoxide hydrolase. To demonstrate the application of this method, the kinetics of BaP-dione formation was studied by incubating BaP with these recombinant enzymes. The present method enhances the detection sensitivity for BaP-diones by more than two orders of magnitude compared with traditional ultraviolet detection. Moreover, the method avoids the time-consuming derivatization or reduction steps required by other methods.