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Pyrazole has been recognized as a pharmacologically important privileged scaffold whose derivatives produce almost all types of pharmacological activities and have attracted much attention in the last decades. Of the various pyrazole derivatives reported as potential therapeutic agents, this article focuses on pyrazole-based kinase inhibitors. Pyrazole-possessing kinase inhibitors play a crucial role in various disease areas, especially in many cancer types such as lymphoma, breast cancer, melanoma, cervical cancer, and others in addition to inflammation and neurodegenerative disorders. In this article, we reviewed the structural and biological characteristics of the pyrazole derivatives recently reported as kinase inhibitors and classified them according to their target kinases in a chronological order. We reviewed the reports including pyrazole derivatives as kinase inhibitors published during the past decade (2011-2020).

The coronavirus disease (COVID-19) is spreading between human populations mainly through nasal droplets. Currently, the vaccines have great hope, but it takes years for testing its efficacy in human. As there is no specific drug treatment available for COVID-19 pandemic, we explored in silico repurposing of drugs with dual inhibition properties by targeting transmembrane serine protease 2 (TMPRSS2) and human angiotensin-converting enzyme 2 (ACE2) from FDA-approved drugs. The TMPRSS2 and ACE2 dual inhibitors in COVID-19 would be a novel antiviral class of drugs called "entry inhibitors." For this purpose, approximately 2800 US-FDA approved drugs were docked using a virtual docking tool with the targets TMPRSS2 and ACE2. The best-fit drugs were selected as per docking scores and visual outcomes. Later on, drugs were selected on the basis of molecular dynamics simulations. The drugs alvimopan, arbekacin, dequalinum, fleroxacin, lopinavir, and valrubicin were shortlisted by visual analysis and molecular dynamics simulations. Among these, lopinavir and valrubicin were found to be superior in terms of dual inhibition. Thus, lopinavir and valrubicin have the potential of dual-target inhibition whereby preventing SARS-CoV-2 entry to the host. For repurposing of these drugs, further screening in vitro and in vivo would help in exploring clinically., (Copyright © 2021 Elsevier B.V. All rights reserved.)

With the introduction of the influenza specific neuraminidase inhibitors (NAIs) in 1999, there were concerns about the emergence and spread of resistant viruses in the community setting. Surveillance and testing of community isolates for their susceptibility to the NAIs was initially carried out by the Neuraminidase Inhibitor Susceptibility Network (NISN) and has subsequently been taken on by the global WHO influenza network laboratories. During the NISN surveillance, we identified two Yamagata lineage influenza B viruses with amino acid substitutions of H134Y (B/Auckland/2/2001) or W438R (B/Yokohama/12/2005) which had slightly elevated IC 50 values for zanamivir and/or oseltamivir, but not sufficiently to be characterized as mild outliers at the time. As it has now been well demonstrated that mixed populations can mask the true magnitude of resistance of a mutant, we re-examined both of these isolates by plaque purification to see if the true susceptibilities were being masked due to mixed populations. Results confirmed that the B/Auckland isolate contained both wild type and H134Y mutant populations, with mutant IC 50 values > 250 nM for both oseltamivir and peramivir in the enzyme inhibition assay. The B/Yokohama isolate also contained both wild type and W438R mutant populations, the latter now demonstrating IC 50 values > 400 nM for zanamivir, oseltamivir and peramivir. In addition, plaque purification of the B/Yokohama isolate identified viruses with other single neuraminidase substitutions H134Y, H134R, H431R, or T436P. H134R and H431R viruses had IC 50 values > 400 nM and >250 nM respectively against all three NAIs. All changes conferred much greater resistance to peramivir than to zanamivir, and less to oseltamivir, and affected the kinetics of binding and dissociation of the NAIs. Most affected affinity (K m ) for the MUNANA substrate, but some had decreased while others had increased affinity. Despite resistance in the enzyme assay, no reduced susceptibility was seen in plaque reduction assays in MDCK cells for any of the mutant viruses. None of these substitutions was in the active site. Modelling suggests that these substitutions affect the 150 and 430-loop regions described for influenza A NAs, suggesting they may also be important for substrate and inhibitor binding for influenza B NAs., (Crown Copyright © 2020. Published by Elsevier B.V. All rights reserved.)

The nickel-dependent urease enzyme is responsible for the hydrolysis of urea to ammonia and carbon dioxide. A number of bacteria produce urease (ureolytic bacteria) and are associated with various infectious diseases and ammonia emissions from agriculture. We report the first comprehensive comparison of the inhibition of urease activity by compounds analysed under the same conditions. Thus, 71 commercially available compounds were screened for their anti-ureolytic properties against both the ureolytic bacterium Klebsiella pneumoniae and purified jack bean urease. Of the tested compounds, 30 showed more than 25% inhibition of the ureolytic activity of Klebsiella pneumoniae or jack bean urease, and among these, carbon disulfide, N-phenylmaleimide, diethylenetriaminepentaacetic acid, sodium pyrrolidinedithiocarbamate, 1,2,4-butanetricarboxylic acid, tannic acid, and gallic acid have not previously been reported to possess anti-ureolytic properties. The diverse effects of metal ion chelators on ureolysis were investigated using a cellular nickel uptake assay. Ethylenediaminetetraacetic acid (EDTA) and dimethylglyoxime (DMG) clearly reduced the nickel import and ureolytic activity of cells, oxalic acid stimulated nickel import but reduced the ureolytic activity of cells, 1,2,4-butanetricarboxylic acid strongly stimulated nickel import and slightly increased the ureolytic activity of cells, while L-cysteine had no effect on nickel import but efficiently reduced the ureolytic activity of cells.

