Linking CoMFA and protein homology models of enzyme-inhibitor interactions: an application to non-steroidal aromatase inhibitors

Towards understanding aromatase inhibitory activity via QSAR modeling

Aromatase is a rate-limiting enzyme for estrogen biosynthesis that is overproduced in breast cancer tissue. To block the growth of breast tumors, aromatase inhibitors (AIs) are employed to bind and inhibit aromatase in order to lower the amount of estrogen produced in the body. Although a number of synthetic aromatase inhibitors have been released for clinical use in the treatment of hormone-receptor positive breast cancer, these inhibitors may lead to undesirable side effects (e.g. increased rash, diarrhea and vomiting; effects on the bone, brain and heart) and therefore, the search for novel AIs continues. Over the past decades, there has been an intense effort in employing medicinal chemistry and quantitative structure-activity relationship (QSAR) to shed light on the mechanistic basis of aromatase inhibition. To the best of our knowledge, this article constitutes the first comprehensive review of all QSAR studies of both steroidal and non-steroidal AIs that have been published in the field. Herein, we summarize the experimental setup of these studies as well as summarizing the key features that are pertinent for robust aromatase inhibition.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

Ligand- and Structure-Based Drug Design of Non-Steroidal Aromatase Inhibitors (NSAIs) in Breast Cancer

Aromatase is a multienzyme complex overexpressed in breast cancer and responsible for estrogen production. It is the potential target for designing anti-breast cancer drugs. Ligand and Structure-Based Drug Designing approaches (LBDD and SBDD) are involved in development of active and more specific Nonsteroidal Aromatase Inhibitors (NSAIs). Different LBDD and SBDD approaches are presented here to understand their utility in designing novel NSAIs. It is observed that molecules should possess a five or six membered heterocyclic nitrogen containing ring to coordinate with heme portion of aromatase for inhibition. Moreover, one or two hydrogen bond acceptor features, hydrophobicity, and steric factors may play crucial roles for anti-aromatase activity. Electrostatic, van der Waals, and p-p interactions are other important factors that determine binding affinity of inhibitors. HQSAR, LDA-QSAR, GQSAR, CoMFA, and CoMSIA approaches, pharmacophore mapping followed by virtual screening, docking, and dynamic simulation may be effective approaches for designing new potent anti-aromatase molecules.

Mechanism-based categorization of aromatase inhibitors: a potential discovery and screening tool

Cytochrome P450 aromatase is a key steroidogenic enzyme that converts androgens to estrogens in vertebrates. There is much interest in aromatase inhibitors (AIs) both because of their use as pharmaceuticals in the treatment of estrogen-sensitive breast cancers, and because a number of environmental contaminants can act as AIs, thereby disrupting endocrine function in humans and wildlife through suppression of circulating estrogen levels. The goal of the current work was to develop a mechanism-based structure-activity relationship (SAR) categorization framework highlighting the most important chemical structural features responsible for inhibition of aromatase activity. Two main interaction mechanisms were discerned: steroidal and non-steroidal. The steroid scaffold is most prominent when the structure of the target chemical is similar to the natural substrates of aromatase - androstenedione and testosterone. Chemicals acting by non-steroidal mechanism(s) possess a heteroatom (N, O, S) able to coordinate the heme iron of the cytochrome P450, and thus interfere with steroid hydroxylation. The specific structural boundaries controlling AI for both analyzed mechanisms were defined, and a software tool was developed that allowed a decision tree (profile) to be built discriminating AIs by mechanism and potency. An input chemical follows a profiling path and the structure is examined at each step to decide whether it conforms with the structural boundaries implemented in the decision tree node. Such a system would aid drug discovery efforts, as well as provide a screening tool to detect environmental contaminants that could act as AIs.

