Competitive CYP2C9 inhibitors: enzyme inhibition studies, protein homology modeling, and three-dimensional quantitative structure-activity relationship analysis

Differential inhibition of human CYP2C8 and molecular docking interactions elicited by sorafenib and its major N-oxide metabolite

The tyrosine kinase inhibitor sorafenib (SOR) is being used increasingly in combination with other anticancer agents like paclitaxel, but this increases the potential for drug toxicity. SOR inhibits several human CYPs, including CYP2C8, which is a major enzyme in the elimination of oncology drugs like paclitaxel and imatinib. It has been reported that CYP2C8 inhibition by SOR in human liver microsomes is potentiated by NADPH-dependent biotransformation. This implicates a SOR metabolite in enhanced inhibition, although the identity of that metabolite is presently unclear. The present study evaluated the capacity of the major N-oxide metabolite of SOR (SNO) to inhibit CYP2C8-dependent paclitaxel 6α-hydroxylation. The IC₅₀ of SNO against CYP2C8 activity was found to be 3.7-fold lower than that for the parent drug (14 μM versus 51 μM). In molecular docking studies, both SOR and SNO interacted with active site residues in CYP2C8, but four additional major hydrogen and halogen bonding interactions were identified between SNO and amino acids in the B-B' loop region and helixes F' and I that comprise the catalytic region of the enzyme. In contrast, the binding of both SOR and SNO to active site residues in the closely related human CYP2C9 enzyme was similar, as were the IC₅₀s determined against CYP2C9-mediated losartan oxidation. These findings suggest that the active metabolite SNO could impair the elimination of coadministered drugs that are substrates for CYP2C8, and mediate toxic adverse events, perhaps in those individuals in whom SNO is formed extensively.

Comparison of Theory and Practice in the Framework for Quantifying Interaction Capacity through Six Interaction Parameters Using tert-Butanol as a Target Molecule

Molecular interactions are important for various areas of research. Interactions between a target molecule and probe molecules having their own interaction capacity can be quantified via six interaction parameters. The theoretical interaction energy can be calculated from the interaction parameters, while that of experimental is measured using a calorimeter. These two methods are proposed in this work to calculate them. The first is based on an equation linking Hansen's and Drago's parameters. The second method is based on an experimental matrix formed by the interaction energies of tert-butanol with the probe molecules characterized by their six interaction parameters. Finally, the quality of the experiment matrix is checked for the effectiveness of the six experimental interaction parameters of the target molecule, which is tert-butanol. Then, these experimental values are compared with theoretical values from interaction parameters.

Computational prediction of cytochrome P450 inhibition and induction

Cytochrome P450 (CYP) enzymes play an important role in the phase I metabolism of many xenobiotics. Most drug-drug interactions (DDIs) associated with CYP are caused by either CYP inhibition or induction. The early detection of potential DDIs is highly desirable in the pharmaceutical industry because DDIs can cause serious adverse events, which can lead to poor patient health and drug development failures. Recently, many computational studies predicting CYP inhibition and induction have been reported. The current computational modeling approaches for CYP metabolism are classified as ligand- and structure-based; various techniques, such as quantitative structure-activity relationships, machine learning, docking, and molecular dynamic simulation, are involved in both the approaches. Recently, combining these two approaches have resulted in improvements in the prediction accuracy of DDIs. In this review, we present important, recent developments in the computational prediction of the inhibition of four clinically crucial CYP isoforms (CYP1A2, 2C9, 2D6, and 3A4) and three nuclear receptors (aryl hydrocarbon receptor, constitutive androstane receptor, and pregnane X receptor) involved in the induction of CYP1A2, 2B6, and 3A4, respectively.

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.