Cryptosporidium parvum and Cryptosporidium hominis have emerged as major enteric pathogens of infants in the developing world, in addition to their known importance in immunocompromised adults. Although there has been recent progress in identifying new small molecules that inhibit Cryptosporidium sp. growth in vitro or in animal models, we lack information about their mechanism of action, potency across the life cycle, and cidal versus static activities. Here, we explored four potent classes of compounds that include inhibitors that likely target phosphatidylinositol 4 kinase (PI4K), phenylalanine-tRNA synthetase (PheRS), and several potent inhibitors with unknown mechanisms of action. We utilized monoclonal antibodies and gene expression probes for staging life cycle development to define the timing of when inhibitors were active during the life cycle of Cryptosporidium parvum grown in vitro These different classes of inhibitors targeted different stages of the life cycle, including compounds that blocked replication (PheRS inhibitors), prevented the segmentation of daughter cells and thus blocked egress (PI4K inhibitors), or affected sexual-stage development (a piperazine compound of unknown mechanism). Long-term cultivation of C. parvum in epithelial cell monolayers derived from intestinal stem cells was used to distinguish between cidal and static activities based on the ability of parasites to recover from treatment. Collectively, these approaches should aid in identifying mechanisms of action and for designing in vivo efficacy studies based on time-dependent concentrations needed to achieve cidal activity. IMPORTANCE Currently, nitazoxanide is the only FDA-approved treatment for cryptosporidiosis; unfortunately, it is ineffective in immunocompromised patients, has varied efficacy in immunocompetent individuals, and is not approved in infants under 1 year of age. Identifying new inhibitors for the treatment of cryptosporidiosis requires standardized and quantifiable in vitro assays for assessing potency, selectivity, timing of activity, and reversibility. Here, we provide new protocols for defining which stages of the life cycle are susceptible to four highly active compound classes that likely inhibit different targets in the parasite. We also utilize a newly developed long-term culture system to define assays for monitoring reversibility as a means of defining cidal activity as a function of concentration and time of treatment. These assays should provide valuable in vitro parameters to establish conditions for efficacious in vivo treatment., (Copyright © 2020 Funkhouser-Jones et al.)

Introduction : A few scorpions are dangerous to humans. Their medical relevance was the initial driving force for venom research. By classical biochemistry and molecular cloning, several venom peptides and their coding transcripts were characterized, mainly those related to toxins. The discovery of other components with novel activities and potential applications has revitalized the interest in the field in the last decade and a half. Nontoxic scorpion species have also attracted major interest. Areas covered : Advances in the identification of scorpion venom components via high-throughput venomics (genomics, transcriptomics and proteomics) up to 2019 are summarized. A classification system for venom-related transcripts and proteins, together with an intuitive systematic nomenclature for RNAseq-generated transcripts are proposed. Venom components classified as Na + , K + , Ca 2+ , Cl - and TRP channel toxins, enzymes, protease inhibitors, host defense peptides and other peptidic molecules are briefly reviewed, giving a comprehensive picture of the venom. Expert opinion : Modern high-throughput technologies applied to scorpion venom studies have resulted in a dramatic increase in both, the number and diversity of available sequences, leading to a deeper understanding of the composition of scorpion venoms. Still, many newly-discovered venom constituents remain to be characterized, to complete the puzzle of scorpion venoms.

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-cX 4 -Ar, (ii) Ar-lX 3 -Ar and (iii) Ar-lX 2 -Ar. This provides an opportunity to cluster flavones, quinolines and quinazolinones under Ar-cX 4 -Ar class, while biphenyl ureas and chalcones under Ar-lX 3 -Ar class. We believe in coming years many chemical scaffolds may be clustered under this framework., (Copyright © 2018 Elsevier Masson SAS. All rights reserved.)

Aldehyde dehydrogenase (ALDH) activity is commonly used as a marker to identify cancer stem-like cells. The three ALDH1A isoforms have all been individually implicated in cancer stem-like cells and in chemoresistance; however, which isoform is preferentially expressed varies between cell lines. We sought to explore the structural determinants of ALDH1A isoform selectivity in a series of small-molecule inhibitors in support of research into the role of ALDH1A in cancer stem cells. An SAR campaign guided by a cocrystal structure of the HTS hit CM39 (7) with ALDH1A1 afforded first-in-class inhibitors of the ALDH1A subfamily with excellent selectivity over the homologous ALDH2 isoform. We also discovered the first reported modestly selective single isoform 1A2 and 1A3 inhibitors. Two compounds, 13g and 13h, depleted the CD133 + putative cancer stem cell pool, synergized with cisplatin, and achieved efficacious concentrations in vivo following IP administration. Compound 13h additionally synergized with cisplatin in a patient-derived ovarian cancer spheroid model.

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., (© 2017 American Society of Plant Biologists. All Rights Reserved.)

Accurate and robust classification models for describing and predicting the activity of 330 chemicals that are sphingosine kinase 1 (SphK1) and/or sphingosine kinase 2 (SphK2) inhibitors were derived. The classification models developed in this work assist in finding selective subspaces in chemical space occupied by particular groups of SphK inhibitors. A combination of a genetic algorithm (GA) and a counter propagation artificial neural network (CPANN) was utilized to select the most efficient subsets of the molecular descriptors. The optimized models in this work reasonably separate active inhibitors of SphK1 from active SphK2 inhibitors. Generally, the CPANN models in this work were used to classify the compounds according to their therapeutic targets and activities. The simplicity of the chosen descriptors and their relative importance sheds some light on the structural features necessary to induce selective inhibitory activity to the studied molecules. The areas under the receiver operating characteristic (ROC) curves for the GA-CPANN models in this work were 0.934 and 0.922 for active SphK1 and SphK2 inhibitors, respectively. Generally, the results in this work suggest some important molecular features and pharmacophores that could help medicinal chemists develop selective and potent SphK inhibitors.

As an important enzyme in Phase I drug metabolism, the flavin-containing monooxygenase (FMO) also metabolizes some xenobiotics with soft nucleophiles. The site of metabolism (SOM) on a molecule is the site where the metabolic reaction is exerted by an enzyme. Accurate prediction of SOMs on drug molecules will assist the search for drug leads during the optimization process. Here, some quantum mechanics features such as the condensed Fukui function and attributes from circular fingerprints (called Molprint2D) are computed and classified using the support vector machine (SVM) for predicting some potential SOMs on a series of drugs that can be metabolized by FMO enzymes. The condensed Fukui function fA- representing the nucleophilicity of central atom A and the attributes from circular fingerprints accounting the influence of neighbors on the central atom. The total number of FMO substrates and non-substrates collected in the study is 85 and they are equally divided into the training and test sets with each carrying roughly the same number of potential SOMs. However, only N-oxidation and S-oxidation features were considered in the prediction since the available C-oxidation data was scarce. In the training process, the LibSVM package of WEKA package and the option of 10-fold cross validation are employed. The prediction performance on the test set evaluated by accuracy, Matthews correlation coefficient and area under ROC curve computed are 0.829, 0.659, and 0.877 respectively. This work reveals that the SVM model built can accurately predict the potential SOMs for drug molecules that are metabolizable by the FMO enzymes., Competing Interests: The authors have declared that no competing interests exist.