Three-dimensional model of cytochrome P450 human aromatase

A three-dimensional (3-D) structure of human aromatase (CYP 19) was modeled on the basis of the crystal structure of rabbit CYP2C5, the first solved X-ray structure of an eukaryotic cytochrome P450 and was evaluated by docking S-fadrozole and the steroidal competitive inhibitor (19R)-10-thiiranylestr-4-ene-3,17-dione, into the enzyme active site. According to a previous pharmacophoric hypothesis described in the literature, the cyano group of S-fadrozole partially mimics the steroid backbone C(17) carbonyl group of (19R)-10-thiiranylestr-4-ene-3,17-dione, and was oriented in a favorable position for H-bonding with the newly identified positively charged residues Lys 119 and Arg435. In addition, this model is consistent with the recent combined mutagenesis/modeling studies already published concerning the roles ofAsp309 and His480 in the aromatization of the steroid A ring.

A 3D QSAR model of 17beta-HSD1 inhibitors based on a thieno[2,3-d]pyrimidin-4(3H)-one core applying molecular dynamics simulations and ligand-protein docking

The 17beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) enzyme plays a crucial role in female hormonal regulation by catalysing the NADPH-dependent reduction of the less potent estrone E1 into the biologically active estradiol E2. Because 17beta-HSD1 is a key enzyme in E2 biosynthesis, it has emerged as an attractive drug target for inhibitor development. Herein we report the plausible binding modes and a 3D QSAR model of 17beta-HSD1 inhibitors based on a (di)cycloalkenothieno[2,3-d]pyrimidin-4(3H)-one core. Two generated enzyme complexes with potent inhibitors were subjected to molecular dynamics simulation to mimic the dynamic process of inhibitor binding. A set of 17beta-HSD1 inhibitors based on the thieno[2,3-d]pyrimidin-4(3H)-one core were docked into the resulting active site, and a CoMFA model employing the most extensive training set to date was generated. The model was validated with an external test set. Active site residues involved in inhibitor binding and CoMFA fields for steric and electrostatic interactions were identified. The model will be used to guide structural modifications of 17beta-HSD1 inhibitors based on a thieno[2,3-d]pyrimidin-4(3H)-one core in order to improve the biological activity as well as in the design of novel 17beta-HSD1 inhibitors.

A combination of 3D-QSAR, docking, local-binding energy (LBE) and GRID study of the species differences in the carcinogenicity of benzene derivatives chemicals

A combination of 3D-QSAR, docking, local-binding energy (LBE) and GRID methods was applied as a tool to study and predict the mechanism of action of 100 carcinogenic benzene derivatives. Two 3D-QSAR models were obtained: (i) model of mouse carcinogenicity on the basis of 100 chemicals (model 1) and (ii) model of the differences in mouse and rat carcinogenicity on the basis of 73 compounds (model 2). 3D-QSAR regression maps indicated the important differences in species carcinogenicity, and the molecular positions associated with them. In order to evaluate the role of P450 metabolic process in carcinogenicity, the following approaches were used. The 3D models of CYP2E1 for mouse and rat were built up. A docking study was applied and the important ligand-protein residues interactions and oxidation positions of the molecules were identified. A new approach for quantitative assessment of metabolism pathways was developed, which enabled us to describe the species differences in CYP2E1 metabolism, and how it can be related to differences in the carcinogenic potential for a subset of compounds. The binding energies of the important substituents (local-binding energy-LBE) were calculated, in order to quantitatively demonstrate the contribution of the substituents in metabolic processes. Furthermore, a computational procedure was used for determining energetically favourable binding sites (GRID examination) of the enzymes. The GRID procedure allowed the identification of some important differences, related to species metabolism in CYP2E1. Comparing GRID, 3D-QSAR maps and LBE results, a similarity was identified, indicating a relationship between P450 metabolic processes and the differences in the carcinogenicity.

