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

Electrochemical γ-Selective Deuteration of Pyridines

Herein, we first report a γ-selective deuteration reaction of pyridines via H/D exchange without the need for preinstalled directing groups and transformable functional groups. The electrochemical process offers an attractive approach to producing γ-deuterated pyridines under gentle conditions. The broad substrate scope, excellent deuterium incorporation, and remarkable selectivity of the electrochemical method make it applicable for the late-stage modification of pharmaceutical molecules.

Descriptors of Cytochrome Inhibitors and Useful Machine Learning Based Methods for the Design of Safer Drugs

Roughly 2.8% of annual hospitalizations are a result of adverse drug interactions in the United States, representing more than 245,000 hospitalizations. Drug-drug interactions commonly arise from major cytochrome P450 (CYP) inhibition. Various approaches are routinely employed in order to reduce the incidence of adverse interactions, such as altering drug dosing schemes and/or minimizing the number of drugs prescribed; however, often, a reduction in the number of medications cannot be achieved without impacting therapeutic outcomes. Nearly 80% of drugs fail in development due to pharmacokinetic issues, outlining the importance of examining cytochrome interactions during preclinical drug design. In this review, we examined the physiochemical and structural properties of small molecule inhibitors of CYPs 3A4, 2D6, 2C19, 2C9, and 1A2. Although CYP inhibitors tend to have distinct physiochemical properties and structural features, these descriptors alone are insufficient to predict major cytochrome inhibition probability and affinity. Machine learning based in silico approaches may be employed as a more robust and accurate way of predicting CYP inhibition. These various approaches are highlighted in the review.

Inhibition of human CYP1 enzymes by a classical inhibitor α-naphthoflavone and a novel inhibitor N-(3, 5-dichlorophenyl)cyclopropanecarboxamide: An in vitro and in silico study

Enzymes in the cytochrome P450 family 1 (CYP1) catalyze metabolic activation of procarcinogens and deactivation of certain anticancer drugs. Inhibition of these enzymes is a potential approach for cancer chemoprevention and treatment of CYP1-mediated drug resistance. We characterized inhibition of human CYP1A1, CYP1A2, and CYP1B1 enzymes by the novel inhibitor N-(3,5-dichlorophenyl)cyclopropanecarboxamide (DCPCC) and α-naphthoflavone (ANF). Depending on substrate, IC₅₀ values of DCPCC for CYP1A1 or CYP1B1 were 10-95 times higher than for CYP1A2. IC₅₀ of DCPCC for CYP1A2 was 100-fold lower than for enzymes in CYP2 and CYP3 families. DCPCC IC₅₀ values were 10-680 times higher than the ones of ANF. DCPCC was a mixed-type inhibitor of CYP1A2. ANF was a competitive tight-binding inhibitor of CYP1A1, CYP1A2, and CYP1B1. CYP1A1 oxidized DCPCC more rapidly than CYP1A2 or CYP1B1 to the same metabolite. Molecular dynamics simulations and binding free energy calculations explained the differences of binding of DCPCC and ANF to the active sites of all three CYP1 enzymes. We conclude that DCPCC is a more selective inhibitor for CYP1A2 than ANF. DCPCC is a candidate structure to modulate CYP1A2-mediated metabolism of procarcinogens and anticancer drugs.

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.

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.

Blocking oestradiol synthesis pathways with potent and selective coumarin derivatives

A comprehensive set of 3-phenylcoumarin analogues with polar substituents was synthesised for blocking oestradiol synthesis by 17-β-hydroxysteroid dehydrogenase 1 (HSD1) in the latter part of the sulphatase pathway. Five analogues produced ≥62% HSD1 inhibition at 5 µM and, furthermore, three of them produced ≥68% inhibition at 1 µM. A docking-based structure-activity relationship analysis was done to determine the molecular basis of the inhibition and the cross-reactivity of the analogues was tested against oestrogen receptor, aromatase, cytochrome P450 1A2, and monoamine oxidases. Most of the analogues are only modestly active with 17-β-hydroxysteroid dehydrogenase 2 – a requirement for lowering effective oestradiol levels in vivo. Moreover, the analysis led to the synthesis and discovery of 3-imidazolecoumarin as a potent aromatase inhibitor. In short, coumarin core can be tailored with specific ring and polar moiety substitutions to block either the sulphatase pathway or the aromatase pathway for treating breast cancer and endometriosis.

