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AbstractAuranofin is an oral gold(I) compound, initially developed for the treatment of rheumatoid arthritis. Currently, Auranofin is under investigation for oncological application within a drug repurposing plan due to the relevant antineoplastic activity observed both in vitro and in vivo tumor models. In this review, we analysed studies in which Auranofin was used as a single drug or in combination with other molecules to enhance their anticancer activity or to overcome chemoresistance. The analysis of different targets/pathways affected by this drug in different cancer types has allowed us to highlight several interesting targets and effects of Auranofin besides the already well‐known inhibition of thioredoxin reductase. Among these targets, inhibitory‐κB kinase, deubiquitinates, protein kinase C iota have been frequently suggested. To rationalize the effects of Auranofin by a system biology‐like approach, we exploited transcriptomic data obtained from a wide range of cell models, extrapolating the data deposited in the Connectivity Maps website and we attempted to provide a general conclusion and discussed the major points that need further investigation. Show less

Advanced stages of cancer are highly associated with short overall survival in patients due to the lack of long-term treatment options following the standard form of care. New options for cancer therapy are needed to improve the survival of cancer patients without disease recurrence. Auranofin is a clinically approved agent against rheumatoid arthritis that is currently enrolled in clinical trials for potential repurposing against cancer. Auranofin mainly targets the anti-oxidative system catalyzed by thioredoxin reductase (TrxR), which protects the cell from oxidative stress and death in the cytoplasm and the mitochondria. TrxR is over-expressed in many cancers as an adaptive mechanism for cancer cell proliferation, rendering it an attractive target for cancer therapy, and auranofin as a potential therapeutic agent for cancer. Inhibiting TrxR dysregulates the intracellular redox state causing increased intracellular reactive oxygen species levels, and stimulates cellular demise. An alternate mechanism of action of auranofin is to mimic proteasomal inhibition by blocking the ubiquitin-proteasome system (UPS), which is critically important in cancer cells to prevent cell death when compared to non-cancer cells, because of its role on cell cycle regulation, protein degradation, gene expression, and DNA repair. This article provides new perspectives on the potential mechanisms used by auranofin alone, in combination with diverse other compounds, or in combination with platinating agents and/or immune checkpoint inhibitors to combat cancer cells, while assessing the feasibility for its repurposing in the clinical setting. Show less

The synthesis and antiproliferative activity of Mes- and iPr-substituted gold(I) bis(1,2,3-triazol-5-ylidene) complexes in various cancer cell lines are reported, showing nanomolar IC₅₀ values of 50 nM (lymphoma cells) and 500 nM (leukemia cells), respectively (Mes < iPr). The compounds exclusively induce apoptosis (50 nM to 5 μM) instead of necrosis in common malignant blood cells (leukemia cells) and do not affect non-malignant leucocytes. Remarkably, the complexes not only overcome resistances against the well-established cytostatic etoposide, cytarabine, daunorubicin, and cisplatin but also promote a synergistic effect of up to 182% when used with daunorubicin. The present results demonstrate that gold(I) bis(1,2,3-triazol-5-ylidene) complexes are highly promising and easily modifiable anticancer metallodrugs. Show less

In this work, a pair of gold(III) complexes derived from the analogous tetrapyridyl ligands H₂biqbpy1 and H₂biqbpy2 was prepared: the rollover, bis-cyclometalated [Au(biqbpy1)Cl ([1]Cl) and its isomer [Au(biqbpy2)Cl ([2]Cl). In [1]⁺, two pyridyl rings coordinate to the metal via a Au-C bond (C^∧N^∧N^∧C coordination) and the two noncoordinated amine bridges of the ligand remain protonated, while in [2]⁺ all four pyridyl rings of the ligand coordinate to the metal via a Au-N bond (N^∧N^∧N^∧N coordination), but both amine bridges are deprotonated. As a result, both complexes are monocationic, which allowed comparison of the sole effect of cyclometalation on the chemistry, protein interaction, and anticancer properties of the gold(III) compounds. Due to their identical monocationic charge and similar molecular shape, both complexes [1]Cl and [2]Cl displaced reference radioligand [³H]dofetilide equally well from cell membranes expressing the K_v11.1 (hERG) potassium channel, and more so than the tetrapyridyl ligands H₂biqbpy1 and H₂biqbpy2. By contrast, cyclometalation rendered [1]Cl coordinatively stable in the presence of biological thiols, while [2]Cl was reduced by a millimolar concentration of glutathione into metastable Au(I) species releasing the free ligand H₂biqbpy2 and TrxR-inhibiting Au⁺ ions. The redox stability of [1]Cl dramatically decreased its thioredoxin reductase (TrxR) inhibition properties, compared to [2]Cl. On the other hand, unlike [2]Cl, [1]Cl aggregated into nanoparticles in FCS-containing medium, which resulted in much more efficient gold cellular uptake. [1]Cl had much more selective anticancer properties than [2]Cl and cisplatin, as it was almost 10 times more cytotoxic to human cancer cells (A549, A431, A375, and MCF7) than to noncancerous cells (MRC5). Mechanistic studies highlight the strikingly different mode of action of the two compounds: while for [1]Cl high gold cellular uptake, nuclear DNA damage, and interaction with hERG may contribute to cell killing, for [2]Cl extracellular reduction released TrxR-inhibiting Au⁺ ions that were taken up in minute amounts in the cytosol, and a toxic tetrapyridyl ligand also capable of binding to hERG. These results demonstrate that bis-cyclometalation is an appealing method to improve the redox stability of Au(III) compounds and to develop gold-based cytotoxic compounds that do not rely on TrxR inhibition to kill cancer cells. Show less