Construction of 3D models of the CYP11B family as a tool to predict ligand binding characteristics

Aldosterone is synthesised by aldosterone synthase (CYP11B2). CYP11B2 has a highly homologous isoform, steroid 11beta-hydroxylase (CYP11B1), which is responsible for the biosynthesis of aldosterone precursors and glucocorticoids. To investigate aldosterone biosynthesis and facilitate the search for selective CYP11B2 inhibitors, we constructed three-dimensional models for CYP11B1 and CYP11B2 for both human and rat. The models were constructed based on the crystal structure of Pseudomonas Putida CYP101 and Oryctolagus Cuniculus CYP2C5. Small steric active site differences between the isoforms were found to be the most important determinants for the regioselective steroid synthesis. A possible explanation for these steric differences for the selective synthesis of aldosterone by CYP11B2 is presented. The activities of the known CYP11B inhibitors metyrapone, R-etomidate, R-fadrazole and S-fadrazole were determined using assays of V79MZ cells that express human CYP11B1 and CYP11B2, respectively. By investigating the inhibitors in the human CYP11B models using molecular docking and molecular dynamics simulations we were able to predict a similar trend in potency for the inhibitors as found in the in vitro assays. Importantly, based on the docking and dynamics simulations it is possible to understand the enantioselectivity of the human enzymes for the inhibitor fadrazole, the R-enantiomer being selective for CYP11B2 and the S-enantiomer being selective for CYP11B1.

Imidazolylmethylbenzophenones as Highly Potent Aromatase Inhibitors

Suppression of tumor and plasma estrogen levels by inhibition of aromatase is one of the most effective treatments for postmenopausal breast cancer patients. Starting from an easy, synthetically accessible, benzophenone scaffold, a new class of potent aromatase inhibitors was synthesized, endowed with high selectivity with respect to 17 alpha-hydroxylase/17,20-lyase (CYP17). Compounds 1b and 1d proved to be among the most potent inhibitors described so far.

A three-dimensional model of CYP19 aromatase for structure-based drug design

Aromatase (CYP450(arom), CYP19) is an enzyme responsible for converting the aliphatic androgens androstenedione and testosterone to the aromatic estrogens estrone and estradiol, respectively. These endogenous hormones are a key factor in cancer tumor formation and proliferation through a cascade starting from estrogen binding to estrogen receptor. To interfere with the overproduction of estrogens especially in tumor tissue, it is possible to inhibit aromatase activity. This can be achieved using aromatase inhibitors. In order to design novel aromatase inhibitors, it is necessary to have an understanding of the active site of aromatase. As no crystal structure of the enzyme has yet been published, we built a homology model of aromatase using the first crystallized mammalian cytochrome enzyme, rabbit 21-progesterone hydroxylase 2C5, as a template structure. The initial model was validated with exhaustive molecular dynamics simulation with and without the natural substrate androstenedione. The resulting enzyme-substrate complex shows very good stability and only two of the residues are in disallowed regions in a Ramachandran plot.

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.

Active site acidic residues and structural analysis of modelled human aromatase: a potential drug target for breast cancer

This study sheds new light on the role of acidic residues present in the active site cavity of human aromatase. Eight acidic residues (E129, D222, E245, E302, D309, E379, D380 and D476) lining the cavity are identified and studied using comparative modeling, docking, molecular dynamics as well as statistical techniques. The structural environment of these acidic residues is studied to assess the stability of the corresponding carboxylate anions. Results indicate that the environment of the residues E245, E302 and D222 is most suitable for carboxylate ion formation in the uncomplexed form. However, the stability of D309, D222 and D476 anions is seen to increase on complexation to steroidal substrates. In particular, the interaction between D309 and T310, which assists proton transfer, is found to be formed following androgen/nor-androgen complexation. The residue D309 is found to be clamped in the presence of substrate which is not observed in the case of the other residues although they exhibit changes in properties following substrate binding. Information entropic analysis indicates that the residues D309, D222 and D476 have more conformational flexibility compared to E302 and E245 prior to substrate binding. Interaction similar to that between D476 and D309, which is expected to assist androgen aromatization, is proposed between E302 and E245. The inhibition of aromatase activity by 4-hydroxy androstenedione (formestane) is attributed to a critical hydrogen bond formation between the hydroxy moiety and T310/D309 as well as the large distance from D476. The results corroborate well with earlier site directed mutagenesis studies.

