Theoretical investigation of substrate specificity for cytochromes P450 IA2, P450 IID6 and P450 IIIA4

Physicochemical and structural characterization, epitope mapping and vaccine potential investigation of a new protein containing Tetratrico Peptide Repeats of Acinetobacter baumannii: An in-silico and in-vivo approach

Acinetobacter baumannii is an opportunistic multidrug-resistant pathogen that causes a significant mortality rate. The proteins containing Tetratrico Peptide Repeats (TPRs) are involved in the pathogenicity and virulence of bacteria and have different roles such as transfer of bacterial virulence factors to host cells, binding to the host cells and inhibition of phagolysosomal maturation. So, in this study, physicochemical properties of a new protein containing TPRs in A. baumannii which was named PcTPRs1 by this study were characterized and its 3D structure was predicted by in-silico tools. The protein B and T cell epitopes were mapped and its vaccine potential was in-silico and in-vivo investigated. Domain analysis indicated that the protein contains the Flp pilus assembly protein TadD domain which has three TPRs. The helix is dominant in the protein structure, and this protein is an outer membrane antigen which, is extremely conserved among A. baumannii strains; thus, has good properties to be applied as a recombinant vaccine. The best-predicted and refined model was applied in ligand-binding sites and conformational epitopes prediction. Based on epitope mapping results, several epitopes were characterized which could stimulate both immune systems. BLAST results showed the introduced epitopes are completely conserved among A. baumannii strains. The in-vivo analysis indicates that a 101 amino acid fragment of the protein which contains the best selected epitope, can produce a good protectivity against A. baumannii as well as the whole TPR protein and thus could be investigated as an effective subunit and potential vaccines.

Structure-Based Drug Design for Cytochrome P450 Family 1 Inhibitors

Cytochromes P450 are a class of metalloproteins which are responsible for electron transfer in a wide spectrum of reactions including metabolic biotransformation of endogenous and exogenous substrates. The superfamily of cytochromes P450 consists of families and subfamilies which are characterized by a specific structure and substrate specificity. Cytochromes P450 family 1 (CYP1s) play a distinctive role in the metabolism of drugs and chemical procarcinogens. In recent decades, these hemoproteins have been intensively studied with the use of computational methods which have been recently developed remarkably to be used in the process of drug design by the virtual screening of compounds in order to find agents with desired properties. Moreover, the molecular modeling of proteins and ligand docking to their active sites provide an insight into the mechanism of enzyme action and enable us to predict the sites of drug metabolism. The review presents the current status of knowledge about the use of the computational approach in studies of ligand-enzyme interactions for CYP1s. Research on the metabolism of substrates and inhibitors of CYP1s and on the selectivity of their action is particularly valuable from the viewpoint of cancer chemoprevention, chemotherapy, and drug-drug interactions.

Technologies for Measuring Pharmacokinetic Profiles

The creation of a pharmacokinetic (PK) curve, which follows the plasma concentration of an administered drug as a function of time, is a critical aspect of the drug development process and includes such information as the drug's bioavailability, clearance, and elimination half-life. Prior to a drug of interest gaining clearance for use in human clinical trials, research is performed during the preclinical stages to establish drug safety and dosing metrics from data obtained from the PK studies. Both in vivo animal models and in vitro platforms have limitations in predicting human reaction to a drug due to differences in species and associated simplifications, respectively. As a result, in silico experiments using computer simulation have been implemented to accurately predict PK parameters in human studies. This review assesses these three approaches (in vitro, in vivo, and in silico) when establishing PK parameters and evaluates the potential for in silico studies to be the future gold standard of PK preclinical studies.

Antiviral Compounds Against Nucleocapsid Protein of Porcine Epidemic Diarrhea Virus

Porcine epidemic diarrhea (PED) is a severe diarrhea disease in swine that is caused by porcine epidemic diarrhea virus (PEDV). Nucleocapsid (N) protein is the RNA-binding protein of PEDV, which plays an important role for virus life cycle. The aim of this research was to screen and characterize the compounds that could inhibit the activity of PEDV N protein. The gene encoding PEDV N protein obtained from PEDV Thai isolate was cloned and expressed in E. coli. Its amino acid sequence was employed to generate the three dimensional structure by homology modeling. There were 1,286 compounds of FDA-approved drug database that could virtually bind to the RNA-binding region of N protein. Three compounds, trichlormethiazide, D-(+) biotin, and glutathione successfully bound to the N protein, in vitro, with the IC₅₀ at 8.754 mg/mL, 0.925 mg/mL, and 2.722 mg/mL. Antiviral activity in PEDV-infected Vero cells demonstrated that the effective concentration of trichlormethiazide, D-(+) biotin, and glutathione in inhibiting PEDV replication were 0.094, 0.094 and 1.5 mg/mL. This study demonstrated a strategy applied for discovery of antiviral agents capable of inhibiting PEDV N protein and PEDV replication. The compounds identified here exhibited a potential use as therapeutic agents for controlling PEDV infection.

