Abstract: PURPOSE: Drug repositioning strategies were employed to explore new therapeutic indications for existing drugs that may exhibit dual negative mGluR1/5 modulating activities as potential treatments for neuropathic pain. METHOD: A customized in silico-in vitro-in vivo drug repositioning scheme was assembled and implemented to search available drug libraries for compounds with dual mGluR1/5 antagonistic activities, that were then evaluated using in vitro functional assays and, for validated hits, in an established animal model for neuropathic pain. RESULTS: Tizoxanide, the primary active metabolite of the FDA approved drug nitazoxanide, fit in silico pharmacophore models constructed for both mGluR1 and mGluR5. Subsequent calcium (Ca++) mobilization functional assays confirmed that tizoxanide exhibited appreciable antagonist activity for both mGluR1 and mGluR5 (IC50 = 1.8 muM and 1.2 muM, respectively). The in vivo efficacy of nitazoxanide administered by intraperitoneal injection was demonstrated in a rat model for neuropathic pain. CONCLUSION: The major aim of the present study was to demonstrate the utility of an in silico-in vitro-in vivo drug repositioning protocol to facilitate the repurposing of approved drugs for new therapeutic indications. As an example, this particular investigation successfully identified nitazoxanide and its metabolite tizoxanide as dual mGluR1/5 negative modulators. A key finding is the vital importance for drug screening libraries to include the structures of drug active metabolites, such as those emanating from prodrugs which are estimated to represent 5-7% of marketed drugs.

Abstract: Solute carrier (SLC) transporters - a family of more than 300 membrane-bound proteins that facilitate the transport of a wide array of substrates across biological membranes - have important roles in physiological processes ranging from the cellular uptake of nutrients to the absorption of drugs and other xenobiotics. Several classes of marketed drugs target well-known SLC transporters, such as neurotransmitter transporters, and human genetic studies have provided powerful insight into the roles of more-recently characterized SLC transporters in both rare and common diseases, indicating a wealth of new therapeutic opportunities. This Review summarizes knowledge on the roles of SLC transporters in human disease, describes strategies to target such transporters, and highlights current and investigational drugs that modulate SLC transporters, as well as promising drug targets.

Abstract: Often in pharmaceutical research the goal is to identify small molecules that can interact with and appropriately modify the biological behavior of a new protein target. Unfortunately, most proteins lack both known structures and small molecule binders, prerequisites of many virtual screening, VS, approaches. For such proteins, ligand homology modeling, LHM, that copies ligands from homologous and perhaps evolutionarily distant template proteins, has been shown to be a powerful VS approach to identify possible binding ligands. However, if we want to target a specific pocket for which there is no homologous holo template protein structure, then LHM will not work. To address this issue, in a new pocket-based approach, PoLi, we generalize LHM by exploiting the fact that the number of distinct small molecule ligand-binding pockets in proteins is small. PoLi identifies similar ligand-binding pockets in a holo template protein library, selectively copies relevant parts of template ligands, and uses them for VS. In practice, PoLi is a hybrid structure and ligand-based VS algorithm that integrates 2D fingerprint-based and 3D shape-based similarity metrics for improved virtual screening performance. On standard DUD and DUD-E benchmark databases, using modeled receptor structures, PoLi achieves an average enrichment factor of 13.4 and 9.6, respectively, in the top 1% of the screened library. In contrast, traditional docking-based VS using AutoDock Vina and homology-based VS using FINDSITE(filt) have an average enrichment of 1.6 (3.0) and 9.0 (7.9) on the DUD (DUD-E) sets, respectively. Experimental validation of PoLi predictions on dihydrofolate reductase, DHFR, using differential scanning fluorimetry, DSF, identifies multiple ligands with diverse molecular scaffolds, thus demonstrating the advantage of PoLi over current state-of-the-art VS methods.