Pharmacophore Modeling and in Silico Screening for New P450 19 (Aromatase) Inhibitors

Cytochrome P450 19 (P450 19, aromatase) constitutes a successful target for the treatment of breast cancer. This study analyzes chemical features common to P450 19 inhibitors to develop ligand-based, selective pharmacophore models for this enzyme. The HipHop and HypoRefine algorithms implemented in the Catalyst software package were employed to create both common feature and quantitative models. The common feature model for P450 19 includes two ring aromatic features in its core and two hydrogen bond acceptors at the ends. The models were used as database search queries to identify active compounds from the NCI database.

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.

Enantioselective nonsteroidal aromatase inhibitors identified through a multidisciplinary medicinal chemistry approach

To identify enantioselective nonsteroidal aromatase inhibitors, a multidisciplinary medicinal chemistry approach was pursued. First, our earlier CoMFA model [Bioorg. Med. Chem. 1998,6, 377-388] was extended taking purposely into account previously discovered enantioselective aromatase inhibitors. The 3D QSAR model was then exploited to design chiral ligands, whose configurational assignment was obtained, after HPLC separation, by means of a combination of circular dichroism measurements and time dependent density functional calculations. Finally, the new enantiomeric inhibitors were separately tested to ascertain both their potency against the cytochrome P450 aromatase (CYP19; EC 1.14.14.1), and their selectivity relative to another enzyme of the P450 family. A satisfactory agreement between experimental and predicted data allowed us to assert that a properly built "enantioselective CoMFA model" might constitute a useful tool for addressing enantioselective ligands design.

Three-dimensional model of the human aromatase enzyme and density functional parameterization of the iron-containing protoporphyrin IX for a molecular dynamics study of heme-cysteinato cytochromes

Mammalian cytochromes P450 (CYP) are enzymes of great biological and pharmaco-toxicological relevance. Due to their membrane-bound nature, the structural characterization of these proteins is extremely difficult, and therefore computational techniques, such as comparative modeling, may help obtaining reliable structures of members of this family. An important feature of CYP is the presence of an iron-containing porphyrin group at the enzyme active site. This calls for quantum chemical calculations to derive charges and parameters suitable for classical force field-based investigations of this proteins family. In this report, we first carried out density functional theory (DFT) computations to derive suitable charges for the Fe2+-containing heme group of P450 enzymes. Then, by means of the homology modeling technique, and taking advantage of the recently published crystal structure of the human CYP2C9, we built a new model of the human aromatase (CYP19) enzyme. Furthermore, to study the thermal stability of the new model as well as to test the suitability of the new DFT-based heme parameters, molecular dynamics (MD) simulations were carried out on both CYP2C9 and CYP19. Finally, the last few ns of aromatase MD trajectories were investigated following the essential dynamics protocol that allowed the detection of some correlated motions among some protein domains.

3D-QSAR and Molecular Mechanics Study for the Differences in the Azole Activity against Yeastlike and Filamentous Fungi and Their Relation to P450DM Inhibition. 1. 3-Substituted-4(3H)-quinazolinones

A combination between 3D-QSAR and molecular mechanics (MM)-docking study was used as a tool to detail and model the mechanism of action of 46 antifungal azoles. Two methods of alignment of the ligands were performed: (i) alignment of the main skeleton without substituents and (ii) alignment of a defined substructure. The best model is characterized by q(2) with the values of 0.70 for yeastlike (yeast), 0.66 for filamentous fungi, and 0.70 for the selectivity against filamentous fungi. 3D-QSAR regression maps derived from six models were used to identify the regions responsible for the differences in the compounds activity against yeast and filamentous fungi. The binding energy of the important substructures (Local Binding Energy-LBE) and its standard deviation were calculated in order to demonstrate quantitatively the contribution of substituents reflecting the diversity of the antifungal activity. The comparisons of these results with the same regions of the contour maps indicated a good correspondence between the 3D-QSAR and MM (LBE) approaches allowing association between the maps and the participating residues in the active sites of P450DM of C. albicans and A. fumigatus. The pi-pi interactions of two or more aromatic groups of the ligands with Phe228 and Tyr132 prove to be most important for the differences in activity against C. albicans. In A. fumigatus there was a better occupation of the inner central I-spiral in the areas around the heme. For the activity against A. fumigatus the pi-pi interactions of aromatic groups of the compounds with Phe509, Phe228, and Tyr132 are significant for the activity.

