Novel approach to predicting P450-mediated drug metabolism: development of a combined protein and pharmacophore model for CYP2D6

Lipophilic Metabolic Efficiency (LipMetE) and Drug Efficiency Indices to Explore the Metabolic Properties of the Substrates of Selected Cytochrome P450 Isoforms

Cytochrome P450 (CYP450) enzymes belong to a superfamily of heme-containing proteins that are involved in the metabolism of structurally diverse endogenous and exogenous compounds. Various proof-of-concept studies indicate that metabolic stability and intrinsic clearance of CYP450 substrates are linked with the respective lipophilicity (log P or log D). This necessitates the normalization of lipophilicity (log P or log D) of a given CYP450 substrate with respect to its metabolic stability (LipMetE) and intrinsic clearance (log₁₀CL_int,u). Therefore, in this article, the LipMetE values of already known substrates of CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4, including some marketed drugs, have been calculated to elucidate the relationship between lipophilicity (log D _7.4) and in vitro clearance. Moreover, various drug efficiency metrics including lipophilic efficiency (LipE) and ligand efficiency (LE) have been evaluated, and the thresholds of these parameters have been defined for the CYP450 substrates exhibiting normalized LipMetE. Our results indicate that for a given range of LipMetE, greater the log D value of the substrate the more avidly it binds to a given CYP450 enzyme and shows more intrinsic clearance (log₁₀CL_int,u). Overall, the majority of the model substrates of CYP450 isoforms and already marketed drugs in our datasets exhibit log D _7.4 values of ∼2.5 with LipMetE values in the range of 0-2.5 and LipE values of ≤3. Overall, consideration of these parameters in ADME profiling could aid in reducing the drug failure rate in the later stages of clinical investigations.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

Recent Advancements in Docking Methodologies

Nowadays molecular docking has become an important methodology in CADD (Computer-Aided Drug Design)-assisted drug discovery process. It is an important computational tool widely used to predict binding mode, binding affinity and binding free energy of a protein-ligand complex. The important factors responsible for accurate results in docking studies are correct binding site prediction, use of suitable small-molecule databases, consistent docking pose, high dock score with good MD (Molecular Dynamics), clarity whether the compound is an inhibitor or agonist, etc. However, still there are several limitations which make it difficult to obtain accurate results from docking studies. In this chapter, the main focus is on recent advancements in various aspects of molecular docking such as ligand sampling, protein flexibility, scoring functions, fragment docking, post-processing, docking into homology models and protein-protein docking.

Construction of Metabolism Prediction Models for CYP450 3A4, 2D6, and 2C9 Based on Microsomal Metabolic Reaction System

During the past decades, there have been continuous attempts in the prediction of metabolism mediated by cytochrome P450s (CYP450s) 3A4, 2D6, and 2C9. However, it has indeed remained a huge challenge to accurately predict the metabolism of xenobiotics mediated by these enzymes. To address this issue, microsomal metabolic reaction system (MMRS)—a novel concept, which integrates information about site of metabolism (SOM) and enzyme—was introduced. By incorporating the use of multiple feature selection (FS) techniques (ChiSquared (CHI), InfoGain (IG), GainRatio (GR), Relief) and hybrid classification procedures (Kstar, Bayes (BN), K-nearest neighbours (IBK), C4.5 decision tree (J48), RandomForest (RF), Support vector machines (SVM), AdaBoostM1, Bagging), metabolism prediction models were established based on metabolism data released by Sheridan et al. Four major biotransformations, including aliphatic C-hydroxylation, aromatic C-hydroxylation, N-dealkylation and O-dealkylation, were involved. For validation, the overall accuracies of all four biotransformations exceeded 0.95. For receiver operating characteristic (ROC) analysis, each of these models gave a significant area under curve (AUC) value >0.98. In addition, an external test was performed based on dataset published previously. As a result, 87.7% of the potential SOMs were correctly identified by our four models. In summary, four MMRS-based models were established, which can be used to predict the metabolism mediated by CYP3A4, 2D6, and 2C9 with high accuracy.