Structural and energetic analysis to provide insight residues of CYP2C9, 2C11 and 2E1 involved in valproic acid dehydrogenation selectivity

Docking and molecular dynamics (MD) simulation have been two computational techniques used to gain insight about the substrate orientation within protein active sites, allowing to identify potential residues involved in the binding and catalytic mechanisms. In this study, both methods were combined to predict the regioselectivity in the binding mode of valproic acid (VPA) on three cytochrome P-450 (CYP) isoforms CYP2C9, CYP2C11, and CYP2E1, which are involved in the biotransformation of VPA yielding reactive hepatotoxic intermediate 2-n-propyl-4-pentenoic acid (4nVPA). There are experimental data about hydrogen atom abstraction of the C4-position of VPA to yield 4nVPA, however, there are not structural evidence about the binding mode of VPA and 4nVPA on CYPs. Therefore, the complexes between these CYP isoforms and VPA or 4nVPA were studied to explore their differences in binding and energetic stabilization. Docking results showed that VPA and 4nVPA are coupled into CYPs binding site in a similar conformation, but it does not explain the VPA hydrogen atom abstraction. On the other hand, MD simulations showed a set of energetic states that reorient VPA at the first ns, then making it susceptible to a dehydrogenation reaction. For 4nVPA, multiple binding modes were observed in which the different states could favor either undergo other reaction mechanism or ligand expulsion from the binding site. Otherwise, the energetic and entropic contribution point out a similar behavior for the three CYP complexes, showing as expected a more energetically favorable binding free energy for the complexes between CYPs and VPA than with 4nVPA.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Identification of cytochrome P450 2D6 and 2C9 substrates and inhibitors by QSAR analysis

This paper presents four new QSAR models for CYP2C9 and CYP2D6 substrate recognition and inhibitor identification based on human clinical data. The models were used to screen a large data set of environmental chemicals for CYP activity, and to analyze the frequency of CYP activity among these compounds. A large fraction of these chemicals were found to be CYP active, and thus potentially capable of affecting human physiology. 20% of the compounds within applicability domain of the models were predicted to be CYP2C9 substrates, and 17% to be inhibitors. The corresponding numbers for CYP2D6 were 9% and 21%. Where the majority of CYP2C9 active compounds were predicted to be both a substrate and an inhibitor at the same time, the CYP2D6 active compounds were primarily predicted to be only inhibitors. It was demonstrated that the models could identify compound classes with a high occurrence of specific CYP activity. An overrepresentation was seen for poly-aromatic hydrocarbons (group of procarcinogens) among CYP2C9 active and mutagenic compounds compared to CYP2C9 inactive and mutagenic compounds. The mutagenicity was predicted with a QSAR model based on Ames in vitro test data.

Prediction of sites of metabolism in a substrate molecule, instanced by carbamazepine oxidation by CYP3A4

In drug discovery process, improvement of ADME/Tox properties of lead compounds including metabolic stability is critically important. Cytochrome P450 (CYP) is one of the major metabolizing enzymes and the prediction of sites of metabolism (SOM) on the given lead compounds is key information to modify the compounds to be more stable against metabolism. There are two factors essentially important in SOM prediction. First is accessibility of each substrate atom to the oxygenated Fe atom of heme in a CYP protein, and the other is the oxidative reactivity of each substrate atom. To predict accessibility of substrate atoms to the heme iron, conventional protein-rigid docking simulations have been applied. However, the docking simulations without consideration of protein flexibility often lead to incorrect answers in the case of very flexible proteins such as CYP3A4. In this study, we demonstrated an approach utilizing molecular dynamics (MD) simulation for SOM prediction in which multiple MD runs were executed using different initial structures. We applied this strategy to CYP3A4 and carbamazepine (CBZ) complex. Through 10 ns MD simulations started from five different CYP3A4-CBZ complex models, our approach correctly predicted SOM observed in experiments. The experimentally known epoxidized sites of CBZ by CYP3A4 were successfully predicted as the most accessible sites to the heme iron that was judged from a numerical analysis of calculated ΔG(binding) and the frequency of appearance. In contrast, the predictions using protein-rigid docking methods hardly provided the correct SOM due to protein flexibility or inaccuracy of the scoring functions. Our strategy using MD simulation with multiple initial structures will be one of the reliable methods for SOM prediction.

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.

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.