Characterization of differences in substrate specificity among CYP1A1, CYP1A2 and CYP1B1: an integrated approach employing molecular docking and molecular dynamics simulations

Recent trends in new drug discovery of anticancer drugs have made oncologists more aware of the fact that the new drug discovery must target the developing mechanism of tumorigenesis to improve the therapeutic efficacy of antineoplastic drugs. The drugs designed are expected to have high affinity towards the novel targets selectively. Current research highlights overexpression of CYP450s, particularly cytochrome P450 1A1 (CYP1A1), in tumour cells, representing a novel target for anticancer therapy. However, the CYP1 family is identified as posing significant problems in selectivity of anticancer molecules towards CYP1A1. Three members have been identified in the human CYP1 family: CYP1A1, CYP1A2 and CYP1B1. Although sequences of the three isoform have high sequence identity, they have distinct substrate specificities. The understanding of macromolecular features that govern substrate specificity is required to understand the interplay between the protein function and dynamics, design novel antitumour compounds that could be specifically metabolized by only CYP1A1 to mediate their antitumour activity and elucidate the reasons for differences in substrate specificity profile among the three proteins. In the present study, we employed a combination of computational methodologies: molecular docking and molecular dynamics simulations. We utilized eight substrates for elucidating the difference in substrate specificity of the three isoforms. Lastly, we conclude that the substrate specificity of a particular substrate depends upon the type of the active site residues, the dynamic motions in the protein structure upon ligand binding and the physico-chemical characteristics of a particular ligand. Copyright © 2016 John Wiley & Sons, Ltd.

Improving Detection of Arrhythmia Drug-Drug Interactions in Pharmacovigilance Data through the Implementation of Similarity-Based Modeling

Identification of Drug-Drug Interactions (DDIs) is a significant challenge during drug development and clinical practice. DDIs are responsible for many adverse drug effects (ADEs), decreasing patient quality of life and causing higher care expenses. DDIs are not systematically evaluated in pre-clinical or clinical trials and so the FDA U. S. Food and Drug Administration relies on post-marketing surveillance to monitor patient safety. However, existing pharmacovigilance algorithms show poor performance for detecting DDIs exhibiting prohibitively high false positive rates. Alternatively, methods based on chemical structure and pharmacological similarity have shown promise in adverse drug event detection. We hypothesize that the use of chemical biology data in a post hoc analysis of pharmacovigilance results will significantly improve the detection of dangerous interactions. Our model integrates a reference standard of DDIs known to cause arrhythmias with drug similarity data. To compare similarity between drugs we used chemical structure (both 2D and 3D molecular structure), adverse drug side effects, chemogenomic targets, drug indication classes, and known drug-drug interactions. We evaluated the method on external reference standards. Our results showed an enhancement of sensitivity, specificity and precision in different top positions with the use of similarity measures to rank the candidates extracted from pharmacovigilance data. For the top 100 DDI candidates, similarity-based modeling yielded close to twofold precision enhancement compared to the proportional reporting ratio (PRR). Moreover, the method helps in the DDI decision making through the identification of the DDI in the reference standard that generated the candidate.

Similarity-based modeling in large-scale prediction of drug-drug interactions

Drug-drug interactions (DDIs) are a major cause of adverse drug effects and a public health concern, as they increase hospital care expenses and reduce patients' quality of life. DDI detection is, therefore, an important objective in patient safety, one whose pursuit affects drug development and pharmacovigilance. In this article, we describe a protocol applicable on a large scale to predict novel DDIs based on similarity of drug interaction candidates to drugs involved in established DDIs. The method integrates a reference standard database of known DDIs with drug similarity information extracted from different sources, such as 2D and 3D molecular structure, interaction profile, target and side-effect similarities. The method is interpretable in that it generates drug interaction candidates that are traceable to pharmacological or clinical effects. We describe a protocol with applications in patient safety and preclinical toxicity screening. The time frame to implement this protocol is 5-7 h, with additional time potentially necessary, depending on the complexity of the reference standard DDI database and the similarity measures implemented.

Exposition and reactivity optimization to predict sites of metabolism in chemicals

Chemical modifications of drugs induced by phase I biotransformations significantly affect their pharmacokinetic properties. Because the metabolites produced can themselves have a pharmacological effect and an intrinsic toxicity, medicinal chemists need to accurately predict the sites of metabolism (SoM) of drugs as early as possible. However, site of metabolism prediction is rarely accompanied by a prediction of the relative abundance of the various metabolites. Such a prediction would be a great help in the study of drug– drug interactions and in the process of reducing the toxicity of potential drug candidates. The aim of this paper is to present recent developments in the prediction of xenobiotic metabolism and to use concrete examples to explain the computational mechanism employed.

In vitro approaches to investigate cytochrome P450 activities: update on current status and their applicability

INTRODUCTION

Cytochromes P450 (CYPs) play a central role in the Phase I metabolism of drugs and other xenobiotics. It is estimated that CYPs can metabolize up to two-thirds of drugs present in humans. Over the past two decades, there have been numerous advances in in vitro methodologies to characterize drug metabolism and interaction involving CYPs.