Further Optimization and Evaluation of Bioavailable, Mixed-Efficacy μ-Opioid Receptor (MOR) Agonists/δ-Opioid Receptor (DOR) Antagonists: Balancing MOR and DOR Affinities

In a previously described peptidomimetic series, we reported the development of bifunctional μ-opioid receptor (MOR) agonist and δ-opioid receptor (DOR) antagonist ligands with a lead compound that produced antinociception for 1 h after intraperitoneal administration in mice. In this paper, we expand on our original series by presenting two modifications, both of which were designed with the following objectives: (1) probing bioavailability and improving metabolic stability, (2) balancing affinities between MOR and DOR while reducing affinity and efficacy at the κ-opioid receptor (KOR), and (3) improving in vivo efficacy. Here, we establish that, through N-acetylation of our original peptidomimetic series, we are able to improve DOR affinity and increase selectivity relative to KOR while maintaining the desired MOR agonist/DOR antagonist profile. From initial in vivo studies, one compound (14a) was found to produce dose-dependent antinociception after peripheral administration with an improved duration of action of longer than 3 h.

Antihypertensive drugs metabolism: an update to pharmacokinetic profiles and computational approaches

Drug discovery and development is a high-risk enterprise that requires significant investments in capital, time and scientific expertise. The studies of xenobiotic metabolism remain as one of the main topics in the research and development of drugs, cosmetics and nutritional supplements.

Antihypertensive drugs are used for the treatment of high blood pressure, which is one the most frequent symptoms of the patients that undergo cardiovascular diseases such as myocardial infraction and strokes. In current cardiovascular disease pharmacology, four drug clusters - Angiotensin Converting Enzyme Inhibitors, Beta-Blockers, Calcium Channel Blockers and Diuretics - cover the major therapeutic characteristics of the most antihypertensive drugs. The pharmacokinetic and specifically the metabolic profile of the antihypertensive agents are intensively studied because of the broad inter-individual variability on plasma concentrations and the diversity on the efficacy response especially due to the P450 dependent metabolic status they present.

Several computational methods have been developed with the aim to: (i) model and better understand the human drug metabolism; and (ii) enhance the experimental investigation of the metabolism of small xenobiotic molecules. The main predictive tools these methods employ are rule-based approaches, quantitative structure metabolism/activity relationships and docking approaches.

This review paper provides detailed metabolic profiles of the major clusters of antihypertensive agents, including their metabolites and their metabolizing enzymes, and it also provides specific information concerning the computational approaches that have been used to predict the metabolic profile of several antihypertensive drugs.

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

Molecular evolution of the CYP2D subfamily in primates: purifying selection on substrate recognition sites without the frequent or long-tract gene conversion

The human cytochrome P450 (CYP) 2D6 gene is a member of the CYP2D gene subfamily, along with the CYP2D7P and CYP2D8P pseudogenes. Although the CYP2D6 enzyme has been studied extensively because of its clinical importance, the evolution of the CYP2D subfamily has not yet been fully understood. Therefore, the goal of this study was to reveal the evolutionary process of the human drug metabolic system. Here, we investigate molecular evolution of the CYP2D subfamily in primates by comparing 14 CYP2D sequences from humans to New World monkey genomes. Window analysis and statistical tests revealed that entire genomic sequences of paralogous genes were extensively homogenized by gene conversion during molecular evolution of CYP2D genes in primates. A neighbor-joining tree based on genomic sequences at the nonsubstrate recognition sites showed that CYP2D6 and CYP2D8 genes were clustered together due to gene conversion. In contrast, a phylogenetic tree using amino acid sequences at substrate recognition sites did not cluster the CYP2D6 and CYP2D8 genes, suggesting that the functional constraint on substrate specificity is one of the causes for purifying selection at the substrate recognition sites. Our results suggest that the CYP2D gene subfamily in primates has evolved to maintain the regioselectivity for a substrate hydroxylation activity between individual enzymes, even though extensive gene conversion has occurred across CYP2D coding sequences.

Contributions of ionic interactions and protein dynamics to cytochrome P450 2D6 (CYP2D6) substrate and inhibitor binding

