Discovery of novel mesangial cell proliferation inhibitors using a three-dimensional database searching method

Ibrolipim attenuates early-stage nephropathy in diet-induced diabetic minipigs: Focus on oxidative stress and fibrogenesis

It is well-recognized that hyperlipidemia and lipid peroxidation contribute to the progression of diabetic nephropathy (DN), which is associated with oxidative stress (OS) and fibrotic lesions. Ibrolipim, a specific lipoprotein lipase activator, has been proved to reduce hyperglycemia and hyperlipidemia, suppress renal lipid deposition, and also protect renal damage. However, the underlying mechanisms of its renoprotective effect are not clearly elaborated. Herein, the present study was to identify whether the putative mechanism of Ibrolipim was related to OS and fibrogenesis in diabetic minipigs fed by high-sucrose and high-fat diet (HSFD) with or without Ibrolipim for 5 months. Compared with the normal control diet, nutrient stress induced by HSFD caused moderate glomerulosclerosis and tubulointerstitial fibrosis, and promoted renal ultrastructural and functional abnormalities. These abnormalities were correlated with renal OS and fibrogenesis characterized by the increased levels of reactive oxygen species (ROS), malondialdehyde, hydroxyproline, collagen type Ⅳ alpha 1 and fibronectin, and decreased contents of reduced glutathione and total antioxidant capacity in kidneys. Ibrolipim significantly ameliorated these abnormalities in HSFD-fed minipigs. In addition, Ibrolipim diminished HSFD-induced nicotinamide-adenine dinucleotide phosphate oxidase-4 activation to reduce ROS production, and enhanced the expression and activity of antioxidant enzymes (i.e. superoxide dismutase 1, catalase and glutathione peroxidase 1) to increase ROS elimination, resulting in obvious suppression of renal OS. Meanwhile, Ibrolipim not only inhibited the upregulation of transforming growth factor-β1 but also partially reversed the downregulation of matrix metalloproteinase 2, and then prevented extracellular matrix (ECM) accumulation. Taken together, Ibrolipim exhibits anti-oxidative and anti-fibrotic effects via modulating the rebalance of renal ROS and ECM metabolism, and ultimately attenuates the progression of nephropathy in diet-induced diabetic minipigs.

In silico design and analysis of a new hyperglycosylated analog of erythropoietin to improve drug efficacy

Background:

The enhancement of glycosylation by applying glycoengineering approaches has become widely used to boost properties for protein therapeutics. The objective of this work was to engineer a new hyperglycosylated analog of erythropoietin (EPO) with appropriately targeted N-linked carbohydrates through bioinformatics tools.

Materials and Methods:

The EPO protein sequence was retrieved from NCBI protein sequence database. Prediction of N-glycosylation sites for the target protein was done using the prediction server, NetNGlyc. The three-dimensional model of glycoengineered EPO (named as kypoetin) was constructed using the homology modeling program. Ramchandran plot obtained from PROCHECK server was used to check stereochemical property. Meanwhile, 3D model of kypoetin with attached N-carbohydrates was built up using the GlyProt server.

Results:

In the new modified analog, three additional N-glycosylation sites at amino-acid positions 30, 34 and 86 were inserted. Ramchandran plot analysis showed 81.6% of the residues in the most favored region, 15.6% in the additional allowed, 1.4% in the generously allowed regions and 1.4% in the disallowed region. 3D structural modeling showed that attached carbohydrates were on the proper spatial position. The whole solvent accessible surface areas of kypoetin (15132.69) were higher than EPO (9938.62).

