CYP2C9 Structure−Metabolism Relationships: Substrates, Inhibitors, and Metabolites

Stereostructure-activity mechanism of cyproconazole by cytochrome P450 in rat liver microsomes: A combined experimental and computational study

Cyproconazole (CPZ), representing the chiral triazole fungicides, is widely used in the pharmaceutical and agricultural fields. To clarify its potential adverse effects on the generalized CYP-mediated processes within mammalian, a comparative experimental and computational approach was employed to investigate the CYP-mediated metabolism processes of CPZ stereoisomers in rat liver microsomes (RLMs). The depletion rate of CPZ stereoisomers in vitro incubation system with RLMs followed the order RR-> SS-> SR-> RS-CPZ. The results of kinetic assays were in line with the depletion rate results. Further inhibition assay confirmed the stereoselective metabolism of CPZ stereoisomers by different CYP isoforms. Molecular dynamics (MD) simulation revealed the stereoselective metabolism mechanism. Several hydrogen bonds and π-stacking restrict the position of CPZ isomers in the active cavity of CYPs so that the 4'-nitrogen on the triazole ring can bind closely to the heme of CYP, which results in the metabolism of CPZ isomers. By combining the computational and experimental approaches, the structure-activity relationship of CPZ and CYP was elucidated, and this method can be further applied to predict the degree of uncertainty in the process of xenobiotic biotransformation of triazole fungicides and serve as a basis for risk assessment.

Reaction phenotyping: advances in the experimental strategies used to characterize the contribution of drug-metabolizing enzymes

During the process of drug discovery, the pharmaceutical industry is faced with numerous challenges. One challenge is the successful prediction of the major routes of human clearance of new medications. For compounds cleared by metabolism, accurate predictions help provide an early risk assessment of their potential to exhibit significant interpatient differences in pharmacokinetics via routes of metabolism catalyzed by functionally polymorphic enzymes and/or clinically significant metabolic drug-drug interactions. This review details the most recent and emerging in vitro strategies used by drug metabolism and pharmacokinetic scientists to better determine rates and routes of metabolic clearance and how to translate these parameters to estimate the amount these routes contribute to overall clearance, commonly referred to as fraction metabolized. The enzymes covered in this review include cytochrome P450s together with other enzymatic pathways whose involvement in metabolic clearance has become increasingly important as efforts to mitigate cytochrome P450 clearance are successful. Advances in the prediction of the fraction metabolized include newly developed methods to differentiate CYP3A4 from the polymorphic enzyme CYP3A5, scaling tools for UDP-glucuronosyltranferase, and estimation of fraction metabolized for substrates of aldehyde oxidase.

Computational methods in drug discovery

Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.

(E)-4-aryl-4-oxo-2-butenoic acid amides, chalcone-aroylacrylic acid chimeras: design, antiproliferative activity and inhibition of tubulin polymerization

Antiproliferative activity of twenty-nine (E)-4-aryl-4-oxo-2-butenoic acid amides against three human tumor cell lines (HeLa, FemX, and K562) is reported. Compounds showed antiproliferative activity in one-digit micromolar to submicromolar concentrations. The most active derivatives toward all the cell lines tested bear alkyl substituents on the aroyl moiety of the molecules. Fourteen compounds showed tubulin assembly inhibition at concentrations <20 μM. The most potent inhibitor of tubulin assembly was unsubstituted compound 1, with IC50 = 2.9 μM. Compound 23 had an oral LD50in vivo of 45 mg/kg in mice. Cell cycle analysis on K562 cells showed that compounds 1, 2 and 23 caused accumulation of cells in the G2/M phase, but inhibition of microtubule polymerization is not the principal mode of action of the compounds. Nevertheless, they may be useful leads for the design of a new class of antitubulin agents.

Investigating metabolite-protein interactions: an overview of available techniques

Metabolites comprise the molar majority of chemical substances in living cells, and metabolite-protein interactions are expected to be quite common. Many interactions have already been identified and have been shown to be involved in the regulation of different types of cellular processes including signaling events, enzyme activities, protein localizations and interactions. Recent technological advances have greatly facilitated the detection of metabolite-protein interactions at high sensitivity and some of these have been applied on a large scale. In this manuscript, we review the available in vitro, in silico and in vivo technologies for mapping small-molecule-protein interactions. Although some of these were developed for drug-protein interactions they can be applied for mapping metabolite-protein interactions. Information gained from the use of these approaches can be applied to the manipulation of cellular processes and therapeutic applications.