Mitochondrial structure and organization is integral to maintaining mitochondrial homeostasis and an emerging biological target in aging, inflammation, neurodegeneration, and cancer. The study of mitochondrial structure and its functional implications remains challenging in part because of the lack of available tools for direct engagement, particularly in a disease setting. Here, we report a gold-based approach to perturb mitochondrial structure in cancer cells. Specifically, the design and synthesis of a series of tricoordinate Au(I) complexes with systematic modifications to group 15 nonmetallic ligands establish structure-activity relationships (SAR) to identify physiologically relevant tools for mitochondrial perturbation. The optimized compound, AuTri-9 selectively disrupts breast cancer mitochondrial structure rapidly as observed by transmission electron microscopy with attendant effects on fusion and fission proteins. This phenomenon triggers severe depolarization of the mitochondrial membrane in cancer cells. The high in vivo tolerability of AuTri-9 in mice demonstrates its preclinical utility. This work provides a basis for rational design of gold-based agents to control mitochondrial structure and dynamics. Show less

Growth factor receptors are activated through dimerization by the binding of their ligands and play pivotal roles in normal cell function. However, the aberrant activity of the receptors has been associated with cancer malignancy. One of the main causes of the aberrant receptor activation is the overexpression of receptors and the resultant formation of unliganded receptor dimers, which can be activated in the absence of external ligand molecules. Thus, the unliganded receptor dimer is a promising target to inhibit aberrant signaling in cancer. Here, we report an aptamer that specifically binds to fibroblast growth factor receptor 2b and inhibits the aberrant receptor activation and signaling. Our investigation suggests that this aptamer inhibits the formation of the receptor dimer occurring in the absence of external ligand molecules. This work presents a new inhibitory function of aptamers and the possibility of oligonucleotide-based therapeutics for cancer. Show less

The task of drug-target interaction prediction holds significant importance in pharmacology and therapeutic drug design. In this paper, we present FRnet-DTI, an auto-encoder based feature manipulation and a convolutional neural network based classifier for drug target interaction prediction. Two convolutional neural networks are proposed: FRnet-Encode and FRnet-Predict. Here, one model is used for feature manipulation and the other one for classification. Using the first method FRnet-Encode, we generate 4096 features for each of the instances in each of the datasets and use the second method, FRnet-Predict, to identify interaction probability employing those features. We have tested our method on four gold standard datasets extensively used by other researchers. Experimental results shows that our method significantly improves over the state-of-the-art method on three out of four drug-target interaction gold standard datasets on both area under curve for Receiver Operating Characteristic (auROC) and area under Precision Recall curve (auPR) metric. We also introduce twenty new potential drug-target pairs for interaction based on high prediction scores. The source codes and implementation details of our methods are available from https://github.com/farshidrayhanuiu/FRnet-DTI/ and also readily available to use as an web application from http://farshidrayhan.pythonanywhere.com/FRnet-DTI/ . Show less

Four new complexes of Au(III), Pd(II), Ni(II), and Cu(II) ions were synthesized, derived from a novel heterocyclic ligand (L) that has both triazole and tetrazole rings. The ligand synthesis was through successive steps to achieve both heterocyclic rings. The synthesized compounds were characterized using conventional techniques like infrared, ultra violet—visible and proton/carbon nuclear magnetic resonance spectroscopy, metal and thermal analyses, and molar conductivity. All complexes were suggested to have square planar geometry, gold, nickel, and palladium complexes were salts while copper neutral complexes have the chemical formulas; [AuL2]Cl.2H2O, [PdL2]Cl2.2H2O, [NiL2]Cl2.2H2O, and [CuL2]. The cytotoxic effect was studied on breast cancer cell line (MCF‐7 cell line) at different concentrations by using the 3‐(4,5‐dimethylthiazol‐2‐yl)‐2,5‐diphenyltetrazolium bromide assay method, for the ligand (L) and complexes. The results showed that gold(III) and nickel(II) complexes have the highest cytotoxicity among all compounds against cancer cell lines. Show less