In silico platform for xenobiotics ADME-T pharmacological properties modeling and prediction. Part I: Beyond the reduction of animal model use

There is an urgent need for efficient in silico ADME-T prediction tools for the selection of potent therapeutic drugs as well as the elimination of toxic compounds. This is particularly important in view of the high costs and ethical issues inherent to the use of animal models for drugs filtering. To achieve this mission, not only does the accuracy of in silico tools need to be improved, but also new experts in the field with skills in theoretical chemistry, clinical and fundamental biology have to be trained. Similarly, clinical biologists committed to the obligation of means and legally responsible for the results they generate could establish a legal framework that defines legal responsibilities when performing in silico predictions.

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.

Single mutations change CYP2F3 from a dehydrogenase of 3-methylindole to an oxygenase

Pulmonary cytochrome P450 2F3 (CYP2F3) catalyzes the dehydrogenation of the pneumotoxin 3-methylindole (3MI) to an electrophilic intermediate, 3-methyleneindolenine, which is responsible for the toxicity of the parent compound. Members of the CYP2F subfamily are the only enzymes known to exclusively dehydrogenate 3MI, without detectable formation of oxygenation products. Thus, CYP2F3 is an attractive model to study dehydrogenation mechanisms. The purpose of this study was to identify specific residues that could facilitate 3MI dehydrogenation. Both single and double mutations were constructed to study the molecular mechanisms that direct dehydrogenation. Double mutations in substrate recognition sites (SRS) 1 produced an inactive enzyme, while double mutants in SRS 4 did not alter 3MI metabolism. However, double mutations in SRS 5 and SRS 6 successfully introduced oxygenase activity to CYP2F3. Single mutations in SRS 5, SRS 6 and near SRS 2 also introduced 3MI oxygenase activity. Mutants S474H and D361T oxygenated 3MI but also increased dehydrogenation rates, while G214L, E215Q and S475I catalyzed 3MI oxygenation exclusively. A homology model of CYP2F3 was precisely consistent with specific dehydrogenation of 3MI via initial hydrogen atom abstraction from the methyl group. In addition, intramolecular kinetic deuterium isotope studies demonstrated an isotope effect ( K H/ K D) of 6.8. This relatively high intramolecular deuterium isotope effect confirmed the initial hydrogen abstraction step; a mutant (D361T) that retained the dehydrogenation reaction exhibited the same deuterium isotope effect. The results showed that a single alteration, such as a serine to isoleucine change at residue 475, dramatically switched catalytic preference from dehydrogenation to oxygenation.

A shuffled CYP2C library with a high degree of structural integrity and functional versatility

Cytochrome P450 (CYP) enzymes involved in mammalian xenobiotic metabolism are attractive targets for the engineering of biocatalysts since they have broad and overlapping substrate and reaction substrate specificities. In this report, a library of chimeric mutants was prepared from CYP2C8, CYP2C9, CYP2C18 and CYP2C19 by DNA family shuffling. Twelve randomly selected clones were fully sequenced and showed 9+/-2 crossovers and 1.5+/-0.5 spontaneous mutations per approximately 1.5kbp open reading frame. CYP hemoprotein expression was observed in 50% (microaerobic culture) to 54% (aerobic culture) of clones. The functional diversity of the library was assessed using three luminogenic substrates, diclofenac and indole as probe substrates. A random sample of 26 clones revealed two clones with activity towards luciferin ME, one towards luciferin H and five towards diclofenac 4'-hydroxylation. One mutant showed activity towards all three substrates. Of 96 clones screened on solid media, one showed elevated indigo production compared to the parental forms. Turnover rates for luciferin ME and H metabolism by CYP2C9 and mutants were at least one order of magnitude higher in experiments with membranes compared to whole cells, consistent with impaired product egress from cells. Apparent K(m) values were increased in whole cell incubations with luciferin H suggesting impaired access of the substrate to the active site of the enzymes in whole cells. Finally screening with a panel of CYP2C ligands using CYP2C9 or active mutants revealed different patterns of inhibition and heteroactivation of metabolism of luciferin analogs.