P-glycoprotein (Pgp) inhibition has been considered as an effective strategy towards combating multidrug-resistant cancers. Owing to the substrate promiscuity of Pgp, the classification of its interacting ligands is not an easy task and is an ongoing issue of debate. Chemical structures can be represented by the simplified molecular input line entry system (SMILES) in the form of linear string of symbols. In this study, the SMILES notations of 2254 Pgp inhibitors including 1341 active, and 913 inactive compounds were used for the construction of a SMILE-based classification model using CORrelation And Logic (CORAL) software. The model provided an acceptable predictive performance as observed from statistical parameters consisting of accuracy, sensitivity and specificity that afforded values greater than 70% and MCC value greater than 0.6 for training, calibration and validation sets. In addition, the CORAL method highlighted chemical features that may contribute to increased and decreased Pgp inhibitory activities. This study highlights the potential of CORAL software for rapid screening of prospective compounds from a large chemical space and provides information that could aid in the design and development of potential Pgp inhibitors.

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 4 S × 4 I matrix). Lists of inhibitors from four different enzyme assays are studied, and the results are compared with the previously described IC 50 -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 IC 50 -shift method. In addition, we discuss some advantages and limitations of the method and provide recommendations for utilization of this MOI classification method.

Plant secondary-thickened cell walls are characterized by the presence of lignin, a recalcitrant and hydrophobic polymer that provides mechanical strength and ensures long-distance water transport. Exactly the recalcitrance and hydrophobicity of lignin put a burden on the industrial processing efficiency of lignocellulosic biomass. Both forward and reverse genetic strategies have been used intensively to unravel the molecular mechanism of lignin deposition. As an alternative strategy, we introduce here a forward chemical genetic approach to find candidate inhibitors of lignification. A high-throughput assay to assess lignification in Arabidopsis (Arabidopsis thaliana) seedlings was developed and used to screen a 10-k library of structurally diverse, synthetic molecules. Of the 73 compounds that reduced lignin deposition, 39 that had a major impact were retained and classified into five clusters based on the shift they induced in the phenolic profile of Arabidopsis seedlings. One representative compound of each cluster was selected for further lignin-specific assays, leading to the identification of an aromatic compound that is processed in the plant into two fragments, both having inhibitory activity against lignification. One fragment, p-iodobenzoic acid, was further characterized as a new inhibitor of CINNAMATE 4-HYDROXYLASE, a key enzyme of the phenylpropanoid pathway synthesizing the building blocks of the lignin polymer. As such, we provide proof of concept of this chemical biology approach to screen for inhibitors of lignification and present a broad array of putative inhibitors of lignin deposition for further characterization., (© 2016 American Society of Plant Biologists. All rights reserved.)

Aldehyde dehydrogenases (ALDH) catalyze the irreversible oxidation of aldehydes to their corresponding carboxylic acid. Alterations in ALDH1A1 activity are associated with such diverse diseases as cancer, Parkinson's disease, obesity, and cataracts. Inhibitors of ALDH1A1 could aid in illuminating the role of this enzyme in disease processes. However, there are no commercially available selective inhibitors for ALDH1A1. Here we characterize two distinct chemical classes of inhibitors that are selective for human ALDH1A1 compared to eight other ALDH isoenzymes. The prototypical members of each structural class, CM026 and CM037, exhibit submicromolar inhibition constants but have different mechanisms of inhibition. The crystal structures of these compounds bound to ALDH1A1 demonstrate that they bind within the aldehyde binding pocket of ALDH1A1 and exploit the presence of a unique glycine residue to achieve their selectivity. These two novel and selective ALDH1A1 inhibitors may serve as chemical tools to better understand the contributions of ALDH1A1 to normal biology and to disease states.

1-Phenylsulfinyl-3-(pyridin-3-yl)naphthalen-2-ols and related compounds were synthesized and evaluated for inhibition of aldosterone synthase (CYP11B2), a potential target for cardiovascular diseases associated with elevated plasma aldosterone levels like congestive heart failure and myocardial fibrosis. Introduction of substituents at the phenylsulfinyl moiety and changes of the substitution pattern at the naphthalene core were examined. Potent compounds were further examined for selectivity versus other important steroidogenic CYP enzymes, i.e. the highly homologous 11β-hydroxylase (CYP11B1), CYP17 and CYP19. The most potent compound (IC50 = 14 nM) discovered was the meta-trifluoromethoxy derivative 11, which also exhibited excellent selectivity toward CYP11B1 (SF = 415), and showed no inhibition of CYP17 and CYP19., (Copyright © 2014 Elsevier Masson SAS. All rights reserved.)

Living organisms belonging to all three domains of life, viz., eubacteria, archaeabacteria, and eukaryotes encode one or more DNA ligases. DNA ligases are indispensable in various DNA repair and replication processes and a deficiency or an inhibition of their activity can lead to accumulation of DNA damage and strand breaks. DNA damage, specially strand breaks at unsustainable levels can lead to replication block and/or cell death. DNA ligases as potential anticancer targets have been realized only recently. There is enough rationale to suggest that ligases have a tremendous potential for novel therapeutics including anticancer and antibacterial therapy, specially when the world is facing acute problems of drug resistance and chemotherapy failure, with an immediate need for new therapeutic targets. Here, we review the current state of the art in the development of human ligase inhibitors, their structures, molecular mechanisms, physiological effects, and their potential in future cancer therapy. Citing examples, we focus on strategies for improving the activity and specificity of existing and novel inhibitors by using structure-based rational approaches. In the end, we describe potential new sites on the ligase I protein that can be targeted for the development of novel inhibitors. This is the first comprehensive review to compile all known human ligase inhibitors and to provide a rationale for the further development of ligase inhibitors for cancer therapy., (© 2013 Wiley Periodicals, Inc.)

Inosine 5'-monophosphate dehydrogenase (IMPDH) is a key enzyme of de novo GMP biosynthesis. The expression and activity of IMPDH can be affected by diseases and physiological process. It is the drug target for anticancer, antiviral, antimicrobial and immunosuppressive therapeutics. Not only catalytic action but the other biological functions of IMPDH also play an important role in diseases. The basic functions, mechanism of catalysis, classification of inhibitors, biological functions and the latest advances to IMPDH will be illustrated in this review. It is expected to be helpful to the discovery of new inhibitors and biological functions of IMPDH.