Structure-Activity Relationship Analysis of 3-Phenylcoumarin-Based Monoamine Oxidase B Inhibitors

Monoamine oxidase B (MAO-B) catalyzes deamination of monoamines such as neurotransmitters dopamine and norepinephrine. Accordingly, small-molecule MAO-B inhibitors potentially alleviate the symptoms of dopamine-linked neuropathologies such as depression or Parkinson's disease. Coumarin with a functionalized 3-phenyl ring system is a promising scaffold for building potent MAO-B inhibitors. Here, a vast set of 3-phenylcoumarin derivatives was designed using virtual combinatorial chemistry or rationally de novo and synthesized using microwave chemistry. The derivatives inhibited the MAO-B at 100 nM−1 μM. The IC₅₀ value of the most potent derivative 1 was 56 nM. A docking-based structure-activity relationship analysis summarizes the atom-level determinants of the MAO-B inhibition by the derivatives. Finally, the cross-reactivity of the derivatives was tested against monoamine oxidase A and a specific subset of enzymes linked to estradiol metabolism, known to have coumarin-based inhibitors. Overall, the results indicate that the 3-phenylcoumarins, especially derivative 1, present unique pharmacological features worth considering in future drug development.

Descriptor selection based on variable stability for predicting inhibitor activity

Quantitative structure-activity relationship (QSAR) has been a technique to study the relationship between chemical structures and properties, and variable selection is an important problem for finding the informative variables and building reliable models. A variable selection method based on variable stability is proposed and used for selecting the informative descriptors in the QSAR model of inhibitors. In the method, a series of models are built by leave-one-out cross validation (LOOCV), and variable stability is defined as the ratio of the absolute mean value and standard deviation of the regression coefficients in the models for a descriptor. Therefore, the descriptors with larger stabilities are more informative to the model. To further enhance the difference among the descriptors, an exponential parameter is used to modify the standard deviation. The results show that 13 descriptors are selected as informative ones from 1217 descriptors for the QSAR model of inhibitors. An effective prediction model can be constructed by them.

Linear Interaction Energy Based Prediction of Cytochrome P450 1A2 Binding Affinities with Reliability Estimation

Prediction of human Cytochrome P450 (CYP) binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD) simulations and Linear Interaction Energy (LIE) theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE) of 4.1 kJ mol-1 and a standard error in prediction (SDEP) in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pKi units).

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.

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.

Three-dimensional quantitative structure-activity relationship analysis of inhibitors of human and rat cytochrome P4503A enzymes

Cytochrome P450 3A4 (CYP3A4) is a member of the CYP family and is an important enzyme in drug metabolism. A compound that inhibits CYP3A4 activity could also affect the pharmacokinetics of other substrates, resulting in drug-drug interactions (DDIs) that could cause side effects. Pharmacokinetic data from drug-development studies in rats often determine the dosage used in human clinical trials. It is therefore useful to understand differences in metabolism in different species at an early stage in drug development. Human and rat CYP3A enzymes show different inhibition profiles with different drugs, although the mechanisms involved are not yet clear. Here we built three-dimensional quantitative structure-activity relationship (3D-QSAR) models using structure-based comparative molecular field analysis (CoMFA), to predict the direct inhibitory activity of ligands for human CYP3A4 and rat CYP3A1, based on computer-ligand docking. The alignment of the ligand docking poses suggested that key amino acid-ligand interactions (e.g., Thr309 in CYP3A4 and Pro310 in CYP3A1) characterized the different potencies with which the ligands inhibited CYP3A4 and CYP3A1. The 3D-QSAR models for human and rat CYP3A family inhibitors predicted the potency of inhibitors and could be useful for assessing DDIs at an early stage in drug discovery.