Conclusions:

Totally, various model evaluation methods indicated that the glycoengineered version of EPO had considerably good geometry and acceptable profiles for clinical studies and could be considered as the effective drug.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods and applications

While the concept of "single component-single target" in drug discovery seems to have come to an end, "Multi-component-multi-target" is considered to be another promising way out in this field. The Traditional Chinese Medicine (TCM), which has thousands of years' clinical application among China and other Asian countries, is the pioneer of the "Multi-component-multi-target" and network pharmacology. Hundreds of different components in a TCM prescription can cure the diseases or relieve the patients by modulating the network of potential therapeutic targets. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. Without thorough investigation of its potential targets and side effects, TCM is not able to generate large-scale medicinal benefits, especially in the days when scientific reductionism and quantification are dominant. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This article firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in details along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Novel 5' untranslated region directed blockers of iron-regulatory protein-1 dependent amyloid precursor protein translation: implications for down syndrome and Alzheimer's disease

We reported that iron influx drives the translational expression of the neuronal amyloid precursor protein (APP), which has a role in iron efflux. This is via a classic release of repressor interaction of APP mRNA with iron-regulatory protein-1 (IRP1) whereas IRP2 controls the mRNAs encoding the L- and H-subunits of the iron storage protein, ferritin. Here, we identified thirteen potent APP translation blockers that acted selectively towards the uniquely configured iron-responsive element (IRE) RNA stem loop in the 5' untranslated region (UTR) of APP mRNA. These agents were 10-fold less inhibitory of 5'UTR sequences of the related prion protein (PrP) mRNA. Western blotting confirmed that the 'ninth' small molecule in the series selectively reduced neural APP production in SH-SY5Y cells at picomolar concentrations without affecting viability or the expression of α-synuclein and ferritin. APP blocker-9 (JTR-009), a benzimidazole, reduced the production of toxic Aβ in SH-SY5Y neuronal cells to a greater extent than other well tolerated APP 5'UTR-directed translation blockers, including posiphen, that were shown to limit amyloid burden in mouse models of Alzheimer's disease (AD). RNA binding assays demonstrated that JTR-009 operated by preventing IRP1 from binding to the IRE in APP mRNA, while maintaining IRP1 interaction with the H-ferritin IRE RNA stem loop. Thus, JTR-009 constitutively repressed translation driven by APP 5'UTR sequences. Calcein staining showed that JTR-009 did not indirectly change iron uptake in neuronal cells suggesting a direct interaction with the APP 5'UTR. These studies provide key data to develop small molecules that selectively reduce neural APP and Aβ production at 10-fold lower concentrations than related previously characterized translation blockers. Our data evidenced a novel therapeutic strategy of potential impact for people with trisomy of the APP gene on chromosome 21, which is a phenotype long associated with Down syndrome (DS) that can also cause familial Alzheimer's disease.

Exploring the ligand-protein networks in traditional chinese medicine: current databases, methods, and applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the “multicomponent-multitarget” and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

Preventive effect of Ibrolipim on suppressing lipid accumulation and increasing lipoprotein lipase in the kidneys of diet-induced diabetic minipigs

Background

The role of renal lipoprotein lipase (LPL) per se in kidney diseases is still controversial and obscure. The purpose of this study was to observe the preventive effects of Ibrolipim, a LPL activator, on lipid accumulation and LPL expression in the kidneys of minipigs fed a high-sucrose and high-fat diet (HSFD).

Methods

Male Chinese Bama minipigs were fed a control diet or HSFD with or without 0.1 g/kg/day Ibrolipim for 5 months. Body weight, plasma glucose, insulin, lipids, LPL activity, and urinary microalbumin were measured. Renal tissue was obtained for detecting LPL activity and contents of triglyceride and cholesterol, observing the renal lipid accumulation by Oil Red O staining, and examining the mRNA and protein expression of LPL by real time PCR, Western Blot and immunohistochemistry.

Results

Feeding HSFD to minipigs caused weight gain, hyperglycemia, hyperinsulinemia, hyperlipidemia and microalbuminuria. HSFD increased plasma LPL activity while it decreased the mRNA and protein expression and activity of LPL in the kidney. The increases in renal triglyceride and cholesterol contents were associated with the decrease in renal LPL activity of HSFD-fed minipigs. In contrast, supplementing Ibrolipim into HSFD lowered body weight, plasma glucose, insulin, triglyceride and urinary albumin concentrations while it increased plasma total cholesterol and HDL-C. Ibrolipim suppressed the renal accumulation of triglyceride and cholesterol, and stimulated the diet-induced down-regulation of LPL expression and activity in the kidney.