CYP2C9 Structure−Metabolism Relationships: Optimizing the Metabolic Stability of COX-2 Inhibitors

The cytochrome P450 (CYP) family is composed of a large group of monooxygenases that mediate the metabolism of xenobiotics and endogenous compounds. CYP2C9, one of the major isoforms of the CYP family, is responsible for the phase I metabolism of a variety of drugs. The aim of the present investigation is to use rational design together with MetaSite, a metabolism site prediction program, to synthesize compounds that retain their pharmacological effects but that are metabolically more stable in the presence of CYP2C9. The model compound for the study is the nonsteroidal anti-inflammatory drug celecoxib, a COX-2 selective inhibitor and known CYP2C9 substrate. Thirteen analogs of celecoxib were designed, synthesized, and evaluated with regard to their metabolic properties and pharmacologic effects. The docking solutions and the predictions from MetaSite gave useful information leading to the design of new compounds with improved metabolic properties.

State-of-the-art tools for computational site of metabolism predictions: comparative analysis, mechanistical insights, and future applications

In drug design, it is crucial to have reliable information on how a chemical entity behaves in the presence of metabolizing enzymes. This requires substantial experimental efforts. Consequently, being able to predict the likely site/s of metabolism in any compound, synthesized or virtual, would be highly beneficial and time efficient. In this work, six different methodologies for predictions of the site of metabolism (SOM) have been compared and validated using structurally diverse data sets of drug-like molecules with well-established metabolic pattern in CYP3A4, CYP2C9, or both. Three of the methods predict the SOM based on the ligand's chemical structure, two additional methods use structural information of the enzymes, and the sixth method combines structure and ligand similarity and reactivity. The SOM is correctly predicted in 50 to 90% of the cases, depending on method and enzyme, which is an encouraging rate. We also discuss the underlying mechanisms of cytochrome P450 metabolism in the light of the results from this comparison.

Structure modeling, ligand binding, and binding affinity calculation (LR-MM-PBSA) of human heparanase for inhibition and drug design

Human heparanase is an endo-beta-D-glycosidase that cleaves heparan sulphate (HS) chains in the extracellular matrix and basement membrane. It is known that the cleavage of HS by heparanase results in cell invasion and metastasis of cancer. Therefore, heparanase is considered an important target for cancer drug development. The three-dimensional structure of heparanase would be useful in the rational design of inhibitors targeted to the enzyme; however, the three-dimensional structure has not yet been determined. In our effort to design inhibitors, we developed a three-dimensional structure of heparanase using a homology-modeling approach. The homology-built structure is consistent to previous bioinformatics and site-mutation experimental results. The heparanase features a (alpha/beta)(8) TIM-barrel fold with two glutamate residues (Glu225 and Glu343) located in the active-site cleft. This feature supports the putative mechanism of proton donor and nucleophilic sites. Docking simulations yielded 41 complex structures, which indicate that the bound inhibitor could block ligand binding into the catalytic site. A free energy of binding model was established for 25 heparanase inhibitors with a training set of 25 heparanase inhibitors using the linear response MM-PBSA approach (LR-MM-PBSA). The correlation between calculated and experimental activity was 0.79 and the reliability of the model was validated with leave-one-out cross-validation method. Its predictive capability was further validated using a test set of 16 inhibitors similar to the training set of inhibitors. The correlation between the predicted and observed activities is significantly improved by the protein "induced-fit" that accounts for the flexibility of the receptor. These interaction and pharmacophore elements provide a unique insight to the rational design of new ligands targeted to the enzyme.

Methods for Predicting Human Drug Metabolism

Drug metabolism information is a necessary component of drug discovery and development. The key issues in drug metabolism include identifying: the enzyme(s) involved, the site(s) of metabolism, the resulting metabolite(s), and the rate of metabolism. Methods for predicting human drug metabolism from in vitro and computational methodologies and determining relationships between the structure and metabolic activity of molecules are also critically important for understanding potential drug interactions and toxicity. There are numerous experimental and computational approaches that have been developed in order to predict human metabolism which have their own limitations. It is apparent that few of the computational tools for metabolism prediction alone provide the major integrated functions needed to assist in drug discovery. Similarly the different in vitro methods for human drug metabolism themselves have implicit limitations. The utilization of these methods for pharmaceutical and other applications as well as their integration is discussed as it is likely that hybrid methods will provide the most success.