3-Pyridyl substituted aliphatic cycles as CYP11B2 inhibitors: aromaticity abolishment of the core significantly increased selectivity over CYP1A2

Aldosterone synthase (CYP11B2) is a promising therapeutic target for the treatment of cardiovascular diseases related to abnormally high aldosterone levels. On the basis of our previously identified lead compounds I–III, a series of 3-pyridinyl substituted aliphatic cycles were designed, synthesized and tested as CYP11B2 inhibitors. Aromaticity abolishment of the core was successfully applied to overcome the undesired CYP1A2 inhibition. This study resulted in a series of potent and selective CYP11B2 inhibitors, with compound 12 (IC₅₀ = 21 nM, SF = 50) as the most promising one, which shows no inhibition toward CYP1A2 at 2 µM. The design conception demonstrated in this study can be helpful in the optimization of CYP inhibitor drugs regarding CYP1A2 selectivity.

Insights on cytochrome p450 enzymes and inhibitors obtained through QSAR studies

The cytochrome P450 (CYP) superfamily of heme enzymes play an important role in the metabolism of a large number of endogenous and exogenous compounds, including most of the drugs currently on the market. Inhibitors of CYP enzymes have important roles in the treatment of several disease conditions such as numerous cancers and fungal infections in addition to their critical role in drug-drug interactions. Structure activity relationships (SAR), and three-dimensional quantitative structure activity relationships (3D-QSAR) represent important tools in understanding the interactions of the inhibitors with the active sites of the CYP enzymes. A comprehensive account of the QSAR studies on the major human CYPs 1A1, 1A2, 1B1, 2A6, 2B6, 2C9, 2C19, 2D6, 2E1, 3A4 and a few other CYPs are detailed in this review which will provide us with an insight into the individual/common characteristics of the active sites of these enzymes and the enzyme-inhibitor interactions.

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 novel CYP2A6 inhibitors by virtual screening

The human CYP2A6 enzyme metabolises several xenobiotics including nicotine, the addictive component in tobacco. Reduced activity of CYP2A6, either for genetic reasons or by administering inhibitors of CYP2A6, reduces tobacco smoking. The aim was to design novel inhibitors of CYP2A6 using 3D-QSAR analysis combined with virtual screening. A 3D-QSAR model was utilised to identify the most important features of the inhibitors, and this knowledge was used to design inhibitors for CYP2A6. Chemical database screening yielded several potent inhibitor candidates such as alkylamine derivatives (compound no. 5, IC(50)=0.1 μM) and 1-benzothiophene-3-carbaldehyde that can be used as lead compounds in the development of drugs for smoking reduction therapy.

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.

Classification of cytochrome P450 inhibitors with respect to binding free energy and pIC50 using common molecular descriptors

Virtual screening of chemical libraries following experimental assays of drug candidates is a common procedure in structure based drug discovery. However, the relationship between binding free energies and biological activities (pIC50) of drug candidates is still an unsolved issue that limits the efficiency and speed of drug development processes. In this study, the relationship between them is investigated based on a common molecular descriptor set for human cytochrome P450 enzymes (CYPs). CYPs play an important role in drug-drug interactions, drug metabolism, and toxicity. Therefore, in silico prediction of CYP inhibition by drug candidates is one of the major considerations in drug discovery. The combination of partial least-squares regression (PLSR) and a variety of classification algorithms were employed by considering this relationship as a classification problem. Our results indicate that PLSR with classification is a powerful tool to predict more than one output such as binding free energy and pIC50 simultaneously. PLSR with mixed-integer linear programming based hyperboxes predicts the binding free energy and pIC50 with a mean accuracy of 87.18% (min: 81.67% max: 97.05%) and 88.09% (min: 79.83% max: 92.90%), respectively, for the cytochrome p450 superfamily using the common 6 molecular descriptors with a 10-fold cross-validation.

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.

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.

