Phytoestrogens and their synthetic analogues as substrate mimic inhibitors of CYP1B1.

Phytoestrogens are class of natural compounds that shares structural similarity with estrogen and has the capacity to alter the fertilization in mammals. Till early 1990s, the natural phytoestrogens as well as their synthetic analogues were explored for their fertility modulating activity. During late 1990s, two findings renewed the interest on phytoestrogens as means to control hormone induced cancer: (i) revelation of overexpression of CYP1B1 in breast & ovarian cancer and (ii) protection offered by alphanapthoflavone (ANF) against hormone induced cancer. The objective of the review is to summarize the CYP1B1 inhibitory activity of phytoestrogens and their synthetic analogues reported till date. This review is an attempt to classify phytoestrogens and their synthetic analogues on their chemical architecture rather than simply by their chemical class (flavones, stilbenes etc.). This provides a broader sense to cluster many chemical classes under a particular chemical architecture/framework. Accordingly, we divided the phytoestrogen into three different classes based on two aryl groups (Ar) separated by linker (X), which may be either cyclic (c) or linear (l). The number in subscript to X denotes number of atoms: (i) Ar-cX4-Ar, (ii) Ar-lX3-Ar and (iii) Ar-lX2-Ar. This provides an opportunity to cluster flavones, quinolines and quinazolinones under Ar-cX4-Ar class, while biphenyl ureas and chalcones under Ar-lX3-Ar class. We believe in coming years many chemical scaffolds may be clustered under this framework.

Three Pectin Methylesterase Inhibitors Protect Cell Wall Integrity for Arabidopsis Immunity to Botrytis.

Infection by necrotrophs is a complex process that starts with the breakdown of the cell wall (CW) matrix initiated by CW-degrading enzymes and results in an extensive tissue maceration. Plants exploit induced defense mechanisms based on biochemical modification of the CW components to protect themselves from enzymatic degradation. The pectin matrix is the main CW target of Botrytis cinerea, and pectin methylesterification status is strongly altered in response to infection. The methylesterification of pectin is controlled mainly by pectin methylesterases (PMEs), whose activity is posttranscriptionally regulated by endogenous protein inhibitors (PMEIs). Here, AtPMEI10, AtPMEI11, and AtPMEI12 are identified as functional PMEIs induced in Arabidopsis (Arabidopsis thaliana) during B. cinerea infection. AtPMEI expression is strictly regulated by jasmonic acid and ethylene signaling, while only AtPMEI11 expression is controlled by PME-related damage-associated molecular patterns, such as oligogalacturonides and methanol. The decrease of pectin methylesterification during infection is higher and the immunity to B. cinerea is compromised in pmei10, pmei11, and pmei12 mutants with respect to the control plants. A higher stimulation of the fungal oxalic acid biosynthetic pathway also can contribute to the higher susceptibility of pmei mutants. The lack of PMEI expression does not affect hemicellulose strengthening, callose deposition, and the synthesis of structural defense proteins, proposed as CW-remodeling mechanisms exploited by Arabidopsis to resist CW degradation upon B. cinerea infection. We show that PME activity and pectin methylesterification are dynamically modulated by PMEIs during B. cinerea infection. Our findings point to AtPMEI10, AtPMEI11, and AtPMEI12 as mediators of CW integrity maintenance in plant immunity.

Matrix-Based Activity Pattern Classification as a Novel Method for the Characterization of Enzyme Inhibitors Derived from High-Throughput Screening.

One of the central questions in the characterization of enzyme inhibitors is determining the mode of inhibition (MOI). Classically, this is done with a number of low-throughput methods in which inhibition models are fitted to the data. The ability to rapidly characterize the MOI for inhibitors arising from high-throughput screening in which hundreds to thousands of primary inhibitors may need to be characterized would greatly help in lead selection efforts. Here we describe a novel method for determining the MOI of a compound without the need for curve fitting of the enzyme inhibition data. We provide experimental data to demonstrate the utility of this new high-throughput MOI classification method based on nonparametric analysis of the activity derived from a small matrix of substrate and inhibitor concentrations (e.g., from a 4S × 4I matrix). Lists of inhibitors from four different enzyme assays are studied, and the results are compared with the previously described IC50-shift method for MOI classification. The MOI results from this method are in good agreement with the known MOI and compare favorably with those from the IC50-shift method. In addition, we discuss some advantages and limitations of the method and provide recommendations for utilization of this MOI classification method.

Building predictive models for mechanism-of-action classification from phenotypic assay data sets.