Juvenile hormone esterase (JHE) plays a key role in the development and metamorphosis of holometabolous insects. Its inhibitors could possibly be targeted for insect control. Conversely, JHE may also be involved in endocrine disruption by xenobiotics, resulting in detrimental effects in beneficial insects. There is therefore a need to know the structural characteristics of the molecules able to monitor JHE activity, and to develop SAR and QSAR studies to estimate their effectiveness. For a large diverse population of 181 trifluoromethylketones (TFKs) - the most potent JHE inhibitors known to date - we recently proposed a binary classification (active/inactive) using a support vector machine and Codessa structural descriptors. We have now examined, using the same data set and with the same descriptors, the applicability and performance of five other machine learning approaches. These have been shown able to handle high dimensional data (with descriptors possibly irrelevant or redundant) and to cope with complex mechanisms, but without delivering explicit directly exploitable models. Splitting the data into five batches (training set 80%, test set 20%) and carrying out leave-one-out cross-validation, led to good results of comparable performance, consistent with our previous support vector classifier (SVC) results. Accuracy was greater than 0.80 for all approaches. A reduced set of 15 descriptors common to all the investigated approaches showed good predictive ability (confirmed using a three-layer perceptron) and gives some clues regarding a mechanistic interpretation.

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.

Plasmodium falciparum glucose-6-phosphate dehydrogenase (PfG6PD) has been considered as a potential target for severe forms of anti-malaria therapy. In this study, several classification models were built to distinguish active and weakly active PfG6PD inhibitors by support vector machine method. Each molecule was initially represented by 1,044 molecular descriptors calculated by ADRIANA.Code. Correlation analysis and attribute selection methods in Weka were used to get the best reduced set of molecular descriptors, respectively. The best model (Model 2w) gave a prediction accuracy (Q) of 93.88 % and a Matthew's correlation coefficient (MCC) of 0.88 on the test set. Some properties such as [Formula: see text] atom charge, [Formula: see text] atom charge, and lone pair electronegativity-related descriptors are important for the interaction between the PfG6PD and the inhibitor.

Chronic obstructive pulmonary disease (COPD) is a major increasing health problem and the World Health Organization (WHO) reports COPD as the fifth leading cause of death worldwide. COPD refers to a condition of inflammation and progressive weakening of the structure of the lung as well as irreversible narrowing of the airways. Current treatment is only palliative and no available drug halts the progression of the disease. Human neutrophil elastase (HNE) is a serine protease, which plays a major role in the COPD inflammatory process. The protease/anti-protease imbalance leads to an excess of extracellular HNE hydrolyzing elastin, the structural protein that confers elasticity to the lung tissue. Although HNE was identified as a therapeutic target for COPD more than 30 years ago, only Sivelestat (ONO-5046), an HNE inhibitor from Ono Pharmaceutical, has been approved for clinical use. Nevertheless, Sivelestat is only approved in Japan and its development in the USA was terminated in 2003. Other inhibitors in pre-clinical or phase I trials were discontinued for various reasons. Hence, there is an urgent need for low-molecular-weight synthetic elastase inhibitors and the present review discusses the recent advances on this field covering acylating agents, transition-state inhibitors, mechanism-based inhibitors, relevant natural products, and major patent disclosures., (© 2011 Wiley Periodicals, Inc.)

To identify the common inhibition types, the putative decision system is unsatisfactory. In a new decision system, Michaelis-Menten constants and maximal reaction rates were plotted versus inhibitor concentrations for deriving K(ik) and K(iv) as the inhibition constants, respectively; their difference was quantified as the ratio of the larger one to the smaller one. Such ratios below 2.0 suggested uncompetitive inhibitors, over 5.0 suggested noncompetitive or competitive inhibitors, and from 2.0 to 5.0 suggested mixed inhibitors. By the new decision system, (i) the simulation recovery of uncompetitive inhibitors under CVs of 2% or 5% was improved by four times, but that of competitive or noncompetitive inhibitors was improved slightly; (ii) the recovery of L-phenylalanine as an uncompetitive inhibitor of intestinal alkaline phosphatase reached 38%, while the putative decision system lost all; the recovery of xanthine as a competitive inhibitor of uricase was improved slightly. Therefore, the new decision system was better.

Glycoconjugated 1H-1,2,3-triazoles (GCTs) comprise a new class of glycosidase inhibitors that are under investigation as promising therapeutic agents for a variety of diseases, including type 2 diabetes mellitus. However, few kinetics studies have been performed to clarify the mode of inhibition of GCTs with their target glycosidases. Our group has previously shown that some methyl-β-D-ribofuranosyl-1H-1,2,3-triazoles that inhibit baker's yeast maltase were also able to reduce post-prandial glucose levels in normal rats. We hypothesized that this hypoglycemiant activity was attributable to inhibition of mammalian α-glucosidases involved in sugar metabolism, such as pancreatic α-amylase. Hence, the aim of this work was to test a series of 26 GCTs on porcine pancreatic α-amylase (PPA) and to characterize their inhibition mechanisms. Six GCTs, all ribofuranosyl-derived GCTs, significantly inhibited PPA, with IC(50) values in the middle to high micromolar range. Our results also demonstrated that ribofuranosyl-derived GCTs are reversible, noncompetitive inhibitors when using 2-chloro-4-nitrophenyl-α-D-maltotrioside as a substrate. E/ES affinity ratios (α) ranged from 0.3 to 1.1, with the majority of ribofuranosyl-derived GCTs preferentially forming stable ternary ESI complexes. Competition assays with acarbose showed that ribofuranosyl-derived GCTs bind to PPA in a mutually exclusive fashion. The data presented here show that pancreatic α-amylase is one of the possible molecular targets in the pharmacological activity of ribofuranosyl-derived GCTs. Our results also provide important mechanistic insight that can be of major help to develop this new class of synthetic small molecules into more potent compounds with anti-diabetic activity through rational drug design., (Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The two major endocannabinoid transmitters, anandamide (AEA) and 2-arachidonoylglycerol (2-AG), are degraded by distinct enzymes in the nervous system, fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), respectively. FAAH and MAGL inhibitors cause elevations in brain AEA and 2-AG levels, respectively, and reduce pain, anxiety, and depression in rodents without causing the full spectrum of psychotropic behavioral effects observed with direct cannabinoid receptor-1 (CB1) agonists. These findings have inspired the development of several classes of endocannabinoid hydrolase inhibitors, most of which have been optimized to show specificity for either FAAH or MAGL or, in certain cases, equipotent activity for both enzymes. Here, we investigate an unusual class of O-hydroxyacetamide carbamate inhibitors and find that individual compounds from this class can serve as selective FAAH or dual FAAH/MAGL inhibitors in vivo across a dose range (0.125-12.5 mg kg(-1)) suitable for behavioral studies. Competitive and click chemistry activity-based protein profiling confirmed that the O-hydroxyacetamide carbamate SA-57 is remarkably selective for FAAH and MAGL in vivo, targeting only one other enzyme in brain, the additional 2-AG hydrolase ABHD6. These data designate O-hydroxyacetamide carbamates as a versatile chemotype for creating endocannabinoid hydrolase inhibitors that display excellent in vivo activity and tunable selectivity for FAAH-anandamide versus MAGL (and ABHD6)-2-AG pathways.