AREAS COVERED

This review focuses on the use of in vitro methodologies to examine CYPs' role in drug metabolism and interaction. There is an emphasis on their current development, applicability, advantages and limitations as well as the use of in silico approaches in complementing and supporting in vitro data. The article also highlights the challenges in extrapolating in vitro data to in vivo situations.

EXPERT OPINION

Advances in in vitro methodologies have been made such that data can be used for in vivo prediction with comfortable degree of confidence. Improved assay designs and analytical techniques have permitted development of miniaturized assay format and automated system with improved sensitivity and throughput capacity. High-quality experimental designs and scientifically rigorous assessment/validation protocols remain crucial in developing reliable and robust in vitro models. With continued progress made in the field, in vitro methodologies will continually be employed in evaluating CYP activities in pharmaceutical industries and laboratories.

Homology modeling a fast tool for drug discovery: current perspectives

Major goal of structural biology involve formation of protein-ligand complexes; in which the protein molecules act energetically in the course of binding. Therefore, perceptive of protein-ligand interaction will be very important for structure based drug design. Lack of knowledge of 3D structures has hindered efforts to understand the binding specificities of ligands with protein. With increasing in modeling software and the growing number of known protein structures, homology modeling is rapidly becoming the method of choice for obtaining 3D coordinates of proteins. Homology modeling is a representation of the similarity of environmental residues at topologically corresponding positions in the reference proteins. In the absence of experimental data, model building on the basis of a known 3D structure of a homologous protein is at present the only reliable method to obtain the structural information. Knowledge of the 3D structures of proteins provides invaluable insights into the molecular basis of their functions. The recent advances in homology modeling, particularly in detecting and aligning sequences with template structures, distant homologues, modeling of loops and side chains as well as detecting errors in a model contributed to consistent prediction of protein structure, which was not possible even several years ago. This review focused on the features and a role of homology modeling in predicting protein structure and described current developments in this field with victorious applications at the different stages of the drug design and discovery.

Substrate selectivity of drug-metabolizing cytochrome P450s predicted from crystal structures and in silico modeling

Enormous efforts toward predicting the metabolic fate of a drug have been driven by the high attrition rate in drug development. To accelerate such efforts, it is critical to elucidate the molecular mechanisms of drug recognition by drug-metabolizing enzymes. Therefore, it is not surprising that an increasing number of crystal structures have been determined (by X-ray crystallography) and numerous insightful in silico (computational) models have been established for the most important metabolic enzymes, cytochrome P450s (CYPs). In this review, we provide a detailed analysis of the available crystal structures for CYPs to reveal the structural features and protein flexibility determining substrate selectivity. The ligand-based in silico models (including pharmacophore and molecular field analysis models) are also discussed, with a focus on their ability to characterize the structural features of the substrates for various CYP isoforms.

Novel natural inhibitors of CYP1A2 identified by in silico and in vitro screening

Inhibition of cytochrome P450 (CYP) is a major cause of herb–drug interactions. The CYP1A2 enzyme plays a major role in the metabolism of drugs in humans. Its broad substrate specificity, as well as its inhibition by a vast array of structurally diverse herbal active ingredients, has indicated the possibility of metabolic herb–drug interactions. Therefore nowadays searching inhibitors for CYP1A2 from herbal medicines are drawing much more attention by biological, chemical and pharmological scientists. In our work, a pharmacophore model as well as the docking technology is proposed to screen inhibitors from herbal ingredients data. Firstly different pharmaphore models were constructed and then validated and modified by 202 herbal ingredients. Secondly the best pharmaphore model was chosen to virtually screen the herbal data (a curated database of 989 herbal compounds). Then the hits (147 herbal compounds) were continued to be filtered by a docking process, and were tested in vitro successively. Finally, five of eighteen candidate compounds (272, 284, 300, 616 and 817) were found to have inhibition of CYP1A2 activity. The model developed in our study is efficient for in silico screening of large herbal databases in the identification of CYP1A2 inhibitors. It will play an important role to prevent the risk of herb–drug interactions at an early stage of the drug development process.

Studies on 16α-Hydroxylation of Steroid Molecules and Regioselective Binding Mode in Homology-Modeled Cytochrome P450-2C11

We investigated the 16α-hydroxylation of steroid molecules and regioselective binding mode in homology-modeled cytochrome P450-2C11 to correlate the biological study with the computational molecular modeling. It revealed that there was a positive relationship between the observed inhibitory potencies and the binding free energies. Docking of steroid molecules into this homology-modeled CYP2C11 indicated that 16α-hydroxylation is favored with steroidal molecules possessing the following components, (1) a bent A-B ring configuration (5β-reduced), (2) C-3 α-hydroxyl group, (3) C-17β-acetyl group, and (4) methyl group at both the C-18 and C-19. These respective steroid components requirements were defined as the inhibitory contribution factor. Overall studies of the male rat CYP2C11 metabolism revealed that the above-mentioned steroid components requirements were essential to induce an effective inhibition of [³H]progesterone 16α-hydroxylation. As far as docking of homology-modeled CYP2C11 against investigated steroids is concerned, they are docked at the active site superimposed with flurbiprofen. It was also found that the distance between heme iron and C16α-H was between 4 to 6 Å and that the related angle was in the range of 180 ± 45°.