Structural features of cytochromes P450 and ligands that affect drug metabolism as revealed by X-ray crystallography and NMR

Cytochromes P450 (P450s) play a major role in the clearance of drugs, toxins, and environmental pollutants. Additionally, metabolism by P450s can result in toxic or carcinogenic products. The metabolism of pharmaceuticals by P450s is a major concern during the design of new drug candidates. Determining the interactions between P450s and compounds of very diverse structures is complicated by the variability in P450-ligand interactions. Understanding the protein structural elements and the chemical attributes of ligands that dictate their orientation in the P450 active site will aid in the development of effective and safe therapeutic agents. The goal of this review is to describe P450-ligand interactions from two perspectives. The first is the various structural elements that microsomal P450s have at their disposal to assume the different conformations observed in X-ray crystal structures. The second is P450-ligand dynamics analyzed by NMR relaxation studies.

From known knowns to known unknowns: predicting in vivo drug metabolites

'It is better to be useful than perfect'. This review attempts to critically cover and assess the currently available approaches and tools to answer the crucial question: Is it possible (and if it is, to what extent is it possible) to predict in vivo metabolites and their abundances on the basis of in vitro and preclinical animal studies? In preclinical drug development, it is possible to produce metabolite patterns from a candidate drug by virtual means (i.e., in silico models), but these are not yet validated. However, they may be useful to cover the potential range of metabolites. In vitro metabolite patterns and apparent relative abundances are produced by various in vitro systems employing tissue preparations (mainly liver) and in most cases using liquid chromatography-mass spectrometry analytical techniques for tentative identification. The pattern of the metabolites produced depends on the enzyme source; the most comprehensive source of drug-metabolizing enzymes is cultured human hepatocytes, followed by liver homogenate fortified with appropriate cofactors. For specific purposes, such as the identification of metabolizing enzyme(s), recombinant enzymes can be used. Metabolite data from animal in vitro and in vivo experiments, despite known species differences, may help pinpoint metabolites that are not apparently produced in in vitro human systems, or suggest alternative experimental approaches. The range of metabolites detected provides clues regarding the enzymes attacking the molecule under study. We also discuss established approaches to identify the major enzymes. The last question, regarding reliability and robustness of metabolite extrapolations from in vitro to in vivo, both qualitatively and quantitatively, cannot be easily answered. There are a number of examples in the literature suggesting that extrapolations are generally useful, but there are only a few systematic and comprehensive studies to validate in vitro-in vivo extrapolations. In conclusion, extrapolation from preclinical metabolite data to the in vivo situation is certainly useful, but it is not known to what extent.

Computational insights into the different catalytic activities of CYP2A13 and CYP2A6 on NNK

The human cytochrome P450 2A13 (CYP2A13) and P450 2A6 (CYP2A6) are 94% identical in amino acid sequence, but they metabolize many substrates with different efficiencies. Previous experimental results have shown that CYP2A13 exhibited catalytic activity that was more than 300-fold higher than CYP2A6 toward 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK), a carcinogen present in tobacco products. At present, however, the structural determinants accounting for the differential catalytic activities of these two isozymes toward NNK remain unclear. In the present study, molecular docking combined with molecular dynamics simulation and binding free energy calculation was performed to investigate the above issue. The results demonstrate that NNK was able to form a hydrogen bond with Asn297 in either CYP2A13 or CYP2A6. The hydrogen-bond acceptor was the pyridine nitrogen of NNK in the CYP2A13 complex, but it changed to the carbonyl oxygen in the CYP2A6 complex. NNK interacted with the residues in helix I and the K-β2 loop in CYP2A13, whereas it preferred to contact with the phenylalanine cluster in CYP2A6. The residues in helix I and the K-β2 loop of CYP2A13 played a vital role in keeping NNK in a more stable binding state. The binding free energies calculated by MM-GBSA were in agreement with the experimental results.

The challenges of in silico contributions to drug metabolism in lead optimization

IMPORTANCE OF THE FIELD

The site of metabolism (SOM) predictions by CYP 3A4 are extremely important during the drug discovery process especially during the lead discovery or library design phases. With the ability to rapidly characterize metabolites from these enzymes, the challenges facing in silico contribution change during the drug optimization phase. Some of the challenges are addressed in this article. Some aspects of the SOM prediction software and methodology are discussed in this opinion article and examples of software utility in overcoming metabolic instability in drug optimization are shown.