Conclusions

Ibrolipim exerts renoprotective and hypolipidemic effects via the increase in renal LPL activity and expression, and thus the increased expression and activity of renal LPL play a vital role in suppressing renal lipid accumulation and ameliorating proteinuria in diet-induced diabetic minipigs.

Design, synthesis, and cytoprotective effect of 2-aminothiazole analogues as potent poly(ADP-ribose) polymerase-1 inhibitors

A series of novel poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors were designed within 2-aminothiazole analogues (4-10) based on a constructed three-dimensional pharmacophore model. After synthesis, the inhibitory effect on PARP-1 activity and the cytoprotective action of these compounds were tested and evaluated. Among them, compounds 4-6 and 10 appeared to be potent PARP-1 inhibitors with IC(50) values less than 1 microM, which had been perfectly predicted by pharmacophore model. These compounds proved to be highly potent against cell injury induced by H(2)O(2) and oxygen-glucose deprivation (OGD) in PC12 cells. These novel 2-aminothiazole analogues are potentially applicable as neuroprotective agents for the treatment of neurological diseases.

In silico pharmacology for drug discovery: applications to targets and beyond

Computational (in silico) methods have been developed and widely applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, similarity searching, pharmacophores, homology models and other molecular modeling, machine learning, data mining, network analysis tools and data analysis tools that use a computer. Such methods have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The first part of this review discussed the methods that have been used for virtual ligand and target-based screening and profiling to predict biological activity. The aim of this second part of the review is to illustrate some of the varied applications of in silico methods for pharmacology in terms of the targets addressed. We will also discuss some of the advantages and disadvantages of in silico methods with respect to in vitro and in vivo methods for pharmacology research. Our conclusion is that the in silico pharmacology paradigm is ongoing and presents a rich array of opportunities that will assist in expediating the discovery of new targets, and ultimately lead to compounds with predicted biological activity for these novel targets.

In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling

Pharmacology over the past 100 years has had a rich tradition of scientists with the ability to form qualitative or semi-quantitative relations between molecular structure and activity in cerebro. To test these hypotheses they have consistently used traditional pharmacology tools such as in vivo and in vitro models. Increasingly over the last decade however we have seen that computational (in silico) methods have been developed and applied to pharmacology hypothesis development and testing. These in silico methods include databases, quantitative structure-activity relationships, pharmacophores, homology models and other molecular modeling approaches, machine learning, data mining, network analysis tools and data analysis tools that use a computer. In silico methods are primarily used alongside the generation of in vitro data both to create the model and to test it. Such models have seen frequent use in the discovery and optimization of novel molecules with affinity to a target, the clarification of absorption, distribution, metabolism, excretion and toxicity properties as well as physicochemical characterization. The aim of this review is to illustrate some of the in silico methods for pharmacology that are used in drug discovery. Further applications of these methods to specific targets and their limitations will be discussed in the second accompanying part of this review.

Advances in the synthesis and recent therapeutic applications of 1,2,4-thiadiazole heterocycles

Chemical properties of 1,2,4-thiadiazole have been reviewed in the last few years. However, the usefulness of 1,2,4-thiadiazole as a privileged system in medicinal chemistry has prompted the advances on the therapeutic potential of this system. This review provides a brief summary of the medicinal chemistry of 1,2,4-thiadiazole system and highlights some examples of 1,2,4-thiadiazole-containing drug substances in the current literature. A survey of representative literature procedures for the preparation of 1,2,4-thiadiazole is presented in sections by generalized synthetic methods.