Mononuclear gold(I) acyclic diaminocarbenes (ADCs) were prepared by the reaction of 1,2-cyclohexanediamine with the corresponding isocyanide complexes [AuCl(CNR)] (R = Cy, ^t Bu). The three-component coupling of aldehydes, amines, and alkynes was investigated by using these gold(I) ADC complexes. The new gold(I) metal complexes are highly efficient catalysts for the synthesis of propargylamines and indolizines in the absence of solvent and in mild conditions. This method affords the corresponding final products with excellent yields in short reaction times. Additionally, chiral gold(I) complexes with ADCs have been prepared and tried in the enantioselective synthesis of propargylamines. Show less

MOTIVATION: With the booming of interactome studies, a lot of interactions can be measured in a high throughput way and large scale datasets are available. It is becoming apparent that many different types of interactions can be potential drug targets. Compared with inhibition of a single protein, inhibition of protein-protein interaction (PPI) is promising to improve the specificity with fewer adverse side-effects. Also it greatly broadens the drug target search space, which makes the drug target discovery difficult. Computational methods are highly desired to efficiently provide candidates for further experiments and hold the promise to greatly accelerate the discovery of novel drug targets. RESULTS: Here, we propose a machine learning method to predict PPI targets in a genomic-wide scale. Specifically, we develop a computational method, named as PrePPItar, to Predict PPIs as drug targets by uncovering the potential associations between drugs and PPIs. First, we survey the databases and manually construct a gold-standard positive dataset for drug and PPI interactions. This effort leads to a dataset with 227 associations among 63 PPIs and 113 FDA-approved drugs and allows us to build models to learn the association rules from the data. Second, we characterize drugs by profiling in chemical structure, drug ATC-code annotation, and side-effect space and represent PPI similarity by a symmetrical S-kernel based on protein amino acid sequence. Then the drugs and PPIs are correlated by Kronecker product kernel. Finally, a support vector machine (SVM), is trained to predict novel associations between drugs and PPIs. We validate our PrePPItar method on the well-established gold-standard dataset by cross-validation. We find that all chemical structure, drug ATC-code, and side-effect information are predictive for PPI target. Moreover, we can increase the PPI target prediction coverage by integrating multiple data sources. Follow-up database search and pathway analysis indicate that our new predictions are worthy of future experimental validation. CONCLUSION: In conclusion, PrePPItar can serve as a useful tool for PPI target discovery and provides a general heterogeneous data integrative framework. AVAILABILITY AND IMPLEMENTATION: PrePPItar is available at http://doc.aporc.org/wiki/PrePPItar. CONTACT: ycwang@nwipb.cas.cn or ywang@amss.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Show less

Gold(I) complexes with phosphane and thiotetrazolate ligands were prepared and investigated as a new type of bioactive gold metallodrugs. The complexes triggered very efficient inhibition of the enzyme thioredoxin reductase (TrxR), which is an important molecular target for gold species. Strong cytotoxic effects were observed in MDA-MB-231 breast adenocarcinoma and HT-29 colon carcinoma cells, and the complexes also caused strong effects in vincristine resistant Nalm-6 leukemia cells. Cellular uptake studies showed elevated cellular gold levels for complexes containing a triphenylphosphane ligand, whereas trifurylphosphane analogues accumulated at significantly lower cellular concentrations. Show less

AbstractMolecular recognition plays a fundamental role in all biological processes, and that is why great efforts have been made to understand and predict protein–ligand interactions. Finding a molecule that can potentially bind to a target protein is particularly essential in drug discovery and still remains an expensive and time‐consuming task. In silico, tools are frequently used to screen molecular libraries to identify new lead compounds, and if protein structure is known, various protein–ligand docking programs can be used. The aim of docking procedure is to predict correct poses of ligand in the binding site of the protein as well as to score them according to the strength of interaction in a reasonable time frame. The purpose of our studies was to present the novel consensus approach to predict both protein–ligand complex structure and its corresponding binding affinity. Our method used as the input the results from seven docking programs (Surflex, LigandFit, Glide, GOLD, FlexX, eHiTS, and AutoDock) that are widely used for docking of ligands. We evaluated it on the extensive benchmark dataset of 1300 protein–ligands pairs from refined PDBbind database for which the structural and affinity data was available. We compared independently its ability of proper scoring and posing to the previously proposed methods. In most cases, our method is able to dock properly approximately 20% of pairs more than docking methods on average, and over 10% of pairs more than the best single program. The RMSD value of the predicted complex conformation versus its native one is reduced by a factor of 0.5 Å. Finally, we were able to increase the Pearson correlation of the predicted binding affinity in comparison with the experimental value up to 0.5. © 2010 Wiley Periodicals, Inc. J Comput Chem 32: 568–581, 2011 Show less