Investigation of the role of cytochrome P450 2B4 active site residues in substrate metabolism based on crystal structures of the ligand-bound enzyme

Based on the X-ray crystal structures of 4-(4-chlorophenyl)imidazole (4-CPI)- and bifonazole (BIF)-bound P450 2B4, eight active site mutants at six positions were created in an N-terminal modified construct termed 2B4dH and characterized for enzyme inhibition and catalysis. I363A showed a >4-fold decrease in differential inhibition by BIF and 4-CPI (IC(50,BIF)/IC(50,4-CPI)). F296A, T302A, I363A, V367A, and V477A showed a 2-fold decreased k(cat) for 7-ethoxy-4-trifluoromethylcoumarin O-deethylation, whereas V367A and V477F showed an altered K(m). T302A, V367L, and V477A showed >4-fold decrease in total testosterone hydroxylation, whereas I363A, V367A, and V477F showed altered stereo- and regioselectivity. Interestingly, I363A showed a 150-fold enhanced k(cat)/K(m) with testosterone, and yielded a new metabolite. Furthermore, testosterone docking into three-dimensional models of selected mutants based on the 4-CPI-bound structure suggested a re-positioning of residues 363 and 477 to yield products. In conclusion, our results suggest that the 4-CPI-bound 2B4dH/H226Y crystal structure is an appropriate model for predicting enzyme catalysis.

Designing better drugs: predicting cytochrome P450 metabolism

Many 3D ligand-based and structure-based computational approaches have been used to predict, and thus help explain, the metabolism catalyzed by the enzymes of the cytochrome P450 superfamily (P450s). P450s are responsible for >90% of the metabolism of all drugs, so the computational prediction of metabolism can help to design out drug-drug interactions in the early phases of the drug discovery process. Computational methodologies have focused on a few P450s that are directly involved in drug metabolism. The recently derived crystal structures for human P450s enable better 3D modelling of these important metabolizing enzymes. Models derived for P450s have evolved from simple comparisons of known substrates to more-elaborate experiments that require considerable computer power involving 3D overlaps and docking experiments. These models help to explain and, more importantly, predict the involvement of P450s in the metabolism of specific compounds and guide the drug-design process.

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.

Pharmacogenetics of oral anticoagulants

The use of oral anticoagulants (OA) is problematic due to its association with hemorrhagic complications. OA metabolism relies on the CYP2C9 complex. Genetic variations compromising metabolic competence of this complex may explain the risk of excessive and hazardous anticoagulation. A pharmacogenetics-based approach to this issue could be beneficial for choosing adequate dose and duration of treatment, in addition to having a better understanding of pharmacological interactions to which OA are susceptible. However, evidence from several basic and clinical studies indicates that both a complicated system of regulation of expression of multiple genes and the influence of a wide variety of epigenetic factors could be responsible for adverse drug reactions associated with the use of OA. Emphasis on understanding the gene-environment interactions could attain new paths to facilitate the use of these important drugs in the quotidian clinical practice.

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.

Computational prediction of human drug metabolism

There is an urgent requirement within the pharmaceutical and biotechnology industries, regulatory authorities and academia to improve the success of molecules that are selected for clinical trials. Although absorption, distribution, metabolism, excretion and toxicity (ADME/Tox) properties are some of the many components that contribute to successful drug discovery and development, they represent factors for which we currently have in vitro and in vivo data that can be modelled computationally. Understanding the possible toxicity and the metabolic fate of xenobiotics in the human body is particularly important in early drug discovery. There is, therefore, a need for computational methodologies for uncovering the relationships between the structure and the biological activity of novel molecules. The convergence of numerous technologies, including high-throughput techniques, databases, ADME/Tox modelling and systems biology modelling, is leading to the foundation of systems-ADME/Tox. Results from experiments can be integrated with predictions to globally simulate and understand the likely complete effects of a molecule in humans. The development and early application of major components of MetaDrug (GeneGo, Inc.) software will be described, which includes rule-based metabolite prediction, quantitative structure-activity relationship models for major drug metabolising enzymes, and an extensive database of human protein-xenobiotic interactions. This represents a combined approach to predicting drug metabolism. MetaDrug can be readily used for visualising Phase I and II metabolic pathways, as well as interpreting high-throughput data derived from microarrays as networks of interacting objects. This will ultimately aid in hypothesis generation and the early triaging of molecules likely to have undesirable predicted properties or measured effects on key proteins and cellular functions.

