Abstract: Little research has been done to address the huge opportunities that may exist to reposition existing approved or generic drugs for alternate uses in cancer therapy. In addition, there has been little work on strategies to reposition experimental cancer agents for testing in alternate settings that could shorten their clinical development time. Progress in each area has lagged, in part, because of the lack of systematic methods to define drug off-target effects (OTE) that might affect important cancer cell signaling pathways. In this study, we addressed this critical gap by developing an OTE-based method to repurpose drugs for cancer therapeutics, based on transcriptional responses made in cells before and after drug treatment. Specifically, we defined a new network component called cancer-signaling bridges (CSB) and integrated it with a Bayesian factor regression model (BFRM) to form a new hybrid method termed CSB-BFRM. Proof-of-concept studies were conducted in breast and prostate cancer cells and in promyelocytic leukemia cells. In each system, CSB-BFRM analysis could accurately predict clinical responses to more than 90% of drugs approved by the U.S. Food and Drug Administration and more than 75% of experimental clinical drugs that were tested. Mechanistic investigation of OTEs for several high-ranking drug-dose pairs suggested repositioning opportunities for cancer therapy, based on the ability to enforce retinoblastoma-dependent repression of important E2F-dependent cell-cycle genes. Together, our findings establish new methods to identify opportunities for drug repositioning or to elucidate the mechanisms of action of repositioned drugs.

Abstract: MOTIVATION: Genome-wide mRNA profiling provides a snapshot of the global state of cells under different conditions. However, mRNA levels do not provide direct understanding of upstream regulatory mechanisms. Here, we present a new approach called Expression2Kinases (X2K) to identify upstream regulators likely responsible for observed patterns in genome-wide gene expression. By integrating chromatin immuno-precipitation (ChIP)-seq/chip and position weight matrices (PWMs) data, protein-protein interactions and kinase-substrate phosphorylation reactions, we can better identify regulatory mechanisms upstream of genome-wide differences in gene expression. We validated X2K by applying it to recover drug targets of food and drug administration (FDA)-approved drugs from drug perturbations followed by mRNA expression profiling; to map the regulatory landscape of 44 stem cells and their differentiating progeny; to profile upstream regulatory mechanisms of 327 breast cancer tumors; and to detect pathways from profiled hepatic stellate cells and hippocampal neurons. The X2K approach can advance our understanding of cell signaling and unravel drugs mechanisms of action. AVAILABILITY: The software and source code are freely available at: http://www.maayanlab.net/X2K. CONTACT: avi.maayan@mssm.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: Since the onset of antiviral therapy, viral resistance has compromised the clinical value of small-molecule drugs targeting pathogen components. As intracellular parasites, viruses complete their life cycle by hijacking a multitude of host-factors. Aiming at the latter rather than the pathogen directly, host-directed antiviral therapy has emerged as a concept to counteract evolution of viral resistance and develop broad-spectrum drug classes. This approach is propelled by bioinformatics analysis of genome-wide screens that greatly enhance insights into the complex network of host-pathogen interactions and generate a shortlist of potential gene targets from a multitude of candidates, thus setting the stage for a new era of rational identification of drug targets for host-directed antiviral therapies. With particular emphasis on human immunodeficiency virus and influenza virus, two major human pathogens, we review screens employed to elucidate host-pathogen interactions and discuss the state of database ontology approaches applicable to defining a therapeutic endpoint. The value of this strategy for drug discovery is evaluated, and perspectives for bioinformatics-driven hit identification are outlined.

Abstract: We describe a label-free integrative pharmacology on-target (iPOT) method to assess the pharmacology of drugs at the beta(2)-adrenergic receptor. This method combines dynamic mass redistribution (DMR) assays using an array of probe molecule-hijacked cells with similarity analysis. The whole cell DMR assays track cell system-based, ligand-directed, and kinetics-dependent biased activities of the drugs, and translates their on-target pharmacology into numerical descriptors which are subject to similarity analysis. We demonstrate that the approach establishes an effective link between the label-free pharmacology and in vivo therapeutic indications of drugs.

Abstract: The degree of applicability of chemogenomic approaches to protein families depends on the accuracy and completeness of pharmacological data and the corresponding level of pharmacological similarity observed among their protein members. The recent public domain availability of pharmacological data for thousands of small molecules on 204 G protein-coupled receptors (GPCRs) provides a firm basis for an in-depth cross-pharmacology analysis of this superfamily. The number of protein targets included in the cross-pharmacology profile of the different GPCRs changes significantly upon varying the ligand similarity and binding affinity criteria. However, with the exception of muscarinic receptors, aminergic GPCRs distinguish themselves from the rest of the members in the family by their remarkably high levels of pharmacological similarity among them. Clusters of non-GPCR targets related by cross-pharmacology with particular GPCRs are identified and the implications for unwanted side-effects, as well as for repurposing opportunities, discussed.

Abstract: There is a critical need for improving the level of chemistry awareness in systems biology. The data and information related to modulation of genes and proteins by small molecules continue to accumulate at the same time as simulation tools in systems biology and whole body physiologically based pharmacokinetics (PBPK) continue to evolve. We called this emerging area at the interface between chemical biology and systems biology systems chemical biology (SCB) (Nat Chem Biol 3: 447-450, 2007).The overarching goal of computational SCB is to develop tools for integrated chemical-biological data acquisition, filtering and processing, by taking into account relevant information related to interactions between proteins and small molecules, possible metabolic transformations of small molecules, as well as associated information related to genes, networks, small molecules, and, where applicable, mutants and variants of those proteins. There is yet an unmet need to develop an integrated in silico pharmacology/systems biology continuum that embeds drug-target-clinical outcome (DTCO) triplets, a capability that is vital to the future of chemical biology, pharmacology, and systems biology. Through the development of the SCB approach, scientists will be able to start addressing, in an integrated simulation environment, questions that make the best use of our ever-growing chemical and biological data repositories at the system-wide level. This chapter reviews some of the major research concepts and describes key components that constitute the emerging area of computational systems chemical biology.