Compound mechanism-of-action information can be critical for drug development decisions but is often challenging for phenotypic drug discovery programs. One concern is that compounds selected by phenotypic screening will have a previously known but undesirable target mechanism. Here we describe a useful method for assigning mechanism class to compounds and bioactive agents using an 84-feature signature from a panel of primary human cell systems (BioMAP systems). For this approach, a reference data set of well-characterized compounds was used to develop predictive models for 28 mechanism classes using support vector machines. These mechanism classes encompass safety and efficacy-related mechanisms, include both target-specific and pathway-based classes, and cover the most common mechanisms identified in phenotypic screens, such as inhibitors of mitochondrial and microtubule function, histone deacetylase, and cAMP elevators. Here we describe the performance and the application of these predictive models in a decision scheme for triaging phenotypic screening hits using a previously published data set of 309 environmental chemicals tested as part of the Environmental Protection Agency's ToxCast program. By providing quantified membership in specific mechanism classes, this approach is suitable for identification of off-target toxicity mechanisms as well as enabling target deconvolution of phenotypic drug discovery hits.

A novel, species-specific class of uncompetitive inhibitors of gamma-glutamyl transpeptidase.

Expression of gamma-glutamyl transpeptidase (GGT) in tumors contributes to resistance to radiation and chemotherapy. GGT is inhibited by glutamine analogues that compete with the substrate for the gamma-glutamyl binding site. However, the glutamine analogues that have been evaluated in clinical trials are too toxic for use in humans. We have used high throughput screening to evaluate small molecules for their ability to inhibit GGT and have identified a novel class of inhibitors that are not glutamine analogues. These compounds are uncompetitive inhibitors, binding the gamma-glutamyl enzyme complex. OU749, the lead compound, has an intrinsic K(i) of 17.6 microm. It is a competitive inhibitor of the acceptor glycyl-glycine, which indicates that OU749 occupies the acceptor site while binding to the gamma-glutamyl substrate complex. OU749 is more than 150-fold less toxic than the GGT inhibitor acivicin toward dividing cells. Inhibition of GGT by OU749 is species-specific, inhibiting GGT isolated from human kidney with 7-10-fold greater potency than GGT isolated from rat or mouse kidney. OU749 does not inhibit GGT from pig cells. Human GGT expressed in mouse fibroblasts is inhibited by OU749 similarly to GGT from human cells, which indicates that the species specificity is determined by differences in the primary structure of the protein rather than species-specific, post-translational modifications. These studies have identified a novel class of inhibitors of GGT, providing the basis for further development of a new group of therapeutics that inhibit GGT by a mechanism distinct from the toxic glutamine analogues.

High throughput screening reveals several new classes of glutamate dehydrogenase inhibitors.

Glutamate dehydrogenase (GDH) has been shown to play a regulatory role in insulin secretion by pancreatic beta-cells. The most compelling evidence of this comes from features of the hyperinsulism/hyperammonemia (HI/HA) syndrome where a dominant mutation causes the loss of inhibition by GTP, and from studies that link leucine (and its analogue BCH) activation of GDH to stimulation of insulin secretion. This suggests that GDH may represent a new and novel drug target to control a variety of insulin disorders. Recently we demonstrated that a subset of green tea polyphenols are potent inhibitors of glutamate dehydrogenase in vitro and can efficaciously block BCH stimulation of insulin secretion. In these current studies, we extend our search for GDH inhibitors using high throughput methods to pan through more than 27,000 compounds. A number of known and new inhibitors were identified with IC50s in the low micromolar range. These new inhibitors were found to act via apparently different mechanisms with some inhibiting the reaction in a positively cooperative manner, the inhibition by only some of the compounds was reversed by ADP, and one compound was found to stabilize the enzyme against thermal denaturation. Therefore, these new compounds not only are new leads in the treatment of hyperactive GDH but also are useful in dissecting the complex allosteric nature of the enzyme.

Novel and specific inhibitors of a poxvirus type I topoisomerase.

Vaccinia DNA topoisomerase (vTopo) is a prototypic pox virus family topoisomerase that shares extensive structural and mechanistic properties with the human type IB enzyme (hTopo) and is important for viral replication. Despite their far-reaching similarities, vTopo and hTopo have surprisingly distinct pharmacological properties. To further exploit these differences, we have developed recently the first high-throughput screen for vTopo, which has allowed rapid screening of a 1990-member small-molecule library for inhibitors. Using this approach, 21 compounds were identified with IC(90) values less than 10 muM, and 19 of these were also found to inhibit DNA supercoil relaxation by vTopo. Four of the most potent compounds were completely characterized and are structurally novel topo I inhibitors with efficacies at nanomolar concentrations. These inhibitors were highly specific for vTopo, showing no inhibition of the human enzyme even at 500- to 2000-fold greater concentrations. We describe a battery of efficient experiments to characterize the unique mechanisms of these vTopo inhibitors and discuss the surprising promiscuity of this enzyme to inhibition by structurally diverse small molecules.