Utility of homology models in the drug discovery process

Advances in bioinformatics and protein modeling algorithms, in addition to the enormous increase in experimental protein structure information, have aided in the generation of databases that comprise homology models of a significant portion of known genomic protein sequences. Currently, 3D structure information can be generated for up to 56% of all known proteins. However, there is considerable controversy concerning the real value of homology models for drug design. This review provides an overview of the latest developments in this area and includes selected examples of successful applications of the homology modeling technique to pharmaceutically relevant questions. In addition, the strengths and limitations of the application of homology models during all phases of the drug discovery process are discussed.

Do Fukui Function Maxima Relate to Sites of Metabolism? A Critical Case Study

The usefulness of local reactivity descriptors for understanding drug metabolism is investigated. Electrophilic Fukui functions are calculated for 18 drugs and 11 agrochemicals and their relation to experimentally observed metabolites is discussed. Maxima of the Fukui functions correspond to major sites of metabolic attack in many examples, facilitating a posteriori understanding of experimental findings. In the second part of the paper, the nature of the electrophilic oxidant species in cytochromes, called "Compound I" (Cpd I), is investigated within the Fukui framework. Nucleophilic Fukui functions are calculated involving the relevant spin states of Cpd I, allowing a more qualitative, intuitive understanding of its reactivity.

Modeling the Similarity and Divergence of Dopamine D2-like Receptors and Identification of Validated Ligand−Receptor Complexes

Focusing on the similarity and divergence of GPCR subtypes and their ligand interactions, we generated dopamine D2, D3, and D4 receptor models based on the rhodopsin crystal structure and refined these with an extensive MM/MD protocol. After validation by diagnostic experimental data, subtype-specific relative positions of TM1, 2, 6, and 7 and bending angles of TM7 were found. To sample the conformational space of the complex, we performed simulated-annealing runs of the receptor protein with the sub-nanomolar antagonist spiperone. Docking a representative set of ligands, we were able to identify one superior model for each subtype when excellent correlations between predicted energies of binding and experimental affinities (r2 = 0.72 for D2, 0.91 for D3 and 0.77 for D4) could be observed. Further analysis revealed general ligand interactions with ASP3.32 and aromatic residues in TM6/7 and individual key interactions with TM1 and TM2 residues of the D3 and D4 receptor models, respectively.

Predictive Models of CYP3A4 Heteroactivation: In Vitro-in Vivo Scaling and Pharmacophore Modeling

Although activation of CYP3A4 is frequently observed in vitro, predictive computational-based models and methods for in vitro-in vivo scaling are scarce. It has been previously shown that in vitro CYP3A4 heteroactivation of carbamazepine (CBZ)-epoxide (ep) formation can be associated with the clinical drug interaction between felbatame and CBZ. The previously reported prediction methodology is applied here to an additional set of in vitro CYP3A4 heteroactivators, some exerting this effect at concentrations relevant in vivo. The antimalarial artemisinin potently increases CBZ-ep formation by a maximum of 500% at 300 microM. Testosterone and progesterone activates by a maximum of 1680 and 920%, respectively, at 150 microM, and quinidine causes a 130% increase at 300 microM. The predicted maximum in vivo decrease in steady-state concentration of carbamazepine (Css(CBZ)) at saturating effector concentrations is 85 to 90% for testosterone and progesterone, 75% for artemisinin, and 45% for quinidine. The corresponding predicted in vivo increase in Css(CBZ-ep) is 50, 60, 55, and 30% for artemisinin, testosterone, progesterone, and quinidine, respectively. At effector concentrations relevant in vivo, the Css(CBZ) change is predicted to </=20% for testosterone, artemisinin, and quinidine and </=10% for progesterone, with a concomitant Css(CBZ-ep) increase of 12% for testosterone and </=10% for progesterone, artemisinin, and quinidine. Structure-heteroactivation relationships were evaluated by generating a pharmacophore. The model includes two hydrogen bond acceptor features separated by hydrophobic features. Internal predictivity is high, and heteroactivation of an external test set correlate to observed in vitro heteroactivation.

