A quantitative structure-activity relationship analysis on a series of alkyl benzenes metabolized by human cytochrome p450 2E1

Case study on the impact of the source of metabolism parameters in next generation physiologically based pharmacokinetic models: Implications for occupational exposures to trimethylbenzenes

Physiologically based pharmacokinetic (PBPK) models are a means of making important linkages between exposure assessment and in vitro toxicity. A key constraint on rapid application of PBPK models in risk assessment is traditional reliance on substance-specific in vivo toxicokinetic data to evaluate model quality. Bounding conditions, in silico, in vitro, and chemical read-across approaches have been proposed as alternative sources for metabolic clearance estimates. A case study to test consistency of predictive ability across these approaches was conducted using trimethylbenzenes (TMB) as prototype chemicals. Substantial concordance was found among TMB isomers with respect to accuracy (or inaccuracy) of approaches to estimating metabolism; for example, the bounding conditions never reproduced the human in vivo toxicokinetic data within two-fold. Using only approaches that gave acceptable prediction of in vivo toxicokinetics for the source compound (1,2,4-TMB) substantially narrowed the range of plausible internal doses for a given external dose for occupational, emergency response, and environmental/community health risk assessment scenarios for TMB isomers. Thus, risk assessments developed using the target compound models with a constrained subset of metabolism estimates (determined for source chemical models) can be used with greater confidence that internal dosimetry will be estimated with accuracy sufficient for the purpose at hand.

Interactions of inhibitor molecules with the human CYP2E1 enzyme active site

CYP2E1 is an important enzyme oxidizing ethanol as well as several drugs and other xenobiotics in the human liver. We determined the inhibition potency of structurally diverse compounds against human CYP2E1, and analyzed their interactions with the enzyme active site by molecular docking and 3D-QSAR approaches. The IC(50) values for the tested compounds varied from 1.4 μM for γ-undecanolactone to over 46 mM for glycerol. This data set was used to create a comparative molecular field analysis (CoMFA) model. The most important interactions for binding of inhibitors were identified by docking and key features for inhibitors were characterized via the COMFA model. Since the active site of CYP2E1 is flexible, long chain lactones and alkyl alcohols fitted best into the larger open form while the other compounds fitted better in the smaller closed form of the active site. Electrostatic interactions near the Thr(303) residue proved to be important for inhibition of the enzyme activity. Thus, docking analysis and the predictive CoMFA model proved to be efficient tools for revealing interactions between inhibiting compounds and CYP2E1. These approaches can be used to analyze CYP2E1-mediated metabolism and drug interactions in the development of new chemical entities.

Contributions of human enzymes in carcinogen metabolism

Considerable support exists for the roles of metabolism in modulating the carcinogenic properties of chemicals. In particular, many of these compounds are pro-carcinogens that require activation to electrophilic forms to exert genotoxic effects. We systematically analyzed the existing literature on the metabolism of carcinogens by human enzymes, which has been developed largely in the past 25 years. The metabolism and especially bioactivation of carcinogens are dominated by cytochrome P450 enzymes (66% of bioactivations). Within this group, six P450s--1A1, 1A2, 1B1, 2A6, 2E1, and 3A4--accounted for 77% of the P450 activation reactions. The roles of these P450s can be compared with those estimated for drug metabolism and should be considered in issues involving enzyme induction, chemoprevention, molecular epidemiology, interindividual variations, and risk assessment.

Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans

The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CL_int) obtained from in vivo animal studies and (ii) to incorporate it with human physiology in a PBPK model for predicting the inhalation pharmacokinetics of VOCs. CL_int, calculated as the ratio of the in vivo V_max (μmol/h/kg bw rat) to the K_m (μM), was obtained for 26 VOCs from the literature. The QPPR model resulting from stepwise linear regression analysis passed the validation step (R² = 0.8; leave-one-out cross-validation Q² = 0.75) for CL_int normalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation of CL_int (L PL/h/kg bw rat) from the input data on log P_ow, log blood: water PC and ionization potential. The predictions of the QPPR as lower and upper bounds of the 95% mean confidence intervals (LMCI and UMCI, resp.) were then integrated within a human PBPK model. The ratio of the maximum (using LMCI for CL_int) to minimum (using UMCI for CL_int) AUC predicted by the QPPR-PBPK model was 1.36 ± 0.4 and ranged from 1.06 (1,1-dichloroethylene) to 2.8 (isoprene). Overall, the integrated QPPR-PBPK modeling method developed in this study is a pragmatic way of characterizing the impact of the lack of knowledge of CL_int in predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty of CL_int on human pharmacokinetics of VOCs.