P450 2D6 contributes significantly to the metabolism of >15% of the 200 most marketed drugs. Open and closed crystal structures of P450 2D6 thioridazine complexes were obtained using different crystallization conditions. The protonated piperidine moiety of thioridazine forms a charge-stabilized hydrogen bond with Asp-301 in the active sites of both complexes. The more open conformation exhibits a second molecule of thioridazine bound in an expanded substrate access channel antechamber with its piperidine moiety forming a charge-stabilized hydrogen bond with Glu-222. Incubation of the crystalline open thioridazine complex with alternative ligands, prinomastat, quinidine, quinine, or ajmalicine, displaced both thioridazines. Quinine and ajmalicine formed charge-stabilized hydrogen bonds with Glu-216, whereas the protonated nitrogen of quinidine is equidistant from Asp-301 and Glu-216 with protonated nitrogen H-bonded to a water molecule in the access channel. Prinomastat is not ionized. Adaptations of active site side-chain rotamers and polypeptide conformations were evident between the complexes, with the binding of ajmalicine eliciting a closure of the open structure reflecting in part the inward movement of Glu-216 to form a hydrogen bond with ajmalicine as well as sparse lattice restraints that would hinder adaptations. These results indicate that P450 2D6 exhibits sufficient elasticity within the crystal lattice to allow the passage of compounds between the active site and bulk solvent and to adopt a more closed form that adapts for binding alternative ligands with different degrees of closure. These crystals provide a means to characterize substrate and inhibitor binding to the enzyme after replacement of thioridazine with alternative compounds.

Selective and Dual Targeting of CCR2 and CCR5 Receptors: A Current Overview

The chemokine receptor 2 (CCR2) and chemokine receptor 5 (CCR5) are important mediators of leukocyte trafficking in inflammatory processes. The emerging evidence for a role of CCR2 and CCR5 receptors in human inflammatory diseases led to a growing interest in CCR2- and CCR5-selective antagonists. In this review, we focus on the recent development of selective CCR2/CCR5 receptor ligands and dual antagonists. Several compounds targeting CCR2, e.g., INCB8761 and MK0812, were developed as promising candidates for clinical trials, but failed to show clinical efficacy as presumed from preclinical models. The role of CCR5 receptors as the second co-receptor for the HIV-host cell fusion led to the development of various CCR5-selective ligands. Maraviroc is the first CCR5-targeting drug for the treatment of HIV-1 infections on the market. The role of CCR5 receptors in the progression of inflammatory processes fueled the use of CCR5 antagonists for the treatment of rheumatoid arthritis. Unfortunately, the use of maraviroc for the treatment of rheumatoid arthritis failed due to its inefficacy. Some of the ligands, e.g., TAK-779 and TAK-652, were also found to be dual antagonists of CCR2 and CCR5 receptors. The fact that CCR2 and CCR5 receptor antagonists contribute to the treatment of inflammatory diseases renders the development of dual antagonists as promising novel therapeutic strategy.

Predicting network of drug-enzyme interaction based on machine learning method

It is important to correctly and efficiently map drugs and enzymes to their possible interaction network in modern drug research. In this work, a novel approach was introduced to encode drug and enzyme molecules with physicochemical molecular descriptors and pseudo amino acid composition, respectively. Based on this encoding method, Random Forest was adopted to build the drug-enzyme interaction network. After selecting the optimal features that are able to represent the main factors of drug-enzyme interaction in our prediction, a total of 129 features were attained which can be clustered into nine categories: Elemental Analysis, Geometry, Chemistry, Amino Acid Composition, Secondary Structure, Polarity, Molecular Volume, Codon Diversity and Electrostatic Charge. It is further found that Geometry features were the most important of all the features. As a result, our predicting model achieved an MCC of 0.915 and a sensitivity of 87.9% at the specificity level of 99.8% for 10-fold cross-validation test, and achieved an MCC of 0.895 and a sensitivity of 95.7% at the specificity level of 95.4% for independent set test. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Predicting drug metabolism by cytochrome P450 2C9: comparison with the 2D6 and 3A4 isoforms

By the use of knowledge gained through modeling of drug metabolism mediated by the cytochrome P450 2D6 and 3A4 isoforms, we constructed a 2D-based model for site-of-metabolism prediction for the cytochrome P450 2C9 isoform. The similarities and differences between the models for the 2C9 and 2D6 isoforms are discussed through structural knowledge from the X-ray crystal structures and trends in experimental data. The final model was validated on an independent test set, resulting in an area under the curve value of 0.92, and a site of metabolism was found among the top two ranked atoms for 77% of the compounds.

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.

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.

Ligand-Based Site of Metabolism Prediction for Cytochrome P450 2D6

A ligand-based method based on the SMARTCyp approach that predicts the sites of cytochrome P450 2D6-mediated metabolism of druglike molecules has been developed. The method uses only two descriptors besides the reactivity from SMARTCyp: the distance to a protonated nitrogen atom and the distance to the end of the molecule. Hence, the site of metabolism is predicted directly from the 2D structure of a molecule, without requiring calculation of electronic properties or generation of 3D structures. Testing on an independent test set gives an area under the curve value of 0.94, and a site of metabolism is found among the top two ranked atoms for 91% of the compounds.