Structure, function, regulation and polymorphism and the clinical significance of human cytochrome P450 1A2

Human CYP1A2 is one of the major CYPs in human liver and metabolizes a number of clinical drugs (e.g., clozapine, tacrine, tizanidine, and theophylline; n > 110), a number of procarcinogens (e.g., benzo[a]pyrene and aromatic amines), and several important endogenous compounds (e.g., steroids). CYP1A2 is subject to reversible and/or irreversible inhibition by a number of drugs, natural substances, and other compounds. The CYP1A gene cluster has been mapped on to chromosome 15q24.1, with close link between CYP1A1 and 1A2 sharing a common 5'-flanking region. The human CYP1A2 gene spans almost 7.8 kb comprising seven exons and six introns and codes a 515-residue protein with a molecular mass of 58,294 Da. The recently resolved CYP1A2 structure has a relatively compact, planar active site cavity that is highly adapted for the size and shape of its substrates. The architecture of the active site of 1A2 is characterized by multiple residues on helices F and I that constitutes two parallel substrate binding platforms on either side of the cavity. A large interindividual variability in the expression and activity of CYP1A2 has been observed, which is largely caused by genetic, epigenetic and environmental factors (e.g., smoking). CYP1A2 is primarily regulated by the aromatic hydrocarbon receptor (AhR) and CYP1A2 is induced through AhR-mediated transactivation following ligand binding and nuclear translocation. Induction or inhibition of CYP1A2 may provide partial explanation for some clinical drug interactions. To date, more than 15 variant alleles and a series of subvariants of the CYP1A2 gene have been identified and some of them have been associated with altered drug clearance and response and disease susceptibility. Further studies are warranted to explore the clinical and toxicological significance of altered CYP1A2 expression and activity caused by genetic, epigenetic, and environmental factors.

New insights into the structural characteristics and functional relevance of the human cytochrome P450 2D6 enzyme

To date, the crystal structures of at least 12 human CYPs (1A2, 2A6, 2A13, 2C8, 2C9, 2D6, 2E1, 2R1, 3A4, 7A1, 8A1, and 46A1) have been determined. CYP2D6 accounts for only a small percentage of all hepatic CYPs (< 2%), but it metabolizes approximately 25% of clinically used drugs with significant polymorphisms. CYP2D6 also metabolizes procarcinogens and neurotoxins, such as 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine, 1,2,3,4-tetrahydroquinoline, and indolealkylamines. Moreover, the enzyme utilizes hydroxytryptamines and neurosteroids as endogenous substrates. Typical CYP2D6 substrates are usually lipophilic bases with an aromatic ring and a nitrogen atom, which can be protonated at physiological pH. Substrate binding is generally followed by oxidation (5-7 A) from the proposed nitrogen-Asp301 interaction. A number of homology models have been constructed to explore the structural features of CYP2D6, while antibody studies also provide useful structural information. Site-directed mutagenesis studies have demonstrated that Glu216, Asp301, Phe120, Phe481, and Phe483 play important roles in determining the binding of ligands to CYP2D6. The structure of human CYP2D6 has been recently determined and shows the characteristic CYP fold observed for other members of the CYP superfamily. The lengths and orientations of the individual secondary structural elements in the CYP2D6 structure are similar to those seen in other human CYP2 members, such as CYP2C9 and 2C8. The 2D6 structure has a well-defined active-site cavity located above the heme group with a volume of approximately 540 A(3), which is larger than equivalent cavities in CYP2A6 (260 A(3)), 1A2 (375 A(3)), and 2E1 (190 A(3)), but smaller than those in CYP3A4 (1385 A(3)) and 2C8 (1438 A(3)). Further studies are required to delineate the molecular mechanisms involved in CYP2D6 ligand interactions and their implications for drug development and clinical practice.

QSAR of cytochrome inhibitors

Cytochrome P450 (CYP450) enzymes are predominantly involved in the Phase I metabolism of xenobiotics. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. Metabolic stability is a prerequisite for sustaining the therapeutically relevant concentrations, and very often drug candidates are sacrificed due to poor metabolic profiles. Computational tools such as quantitative structure-activity relationships are widely used to study different metabolic end points successfully to accelerate the drug discovery process. There are a lot of computational studies on clinically important CYPs already reported in recent years. But other clinically significant families are to yet be explored computationally. Powerfulness of quantitative structure-activity relationship will drive computational chemists to develop new potent and selective inhibitors of different classes of CYPs for the treatment of different diseases with least drug-drug interactions. Furthermore, there is a need to enhance the accuracy, interpretability and confidence in the computational models in accelerating the drug discovery pathways.