Abstract: BACKGROUND: Dual-specificity phosphatase-5 (DUSP5) plays a central role in vascular development and disease. We present a p-nitrophenol phosphate (pNPP) based enzymatic assay to screen for inhibitors of the phosphatase domain of DUSP5. METHODS: pNPP is a mimic of the phosphorylated tyrosine on the ERK2 substrate (pERK2) and binds the DUSP5 phosphatase domain with a Km of 7.6 +/- 0.4 mM. Docking followed by inhibitor verification using the pNPP assay identified a series of polysulfonated aromatic inhibitors that occupy the DUSP5 active site in the region that is likely occupied by the dual-phosphorylated ERK2 substrate tripeptide (pThr-Glu-pTyr). Secondary assays were performed with full length DUSP5 with ERK2 as substrate. RESULTS: The most potent inhibitor has a naphthalene trisulfonate (NTS) core. A search for similar compounds in a drug database identified suramin, a dimerized form of NTS. While suramin appears to be a potent and competitive inhibitor (25 +/- 5 muM), binding to the DUSP5 phosphatase domain more tightly than the monomeric ligands of which it is comprised, it also aggregates. Further ligand-based screening, based on a pharmacophore derived from the 7 A separation of sulfonates on inhibitors and on sulfates present in the DUSP5 crystal structure, identified a disulfonated and phenolic naphthalene inhibitor (CSD (3) _2320) with IC(5)(0) of 33 muM that is similar to NTS and does not aggregate. CONCLUSIONS: The new DUSP5 inhibitors we identify in this study typically have sulfonates 7 A apart, likely positioning them where the two phosphates of the substrate peptide (pThr-Glu-pTyr) bind, with one inhibitor also positioning a phenolic hydroxyl where the water nucleophile may reside. Polysulfonated aromatic compounds do not commonly appear in drugs and have a tendency to aggregate. One FDA-approved polysulfonated drug, suramin, inhibits DUSP5 and also aggregates. Docking and modeling studies presented herein identify polysulfonated aromatic inhibitors that do not aggregate, and provide insights to guide future design of mimics of the dual-phosphate loops of the ERK substrates for DUSPs.

Abstract: A large quantity of high throughput screening (HTS) data for antimalarial activity has become available in recent years. This includes both phenotypic and target-based activity. Realising the maximum value of these data remains a challenge. In this respect, methods that allow such data to be used for virtual screening maximise efficiency and reduce costs. In this study both in vitro antimalarial activity and inhibitory data for beta-haematin formation, largely obtained from publically available sources, has been used to develop Bayesian models for inhibitors of beta-haematin formation and in vitro antimalarial activity. These models were used to screen two in silico compound libraries. In the first, the 1510 U.S. Food and Drug Administration approved drugs available on PubChem were ranked from highest to lowest Bayesian score based on a training set of beta-haematin inhibiting compounds active against Plasmodium falciparum that did not include any of the clinical antimalarials or close analogues. The six known clinical antimalarials that inhibit beta-haematin formation were ranked in the top 2.1% of compounds. Furthermore, the in vitro antimalarial hit-rate for this prioritised set of compounds was found to be 81% in the case of the subset where activity data are available in PubChem. In the second, a library of about 5000 commercially available compounds (Aldrich(CPR)) was virtually screened for ability to inhibit beta-haematin formation and then for in vitro antimalarial activity. A selection of 34 compounds was purchased and tested, of which 24 were predicted to be beta-haematin inhibitors. The hit rate for inhibition of beta-haematin formation was found to be 25% and a third of these were active against P. falciparum, corresponding to enrichments estimated at about 25- and 140-fold relative to random screening, respectively.

Abstract: There is a wealth of valuable chemical information in publicly available databases for use by scientists undertaking drug discovery. However finite curation resource, limitations of chemical structure software and differences in individual database applications mean that exact chemical structure equivalence between databases is unlikely to ever be a reality. The ability to identify compound equivalence has been made significantly easier by the use of the International Chemical Identifier (InChI), a non-proprietary line-notation for describing a chemical structure. More importantly, advances in methods to identify compounds that are the same at various levels of similarity, such as those containing the same parent component or having the same connectivity, are now enabling related compounds to be linked between databases where the structure matches are not exact.