QSARs for PBPK modelling of environmental contaminants

Physiologically-based pharmacokinetic (PBPK) models are increasingly finding use in risk assessment applications of data-rich compounds. However, it is a challenge to determine the chemical-specific parameters for these models, particularly in time- and resource-limiting situations. In this regard, SARs, QSARs and QPPRs are potentially useful for computing the chemical-specific input parameters of PBPK models. Based on the frequency of occurrence of molecular fragments (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure) and exposure conditions, the available QSAR-PBPK models facilitate the simulation of tissue and blood concentrations for some inhaled volatile organic chemicals. The application domain of existing QSARs for developing PBPK models is limited, due to lack of relevant data for diverse chemicals and mechanisms. Even though this approach is conceptually applicable to non-volatile and high molecular weight organics as well, it is more challenging to predict the other PBPK model parameters required for modelling the kinetics of these chemicals (particularly tissue diffusion coefficients, association constants for binding and oral absorption rates). As the level of our understanding of the mechanistic basis of toxicokinetic processes improves, QSARs to provide a priori predictions of key chemical-specific PBPK parameters can be developed to expedite the internal dose-based health risk assessments in data-poor situations.

Advances in the interpretation and prediction of CYP2E1 metabolism from a biochemical perspective

BACKGROUND

Cytochrome P450 2E1 (CYP2E1) plays a central role in the metabolism and metabolic activation of a large number of small, mostly xenobiotic compounds. These qualities distinguish CYP2E1 from traditional enzymes and pose significant challenges to understanding the role and consequences of CYP2E1-mediated metabolism.

OBJECTIVE

This review discusses recent advances in kinetic profiling, quantitative structure-activity relationships and structural studies that have furthered the development of tools to interpret and predict CYP2E1 metabolism.

METHODS

Analysis of kinetic profiles by specific mechanisms produces important parameters describing specificity, stoichiometry and metabolism of molecules. Quantitative structure-activity relationships reveal a more specific basis for molecular recognition by CYP2E1. The corresponding protein structures imparting these interactions are the focus of chemical modifications, site-directed mutagenesis and homology modeling studies.

RESULTS

Compilation of kinetic profiling for CYP2E1 substrates established the selectivity for small substrates, whose characteristics could be generalized in parameters for hydrophobicity and steric properties as determined by quantitative structure-activity relationships. The possibility of an effector site for monocyclic compounds added an interesting variable to these modeling efforts. Various structural studies identified important residues contributing to binding and catalysis as well as the volume and location of the active site relative to the heme moiety. Pressure and carbon monoxide-binding experiments also demonstrated the inherent conformational flexibility of CYP2E1 that may contribute to rate-limiting steps during catalytic turnover.

CONCLUSION

Although combinations of these approaches have reinforced important observations, more work is needed to verify findings and seek broader impacts for various interpretative and predictive tools.

CYP2E1 substrate inhibition. Mechanistic interpretation through an effector site for monocyclic compounds

In this study we offer a mechanistic interpretation of the previously known but unexplained substrate inhibition observed for CYP2E1. At low substrate concentrations, p-nitrophenol (pNP) was rapidly turned over (47 min(-1)) with relatively low K(m) (24 microM); nevertheless, at concentrations of >100 microM, the rate of pNP oxidation gradually decreased as a second molecule bound to CYP2E1 through an effector site (K(ss) = 260 microm), which inhibited activity at the catalytic site. 4-Methylpyrazole (4MP) was a potent inhibitor for both sites through a mixed inhibition mechanism. The K(i) for the catalytic site was 2.0 microM. Although we were unable to discriminate whether an EIS or ESI complex formed, the respective inhibition constants were far lower than K(ss). Bicyclic indazole (IND) inhibited catalysis through a single CYP2E1 site (K(i) = 0.12 microM). Similarly, 4MP and IND yielded type II binding spectra that reflected the association of either two 4MP or one IND molecule(s) to CYP2E1, respectively. Based on computational docking studies with a homology model for CYP2E1, the two sites for monocyclic molecules, pNP and 4MP, exist within a narrow channel connecting the active site to the surface of the enzyme. Because of the presence of the heme iron, one site supports catalysis, whereas the other more distal effector site binds molecules that can influence the binding orientation and egress of molecules for the catalytic site. Although IND did not bind these sites simultaneously, the presence of IND at the catalytic site blocked binding at the effector site.