Characterization of compound mechanisms and secondary activities by BioMAP analysis.

INTRODUCTION: Unexpected drug activities account for many of the failures of new chemical entities in clinical trials. These activities can be target-dependent, resulting from feedback mechanisms downstream of the primary target, or they can occur as a result of unanticipated secondary target(s). Methods that would provide rapid and efficient characterization of compounds with respect to a broad range of biological pathways and mechanisms relevant to human disease have the potential to improve preclinical and clinical success rates. METHODS: BioMAP assays containing primary human cells (endothelial cells and co-cultures with peripheral blood leukocytes) were stimulated in complex formats (specific combinations of inflammatory mediators) for 24 h in the presence or absence of test agents (drugs, experimental compounds, etc.). The levels of selected protein readouts (adhesion receptors, cytokines, enzymes, etc.) were measured and activity profiles (normalized data sets comprising BioMAP profiles) were generated for each test agent. The resulting profiles were compared by statistical methods to identify similarities and mechanistic insights. RESULTS: Compounds with known mechanisms including inhibitors of histamine H1 receptor, angiotensin converting enzyme, IkappaB kinase-2, beta2 adrenergic receptor and others were shown to generate reproducible and distinguishable BioMAP activity profiles. Similarities were observed between compounds targeting components within the same signal transduction pathway (e.g. NFkappaB), and also between compounds that share secondary targets (e.g. ibuprofen and FMOC-L-leucine, a PPARgamma agonist). DISCUSSION: Complex primary cell-based assays can be applied for detecting and distinguishing unexpected activities that may be of relevance to drug action in vivo. The ability to rapidly test compounds prior to animal or clinical studies may reduce the number of compounds that unexpectedly fail in preclinical or clinical studies.

Regulation of MMP-9 gene expression for the development of novel molecular targets against cancer and inflammatory diseases.

The need to pharmacologically control the proteolytic activity of matrix metalloproteinases (MMPs) has been commonly acknowledged, despite its limited efficacy in clinical trials. Among the reasons that explain this failure is our limited understanding of the signals that control the expression of MMPs in different cell types during different pathological conditions. Thus, future therapies must rely on more selective approaches. With the continually increasing body of proof implicating MMPs in a large number of diseases, it has become a priority to establish the pertinence of molecules involved in the signalling pathways leading to the expression of these enzymes. MMP-9 is a case in point: its dramatic overexpression in cancer and various inflammatory conditions clearly points to the molecular mechanisms controlling its expression as a potential target for eventual rational therapeutic intervention. In this article, recent progress in the signalling pathways that regulate MMP-9 expression is reviewed, and the latest strategies to be considered in the search for a specific inhibitor of its expression are presented.

Classification of kinase inhibitors using a Bayesian model.

The use of Bayesian statistics to model both general (multifamily) and specific (single-target) kinase inhibitors is investigated. The approach demonstrates an alternative to current computational methods applied to heterogeneous structure/activity data sets. This approach operates rapidly and is readily modifiable as required. A generalized model generated using inhibitor data from multiple kinase classes shows meaningful enrichment for several specific kinase targets. Such an approach can be used to prioritize compounds for screening or to optimally select compounds from third-party data collections. The observed benefit of the approach is finding compounds that are not structurally related to known actives, or novel targets for which there is not enough information to build a specific kinase model. The general kinase model described was built from a basis of mostly tyrosine kinase inhibitors, with some serine/threonine inhibitors; all the test cases used in prediction were also on tyrosine kinase targets. Confirming the applicability of this technique to other kinase families will be determined once those biological assays become available.

Aromatase inhibitors for hormonal therapy of early-stage breast cancer.

Aromatase inhibitors block estrogen synthesis by inhibiting aromatase, the enzyme responsible for the conversion of adrenal steroids to estrogen. They are approved for the first-line treatment of metastatic breast cancer in hormone receptor-positive women. Three large, well-designed randomized trials have been reported which support the use of aromatase inhibitors in the treatment of early stage breast cancer. This review focuses on data on aromatase inhibitors in the adjuvant setting and looks to the future potential of these drugs in both ductal carcinoma in situ and chemoprevention.

Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition.