Generation of a set of simple, interpretable ADMET rules of thumb

A set of simple, consistent structure-property guides have been determined from an analysis of a number of key ADMET assays run within GSK: solubility, permeability, bioavailability, volume of distribution, plasma protein binding, CNS penetration, brain tissue binding, P-gp efflux, hERG inhibition, and cytochrome P450 1A2/2C9/2C19/2D6/3A4 inhibition. The rules have been formulated using molecular properties that chemists intuitively know how to alter in a molecule, namely, molecular weight, logP, and ionization state. The rules supplement the more predictive black-box models available to us by clearly illustrating the key underlying trends, which are in line with reports in the literature. It is clear from the analyses reported herein that almost all ADMET parameters deteriorate with either increasing molecular weight, logP, or both, with ionization state playing either a beneficial or detrimental affect depending on the parameter in question. This study re-emphasizes the need to focus on a lower molecular weight and logP area of physicochemical property space to obtain improved ADMET parameters.

Structure-activity relationships for the inhibition of recombinant human cytochromes P450 by curcumin analogues

Inhibition of cytochrome P450 (CYP) is a major cause of drug-drug interactions. In this work, inhibitory potentials of 33 curcumin analogues, i.e. 2,6-dibenzylidenecyclohexanone (A series), 2,5-dibenzylidenecyclopentanone (B series) and 1,4-pentadiene-3-one (C series) substituted analogues of curcumin towards recombinant human CYP1A2, CYP3A4, CYP2B6, CYP2C9 and CYP2D6, all important for drug metabolism, were studied in vitro. Fluorescence plate reader and high performance liquid chromatography (HPLC) assays were used to evaluate CYP-inhibitory activities. MOE-based Quantitative structure-activity relationship (QSAR) analysis suggested that electrostatic and hydrophobic interactions and lipophilicity are important factors for CYP inhibition. Apart from insights in important molecular properties for CYP inhibition, the present results may also guide further design of curcumin analogues with less susceptibility to drug-drug interactions.

Identification of inhibitors of the nicotine metabolising CYP2A6 enzyme--an in silico approach

The compulsive nature of tobacco use is attributable to nicotine addiction. Nicotine is eliminated by metabolism through the cytochrome P450 2A6 (CYP2A6) enzyme in liver. Inhibition of CYP2A6 by chemical compounds may represent a potential supplement to anti-smoking therapy. The purpose of this study was to rationally design potent inhibitors of CYP2A6. 3D-QSAR models were constructed to find out which structural characteristics are important for inhibition potency. Specifically located hydrophobic and hydrogen donor features were found to affect inhibition potency. These features were used in virtual screening of over 60,000 compounds in the Maybridge chemical database. A total of 22 candidate molecules were selected and tested for inhibition potency. Four of these were potent and selective CYP2A6 inhibitors with IC(50) values lower than 1 muM. They represent novel structures of CYP2A6 inhibitors, especially N1-(4-fluorophenyl)cyclopropane-1-carboxamide. This compound can be used as a lead in the design of CYP2A6 inhibitor drugs to combat nicotine addiction.

Tolfenamic acid is a potent CYP1A2 inhibitor in vitro but does not interact in vivo: correction for protein binding is needed for data interpretation

OBJECTIVE

Our aim was to correlate the in vitro and in vivo CYP1A2 inhibition potential of tolfenamic acid, an NSAID highly (99.7%) bound to plasma proteins, to study the significance of protein binding of inhibitor in metabolic drug interactions.

METHODS

The effect of tolfenamic acid on CYP1A2 (phenacetin O-deethylation) was studied using human liver microsomes, with and without albumin (0-10 mg/ml). In a randomized, crossover study, 10 volunteers took 200 mg tolfenamic acid or placebo t.i.d. for 3 days. On day 2, a caffeine test was performed. On day 3, each ingested 4 mg of the CYP1A2 substrate tizanidine. Plasma tizanidine, its metabolites (M) and tolfenamic acid, and pharmacodynamic variables were measured.

RESULTS

Tolfenamic acid strongly inhibited phenacetin-O-deethylation in vitro (IC(50) 1.8 microM without albumin). Albumin decreased its inhibitory effect in a concentration-dependent manner; the IC(50) exceeded 100 microM with 10 mg/ml of albumin. Tolfenamic acid had no effect on the area under the concentration-time curve (AUC(0-oo)), peak concentration, time of peak concentration or half-life of tizanidine or M-3; only the AUC(0-oo) of secondary metabolite M-4 was slightly decreased (13%, P = 0.004). The caffeine test and the pharmacodynamic effects of tizanidine were unchanged.