Background: The phylogenetic profile is widely used to characterize functional linkage and conservation between proteins without amino acid sequence similarity. To survey the conservative regulatory properties of rate-limiting enzymes (RLEs) in metabolic inhibitory network across different species, we define the enzyme inhibiting pair as: where the first enzyme in a pair is the inhibitor provider and the second is the target of the inhibitor. Phylogenetic profiles of enzymes in the inhibiting pairs are further generated to measure the functional linkage of these enzymes during evolutionary history., Results: We find that the RLEs generate, on average, over half of all in vivo inhibitors in each surveyed model organism. And these inhibitors inhibit on average over 85% targets in metabolic inhibitory network and cover the majority of targets of cross-pathway inhibiting relations. Furthermore, we demonstrate that the phylogenetic profiles of the enzymes in inhibiting pairs in which at least one enzyme is rate-limiting often show higher similarities than those in common inhibiting enzyme pairs. In addition, RLEs, compared to common metabolic enzymes, often tend to produce ADP instead of AMP in conservative inhibitory networks., Conclusions: Combined with the conservative roles of RLEs in their efficiency in sensing metabolic signals and transmitting regulatory signals to the rest of the metabolic system, the RLEs may be important molecules in balancing energy homeostasis via maintaining the ratio of ATP to ADP in living cells. Furthermore, our results indicate that similarities of phylogenetic profiles of enzymes in the inhibiting enzyme pairs are not only correlated with enzyme topological importance, but also related with roles of the enzymes in metabolic inhibitory network.

The central idea of supervised classification in chemoinformatics is to design a classifying algorithm that accurately assigns a new molecule to one of a set of predefined classes. Tipping has devised a classifying scheme, the Relevance Vector Machine (RVM), which is in terms of sparsity equivalent to the Support Vector Machine (SVM). However, unlike SVM classifiers, the RVM classifiers are probabilistic in nature, which is crucial in the field of decision making and risk taking. In this work, we investigate the performance of RVM binary classifiers on classifying a subset of the MDDR data set, a standard molecular benchmark data set, into active and inactive compounds. Additionally, we present results that compare the performance of SVM and RVM binary classifiers.

Adverse side effects of drug-drug interactions induced by human cytochrome P450 (CYP) inhibition is an important consideration, especially, during the research phase of drug discovery. It is highly desirable to develop computational models that can predict the inhibitive effect of a compound against a specific CYP isoform. In this study, inhibitor predicting models were developed for five major CYP isoforms, namely 1A2, 2C9, 2C19, 2D6, and 3A4, using a combined classifier algorithm on a large data set containing more than 24,700 unique compounds, extracted from PubChem. The combined classifiers algorithm is an ensemble of different independent machine learning classifiers including support vector machine, C4.5 decision tree, k-nearest neighbor, and naïve Bayes, fused by a back-propagation artificial neural network (BP-ANN). All developed models were validated by 5-fold cross-validation and a diverse validation set composed of about 9000 diverse unique compounds. The range of the area under the receiver operating characteristic curve (AUC) for the validation sets was 0.764 to 0.815 for CYP1A2, 0.837 to 0.861 for CYP2C9, 0.793 to 0.842 for CYP2C19, 0.839 to 0.886 for CYP2D6, and 0.754 to 0.790 for CYP3A4, respectively, using the new developed combined classifiers. The overall performance of the combined classifiers fused by BP-ANN was superior to that of three classic fusion techniques (Mean, Maximum, and Multiply). The chemical spaces of data sets were explored by multidimensional scaling plots, and the use of applicability domain improved the prediction accuracies of models. In addition, some representative substructure fragments differentiating CYP inhibitors and noninhibitors were characterized by the substructure fragment analysis. These classification models are applicable for virtual screening of the five major CYP isoforms inhibitors or can be used as simple filters of potential chemicals in drug discovery.

Background: In an effort to discover new drugs to treat tuberculosis (TB) we chose alanine racemase as the target of our drug discovery efforts. In Mycobacterium tuberculosis, the causative agent of TB, alanine racemase plays an essential role in cell wall synthesis as it racemizes L-alanine into D-alanine, a key building block in the biosynthesis of peptidoglycan. Good antimicrobial effects have been achieved by inhibition of this enzyme with suicide substrates, but the clinical utility of this class of inhibitors is limited due to their lack of target specificity and toxicity. Therefore, inhibitors that are not substrate analogs and that act through different mechanisms of enzyme inhibition are necessary for therapeutic development for this drug target., Methodology/principal Findings: To obtain non-substrate alanine racemase inhibitors, we developed a high-throughput screening platform and screened 53,000 small molecule compounds for enzyme-specific inhibitors. We examined the 'hits' for structural novelty, antimicrobial activity against M. tuberculosis, general cellular cytotoxicity, and mechanism of enzyme inhibition. We identified seventeen novel non-substrate alanine racemase inhibitors that are structurally different than any currently known enzyme inhibitors. Seven of these are active against M. tuberculosis and minimally cytotoxic against mammalian cells., Conclusions/significance: This study highlights the feasibility of obtaining novel alanine racemase inhibitor lead compounds by high-throughput screening for development of new anti-TB agents.

The aryl hydrocarbon (dioxin) receptor (AhR) is a ligand-dependent transcription factor that produces a wide range of biological and toxic effects in many species and tissues. Whereas the best-characterized high-affinity ligands include structurally related halogenated aromatic hydrocarbons (HAHs) and polycyclic aromatic hydrocarbons (PAHs), the AhR is promiscuous and can also be activated by structurally diverse exogenous and endogenous chemicals. However, little is known about how these diverse ligands actually bind to and activate the AhR. Utilizing AhR ligand binding, DNA binding, and reporter gene expression assays, we have identified a novel ligand-selective antagonist (CH223191) that preferentially inhibits the ability of some classes of AhR agonists (2,3,7,8-tetrachlorodibenzo-p-dioxin and related HAHs), but not others (PAHs, flavonoids, or indirubin), to bind to and/or activate the AhR and AhR signal transduction. HAH-specific antagonism of AhR-dependent reporter gene expression by CH223191 was observed with mouse, rat, human, and guinea pig cell lines. Ligand- and species-selective antagonism was also observed with the AhR antagonists 3'-methoxy-4'-nitroflavone and 6,2',4',-trimethoxyflavone. Our results suggest that the differences in the binding by various ligands to the AhR contribute to the observed structural diversity of AhR ligands and could contribute in ligand-specific variation in AhR functionality and the toxic and biological effects of various classes of AhR agonists.