IDSite: An accurate approach to predict P450-mediated drug metabolism

Accurate prediction of drug metabolism is crucial for drug design. Since a large majority of drugs metabolism involves P450 enzymes, we herein describe a computational approach, IDSite, to predict P450-mediated drug metabolism. To model induced-fit effects, IDSite samples the conformational space with flexible docking in Glide followed by two refinement stages using the Protein Local Optimization Program (PLOP). Sites of metabolism (SOMs) are predicted according to a physical-based score that evaluates the potential of atoms to react with the catalytic iron center. As a preliminary test, we present in this paper the prediction of hydroxylation and O-dealkylation sites mediated by CYP2D6 using two different models: a physical-based simulation model, and a modification of this model in which a small number of parameters are fit to a training set. Without fitting any parameters to experimental data, the Physical IDSite scoring recovers 83% of the experimental observations for 56 compounds with a very low false positive rate. With only 4 fitted parameters, the Fitted IDSite was trained with the subset of 36 compounds and successfully applied to the other 20 compounds, recovering 94% of the experimental observations with high sensitivity and specificity for both sets.

Toward in silico structure-based ADMET prediction in drug discovery

Quantitative structure-activity relationship (QSAR) methods and related approaches have been used to investigate the molecular features that influence the absorption, distribution, metabolism, excretion and toxicity (ADMET) of drugs. As the three-dimensional structures of several major ADMET proteins become available, structure-based (docking-scoring) computations can be carried out to complement or to go beyond QSAR studies. Applying docking-scoring methods to ADMET proteins is a challenging process because they usually have a large and flexible binding cavity; however, promising results relating to metabolizing enzymes have been reported. After reviewing current trends in the field we applied structure-based methods in the context of receptor flexibility in a case study involving the phase II metabolizing sulfotransferases. Overall, the explored concepts and results suggested that structure-based ADMET profiling will probably join the mainstream during the coming years.

Selecting Relevant Descriptors for Classification by Bayesian Estimates: A Comparison with Decision Trees and Support Vector Machines Approaches for Disparate Data Sets

Classification algorithms suffer from the curse of dimensionality, which leads to overfitting, particularly if the problem is over-determined. Therefore it is of particular interest to identify the most relevant descriptors to reduce the complexity. We applied Bayesian estimates to model the probability distribution of descriptors values used for binary classification using n-fold cross-validation. As a measure for the discriminative power of the classifiers, the symmetric form of the Kullback-Leibler divergence of their probability distributions was computed. We found that the most relevant descriptors possess a Gaussian-like distribution of their values, show the largest divergences, and therefore appear most often in the cross-validation scenario. The results were compared to those of the LASSO feature selection method applied to multiple decision trees and support vector machine approaches for data sets of substrates and nonsubstrates of three Cytochrome P450 isoenzymes, which comprise strongly unbalanced compound distributions. In contrast to decision trees and support vector machines, the performance of Bayesian estimates is less affected by unbalanced data sets. This strategy reveals those descriptors that allow a simple linear separation of the classes, whereas the superior accuracy of decision trees and support vector machines can be attributed to nonlinear separation, which are in turn more prone to overfitting.

Design and discovery of a selective small molecule κ opioid antagonist (2-methyl-N-((2'-(pyrrolidin-1-ylsulfonyl)biphenyl-4-yl)methyl)propan-1-amine, PF-4455242)

By use of parallel chemistry coupled with physicochemical property design, a series of selective κ opioid antagonists have been discovered. The parallel chemistry strategy utilized key monomer building blocks to rapidly expand the desired SAR space. The potency and selectivity of the in vitro κ antagonism were confirmed in the tail-flick analgesia model. This model was used to build an exposure-response relationship between the κ K(i) and the free brain drug levels. This strategy identified 2-methyl-N-((2'-(pyrrolidin-1-ylsulfonyl)biphenyl-4-yl)methyl)propan-1-amine, PF-4455242, which entered phase 1 clinical testing and has demonstrated target engagement in healthy volunteers.