CONCLUSIONS

Tolfenamic acid potently inhibits CYP1A2 in vitro when studied without albumin, but not in vivo. This apparent discrepancy is due to the high protein binding of tolfenamic acid. To avoid overestimation of the interaction potential, the inhibitory effect of highly albumin-bound compounds should also be studied in vitro with albumin, or their exact unbound plasma concentration should be used in predictions.

New potent and selective cytochrome P450 2B6 (CYP2B6) inhibitors based on three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis

BACKGROUND AND PURPOSE

The cytochrome P450 2B6 (CYP2B6) enzyme metabolises a number of clinically important drugs. Drug-drug interactions resulting from inhibition or induction of CYP2B6 activity may cause serious adverse effects. The aims of this study were to construct a three-dimensional structure-activity relationship (3D-QSAR) model of the CYP2B6 protein and to identify novel potent and selective inhibitors of CYP2B6 for in vitro research purposes.

EXPERIMENTAL APPROACH

The inhibition potencies (IC(50) values) of structurally diverse chemicals were determined with recombinant human CYP2B6 enzyme. Two successive models were constructed using Comparative Molecular Field Analysis (CoMFA).

KEY RESULTS

Three compounds proved to be very potent and selective competitive inhibitors of CYP2B6 in vitro (IC(50)<1 microM): 4-(4-chlorobenzyl)pyridine (CBP), 4-(4-nitrobenzyl)pyridine (NBP), and 4-benzylpyridine (BP). A complete inhibition of CYP2B6 activity was achieved with 0.1 microM CBP, whereas other CYP-related activities were not affected. Forty-one compounds were selected for further testing and construction of the final CoMFA model. The created CoMFA model was of high quality and predicted accurately the inhibition potency of a test set (n=7) of structurally diverse compounds.

CONCLUSIONS AND IMPLICATIONS

Two CoMFA models were created which revealed the key molecular characteristics of inhibitors of the CYP2B6 enzyme. The final model accurately predicted the inhibitory potencies of several structurally unrelated compounds. CBP, BP and NBP were identified as novel potent and selective inhibitors of CYP2B6 and CBP especially is a suitable inhibitor for in vitro screening studies.

Drug-induced changes in P450 enzyme expression at the gene expression level: a new dimension to the analysis of drug-drug interactions

Drug-drug interactions (DDIs) caused by direct chemical inhibition of key drug-metabolizing cytochrome P450 enzymes by a co-administered drug have been well documented and well understood. However, many other well-documented DDIs cannot be so readily explained. Recent investigations into drug and other xenobiotic-mediated expression changes of P450 genes have broadened our understanding of drug metabolism and DDI. In order to gain additional information on DDI, we have integrated existing information on drugs that are substrates, inhibitors, or inducers of important drug-metabolizing P450s with new data on drug-mediated expression changes of the same set of cytochrome P450s from a large-scale microarray gene expression database of drug-treated rat tissues. Existing information on substrates and inhibitors has been updated and reorganized into drug-cytochrome P450 matrices in order to facilitate comparative analysis of new information on inducers and suppressors. When examined at the gene expression level, a total of 119 currently marketed drugs from 265 examined were found to be cytochrome P450 inducers, and 83 were found to be suppressors. The value of this new information is illustrated with a more detailed examination of the DDI between PPARalpha agonists and HMG-CoA reductase inhibitors. This paper proposes that the well-documented, but poorly understood, increase in incidence of rhabdomyolysis when a PPARalpha agonist is co-administered with a HMG-CoA reductase inhibitor is at least in part the result of PPARalpha-induced general suppression of drug metabolism enzymes in liver. The authors believe this type of information will provide insights to other poorly understood DDI questions and stimulate further laboratory and clinical investigations on xenobiotic-mediated induction and suppression of drug metabolism.