AREAS COVERED IN THIS REVIEW

SOM prediction by various approaches is discussed. Two ways of overcoming metabolic instability, blocking the metabolic softspots and rational modification of the instable molecule to avoid interaction with the CYP pocket, are discussed. The contribution plot in MetaSite and its use are discussed.

WHAT THE READER WILL GAIN

The reader will gain an understanding of possible approaches to either blocking the metabolic softspot or rationally modifying the molecule using MetaSite software or docking approaches. Blocking metabolism using fluorination has risks especially introducing multifluorinated benzene rings in the molecule.

TAKE HOME MESSAGE

During the lead optimization phase of drug discovery, when metabolic instability is an issue in a series, in silico approaches can be used to modify the molecule in order to decrease clearance due to metabolism, even that due to CYP3A4.

Site of metabolism prediction on cytochrome P450 2C9: a knowledge-based docking approach

A novel structure-based approach for site of metabolism prediction has been developed. This knowledge-based method consists of three steps: (1) generation of possible metabolites, (2) docking the predicted metabolites to the CYP binding site and (3) selection of the most probable metabolites based on their complementarity to the binding site. As a proof of concept we evaluated our method by using MetabolExpert for metabolite generation and Glide for docking into the binding site of the CYP2C9 crystal structure. Our method could identify the correct metabolite among the three best-ranked compounds in 69% of the cases. The predictive power of our knowledge-based method was compared to that achieved by substrate docking and two alternative literature approaches.

Computer-based substrate specifity prediction for cytochrome P450

Cytochrome P450 is important class of enzymes metabolizing numerous drugs. The composition and activity of these enzymes are determined the drug distribution in organism, its pharmacological and toxic effect. Thus the prediction of the behaviour of compounds in organism is essential for discovery and development of new drugs in the early stages of this process. The different isoforms of cytochrome P450 can oxidized wide range of chemical compounds and their substrate specifity do not correlate with their taxonomical classification. The main methods of cytochrome P450 substrate specifity prediction is reviewed. These methods divided based on primary informations that used: prediction based on amino acid sequences, ligand-based (pharmacophore and QSAR models) and structure-based (molecular docking, affinity prediction) methods. The common problem of cytochrome P450 substrate prediction and advantage and disadvantages of these methods are discussed.

EaMEAD: Activation energy prediction of cytochrome P450 mediated metabolism with effective atomic descriptors

In an effort to improve drug design and predictions for pharmacokinetics (PK), an empirical model was developed to predict the activation energies (Ea) of cytochrome P450 (CYP450) mediated metabolism. The model, EaMEAD (Activation energy of Metabolism reactions with Effective Atomic Descriptors), predicts the Ea of four major metabolic reactions of the CYP450 enzyme: aliphatic hydroxylation, N-dealkylation, O-dealkylation, and aromatic hydroxylation. To build and validate the empirical model, the E(a) values of the substrates with diverse chemical structures (394 metabolic sites for aliphatic hydroxylation, 27 metabolic sites for N-dealkylation, 9 metabolic sites for O-dealkylation, and 85 metabolic sites for aromatic hydroxylation) were calculated by AM1 molecular orbital (MO). Empirical equations, Quantitative Structure Activity Relationship (QSAR) models, were derived using effective atomic charge, effective atomic polarizability, and bond dipole moments of the substrates as descriptors. EaMEAD is shown to accurately predict Ea with a correlation coefficient (R) of 0.94 and root-mean-square error (RMSE, unit is kcal/mol) of 0.70 for aliphatic hydroxylation, N-dealkylation, and O-dealkylation, and R of 0.83 and RMSE of 0.80 for aromatic hydroxylation, respectively. Physical origin and the role of the effective atomic descriptors of the models are presented in detail. With this model, the Ea of the metabolism can be rapidly predicted without any experimental parameters or time-consuming QM calculation. Regioselectivity prediction with our model is presented in the case of CYP3A4 metabolism. The reliability and ease of use of this model will greatly facilitate early stage PK predictions and rational drug design. Moreover, the model can be applied to develop the Ea prediction model of various types of chemical reactions.