Background: Herbicides that affect lipid metabolism have been used commercially for many years. Here, napropamide, diphenamid, dimethachlor and cafenstrole are compared; these have all been classified by the Herbicide Resistance Action Committee (HRAC) as K(3) herbicides and inhibitors of cell division and/or synthesis of very-long-chain fatty acids (VLCFAs). In addition, spiro-decanedione A and pinoxaden dione are compared as inhibitors of lipid synthesis through inhibition of acetyl-CoA carboxylase (ACCase)., Results: Whereas the chloracetamide dimethachlor and the carboxyamide cafenstrole potently inhibited VLCFA synthesis in both barley and cucumber, the acetamides napropamide and diphenamid which are also classified as K(3) herbicides and likewise the unclassified herbicide cinmethylin did not. The graminicide pinoxaden dione inhibited de novo fatty acid synthesis in barley, but not in cucumber, and correspondingly inhibited the plastid form of maize ACCase much more than the cytosolic form (IC(50) values of 0.1 and 17 microM). By contrast, spiro-decanedione A exhibited herbicidal effects not only on grasses but also on broad leaves, strongly inhibited maize cytosolic ACCase and inhibited synthesis of VLCFAs in cucumber., Conclusions: The acetamides napropamide and diphenamid, which do not inhibit VLCFA synthesis, should be classified separately from K(3) herbicides that do. Pinoxaden dione and spiro-decanedione A represent new classes of chemicals acting on plant lipid synthesis., (Copyright (c) 2010 Society of Chemical Industry.)

Macrophage migration inhibitory factor (MIF) is a major mediator of innate immunity and inflammation and presents a potential therapeutic target for various inflammatory, infectious, and autoimmune diseases, including cancer. Although a number of inhibitors have been identified and designed based on the modification of known nonphysiological substrates, the lack of a suitable high-throughput assay has hindered the screening of chemical libraries and the discovery of more diverse inhibitors. Herein the authors report the development and optimization of a robust high-throughput kinetic-based activity assay for the identification of new MIF inhibitors. Using this assay, they screened 80,000 small molecules and identified and validated 13 novel inhibitors of MIF catalytic activity. These small molecules demonstrated inhibition constant (K(i,app)) values ranging from 0.5 to 13 microM.

New classes of de novo designed renin inhibitors are reported. Some of these compounds display excellent in vitro and in vivo activities toward human renin in a TGR model. The synthesis of these new types of mono- and bicyclic scaffolds are reported, and properties of selected compounds discussed.

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.

Evasion of apoptosis can be caused by epigenetic silencing of caspase-8, a key component of the extrinsic apoptosis pathway. Loss of caspase-8 correlates with poor prognosis in medulloblastoma, which highlights the relevance of strategies to upregulate caspase-8 to break apoptosis resistance. Here, we develop a new combinatorial approach, that is treatment using histone deacetylase inhibitors (HDACI) together with interferon (IFN)-gamma, to restore caspase-8 expression and to overcome resistance to the death-receptor ligand TNF-related apoptosis-inducing ligand (TRAIL) in medulloblastoma in vitro and in vivo. HDACI, for example, valproic acid (VA), suberoylanilide hydroxamic acid (SAHA) and MS-275, cooperate with IFN-gamma to upregulate caspase-8 in cancer cells lacking caspase-8, thereby restoring sensitivity to TRAIL-induced apoptosis. Molecular studies show that VA promotes histone acetylation and acts in concert with IFN-gamma to stimulate caspase-8 promoter activity. The resulting increase in caspase-8 mRNA and protein expression leads to enhanced TRAIL-induced activation of caspase-8 at the death-inducing signaling complex, mitochondrial outer-membrane permeabilization and caspase-dependent cell death. Intriguingly, pharmacological or genetic inhibition of caspase-8 also abolishes the VA/IFN-gamma-mediated sensitization for TRAIL-induced apoptosis. It is important to note that VA and IFN-gamma restore caspase-8 expression and sensitivity to TRAIL in primary medulloblastoma samples and significantly potentiate TRAIL-mediated suppression of medulloblastoma growth in vivo. These findings provide the rationale for further (pre)clinical evaluation of VA and IFN-gamma to restore caspase-8 expression and apoptosis sensitivity in cancers with caspase-8 silencing and open new perspectives to overcome TRAIL resistance.

The role of histone deacetylases (HDAC) and the potential of these enzymes as therapeutic targets for cancer, neurodegenerative diseases and a number of other disorders is an area of rapidly expanding investigation. There are 18 HDACs in humans. These enzymes are not redundant in function. Eleven of the HDACs are zinc dependent, classified on the basis of homology to yeast HDACs: Class I includes HDACs 1, 2, 3, and 8; Class IIA includes HDACs 4, 5, 7, and 9; Class IIB, HDACs 6 and 10; and Class IV, HDAC 11. Class III HDACs, sirtuins 1-7, have an absolute requirement for NAD(+), are not zinc dependent and generally not inhibited by compounds that inhibit zinc dependent deacetylases. In addition to histones, HDACs have many nonhistone protein substrates which have a role in regulation of gene expression, cell proliferation, cell migration, cell death, and angiogenesis. HDAC inhibitors (HDACi) have been discovered of different chemical structure. HDACi cause accumulation of acetylated forms of proteins which can alter their structure and function. HDACi can induce different phenotypes in various transformed cells, including growth arrest, apoptosis, reactive oxygen species facilitated cell death and mitotic cell death. Normal cells are relatively resistant to HDACi induced cell death. Several HDACi are in various stages of development, including clinical trials as monotherapy and in combination with other anti-cancer drugs and radiation. The first HDACi approved by the FDA for cancer therapy is suberoylanilide hydroxamic acid (SAHA, vorinostat, Zolinza), approved for treatment of cutaneous T-cell lymphoma., (2009 Wiley-Liss, Inc.)