Comparative QSAR studies of CYP1A2 inhibitor flavonoids using 2D and 3D descriptors

Comparative Quantitative Structure Activity Relationship (QSAR) analyses have been performed with 21 naturally occurring flavonoids for their inhibitory effects on cytochrome P450 1A2 enzyme using two-dimensional (topological, structural, and thermodynamic) and three-dimensional (spatial) descriptors. The chemometric tools used for the analyses are stepwise multiple linear regression, partial least squares, genetic function approximation, and genetic partial least squares. The data set was divided into a training set (n = 15) and test set (n = 6), based on K-means clustering technique applied on standardized two-dimensional descriptor matrix, and models were developed from the training set compounds. The best model (genetic partial least squares model using two-dimensional descriptors) was selected based on the highest external predictive R(2) (R(2)(pred)) value (0.840) and the lowest root mean square error of prediction value (0.351). The developed QSAR equations suggest the importance of the double bond present at 2 and 3 positions and requirement of absence of hydroxyl substituent or glycosidic linkage at 3 position of the 1,4-benzopyrone nucleus. Furthermore, the phenyl ring present at 2 position of the 1,4-benzopyrone ring should not be substituted with hydroxyl group. Moreover, hydroxyl groups present at 5 and 7 positions of the benzopyran nucleus should not be glycosylated for good cytochrome P450 1A2 enzyme inhibitory activity.

Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450

BACKGROUND

Early in-vitro consideration of metabolism and inhibition of cytochrome P450 has proven its merits over the last 15 years. Simultaneously, many computational drug-design methods have been developed, and are being applied to study the interactions between drug candidates and cytochrome P450 enzymes (P450s).

OBJECTIVE

This review discusses the recent advances of these methods and the implications that are specific for P450s.

METHODS

Mainly focusing on the prediction of binding affinity and ligand selectivity, we outline the applicability of the different methods to answer specific questions. Special emphasis is put on the different levels of theory that are being used in recent computational descriptions of ligand-P450 interactions.

CONCLUSION

P450s offer an additional challenge for computational methods, considering the ambiguities of the catalytic cycle and the significant flexibility of the active site. Different computational methods display different limitations, which is crucial to take into account when choosing the method appropriate to each application.

Homology modelling of CYP3A4 from the CYP2C5 crystallographic template: analysis of typical CYP3A4 substrate interactions

1. The results of homology modelling of cytochrome P4503A4 (CYP3A4), which is a human enzyme of major importance for the Phase 1 metabolism of drug substrates, from the CYP2C5 crystal structure is reported. 2. The overall homology between the two protein sequences was generally good (46%) with 24% of amino acid residues being identical and a 22% similarity between matched pairs in the CYP3A4 and CYP2C5 aligned sequences, thus indicating that CYP2C5 represents a viable template for modelling CYP3A4 by homology. 3. The CYP3A4 model appears to show consistency with the reported findings from the extensive site-directed mutagenesis studies already published. 4. Typical CYP3A4 substrates, such as midazolam, testosterone, nifedipine and verapamil, are shown to fit the putative active site of the enzyme structure in a manner consistent with their known positions of metabolism.

Exploration of Enzyme−Ligand Interactions in CYP2D6 & 3A4 Homology Models and Crystal Structures Using a Novel Computational Approach

New crystal structures of human CYP2D6 and CYP3A4 have recently been reported, and in this study, we wanted to compare them with previously used homology models with respect to predictions of site of metabolism and ligand-enzyme interactions. The data set consisted of a family of synthetic opioid analgesics with the aim to cover both CYP2D6 and CYP3A4, as most of these compounds are metabolized by both isoforms. The program MetaSite was used for the site of metabolism predictions, and the results were validated by experimental assessment of the major metabolites formed with recombinant CYP450s. This was made on a selection of 14 compounds in the data set. The prediction rates for MetaSite were 79-100% except for the CYP3A4 homology model, which picked the correct site in half of the cases. Despite differences in orientation of some important amino acids in the active sites, the MetaSite-predicted sites were the same for the different structures, with the exception of the CYP3A4 homology model. Further exploration of interactions with ligands was done by docking substrates/inhibitors in the different structures with the docking program GLUE. To address the challenge in interpreting patterns of enzyme-ligand interactions for the large number of different docking poses, a new computational tool to handle the results from the dockings was developed, in which the output highlights the relative importance of amino acids in CYP450-substrate/inhibitor interactions. The method is based on calculations of the interaction energies for each pose with the surrounding amino acids. For the CYP3A4 structures, this method was compared with consensus principal component analysis (CPCA), a commonly used method for structural comparison to evaluate the usefulness of the new method. The results from the two methods were comparable with each other, and the highlighted amino acids resemble those that were identified to have a different orientation in the compared structures. The new method has clear advantages over CPCA in that it is far simpler to interpret and there is no need for protein alignment. The methodology enables structural comparison but also gives insights on important amino acid substrate/inhibitor interactions and can therefore be very useful when suggesting modifications of new chemical entities to improve their metabolic profiles.