Probing small-molecule binding to cytochrome P450 2D6 and 2C9: An in silico protocol for generating toxicity alerts

Drug metabolism, toxicity, and their interaction profiles are major issues in the drug-discovery and lead-optimization processes. The cytochromes P450 (CYPs) 2D6 and 2C9 are enzymes involved in the oxidative metabolism of a majority of marketed drugs. Therefore, the prediction of the binding affinity towards CYP2D6 and CYP2C9 would be beneficial for identifying cytochrome-mediated adverse effects triggered by drugs or chemicals (e.g., toxic reactions, drug-drug, and food-drug interactions). By identifying the binding mode by using pharmacophore prealignment, automated flexible docking, and by quantifying the binding affinity by multidimensional QSAR (mQSAR), we validated a model family of 56 compounds (46 training, 10 test) and 85 compounds (68 training, 17 test) for CYP2D6 and CYP2C9, respectively. The correlation with the experimental data (cross-validated r²=0.811 for CYP2D6 and 0.687 for CYP2C9) suggests that our approach is suited for predicting the binding affinity of compounds towards CYP2D6 and CYP2C9. The models were challenged by Y-scrambling and by testing an external dataset of binding compounds (15 compounds for CYP2D6 and 40 for CYP2C9). To assess the probability of false-positive predictions, datasets of nonbinders (64 compounds for CYP2D6 and 56 for CYP2C9) were tested by using the same protocol. The two validated mQSAR models were subsequently added to the VirtualToxLab (VTL, http://www.virtualtoxlab.org).

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.

Identification of a brain penetrant PDE9A inhibitor utilizing prospective design and chemical enablement as a rapid lead optimization strategy

By use of chemical enablement and prospective design, a novel series of selective, brain penetrant PDE9A inhibitors have been identified that are capable of producing in vivo elevations of brain cGMP.

Antagonists of the calcium receptor. 2. Amino alcohol-based parathyroid hormone secretagogues

When administered as a single agent to rats, the previously reported calcium receptor antagonist 3 elicited a sustained elevation of plasma PTH resulting in no increase in overall bone mineral density. The lack of a bone building effect for analogue 3 was attributed to the large volume of distribution (V(dss)(rat) = 11 L/kg), producing a protracted plasma PTH profile. Incorporation of a carboxylic acid functionality into the amino alcohol template led to the identification of 12 with a lower volume of distribution (V(dss)(12) = 1.18 L/kg) and a shorter half-life. The zwitterionic nature of antagonist 12 necessitated the utility of an ester prodrug approach to increase overall permeability. Antagonist 12 elicited a rapid and transient increase in circulating levels of PTH following oral dosing of the ester prodrug 11 in the dog. The magnitude and duration of the increases in plasma levels of PTH would be expected to stimulate new bone formation.

Understanding CYP2D6 interactions

Owing to the polymorphic nature of CYP2D6, clinically significant issues can arise when drugs rely on that enzyme either for clearance, or metabolism to an active metabolite. Available screening methods to determine if the compound is likely to cause drug-drug interactions, or is likely to be a victim of inhibition of CYP2D6 by other compounds will be described. Computational models and examples will be given on strategies to design out the CYP2D6 liabilities for both heme-binding compounds and non-heme-binding compounds.

SPORCalc: A development of a database analysis that provides putative metabolic enzyme reactions for ligand-based drug design

Understanding both the enzyme reactions that contribute to intermediate metabolism and the biochemical fate of candidate therapeutic and toxic agents are essential for drug design. Traditional metabolic databases indicate whether reactions have been observed but do not provide the likelihoods of reactions occurring, for example those of mixed function oxygenases and oxidases, during phase I metabolism. The desire for more quantitative predictions motivated the development of the recently introduced Substrate Product Occurrence Ratio Calculator (SPORCalc) that identifies metabolically labile atom positions in candidate compounds. This paper describes a further development and provides a clearer explanation of SPORCalc for the computational pharmacology, medicinal chemistry and drug design communities interested in metabolic prediction of xenobiotics using chemical databases of biotransformations. Examples of reaction centre detection in Metabolite are described followed by a demonstration of almokalant, an anti-arrhythmic agent, undergoing phase I metabolism. In general, occurrence ratio (OR) values are calculated throughout a compound and its transformed metabolites to give propensity (p) values at each atom position. The OR values from substrates and products in the database are essential for addition and elimination reactions. For almokalant, the resulting p values ranged from 10(-1) to 10(-5) and their order of magnitude reflected the known and experimentally observed metabolites. SPORCalc depends entirely on the level of detail from isoform- or species-specific reaction classes in Metabolite. Labile atom positions (sites of metabolism) are identified in both the candidate compound and its metabolites. In general, the likelihood of one enzyme isoform-dependent reaction occurring relative to another and the putative metabolic routes from different isoforms can be investigated. SPORCalc can be developed further to include suitable three-dimensional, structure-activity and physiochemical information.