Design, synthesis, and 3D QSAR of novel potent and selective aromatase inhibitors

The design, synthesis, and biological evaluation of a series of new aromatase inhibitors bearing an imidazole or triazole ring linked to a fluorene (A), indenodiazine (B), or coumarin scaffold (C) are reported. Properly substituted coumarin derivatives displayed the highest aromatase inhibitory potency and selectivity over 17-alpha-hydroxylase/17-20 lyase. The modeling of the aromatase inhibition data by Comparative Molecular Field Analysis (CoMFA/GOLPE 3D QSAR approach) led to the development of a PLS model with good fitting and predictive powers (n = 22, ONC = 3, r(2) = 0.949, s = 0.216, and q(2) = 0.715). The relationship between aromatase inhibition and the steric and electrostatic fields generated by the examined azole inhibitors enables a clear understanding of the nature and spatial location of the main interactions modulating the aromatase inhibitory potency.

Rational design of an indolebutanoic acid derivative as a novel aldose reductase inhibitor based on docking and 3D QSAR studies of phenethylamine derivatives

A series of 45 phenethylamine derivatives were synthesized and evaluated for their inhibitory activity against pig kidney aldose reductase (ALR2, EC 1.1.1.21). Their IC(50) values ranged from 400 microM to 24 microM. The binding modes of compounds at the active site of ALR2 were examined using flexible docking. The results indicated that phenethylamine derivatives nicely fit into the active pocket of ALR2 by forming various hydrogen bonding and hydrophobic interactions. 3D-QSAR analysis was also conducted using FlexX-docked alignment of the compounds. The best prediction was obtained by CoMSIA combined with hydrophobic and hydrogen bond donor/acceptor field (q(2) = 0.557, r(2) = 0.934). A new derivative, 4-oxo-4-(4-hydroxyindole)butanoic acid, was designed, taking into account the CoMSIA field and the binding mode derived by FlexX docking. This rationally designed compound exhibits an ALR2 inhibition with an IC(50) value of 7.4 microM, which compares favorably to that of a well-known ALR2 inhibitor, tolrestat (IC(50) = 16 microM) and represents a potency approximately 240-fold higher than that of an original phenethylamine lead compound, YUA001.

QT prolongation through hERG K+ channel blockade: Current knowledge and strategies for the early prediction during drug development

Prolongation of the QT interval of the electrocardiogram is a typical effect of Class III antiarrhythmic drugs, achieved through blockade of potassium channels. In the past decade, evidence has accrued that several classes of drugs used for non-cardiovascular indications may prolong the QT interval with the same mechanism (namely, human ether-a-go-go-related gene (hERG) K(+) channel blockade). The great interest in QT prolongation is because of several reasons. First, drug-induced QT prolongation increases the likelihood of a polymorphous ventricular arrhythmia (namely, torsades de pointes, TdP), which may cause syncope and degenerate into ventricular fibrillation and sudden death. Second, the fact that several classes of drugs, such as antihistamines, fluoroquinolones, macrolides, and neuroleptics may cause the long QT syndrome (LQTS) raises the question whether this is a class effect (e.g., shared by all agents of a given pharmacological class) or a specific effect of single agents within a class. There is now consensus that, in most cases, only a few agents within a therapeutic class share the ability to significantly affect hERG K(+) channels. These compounds should be identified as early as possible during drug development. Third, QT prolongation and interaction with hERG K(+) channels have become surrogate markers of cardiotoxicity and have received increasing regulatory attention. This review briefly outlines the mechanisms leading to QT prolongation and the different strategies that can be followed to predict this unwanted effect. In particular, it will focus on the approaches recently proposed for the in silico screening of new compounds.