A new in silico model is developed to predict cytochrome P450 2D6 inhibition from 2D chemical structure. Using a diverse training set of 100 compounds with published inhibition constants, an ensemble approach to recursive partitioning is applied to create a large number of classification trees, each of which yields a yes/no prediction about inhibition for a given compound. These binary classifications are combined to provide an overall prediction, which answers the yes/no question about inhibition and provides a measure of confidence about that prediction. Compared to single-tree models, the ensemble approach is less sensitive to noise in the experimental data as well as to changes in the training set. Internal validation tests indicated an overall classification accuracy of 75%, whereas predictions applied to an external set of 51 compounds yielded 80% accuracy, with all inhibitors correctly identified. The speed and 2D nature of this model make it appropriate for high-throughput processing of large chemical libraries, and the confidence level provides a continuous scale on which to prioritize compounds.

Inhibition of purified factor Xa amidolytic activity may not be predictive of inhibition of in vivo thrombosis: implications for identification of therapeutically active inhibitors.

OBJECTIVE: In this study we test the hypothesis that blood/plasma-based prothrombinase assays, rather than inhibition of purified factor Xa (fXa), are predictive of in vivo antithrombotic activity. METHODS AND RESULTS: Six fXa inhibitors with equivalent nanomolar Ki were studied in thrombin generation assays using human plasma/blood and endogenous macromolecular substrate. In all assays, benzamidine inhibitors were more potent (100 to 800 nmol/L) than the aminoisoquinolines (5 to 58 micromol/L) or neutral inhibitors (3 to 10 micromol/L). A similar rank order of compound inhibition was also seen in purified prothrombinase assays as well as in a rabbit model of deep vein thrombosis. CONCLUSIONS: Assays using prothrombinase with protein substrates are better predictors of in vivo efficacy than fXa Ki using amidolytic substrates.

Novel inhibitors of glutamyl-tRNA(Glu) reductase identified through cell-based screening of the heme/chlorophyll biosynthetic pathway.

The metabolite 5-aminolevulinic acid (ALA) is an early committed intermediate in the biosynthetic pathway of heme and chlorophyll formation. In plants, 5-aminolevulinic acid is synthesized via a two-step pathway in which glutamyl-tRNA(Glu) is reduced by glutamyl-tRNA(Glu) reductase (GluTR) to glutamate 1-semialdehyde, followed by transformation to 5-aminolevulinic acid catalyzed by glutamate 1-semialdehyde aminotransferase. Using an Escherichia coli cell-based high-throughput assay to screen small molecule libraries, we identified several chemical classes that specifically inhibit heme/chlorophyll biosynthesis at this point by demonstrating that the observed cell growth inhibition is reversed by supplementing the medium with 5-aminolevulinic acid. These compounds were further tested in vitro for inhibition of the purified enzymes GluTR and glutamate 1-semialdehyde aminotransferase as confirmation of the specificity and site of action. Several promising compounds were identified from the high-throughput screen that inhibit GluTR with an I(0.5) of less than 10 microM. Our results demonstrate the efficacy of cell-based high-throughput screening for identifying inhibitors of 5-aminolevulinic acid biosynthesis, thus representing the first report of exogenous inhibitors of this enzyme.

Use of aromatase inhibitors in breast carcinoma.

Aromatase, a cytochrome P-450 enzyme that catalyzes the conversion of androgens to estrogens, is the major mechanism of estrogen synthesis in the post-menopausal woman. We review some of the recent scientific advances which shed light on the biologic significance, physiology, expression and regulation of aromatase in breast tissue. Inhibition of aromatase, the terminal step in estrogen biosynthesis, provides a way of treating hormone-dependent breast cancer in older patients. Aminoglutethimide was the first widely used aromatase inhibitor but had several clinical drawbacks. Newer agents are considerably more selective, more potent, less toxic and easier to use in the clinical setting. This article reviews the clinical data supporting the use of the potent, oral competitive aromatase inhibitors anastrozole, letrozole and vorozole and the irreversible inhibitors 4-OH androstenedione and exemestane. The more potent compounds inhibit both peripheral and intra-tumoral aromatase. We discuss the evidence supporting the notion that aromatase inhibitors lack cross-resistance with antiestrogens and suggest that the newer, more potent compounds may have a particular application in breast cancer treatment in a setting of adaptive hypersensitivity to estrogens. Currently available aromatase inhibitors are safe and effective in the management of hormone-dependent breast cancer in post-menopausal women failing antiestrogen therapy and should now be used before progestational agents. There is abundant evidence to support testing these compounds as first-line hormonal therapy for metastatic breast cancer as well as part of adjuvant regimens in older patients and quite possibly in chemoprevention trials of breast cancer.