AmineDB: Large scale docking of amines with CYP2D6 and scoring for druglike properties—towards defining the scope of the chemical defense against foreign amines in humans

Organic amines are prevalent in nature and in drugs, especially the psychotherapeutic agents, and a major defense against potentially toxic amines is metabolism by CYP2D6. In order to understand better the constraints on the broad specificity of CYP2D6, 4207 amines were docked into the binding site of this enzyme. Docking poses were found predominantly with the positively charged amino groups closest to Asp301, with aromatic rings close to Phe120 and sometimes extending as far as Phe483. Organic amines that bind best to CYP2D6 tend to have larger molecular weights and logP values. Organic amines that score highly as being druglike, based on a Bayesian model constructed using a 5223-drug training set, are least likely to bind to CYP2D6. This correlation suggests that the set of known drugs, which have been largely designed or selected to avoid high affinity CYP binding, partially encodes the binding site preferences (or rather anti-preferences) of CYP2D6. Finally, in order to benchmark our docking and druglike scoring procedures, an analysis of psychotherapeutic agents is presented. All of these data, including the 4207 AM1-optimized ligand structures in proper ionization states, docking poses and scores, Druglike Scores and Lipinski properties, can be viewed from an online database, the AmineDB.

Application of molecular modeling for prediction of substrate specificity in cytochrome P450 1A2 mutants

Molecular dynamics (MD) simulations of 7-ethoxy- and 7-methoxyresorufin bound in the active site of cytochrome P450 (P450) 1A2 wild-type and various mutants were used to predict changes in substrate specificity of the mutants. A total of 26 multiple mutants representing all possible combinations of five key amino acid residues, which are different between P450 1A1 and 1A2, were examined. The resorufin substrates were docked in the active site of each enzyme in the productive binding orientation, and MD simulations were performed on the enzyme-substrate complex. Ensembles collected from MD trajectories were then scored on the basis of geometric parameters relating substrate position with respect to the activated oxoheme cofactor. The results showed a high correlation between the previous experimental data on P450 1A2 wild-type and single mutants with respect to the ratio between 7-ethoxyresorufin-O-deethylase (EROD) and 7-methoxyresorufin-O-demethylase (MROD) activities and the equivalent in silico "E/M scores" (the ratio of hits obtained with 7-ethoxyresorufin to those obtained with 7-methoxyresorufin). Moreover, this correlation served to establish linear regression models used to evaluate E/M scores of multiple P450 1A2 mutants. Seven mutants, all of them incorporating the L382V substitution, were predicted to shift specificity to that of P450 1A1. The predictions were then verified experimentally. The appropriate P450 1A2 multiple mutants were constructed by site-directed mutagenesis, expressed in Escherichia coli, and assayed for EROD and MROD activities. Of six mutants, five demonstrated an increased EROD/MROD ratio, confirming modeling predictions.

Designing rapid onset selective serotonin re-uptake inhibitors. Part 3: Site-directed metabolism as a strategy to avoid active circulating metabolites: structure-activity relationships of (thioalkyl)phenoxy benzylamines

A series of thio-alkyl containing diphenylethers were designed and evaluated, as a strategy to competitively direct metabolism away from unwanted amine N-demethylation and deliver a pharmacologically inactive S-oxide metabolite. Overall, sulfonamide 20 was found to possess the best balance of target pharmacology, pharmacokinetics and metabolism profile.

Regioselectivity prediction of CYP1A2-mediated phase I metabolism

A kinetic, reactivity-binding model has been proposed to predict the regioselectivity of substrates meditated by the CYP1A2 enzyme, which is responsible for the metabolism of planar-conjugated compounds such as caffeine. This model consists of a docking simulation for binding energy and a semiempirical molecular orbital calculation for activation energy. Possible binding modes of CYP1A2 substrates were first examined using automated docking based on the crystal structure of CYP1A2, and binding energy was calculated. Then, activation energies for CYP1A2-mediated metabolism reactions were calculated using the semiempirical molecular orbital calculation, AM1. Finally, the metabolic probability obtained from two energy terms, binding and activation energies, was used for predicting the most probable metabolic site. This model predicted 8 out of 12 substrates accurately as the primary preferred site among all possible metabolic sites, and the other four substrates were predicted into the secondary preferred site. This method can be applied for qualitative prediction of drug metabolism mediated by CYP1A2 and other CYP450 family enzymes, helping to develop drugs efficiently.