Lithiation of 1-benzyl-1H-tetrazole followed by transmetallation with [AuCl(PPh3)], [Au(C6F5)(tht)] or [AuCl(tht)] (tht = tetrahydrothiophene) and subsequent alkylation afforded cationic 1-benzyl-4-methyl-4,5-dihydro-1H-1,2,3,4-tetrazol-5-ylidene(triphenylphosphine)gold(I), 1, neutral 1-benzyl-4-methyl-4,5-dihydro-1H-1,2,3,4-tetrazol-5-ylidene(pentafluorophenyl)gold(I), 2, and a cationic biscarbene complex, bis(1-benzyl-4-methyl-4,5-dihydro-1H-1,2,3,4-tetrazol-5-ylidene)gold(I), 3. The first complex underwent a homoleptic rearrangement in solution to form 3. Reaction of [Au(N3)PPh3] with the three isocyanides (CH3)2C6H3NC, tBuNC and CyNC, respectively, yielded the corresponding neutral tetrazolyl(phosphine) complexes of gold, [1-(2,6-dimethylphenyl)-1H-tetrazol-5-yl](triphenylphosphine)gold(I), 4, [1-(tert-butyl)-1H-tetrazol-5-yl](triphenylphosphine)gold(I), 6, and [1-(cyclohexyl)-1H-tetrazol-5-yl](triphenylphosphine)gold(I), 7. Alkylation of 4 with methyl triflate on N4 allowed isolation of the crystalline carbene complex 1-(2,6-dimethylphenyl)-4-methyl-4,5-dihydro-1H-1,2,3,4-tetrazol-5-ylidene)(triphenylphosphine)gold(I), 5. Complex 7 was not isolable in pure form but converts by isocyanide substitution of triphenylphosphine into [1-cyclohexylisocyanide][1-(cyclohexyl)-1H-tetrazol-5-yl]gold(I), 8. From a product mixture of 7 and 8 the transformed molecules [(cyclohexylamino)(ethoxy)carbene](1-cyclohexyl-1H-tetrazol-5-yl)gold(I), 9, and [bis(cyclohexylamino)carbene](1-cyclohexyltetrazol-5-yl)gold(I), 10, co-crystallised spontaneously after a long time at −20 °C. Show less

AbstractEight docking programs (DOCK, FLEXX, FRED, GLIDE, GOLD, SLIDE, SURFLEX, and QXP) that can be used for either single‐ligand docking or database screening have been compared for their propensity to recover the X‐ray pose of 100 small‐molecular‐weight ligands, and for their capacity to discriminate known inhibitors of an enzyme (thymidine kinase) from randomly chosen “drug‐like” molecules. Interestingly, both properties are found to be correlated, since the tools showing the best docking accuracy (GLIDE, GOLD, and SURFLEX) are also the most successful in ranking known inhibitors in a virtual screening experiment. Moreover, the current study pinpoints some physicochemical descriptors of either the ligand or its cognate protein‐binding site that generally lead to docking/scoring inaccuracies. Proteins 2004. © 2004 Wiley‐Liss, Inc. Show less

AbstractProtein‐based virtual screening of chemical libraries is a powerful technique for identifying new molecules that may interact with a macromolecular target of interest. Because of docking and scoring limitations, it is more difficult to apply as a lead optimization method because it requires that the docking/scoring tool is able to propose as few solutions as possible and all of them with a very good accuracy for both the protein‐bound orientation and the conformation of the ligand. In the present study, we present a consensus docking approach (ConsDock) that takes advantage of three widely used docking tools (Dock, FlexX, and Gold). The consensus analysis of all possible poses generated by several docking tools is performed sequentially in four steps: (i) hierarchical clustering of all poses generated by a docking tool into families represented by a leader; (ii) definition of all consensus pairs from leaders generated by different docking programs; (iii) clustering of consensus pairs into classes, represented by a mean structure; and (iv) ranking the different means starting from the most populated class of consensus pairs. When applied to a test set of 100 protein–ligand complexes from the Protein Data Bank, ConsDock significantly outperforms single docking with respect to the docking accuracy of the top‐ranked pose. In 60% of the cases investigated here, ConsDock was able to rank as top solution a pose within 2 Å RMSD of the X‐ray structure. It can be applied as a postprocessing filter to either single‐ or multiple‐docking programs to prioritize three‐dimensional guided lead optimization from the most likely docking solution. Proteins 2002;47:521–533. © 2002 Wiley‐Liss, Inc. Show less