Background and Purpose: Monoacylglycerol lipase (MGL) is a presynaptic serine hydrolase that inactivates the endocannabinoid neurotransmitter, 2-arachidonoyl-sn-glycerol. Recent studies suggest that cysteine residues proximal to the enzyme active site are important for MGL function. In the present study, we characterize the role of cysteines in MGL function and identify a series of cysteine-reactive agents that inhibit MGL activity with nanomolar potencies by interacting with cysteine residue 208., Experimental Approach: A series of cysteine traps were screened for the ability to inhibit MGL in vitro. Rapid dilution assays were performed to determine reversibility of inhibition. Molecular modelling and site-directed mutagenesis were utilized to identify cysteine residues targeted by the inhibitors., Key Results: The screening revealed that 2-octyl-4-isothiazolin-3-one (octhilinone) inhibited purified rat recombinant MGL (IC(50)= 88 +/- 12 nM) through a partially reversible mechanism. Initial structure-activity relationship studies showed that substitution of the n-octyl group of octhilinone with a more lipophilic oleoyl group increased inhibitor potency (IC(50)= 43 +/- 8 nM), while substitution with a methyl group produced the opposite effect (IC(50)= 239 +/- 68 nM). The inhibitory potency of octhilinone was selectively decreased by mutating cysteine 208 in MGL to glycine (IC(50); wild-type, 151 +/- 17 nM; C208G, 722 +/- 74 nM), but not by mutation of other cysteine residues (C32, C55, C201, C208 and C242)., Conclusions and Implications: The results indicated that cysteine 208 plays an important role in MGL function and identified a novel class of isothiazolinone-based MGL inhibitors with nanomolar potency in vitro.

Although electrostatic interactions contribute only a part of the interaction energies between macromolecules, unlike dispersion forces they are highly directional and therefore dominate the nature of molecular packing in crystals and in biological complexes and contribute significantly to differences in inhibition strength among related enzyme inhibitors. In the reported study, a wide range of complexes of influenza neuraminidases with inhibitor molecules (sialic acid derivatives and others) have been analyzed using charge densities from a transferable aspherical-atom data bank. The strongest interactions of the residues are with the acidic group at the C2 position of the inhibitor ( approximately -300 kJ mol(-1) for -COO(-) in non-aromatic inhibitors, approximately -120-210 kJ mol(-1) for -COO(-) in aromatic inhibitors and approximately -450 kJ mol(-1) for -PO(3)(2-)) and with the amino and guanidine groups at C4 ( approximately -250 kJ mol(-1)). Other groups contribute less than approximately 100 kJ mol(-1). Residues Glu119, Asp151, Glu227, Glu276 and Arg371 show the largest variation in electrostatic energies of interaction with different groups of inhibitors, which points to their important role in the inhibitor recognition. The Arg292-->Lys mutation reduces the electrostatic interactions of the enzyme with the acidic group at C2 for all inhibitors that have been studied (SIA, DAN, 4AM, ZMR, G20, G28, G39 and BCZ), but enhances the interactions with the glycerol group at C6 for inhibitors that contain it. This is in agreement with the lower level of resistance of the mutated virus to glycerol-containing inhibitors compared with the more hydrophobic derivatives.

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.

The cytochrome P450 (P450) superfamily plays an important role in the metabolism of drug compounds, and it is therefore highly desirable to have models that can predict whether a compound interacts with a specific isoform of the P450s. In this work, we provide in silico models for classification of CYP1A2 inhibitors and noninhibitors. Training and test sets consisted of approximately 400 and 7000 compounds, respectively. Various machine learning techniques, such as binary quantitative structure activity relationship, support vector machine (SVM), random forest, kappa nearest neighbor (kNN), and decision tree methods were used to develop in silico models, based on Volsurf and Molecular Operating Environment descriptors. The best models were obtained using the SVM, random forest, and kNN methods in combination with the BestFirst variable selection method, resulting in models with 73 to 76% of accuracy on the test set prediction (Matthews correlation coefficients of 0.51 and 0.52). Finally, a decision tree model based on Lipinski's Rule-of-Five descriptors was also developed. This model predicts 67% of the compounds correctly and gives a simple and interesting insight into the issue of classification. All of the models developed in this work are fast and precise enough to be applicable for virtual screening of CYP1A2 inhibitors or noninhibitors or can be used as simple filters in the drug discovery process.

Guidance from the Food and Drug Administration on drug interaction studies does not include a specific section on contributions of metabolites to observed inhibitory drug-drug interactions, and the quantitative role of drug metabolites in inhibitory drug-drug interactions is not presently known. The current work was undertaken to evaluate what fraction of inhibitors of common drug-metabolizing enzymes [cytochrome P450 (P450) 1A2, 2E1, 2D6, 2C9, 2C19, 2C8, 2B6, and 3A4] have circulating metabolites that may contribute to observed in vivo interactions. A literature analysis was conducted using the Metabolism and Transport Drug Interaction Database to identify all precipitants (i.e., inhibitors) that cause more than a 20% increase in the area under the plasma concentration-time curve (AUC) of marker substrates. The database, PubMed, and product labels were then used to determine whether circulating metabolites were present after administration of these inhibitors. Of the total of 129 inhibitors identified, 106 were confirmed to have metabolites that circulate in plasma. An additional 14 inhibitors were identified that are extensively metabolized but whose metabolites either have not been identified or have not been investigated. Hence, only 7% of the inhibitors did not have circulating metabolites. Of the 21 potent inhibitors (>or=5-fold increase in AUC) currently known, 17 had circulating metabolites, and the remaining four were all extensively metabolized. On the basis of available in vitro data, 24 of all of the inhibitors were mechanism-based inactivators of P450 enzymes, while 105 were characterized as reversible inhibitors. In vitro evaluation of inhibition potential was conducted for only 32% of the circulating metabolites of the inhibitors. In conclusion, circulating metabolites are often present with inhibitors of P450 enzymes, suggesting a need for increased efforts to characterize the inhibitory potency of metabolites of candidate drugs and for newer models for in vitro to in vivo extrapolations.

The amount of high-throughput screening (HTS) data readily available has significantly increased because of the PubChem project (http://pubchem.ncbi.nlm.nih.gov/). There is considerable opportunity for data mining of small molecules for a variety of biological systems using cheminformatic tools and the resources available through PubChem. In this work, we trained a support vector machine (SVM) classifier using the Signature molecular descriptor on factor XIa inhibitor HTS data. The optimal number of Signatures was selected by implementing a feature selection algorithm of highly correlated clusters. Our method included an improvement that allowed clusters to work together for accuracy improvement, where previous methods have scored clusters on an individual basis. The resulting model had a 10-fold cross-validation accuracy of 89%, and additional validation was provided by two independent test sets. We applied the SVM to rapidly predict activity for approximately 12 million compounds also deposited in PubChem. Confidence in these predictions was assessed by considering the number of Signatures within the training set range for a given compound, defined as the overlap metric. To further evaluate compounds identified as active by the SVM, docking studies were performed using AutoDock. A focused database of compounds predicted to be active was obtained with several of the compounds appreciably dissimilar to those used in training the SVM. This focused database is suitable for further study. The data mining technique presented here is not specific to factor XIa inhibitors, and could be applied to other bioassays in PubChem where one is looking to expand the search for small molecules as chemical probes.