Cytochrome P450 (CYP) inhibition screening: Comparison of three tests

There are several different experimental systems for screening of in vitro inhibitory potency of drugs under development. In this study we compared three different types of cytochrome P450 (CYP) inhibition tests: the traditional single substrate assays, the fluorescent probe method with recombinant human CYPs, and a novel n-in-one technique. All major hepatic drug-metabolizing CYPs were included (1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, 3A4). Six compounds (sotalol, propranolol, citalopram, fluoxetine, oxazepam and diazepam) were selected for detailed comparisons. The IC50 values of each of these compounds were measured using the three assay types. The inhibitory potencies of these model drugs were generally within the same order of magnitude and followed similar inhibition profiles in all the assay types. Clinically observed inhibitory interactions, or lack thereof, were predictable with all three assays. Comparison of potencies of 'diagnostic' inhibitors revealed also some notable differences between the assays, especially regarding CYP2E1. The potency of inhibitors towards CYP3A4 was dependent on the substrate and reaction measured. Generally all three assays gave reasonably comparable results, although some unexplained differences were also noted.

Recursive Partitioning for the Prediction of Cytochromes P450 2D6 and 1A2 Inhibition: Importance of the Quality of the Dataset

The purpose of this study was to explore the use of detailed biological data in combination with a statistical learning method for predicting the CYP1A2 and CYP2D6 inhibition. Data were extracted from the Aureus-Pharma highly structured databases which contain precise measures and detailed experimental protocol concerning the inhibition of the two cytochromes. The methodology used was Recursive Partitioning, an easy and quick method to implement. The building of models was preceded by the evaluation of the chemical space covered by the datasets. The descriptors used are available in the MOE software suite. The models reached at least 80% of Accuracy and often exceeded this percentage for the Sensitivity (Recall), Specificity, and Precision parameters. CYP2D6 datasets provided 11 models with Accuracy over 80%, while CYP1A2 datasets counted 5 high-accuracy models. Our models can be useful to predict the ADME properties during the drug discovery process and are indicated for high-throughput screening.

Structure-based methods for the prediction of drug metabolism

There is a tantalising possibility that we may be able to predict the metabolism of a drug directly from its structure, thus obviating the requirement for animal tests in this area. There are a number of techniques that can be used to estimate a range of events associated with metabolism, and may allow us to achieve this aim. This paper considers the role of (quantitative) structure-activity relationships, and pharmacophore and homology modelling in the prediction of metabolism. Examples are also presented where such approaches have been formalised into expert systems. Clearly, many advances have been made in this area in recent years. Discussed herein is the importance of fully integrating the diverse systems and approaches available to fulfil the aspiration to predict metabolism directly from structure.

Functional interaction of nitrogenous organic bases with cytochrome P450: A critical assessment and update of substrate features and predicted key active-site elements steering the access, binding, and orientation of amines

The widespread use of nitrogenous organic bases as environmental chemicals, food additives, and clinically important drugs necessitates precise knowledge about the molecular principles governing biotransformation of this category of substrates. In this regard, analysis of the topological background of complex formation between amines and P450s, acting as major catalysts in C- and N-oxidative attack, is of paramount importance. Thus, progress in collaborative investigations, combining physico-chemical techniques with chemical-modification as well as genetic engineering experiments, enables substantiation of hypothetical work resulting from the design of pharmacophores or homology modelling of P450s. Based on a general, CYP2D6-related construct, the majority of prospective amine-docking residues was found to cluster near the distal heme face in the six known SRSs, made up by the highly variant helices B', F and G as well as the N-terminal portion of helix C and certain beta-structures. Most of the contact sites examined show a frequency of conservation < 20%, hinting at the requirement of some degree of conformational versatility, while a limited number of amino acids exhibiting a higher level of conservation reside close to the heme core. Some key determinants may have a dual role in amine binding and/or maintenance of protein integrity. Importantly, a series of non-SRS elements are likely to be operative via long-range effects. While hydrophobic mechanisms appear to dominate orientation of the nitrogenous compounds toward the iron-oxene species, polar residues seem to foster binding events through H-bonding or salt-bridge formation. Careful uncovering of structure-function relationships in amine-enzyme association together with recently developed unsupervised machine learning approaches will be helpful in both tailoring of novel amine-type drugs and early elimination of potentially toxic or mutagenic candidates. Also, chimeragenesis might serve in the construction of more efficient P450s for activation of amine drugs and/or bioremediation.