Ligand-Based Molecular Modeling Study on a Chemically Diverse Series of Cholecystokinin-B/Gastrin Receptor Antagonists: Generation of Predictive Model

Pharmacophore hypotheses were developed for six structurally diverse series of cholecystokinin-B/gastrin receptor (CCK-BR) antagonists. A training set consisting of 33 compounds was carefully selected. The activity spread of the training set molecules was from 0.1 to 2100 nM. The most predictive pharmacophore model (hypothesis 1), consisting of four features, namely, two hydrogen bond donors, one hydrophobic aliphatic, and one hydrophobic aromatic feature, had a correlation (r) of 0.884 and a root-mean-square deviation of 1.1526, and the cost difference between null cost and fixed cost was 81.5 bits. The model was validated on a test set consisting of six different series of 27 structurally diverse compounds and performed well in classifying active and inactive molecules correctly. This validation approach provides confidence in the utility of the predictive pharmacophore model developed in this work as a 3D query tool in the virtual screening of drug-like molecules to retrieve new chemical entities as potent CCK-BR antagonists. The model can also be used to predict the biological activities of compounds prior to their costly and time-consuming synthesis.

The use of a pharmacophore model for identification of novel ligands for the benzodiazepine binding site of the GABAA receptor

A Catalyst pharmacophore model has been developed for the benzodiazepine site within the GABA(A) receptor complex. The model is based on a pharmacophore model originally proposed by Cook and co-workers (Drug Des. Discovery 1995, 12, 193-248) and further developed by Kahnberg et al. (J. Med. Chem. 2002, 45, 4188-4201). The Catalyst pharmacophore model has been validated by using a series of flavonoids with varying affinities for the benzodiazepine receptor and has then been used as a search query in database searching with the aim of finding novel structures which have the possibility to be modified into novel lead compounds. Five of the hits from the database searching were purchased and their affinities for the benzodiazepine site of the GABA(A) receptor were determined. Two of the compounds displayed K(i) values below 10 microM. The substance showing highest potency in-vitro displayed an affinity of 121 nM making it an interesting compound for optimization. The false positive compounds (K(i) values >10 microM affinities) have been analysed in terms of conformational energy penalties and possibilities for hydrogen bond interactions. The analysis clearly demonstrates the need for post processing of Catalyst hits.

Chemical Function Based Pharmacophore Generation of Endothelin-A Selective Receptor Antagonists

Both quantitative and qualitative chemical function based pharmacophore models of endothelin-A (ET(A)) selective receptor antagonists were generated by using the two algorithms HypoGen and HipHop, respectively, which are implemented in the Catalyst molecular modeling software. The input for HypoGen is a training set of 18 ET(A) antagonists exhibiting IC(50) values ranging between 0.19 nM and 67 microM. The best output hypothesis consists of five features: two hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI) function. The highest scoring Hip Hop model consists of six features: three hydrophobic (HY), one ring aromatic (RA), one hydrogen bond acceptor (HBA), and one negative ionizable (NI). It is the result of an input of three highly active, selective, and structurally diverse ET(A) antagonists. The predictive power of the quantitative model could be approved by using a test set of 30 compounds, whose activity values spread over 6 orders of magnitude. The two pharmacophores were tested according to their ability to extract known endothelin antagonists from the 3D molecular structure database of Derwent's World Drug Index. Thereby the main part of selective ET(A) antagonistic entries was detected by the two hypotheses. Furthermore, the pharmacophores were used to screen the Maybridge database. Six compounds were chosen from the output hit lists for in vitro testing of their ability to displace endothelin-1 from its receptor. Two of these are new potential lead compounds because they are structurally novel and exhibit satisfactory activity in the binding assay.

Non-peptide angiotensin II receptor antagonists: chemical feature based pharmacophore identification