Advances in homology protein structure modeling

Homology modeling plays a central role in determining protein structure in the structural genomics project. The importance of homology modeling has been steadily increasing because of the large gap that exists between the overwhelming number of available protein sequences and experimentally solved protein structures, and also, more importantly, because of the increasing reliability and accuracy of the method. In fact, a protein sequence with over 30% identity to a known structure can often be predicted with an accuracy equivalent to a low-resolution X-ray structure. The recent advances in homology modeling, especially in detecting distant homologues, aligning sequences with template structures, modeling of loops and side chains, as well as detecting errors in a model, have contributed to reliable prediction of protein structure, which was not possible even several years ago. The ongoing efforts in solving protein structures, which can be time-consuming and often difficult, will continue to spur the development of a host of new computational methods that can fill in the gap and further contribute to understanding the relationship between protein structure and function.

Computational approaches for predicting CYP-related metabolism properties in the screening of new drugs

The site of biotransformation, the extent and rate of metabolism and the number of active metabolic pathways are among the most important characteristics of the pharmacokinetics of a drug. The catalytic activity of drug metabolizing enzymes is likely the most influential determinant of the pharmacokinetic variability. Metabolic stability is the prerequisite for sustaining the therapeutically relevant concentrations. Metabolic inhibition and induction can give rise to clinically important drug-drug interactions. A variety of computational approaches are currently available for predicting different cytochrome P450 (CYP)-related metabolism endpoints. The present review will describe these approaches and their impact on drug development process. Indications on the available software for the implementation will also be given.

COMPARISON OF METHODS FOR THE PREDICTION OF THE METABOLIC SITES FOR CYP3A4-MEDIATED METABOLIC REACTIONS

Predictions of the metabolic sites for new chemical entities, synthesized or only virtual, are important in the early phase of drug discovery to guide chemistry efforts in the synthesis of new compounds with reduced metabolic liability. This information can now be obtained from in silico predictions, and therefore, a thorough and unbiased evaluation of the computational techniques available is needed. Several computational methods to predict the metabolic hot spots are emerging. In this study, metabolite identification using MetaSite and a docking methodology, GLUE, were compared. Moreover, the published CYP3A4 crystal structure and computed CYP3A4 homology models were compared for their usefulness in predicting metabolic sites. A total of 227 known CYP3A4 substrates reported to have one or more metabolites adding up to 325 metabolic pathways were analyzed. Distance-based fingerprints and four-point pharmacophore derived from GRID molecular interaction fields were used to characterize the substrate and protein in MetaSite and the docking methodology, respectively. The CYP3A4 crystal structure and homology model with the reactivity factor enabled achieved a similar prediction success (78%) using the MetaSite method. The docking method had a relatively lower prediction success (approximately 57% for the homology model), although it still may provide useful insights for interactions between ligand and protein, especially for uncommon reactions. The MetaSite methodology is automated, rapid, and has relatively accurate predictions compared with the docking methodology used in this study.

Crystal Structure of Human Cytochrome P450 2D6

Cytochrome P450 2D6 is a heme-containing enzyme that is responsible for the metabolism of at least 20% of known drugs. Substrates of 2D6 typically contain a basic nitrogen and a planar aromatic ring. The crystal structure of human 2D6 has been solved and refined to 3.0A resolution. The structure shows the characteristic P450 fold as seen in other members of the family, with the lengths and orientations of the individual secondary structural elements being very similar to those seen in 2C9. There are, however, several important differences, the most notable involving the F helix, the F-G loop, the B'helix, beta sheet 4, and part of beta sheet 1, all of which are situated on the distal face of the protein. The 2D6 structure has a well defined active site cavity above the heme group, containing many important residues that have been implicated in substrate recognition and binding, including Asp-301, Glu-216, Phe-483, and Phe-120. The crystal structure helps to explain how Asp-301, Glu-216, and Phe-483 can act as substrate binding residues and suggests that the role of Phe-120 is to control the orientation of the aromatic ring found in most substrates with respect to the heme. The structure has been compared with published homology models and has been used to explain much of the reported site-directed mutagenesis data and help understand the metabolism of several compounds.

MetaSite: Understanding Metabolism in Human Cytochromes from the Perspective of the Chemist

Identification of metabolic biotransformations can significantly affect the drug discovery process. Since bioavailability, activity, toxicity, distribution, and final elimination all depend on metabolic biotransformations, it would be extremely advantageous if this information could be produced early in the discovery phase. Once obtained, this information can help chemists to judge whether a potential candidate should be eliminated from the pipeline or modified to improve chemical stability or safety of new compounds. The use of in silico methods to predict the site of metabolism in phase I cytochrome-mediated reactions is a starting point in any metabolic pathway prediction. This paper presents a new method, specifically designed for chemists, that provides the cytochrome involved and the site of metabolism for any human cytochrome P450 (CYP) mediated reaction acting on new substrates. The methodology can be applied automatically to all the cytochromes for which 3D structure is known and can be used by chemists to detect positions that should be protected in order to avoid metabolic degradation or to check the suitability of a new scaffold or prodrug. The fully automated procedure is also a valuable new tool in early ADME-Tox assays (absorption, distribution, metabolism, and excretion toxicity assays), where drug safety and metabolic profile patterns must be evaluated as soon, and as early, as possible.