Physiologically based modeling of the inhalation pharmacokinetics of ethylbenzene in B6C3F1 mice

A physiologically based pharmacokinetic (PBPK) model was developed for inhaled ethylbenzene (EB) in B6C3F1 mice. The mouse physiological parameters were obtained from the literature, but the blood:air and tissue:air partition coefficients were determined by vial equilibration technique. The maximal velocity for hepatic metabolism (Vmax) obtained from a previously published rat study was increased by a factor of approximately 3 to account for enzyme induction during repeated exposures. The Michaelis affinity constant (Km) for hepatic metabolism of EB, obtained from a previously published rat PBPK modeling study, was kept unchanged during single and repeated exposure scenarios. Hepatic metabolism alone could not adequately describe the clearance of EB from mouse blood. Additional metabolism was assumed to be localized in the lung. The parameters for pulmonary metabolism were obtained by optimization of PBPK model fits to kinetic data collected following exposures to 75-1000 ppm. The PBPK model successfully predicted all available blood and tissue concentration data in mice exposed to 75 or 750 ppm EB. Overall, the results indicate that the rate of EB clearance is markedly higher in B6C3F1 mice than rats or humans and exceeds the hepatic metabolism capacity. Available biochemical evidence is consistent with a significant role for pulmonary metabolism; however, the extent to which the extrahepatic metabolism is localized in the lung is unclear. Overall, the PBPK model developed for the mouse adequately simulated the blood and tissue kinetics of EB by accounting for its high rate of clearance.

Progress in cytochrome P450 active site modeling

Models capable of predicting the possible involvement of cytochromes P450 in the metabolism of drugs or drug candidates are important tools in drug discovery and development. Ideally, functional information would be obtained from crystal structures of all the cytochromes P450 of interest. Initially, only crystal structures of distantly related bacterial cytochromes P450 were available-comparative modeling techniques were used to bridge the gap and produce structural models of human cytochromes P450, and thereby obtain some useful functional information. A significant step forward in the reliability of these models came four years ago with the first crystal structure of a mammalian cytochrome P450, rabbit CYP2C5, followed by the structures of two human enzymes, CYP2C8 and CYP2C9, and a second rabbit enzyme, CYP2B4. The evolution of a CYP2D6 model, leading to the validation of the model as an in silico tool for predicting binding and metabolism, is presented as a case study.

PREDICTINGHUMANDRUGGLUCURONIDATIONPARAMETERS: Application of In Vitro and In Silico Modeling Approaches

Cytochrome P450 (CYP) and UDP-glucuronosyltransferase (UGT), which both exist as enzyme "superfamilies," are together responsible for the metabolism of most hepatically cleared drugs. There is currently intense interest in the development of techniques that permit identification of the CYP and UGT isoform(s) involved in the metabolism of a newly discovered drug, and hence prediction of factors likely to alter elimination in vivo. In addition, the quantitative scaling of kinetic parameters for a metabolic pathway assumes importance for identifying newly discovered drugs with undesirable in vivo pharmacokinetic properties. Although qualitative and quantitative in vitro-in vivo correlation based on data generated using human liver tissue or recombinant enzymes have been applied successfully to many drugs eliminated by CYP, these strategies have proved less definitive for glucuronidated compounds. Computational (in silico) modeling techniques that potentially provide a facile and economic alternative to the in vitro methods are now emerging. This review assesses the utility of in vitro and in silico approaches for the qualitative and quantitative prediction of drug glucuronidation parameters and the challenges facing the development of generalizable models.