Chemical feature based pharmacophore models were elaborated for angiotensin II receptor subtype 1 (AT(1)) antagonists using both a quantitative and a qualitative approach (Catalyst HypoGen and HipHop algorithms, respectively). The training sets for quantitative model generation consisted of 25 selective AT(1) antagonists exhibiting IC(50) values ranging from 1.3 nM to 150 microM. Additionally, a qualitative pharmacophore hypothesis was derived from multiconformational structure models of the two highly active AT(1) antagonists 4u (IC(50) = 0.2 nM) and 3k (IC(50) = 0.7 nM). In the case of the quantitative model, the best pharmacophore hypothesis consisted of a five-features model (Hypo1: seven points, one hydrophobic aromatic, one hydrophobic aliphatic, a hydrogen bond acceptor, a negative ionizable function, and an aromatic plane function). The best qualitative model consisted of seven features (Hypo2: 11 points, two aromatic rings, two hydrogen bond acceptors, a negative ionizable function, and two hydrophobic functions). The obtained pharmacophore models were validated on a wide set of test molecules. They were shown to be able to identify a range of highly potent AT(1) antagonists, among those a number of recently launched drugs and some candidates presently undergoing clinical tests and/or development phases. The results of our study provide confidence for the utility of the selected chemical feature based pharmacophore models to retrieve structurally diverse compounds with desired biological activity by virtual screening.

Mesangial cell proliferation inhibitors for the treatment of proliferative glomerular disease

Mesangial cells (MC) serve a number of functions in the renal glomerular capillary including structural support of the capillary tuft, modulation of glomerular hemodynamics, and a phagocytic function allowing removal of macromolecules and immune complexes. The proliferation of MC is a prominent feature of glomerular disease including IgA nephropathy, membranoproliferative glomerulonephritis, lupus nephritis, and diabetic nephropathy. In experimental animal models of nephritis, MC proliferation frequently precedes and is linked to the increase of extracellular matrix in the mesangium and glomerulosclerosis. Reduction of MC proliferation in glomerular disease models by treatment with heparin, low-protein diet, or antibodies to platelet-derived growth factor (PDGF), have been shown to reduce extracellular matrix expansion and glomerulosclerotic changes. Therefore, MC proliferation inhibitors may offer therapeutic opportunities for the treatment of proliferative glomerular disease. It is also known that the MC proliferation is inhibited by many kinds of pharmacological drugs, for example, angiotensin converting enzyme (ACE) inhibitors, leukotriene D(4) (LTD(4)) antagonists, PDGF inhibitors, matrix metalloproteinases (MMP) inhibitors, 3-hydroxy-3 methyl glutaryl-coenzymeA (HMG-CoA) inhibitors, cyclin-dependent kinases (CDK) inhibitors, and others. This review summarizes the recently reported MC proliferation inhibitors with their pharmacological properties on the basis of their chemical structures.

Pharmacophore mapping of a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex dihydrofolate reductase

Pharmacophore hypotheses were developed for a series of 2,4-diamino-5-deazapteridine inhibitors of Mycobacterium avium complex (MAC) and human dihydrofolate reductase (hDHFR). Training sets consisting of 20 inhibitors were selected in each case on the basis of the information content of the structures and activity data as required by the HypoGen program in the Catalyst software. In the case of MAC DHFR inhibitors, the best pharmacophore in terms of statistics and predictive value consisted of four features: two hydrogen bond acceptors (HA), one hydrophobic (HY) feature, and one ring aromatic (RA) feature. The selected pharmacophore hypothesis yielded an rms deviation of 0.730 and a correlation coefficient of 0.967 with a cost difference (null cost minus total cost) of approximately 52. The pharmacophore was validated on a large set of test inhibitors. For the test series, a classification scheme was used to distinguish highly active from moderately active and inactive compounds on the basis of activity ranges. This classification scheme is more practical than actual estimated values because these values have no meaning for compounds yet to be tested except that they indicate whether the compounds will be active or inactive in a biological assay. For the training set, the success rate for predicting active and inactive compounds was 100%. For the test set, the success rate in predicting active compounds was greater than 92% while about 7% of the inactive compounds were predicted to be active. This successful prediction was further validated on three structurally diverse compounds active against MAC DHFR. Two compounds mapped well onto three of the four features of the pharmacophore. The third compound was mapped to all four features of the pharmacophore. This validation study provided confidence for the usefulness of the selected pharmacophore model to identify compounds with diverse structures from a database search. Comparison of pharmacophores for inhibitors of human and MAC DHFR is expected to reveal fundamental differences between these two pharmacophores that may be effectively exploited to identify and design compounds with high selectivity for MAC DHFR.