A 3D-QSAR model for CYP2D6 inhibition in the aryloxypropanolamine series

A comparative molecular similarity index analysis (CoMSiA) has been performed for cytochrome P450 2D6 inhibition on a series of aryloxypropanolamines to determine the factors contributing to this activity. The model is in agreement with a CYP2D6 homology model constructed on the basis of the mammalian CYP2C5 crystal structure. The energy minimized conformations were generated using the systematic search methodology in Sybyl 6.7. The model not only elucidated the relationship between structure and biological activity but, more importantly, provided useful strategies to modulate CYP2D6 affinity in the aryloxypropanolamine series.

Predictive three-dimensional quantitative structure-activity relationship of cytochrome P450 1A2 inhibitors

The purpose of this study was to determine the cytochrome P450 1A2 (CYP1A2) inhibition potencies of structurally diverse compounds to create a comprehensive three-dimensional quantitative structure-activity relationship (3D-QSAR) model of CYP1A2 inhibitors and to use this model to predict the inhibition potencies of an external set of compounds. Fifty-two compounds including naphthalene, lactone and quinoline derivatives were assayed in a 96-well plate format for CYP1A2 inhibition activity using 7-ethoxyresorufin O-dealkylation as the probe reaction. The IC50 values of the tested compounds varied from 2.3 microM to over 40,000 microM. On the basis of this data set, a comparative molecular field analysis (CoMFA) and GRID/GOLPE models were created that yielded novel structural information about the interaction between inhibitory molecules and the CYP1A2 active site. The created CoMFA model was able to accurately predict inhibitory potencies of several structurally unrelated compounds, including selective inhibitors of other cytochrome P450 forms.

Theoretical quantitative structure–activity relationships of flavone ligands interacting with cytochrome P450 1A1 and 1A2 isozymes

Theoretical descriptors obtained from quantum mechanical calculations on isolated ligands in different media and molecular dynamics simulations of ligand-enzyme complexes have been used to obtain a quantitative rationalization of the inhibition of CYP1A2 and CYP1A2 by three series of flavonoids. Predictive models obtained through one-descriptor QSAR studies and mechanistic explanations have been obtained for recognition and selectivity.

In silico approaches for predicting ADME properties of drugs

Combinatorial chemistry and high-throughput screening have increased the possibility of finding new lead compounds at much shorter time periods than conventional medicinal chemistry. However, too much promising drug candidates often fail because of unsatisfactory ADME properties. In silico ADME studies are expected to reduce the risk of late-stage attrition of drug development and to optimize screening and testing by looking at only the promising compounds. To this end, many in silico approaches for predicting ADME properties of compounds from their chemical structure have been developed, ranging from data-based approaches such as quantitative structure-activity relationship (QSAR), similarity searches, and 3-dimensional QSAR, to structure-based methods such as ligand-protein docking and pharmacophore modelling. In addition, several methods of integrating ADME properties to predict pharmacokinetics at the organ or body level have been studied. In this article, we briefly summarize in silico ADME approaches.

Functional analysis of CYP2D6.31 variant: Homology modeling suggests possible disruption of redox partner interaction by Arg440His substitution

Cytochrome P450 2D6 (CYP2D6) is an important human drug-metabolizing enzyme that exhibits a marked genetic polymorphism. Numerous CYP2D6 alleles have been characterized at a functional level, although the consequences for expression and/or catalytic activity of a substantial number of rare variants remain to be investigated. One such allele, CYP2D6*31, is characterized by mutations encoding three amino acid substitutions: Arg296Cys, Arg440His and Ser486Thr. The identification of this allele in an individual with an apparent in vivo poor metabolizer phenotype prompted us to analyze the functional consequence of these substitutions on enzyme activity using yeast as a heterologous expression system. We demonstrated that the Arg440His substitution, alone or in combination with Arg296Cys and/or Ser486Thr, altered the respective kinetic parameters [Km (microM) and kcat (min(-1))] of debrisoquine 4-hydroxylation (wild-type, 25; 0.92; variants, 43-68; 0.05-0.11) and dextromethorphan O-demethylation (wild-type, 1; 4.72; variants, 12-23; 0.64-1.43), such that their specificity constants (kcat/Km) were decreased by more than 95% compared to those observed with the wild-type enzyme. The rates of oxidation of rac-metoprolol at single substrate concentrations of 40 and 400 microM were also markedly decreased by approximately 90% with each CYP2D6 variant containing the Arg440His substitution. These in vitro data confirm that the CYP2D6*31 allele encodes an enzyme with a severely impaired but residual catalytic activity and, furthermore, that the Arg440His exchange alone is the inactivating mutation. A homology model of CYP2D6 based on the crystal structure of rabbit CYP2C5 locates Arg440 on the proximal surface of the protein. Docking the structure of the FMN domain of human cytochrome P450 reductase to the CYP2D6 model suggests that Arg440 is a key member of a cluster of basic amino acid residues important for reductase binding.