We report the discovery and optimization of a potent inhibitor against the papain-like protease (PLpro) from the coronavirus that causes severe acute respiratory syndrome (SARS-CoV). This unique protease is not only responsible for processing the viral polyprotein into its functional units but is also capable of cleaving ubiquitin and ISG15 conjugates and plays a significant role in helping SARS-CoV evade the human immune system. We screened a structurally diverse library of 50,080 compounds for inhibitors of PLpro and discovered a noncovalent lead inhibitor with an IC(50) value of 20 microM, which was improved to 600 nM via synthetic optimization. The resulting compound, GRL0617, inhibited SARS-CoV viral replication in Vero E6 cells with an EC(50) of 15 microM and had no associated cytotoxicity. The X-ray structure of PLpro in complex with GRL0617 indicates that the compound has a unique mode of inhibition whereby it binds within the S4-S3 subsites of the enzyme and induces a loop closure that shuts down catalysis at the active site. These findings provide proof-of-principle that PLpro is a viable target for development of antivirals directed against SARS-CoV, and that potent noncovalent cysteine protease inhibitors can be developed with specificity directed toward pathogenic deubiquitinating enzymes without inhibiting host DUBs.

A facile synthesis of oligosaccharide-thiazoline derivatives of N-glycans as a novel class of inhibitors for endo-beta-N-acetylglucosaminidases was described. It was found that the external sugar residues on the N-glycan core could enhance the inhibitory potency. While the Manbeta1,4GlcNAc- and Man3GlcNAc-thiazolines were only moderate inhibitors, the large Man9GlcNAc-thiazoline demonstrated potent inhibitory activity, with an IC(50) of 0.22 and 0.42 microM against the Arthrobacter enzyme (Endo-A) and the human endo-beta-N-acetylglycosaminidase (hENGase), respectively. It was also observed that the oligosaccharide thiazolines could differentially inhibit endo-beta-N-acetylglucosaminidases from different sources. These oligosaccharide thiazolines represent the first class of endo-beta-N-acetylglucosaminidase inhibitors.

Inhibition of cholesterol biosynthesis offers the opportunity for treatment of cardiovascular diseases. Numerous enzymes are involved in the post-squalene part of this biosynthesis, and selective inhibitors for almost all of the enzymes involved there have been described in literature. The only exception is the enzyme lathosterol oxidase (EC 1.14.21.6), for which up to now no selective inhibitor has been found. Up to date only triarimol has been reported as a weak inhibitor. In this paper we report on lathosterol side chain amides as a new class of selective lathosterol oxidase inhibitors. To study the influence of different sterol amides on inhibition of this enzyme, numerous compounds were prepared and the sterol patterns resulting from incubation of HL 60 cells with these enzyme inhibitors were monitored in a whole cell screening assay by means of GC/MS analysis. Small alkyl residues at the amide nitrogen (hydrogen and methyl) lead to an inhibition of the enzyme Delta24-reductase, the N-ethyl and N-propyl derivatives show a dual action, inhibiting both Delta24-reductase and lathosterol oxidase. Lathosterol-derived amides with larger substituents (butyl, isobutyl, tert-butyl, pentyl) at the amide nitrogen were found to be selective inhibitors of lathosterol oxidase. The corresponding 3beta-acetoxy derivatives showed comparable activities and can be considered as prodrugs, since they are transformed into the 3beta-hydroxy derivatives under the test conditions, as proven by HPLC analysis.

The topopyrones represent a new class of highly cytotoxic topoisomerase I poisons. Efficient total syntheses of all four naturally occurring members of this class have been accomplished. Key elements of the syntheses include Diels-Alder reactions employing two novel dienes and a titanium-mediated ortho-directed Friedel-Crafts acylation. Additionally, the syntheses of two chlorinated analogues accessible from an advanced intermediate are described.

The family of the endogenous agonists of the cannabinoid receptors--i.e., the endocannabinoids--includes several polyunsaturated fatty acid amides and esters. Arachidonoylethanolamide (anandamide, AEA) and 2-arachidon-oylglycerol (2-AG) are, respectively, the leads of these chemical families. So far, two enzymes responsible for the metabolism of AEA and 2-AG have been described: fatty acid amide hydrolase (FAAH) which hydrolyzes AEA and in some cells 2-AG, and Monoacylglycerol Lipase (MAGL) which hydrolyzes 2-AG. In spite of the early characterisation of MAGL and the nearly simultaneous clonings of the two enzymes, most of the efforts were dedicated to the study of FAAH and consequentially, the range of FAAH inhibitors available nowadays exceeds the number of compounds active upon MAGL. FAAH inhibitors can be divided in two major groups, the first one includes the inhibitors inspired by the chemical structures of FAAH substrates, which carry an arachidonoyl-, oleoyl- or palmitoyl-carbon chain that mimic the fatty acid chains of anandamide, oleamide and palmitoylethanolamide. The second group involves compounds that do not share similarities with the endocannabinoids, such as the carbamates, oxazolopyridins, 2-thioxoimidazolidin-4-ones, imidazolidine-2,4-diones and the non-steroidal anti-inflammatory drugs. However, the family of MAGL inhibitors contains few members and most of them exhibit a lack of selectivity. The purpose of this review is to give an overview of the families of synthetic inhibitors of FAAH and MAGL. The synthetic pathways, the chemical features, potencies, selectivities and modes of inhibition are listed and discussed in order to facilitate their comparison.

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.

A new series of non-peptidic, mono-acid protein tyrosine phosphatase 1B (PTP1B) inhibitors has been identified by structure-based design. Compounds with 2-(indol-3-yl)- and 2-phenyl-3,3,3-trifluoro-2-hydroxypropionic acid core units targeted at the enzyme's primary site and a hydrophobic chlorophenylthiazole extension in its 2 degrees site exhibit 3-60microM IC(50)s for PTP1B inhibition in an Sf9 cell-based assay.