Abstract: The published biomedical research literature encompasses most of our understanding of how drugs interact with gene products to produce physiological responses (phenotypes). Unfortunately, this information is distributed throughout the unstructured text of over 23 million articles. The creation of structured resources that catalog the relationships between drugs and genes would accelerate the translation of basic molecular knowledge into discoveries of genomic biomarkers for drug response and prediction of unexpected drug-drug interactions. Extracting these relationships from natural language sentences on such a large scale, however, requires text mining algorithms that can recognize when different-looking statements are expressing similar ideas. Here we describe a novel algorithm, Ensemble Biclustering for Classification (EBC), that learns the structure of biomedical relationships automatically from text, overcoming differences in word choice and sentence structure. We validate EBC's performance against manually-curated sets of (1) pharmacogenomic relationships from PharmGKB and (2) drug-target relationships from DrugBank, and use it to discover new drug-gene relationships for both knowledge bases. We then apply EBC to map the complete universe of drug-gene relationships based on their descriptions in Medline, revealing unexpected structure that challenges current notions about how these relationships are expressed in text. For instance, we learn that newer experimental findings are described in consistently different ways than established knowledge, and that seemingly pure classes of relationships can exhibit interesting chimeric structure. The EBC algorithm is flexible and adaptable to a wide range of problems in biomedical text mining.

Abstract: PURPOSE: Predicting human skin permeability of chemical compounds accurately and efficiently is useful for developing dermatological medicines and cosmetics. However, previous work have two problems; 1) quality of databases used, and 2) methods for prediction models. In this paper, we attempt to solve these two problems. METHODS: We first compile, by carefully screening from the literature, a novel dataset of chemical compounds with permeability coefficients, measured under consistent experimental conditions. We then apply machine learning techniques such as support vector regression (SVR) and random forest (RF) to our database to develop prediction models. Molecular descriptors are fully computationally obtained, and greedy stepwise selection is employed for descriptor selection. Prediction models are internally and externally validated. RESULTS: We generated an original, new database on human skin permeability of 211 different compounds from aqueous donors. Nonlinear SVR achieved the best performance among linear SVR, nonlinear SVR, and RF. The determination coefficient, root mean square error, and mean absolute error of nonlinear SVR in external validation were 0.910, 0.342, and 0.282, respectively. CONCLUSIONS: We provided one of the largest datasets with purely experimental log kp and developed reliable and accurate prediction models for screening active ingredients and seeking unsynthesized compounds of dermatological medicines and cosmetics.

Abstract: The human gut microbiota performs essential functions for host and well-being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health-relevant metabolites being considered 'mammalian-microbial co-metabolites'. To systematically investigate this complex host-microbial co-metabolism, a systems biology approach integrating high-throughput data and computational network models is required. Here, we review established top-down and bottom-up systems biology approaches that have successfully elucidated relationships between gut microbiota-derived metabolites and host health and disease. We focus particularly on the constraint-based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host-microbe interactions on the molecular level. We illustrate that constraint-based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host-microbe interactions, yielding, e.g., in potential novel drugs and biomarkers.

Abstract: Campylobacters are a major global health burden and a cause of food-borne diarrheal illness and economic loss worldwide. In developing countries, Campylobacter infections are frequent in children under age two and may be associated with mortality. In developed countries, they are a common cause of bacterial diarrhea in early adulthood. In the United States, antibiotic resistance against Campylobacter is notably increased from 13% in 1997 to nearly 25% in 2011. Novel drug targets are urgently needed but remain a daunting task to accomplish. We suggest that omics-guided drug discovery is timely and worth considering in this context. The present study employed an integrated subtractive genomics and comparative metabolic pathway analysis approach. We identified 16 unique pathways from Campylobacter when compared against H. sapiens with 326 non-redundant proteins; 115 of these were found to be essential in the Database of Essential Genes. Sixty-six proteins among these were non-homologous to the human proteome. Six membrane proteins, of which four are transporters, have been proposed as potential vaccine candidates. Screening of 66 essential non-homologous proteins against DrugBank resulted in identification of 34 proteins with drug-ability potential, many of which play critical roles in bacterial growth and survival. Out of these, eight proteins had approved drug targets available in DrugBank, the majority serving crucial roles in cell wall synthesis and energy metabolism and therefore having the potential to be utilized as drug targets. We conclude by underscoring that screening against these proteins with inhibitors may aid in future discovery of novel therapeutics against campylobacteriosis in ways that will be pathogen specific, and thus have minimal toxic effect on host. Omics-guided drug discovery and bioinformatics analyses offer the broad potential for veritable advances in global health relevant novel therapeutics.