Homotropic cooperativity of monomeric cytochrome P450 3A4 in a nanoscale native bilayer environment

Mechanistic studies of mammalian cytochrome P450s are often obscured by the phase heterogeneity of solubilized preparations of membrane enzymes. The various protein-protein aggregation states of microsomes, detergent solubilized cytochrome or a family of aqueous multimeric complexes can effect measured substrate binding events as well as subsequent steps in the reaction cycle. In addition, these P450 monooxygenases are normally found in a membrane environment and the bilayer composition and dynamics can also effect these catalytic steps. Here, we describe the structural and functional characterization of a homogeneous monomeric population of cytochrome P450 3A4 (CYP 3A4) in a soluble nanoscale membrane bilayer, or Nanodisc [Nano Lett. 2 (2002) 853]. Cytochrome P450 3A4:Nanodisc assemblies were formed and purified to yield a 1:1 ratio of CYP 3A4 to Nanodisc. Solution small angle X-ray scattering was used to structurally characterize this monomeric CYP 3A4 in the membrane bilayer. The purified CYP 3A4:Nanodiscs showed a heretofore undescribed high level of homotropic cooperativity in the binding of testosterone. Soluble CYP 3A4:Nanodisc retains its known function and shows prototypic hydroxylation of testosterone when driven by hydrogen peroxide. This represents the first functional characterization of a true monomeric preparation of cytochrome P450 monooxygenase in a phospholipid bilayer and elucidates new properties of the monomeric form.

Homology modeling of rat and human cytochrome P450 2D (CYP2D) isoforms and computational rationalization of experimental ligand-binding specificities

The ligand-binding characteristics of rat and human CYP2D isoforms, i.e., rat CYP2D1-4 and human CYP2D6, were investigated by measuring IC(50) values of 11 known CYP2D6 ligands using 7-methoxy-4-(aminomethyl)coumarin (MAMC) as substrate. Like CYP2D6, all rat CYP2D isozymes catalyzed the O-demethylation of MAMC with K(m) and V(max) values ranging between 78 and 145 microM and 0.048 and 1.122 min(-1), respectively. To rationalize observed differences in the experimentally determined IC(50) values, homology models of the CYP2D isoforms were constructed. A homology model of CYP2D6 was generated on the basis of crystallized rabbit CYP2C5 and was validated on its ability to reproduce binding orientations corresponding to metabolic profiles of the substrates and to remain stable during unrestrained molecular dynamics simulations at 300 K. Twenty-two active site residues, sharing up to 59% sequence identity, were identified in the CYP2D binding pockets and included CYP2D6 residues Phe120, Glu216, and Asp301. Electrostatic potential calculations displayed large differences in the negative charge of the CYP2D active sites, which was consistent with observed differences in absolute IC(50) values. MD studies on the binding mode of sparteine, quinidine, and quinine in CYP2D2 and CYP2D6 furthermore concurred well with experimentally determined IC(50) values and metabolic profiles. The current study thus provides new insights into differences in the active site topology of the investigated CYP2D isoforms.

Pharmacophore modeling of cytochromes P450

Understanding the binding of ligands in the active site of a membrane-bound protein is difficult in the absence of a crystal structure. When these proteins are the enzymes involved in drug metabolism, it leaves little option but to use site-directed mutagenesis and in vitro studies to provide critical information relating to determinants of binding affinity. Pharmacophore models and three-dimensional quantitative structure-activity relationships have been used either alone or in combination with protein homology models to provide this information for cytochrome P450s. At present, their application has been directed to the major enzymes but this may escalate in future as more in vitro data are generated for other P450s. The following review outlines the methodologies and models as well as future prospects for applying these technologies to P450s in the hope that future drugs will be selected with increased metabolic stability and fewer incidences of undesirable drug-drug interactions.

Application of in silico approaches to predicting drug--drug interactions

In an environment driven to find the next blockbuster drug, failure years into a project should not be an option. Recent studies have shown that poor absorption, distribution, metabolism, and excretion (ADME), and the related properties of toxicity and pharmacokinetics are responsible for a large proportion of failures. One way to understand and potentially predict molecules likely to be successful in humans as drugs from an ADME point of view is to use simulations. Such simulations may include simple rule-based approaches, structure--activity relationships, three-dimensional quantitative structure--activity relationships (3D-QSAR), and pharmacophores. All of these represent useful tools in understanding metabolism by the cytochromes P450, predicting drug--drug interactions (DDIs), and other pharmacokinetic parameters. The present paper briefly reviews the application of some computational tools applied to predicting DDIs and will provide the reader with an idea of their utility.

Recent advances in structure-based rational drug design

Two approaches to structure-based drug design, that is, the docking of known compounds into a target protein and molecular assembly in situ, are seen to be merging technologies. The need for structural information about drug-protein complexes is now fundamental for drug discovery.