Predictive ADMET studies, the challenges and the opportunities

Predictive ADMET is the new 'hip' area in drug discovery. The aim is to use large databases of ADMET data associated with structures to build computational models that link structural changes with changes in response, from which compounds with improved properties can be designed and predicted. These databases also provide the means to enable predictions of human ADMET properties to be made from human in vitro and animal in vivo ADMET measurements. Both methods are limited by the amount of data available to build such predictive models, the limitations of modelling methods and our understanding of the systems we wish to model. The current failures, successes and opportunities are reviewed.

Metabolic stability for drug discovery and development: pharmacokinetic and biochemical challenges

Metabolic stability refers to the susceptibility of compounds to biotransformation in the context of selecting and/or designing drugs with favourable pharmacokinetic properties. Metabolic stability results are usually reported as measures of intrinsic clearance, from which secondary pharmacokinetic parameters such as bioavailability and half-life can be calculated when other data on volume of distribution and fraction absorbed are available. Since these parameters are very important in defining the pharmacological and toxicological profile of drugs as well as patient compliance, the pharmaceutical industry has a particular interest in optimising for metabolic stability during the drug discovery and development process. In the early phases of drug discovery, new chemical entities cannot be administered to humans; hence, predictions of these properties have to be made from in vivo animal, in vitro cellular/subcellular and computational systems. The utility of these systems to define the metabolic stability of compounds that is predictive of the human situation will be reviewed here. The timing of performing the studies in the discovery process and the impact of recent advances in research on drug absorption, distribution, metabolism and excretion (ADME) will be evaluated with respect to the scope and depth of metabolic stability issues. Quantitative prediction of in vivo clearance from in vitro metabolism data has, for many compounds, been shown to be poor in retrospective studies. One explanation for this may be that there are components used in the equations for scaling that are missing or uncertain and should be an area of more research. For example, as a result of increased biochemical understanding of drug metabolism, old assumptions (e.g. that the liver is the principal site of first-pass metabolism) need revision and new knowledge (e.g. the relationship between transporters and drug metabolising enzymes) needs to be incorporated into in vitro-in vivo correlation models. With ADME parameters increasingly being determined on automated platforms, instead of using results from high throughput screening (HTS) campaigns as simple go/no-go filters, the time saved and the many compounds analysed using the robots should be invested in careful processing of the data. A logical step would be to investigate the potential to construct computational models to understand the factors governing metabolic stability. A rational approach to the use of HTS assays should aim to screen for many properties (e.g. physicochemical parameters, absorption, metabolism, protein binding, pharmacokinetics in animals and pharmacology) in an integrated manner rather than screen against one property on many compounds, since it is likely that the final drug will represent a global average of these properties.

Predicting undesirable drug interactions with promiscuous proteins in silico

Although computational tools have been used to predict toxic responses resulting from molecules binding either as substrates or inhibitors to proteins, there are complications to be resolved. Some proteins appear promiscuous in their ability to bind a diverse array of hydrophobic molecules. This promiscuity arises from the binding site simultaneously accommodating more than one molecule, multiple separate binding sites, protein flexibility, or a combination of all these properties. With the availability of more crystal structures for these non-target proteins, we should be able to predict binding in silico with a greater accuracy, thus avoiding or managing toxic side effects, therefore ultimately improving the success of drug discovery.