Comparative molecular field analysis of substrates for an aryl sulfotransferase based on catalytic mechanism and protein homology modeling

Comparative Molecular Field Analysis (CoMFA) methods were used to produce a 3D-QSAR model that correlated the catalytic efficiency of rat hepatic aryl sulfotransferase (AST) IV, expressed as log(k(cat)/K(m)), with the molecular structures of its substrates. A total of 35 substrate molecules were used to construct a CoMFA model that was evaluated on the basis of its leave-one-out cross-validated partial least-squares value (q(2)) and its ability to predict the activity of six additional substrates not used in the training set. The model was constructed using substrate conformations that favored (1) proton abstraction by the catalytic histidine residue, (2) an in-line sulfuryl-group transfer mechanism, and (3) constraints imposed by the residues lining the substrate binding pocket of a homology model of AST IV. This CoMFA model had a q(2) value of 0.691, and it successfully predicted the activities of the six molecules not used in the training set. A final CoMFA model was constructed using the same methodology but with molecules from both the training set and the test set. Its q(2) value was 0.701, and it had a non-cross-validated r(2) value of 0.922. The contour coefficient map generated by this CoMFA was overlaid on the amino acids in the substrate-binding pocket of the homology model of AST IV and found to show a good fit. Additionally external validation was obtained by using the CoMFA model to design substrates that show high activities. These results establish a methodology for prediction of the substrate specificity of this sulfotransferase based on CoMFA methods that are guided by both the homology model and the catalytic mechanism of the enzyme.

Toward a pharmacophore for drugs inducing the long QT syndrome: insights from a CoMFA study of HERG K(+) channel blockers

In this paper, we present a pharmacophore for QT-prolonging drugs, along with a 3D QSAR (CoMFA) study for a series of very structurally variegate HERG K(+) channel blockers. The blockade of HERG K(+) channels is one of the most important molecular mechanisms through which QT-prolonging drugs increase cardiac action potential duration. Since QT prolongation is one of the most undesirable side effects of drugs, we first tried to identify the minimum set of molecular features responsible for this action and then we attempted to develop a quantitative model correlating the 3D stereoelectronic characteristics of the molecules with their HERG blocking potency. Having considered an initial set of 31 QT-prolonging drugs for which the HERG K(+) channel blocking activity was measured on mammalian transfected cells, we started the construction of a theoretical screening tool able to predict whether a new molecule can interact with the HERG channel and eventually induce the long QT syndrome. This in silico tool might be useful in the design of new drug candidates devoid of the physicochemical features likely to cause the above-mentioned side effect.

Nonsteroidal aromatase inhibitors: recent advances

Aromatase is the cytochrome P450 enzyme responsible for the last step of estrogen biosynthesis, and aromatase inhibitors constitute an important class of drugs in clinical use for the treatment of breast cancer. Nonsteroidal aromatase inhibitors (NSAIs) are competitive inhibitors of aromatase, which bind to the enzyme active site by coordinating the iron atom present in the heme group of the P450 protein. Presently, third generation NSAIs are in use, and research efforts are being carried out both to identify new molecules of therapeutic interest and to clarify the mechanism of action. In this article, we present a survey of the compounds that have been recently reported as NSAIs, to provide a broad view on the general structure-activity relationships of the class. Moreover, starting from the current knowledge of the mechanistic aspects of aromatase action and from recent theoretical work on the molecular modeling of both enzyme and inhibitors, we try to indicate a way to integrate these different studies in view of a more general understanding of the aromeatase-inhibitor system. Finally, some aspects regarding the possible future development of the field are considered briefly.

Looking for selectivity among cytochrome P450s inhibitors

Cytochrome P450s 19 and 17 are very important pharmacological targets in two different fields of cancer chemotherapy. We present here a theoretical study aimed at explaining the molecular basis of inhibitor affinity and selectivity for either P450 19 or P450 17. Docking simulations of two compounds pointed out the major physicochemical features associated with inhibitory activity. Our results, in agreement with site-directed mutagenesis experiments, could be of relevant utility when designing new P450 19 and P450 17 inhibitors.