Abstract: The Plasmodium falciparum lactate dehydrogenase enzyme (PfLDH) has been considered as a potential molecular target for antimalarials due to this parasite's dependence on glycolysis for energy production. Because the LDH enzymes found in P. vivax, P. malariae and P. ovale (pLDH) all exhibit approximately 90% identity to PfLDH, it would be desirable to have new anti-pLDH drugs, particularly ones that are effective against P. falciparum, the most virulent species of human malaria. Our present work used docking studies to select potential inhibitors of pLDH, which were then tested for antimalarial activity against P. falciparum in vitro and P. berghei malaria in mice. A virtual screening in DrugBank for analogs of NADH (an essential cofactor to pLDH) and computational studies were undertaken, and the potential binding of the selected compounds to the PfLDH active site was analyzed using Molegro Virtual Docker software. Fifty compounds were selected based on their similarity to NADH. The compounds with the best binding energies (itraconazole, atorvastatin and posaconazole) were tested against P. falciparum chloroquine-resistant blood parasites. All three compounds proved to be active in two immunoenzymatic assays performed in parallel using monoclonals specific to PfLDH or a histidine rich protein (HRP2). The IC(50) values for each drug in both tests were similar, were lowest for posaconazole (<5 microM) and were 40- and 100-fold less active than chloroquine. The compounds reduced P. berghei parasitemia in treated mice, in comparison to untreated controls; itraconazole was the least active compound. The results of these activity trials confirmed that molecular docking studies are an important strategy for discovering new antimalarial drugs. This approach is more practical and less expensive than discovering novel compounds that require studies on human toxicology, since these compounds are already commercially available and thus approved for human use.

Abstract: A vast majority of the burden from neglected tropical diseases result from helminth infections (nematodes and platyhelminthes). Parasitic helminthes infect over 2 billion, exerting a high collective burden that rivals high-mortality conditions such as AIDS or malaria, and cause devastation to crops and livestock. The challenges to improve control of parasitic helminth infections are multi-fold and no single category of approaches will meet them all. New information such as helminth genomics, functional genomics and proteomics coupled with innovative bioinformatic approaches provide fundamental molecular information about these parasites, accelerating both basic research as well as development of effective diagnostics, vaccines and new drugs. To facilitate such studies we have developed an online resource, HelmCoP (Helminth Control and Prevention), built by integrating functional, structural and comparative genomic data from plant, animal and human helminthes, to enable researchers to develop strategies for drug, vaccine and pesticide prioritization, while also providing a useful comparative genomics platform. HelmCoP encompasses genomic data from several hosts, including model organisms, along with a comprehensive suite of structural and functional annotations, to assist in comparative analyses and to study host-parasite interactions. The HelmCoP interface, with a sophisticated query engine as a backbone, allows users to search for multi-factorial combinations of properties and serves readily accessible information that will assist in the identification of various genes of interest. HelmCoP is publicly available at: http://www.nematode.net/helmcop.html.

Abstract: Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. Many such ontologies are currently being developed to represent objects across all the domains of science. Ontologies enable the definition, classification, and support for querying objects in a particular domain, enabling intelligent computer applications to be built which support the work of scientists both within the domain of interest and across interrelated neighbouring domains. Modern chemical research relies on computational techniques to filter and organise data to maximise research productivity. The objects which are manipulated in these algorithms and procedures, as well as the algorithms and procedures themselves, enjoy a kind of virtual life within computers. We will call these information entities. Here, we describe our work in developing an ontology of chemical information entities, with a primary focus on data-driven research and the integration of calculated properties (descriptors) of chemical entities within a semantic web context. Our ontology distinguishes algorithmic, or procedural information from declarative, or factual information, and renders of particular importance the annotation of provenance to calculated data. The Chemical Information Ontology is being developed as an open collaborative project. More details, together with a downloadable OWL file, are available at http://code.google.com/p/semanticchemistry/ (license: CC-BY-SA).

Abstract: BACKGROUND: The human progesterone receptor (hPR) belongs to the steroid receptor family. It may be found as monomers (A and B) and or as a dimer (AB). hPR is regarded as the prognostic biomarker for breast cancer. In a cellular dimer system, AB is the dominant species in most cases. However, when a cell coexpresses all three isoforms of hPR, the complexity of the action of this receptor increases. For example, hPR A suppresses the activity of hPR B, and the ratio of hPR A to hPR B may determine the physiology of a breast tumor. Also, persistent exposure of hPRs to nonendogenous ligands is a common risk factor for breast cancer. Hence we aimed to study progesterone and some nonendogenous ligand interactions with hPRs and their molecular docking. METHODS AND RESULTS: A pool of steroid derivatives, namely, progesterone, cholesterol, testosterone, testolectone, estradiol, estrone, norethindrone, exemestane, and norgestrel, was used for this in silico study. Dockings were performed on AutoDock 4.2. We found that estrogens, including estradiol and estrone, had a higher affinity for hPR A and B monomers in comparison with the dimer, hPR AB, and that of the endogenous progesterone ligand. hPR A had a higher affinity to all the docked ligands than hPR B. CONCLUSION: This study suggests that the exposure of estrogens to hPR A as well as hPR B, and more particularly to hPR A alone, is a risk factor for breast cancer.