Abstract: Large scale data analysis is nowadays a crucial part of drug discovery. Biologists and chemists need to quickly explore and evaluate potentially effective yet safe compounds based on many datasets that are in relationship with each other. However, there is a lack of tools that support them in these processes. To remedy this, we developed ConTour, an interactive visual analytics technique that enables the exploration of these complex, multi-relational datasets. At its core ConTour lists all items of each dataset in a column. Relationships between the columns are revealed through interaction: selecting one or multiple items in one column highlights and re-sorts the items in other columns. Filters based on relationships enable drilling down into the large data space. To identify interesting items in the first place, ConTour employs advanced sorting strategies, including strategies based on connectivity strength and uniqueness, as well as sorting based on item attributes. ConTour also introduces interactive nesting of columns, a powerful method to show the related items of a child column for each item in the parent column. Within the columns, ConTour shows rich attribute data about the items as well as information about the connection strengths to other datasets. Finally, ConTour provides a number of detail views, which can show items from multiple datasets and their associated data at the same time. We demonstrate the utility of our system in case studies conducted with a team of chemical biologists, who investigate the effects of chemical compounds on cells and need to understand the underlying mechanisms.

Abstract: One approach to experimental science involves creating hypotheses, then testing them by varying one or more independent variables, and assessing the effects of this variation on the processes of interest. We use this strategy to compare the intellectual status and available evidence for two models or views of mechanisms of transmembrane drug transport into intact biological cells. One (BDII) asserts that lipoidal phospholipid Bilayer Diffusion Is Important, while a second (PBIN) proposes that in normal intact cells Phospholipid Bilayer diffusion Is Negligible (i.e., may be neglected quantitatively), because evolution selected against it, and with transmembrane drug transport being effected by genetically encoded proteinaceous carriers or pores, whose "natural" biological roles, and substrates are based in intermediary metabolism. Despite a recent review elsewhere, we can find no evidence able to support BDII as we can find no experiments in intact cells in which phospholipid bilayer diffusion was either varied independently or measured directly (although there are many papers where it was inferred by seeing a covariation of other dependent variables). By contrast, we find an abundance of evidence showing cases in which changes in the activities of named and genetically identified transporters led to measurable changes in the rate or extent of drug uptake. PBIN also has considerable predictive power, and accounts readily for the large differences in drug uptake between tissues, cells and species, in accounting for the metabolite-likeness of marketed drugs, in pharmacogenomics, and in providing a straightforward explanation for the late-stage appearance of toxicity and of lack of efficacy during drug discovery programmes despite macroscopically adequate pharmacokinetics. Consequently, the view that Phospholipid Bilayer diffusion Is Negligible (PBIN) provides a starting hypothesis for assessing cellular drug uptake that is much better supported by the available evidence, and is both more productive and more predictive.

Abstract: The current therapeutic arsenal against viral infections remains limited, with often poor efficacy and incomplete coverage, and appears inadequate to face the emergence of drug resistance. Our understanding of viral biology and pathophysiology and our ability to develop a more effective antiviral arsenal would greatly benefit from a more comprehensive picture of the events that lead to viral replication and associated symptoms. Towards this goal, the construction of virus-host interactomes is instrumental, mainly relying on the assumption that a viral infection at the cellular level can be viewed as a number of perturbations introduced into the host protein network when viral proteins make new connections and disrupt existing ones. Here, we review advances in interactomic approaches for viral infections, focusing on high-throughput screening (HTS) technologies and on the generation of high-quality datasets. We show how these are already beginning to offer intriguing perspectives in terms of virus-host cell biology and the control of cellular functions, and we conclude by offering a summary of the current situation regarding the potential development of host-oriented antiviral therapeutics.

Abstract: One of the many factors involved in determining the distribution and metabolism of a compound is the strength of its binding to human serum albumin. While experimental and QSAR approaches for determining binding to albumin exist, various factors limit their ability to provide accurate binding affinity for novel compounds. Thus, to complement the existing tools, we have developed a structure-based model of serum albumin binding. Our approach for predicting binding incorporated the inherent flexibility and promiscuity known to exist for albumin. We found that a weighted combination of the predicted logP and docking score most accurately distinguished between binders and nonbinders. This model was successfully used to predict serum albumin binding in a large test set of therapeutics that had experimental binding data.

Abstract: Compound promiscuity is rationalized as the specific interaction of a small molecule with multiple biological targets (as opposed to non-specific binding events) and represents the molecular basis of polypharmacology, an emerging theme in drug discovery and chemical biology. This concise review focuses on recent studies that have provided a detailed picture of the degree of promiscuity among different categories of small molecules. In addition, an exemplary computational approach is discussed that is designed to navigate multi-target activity spaces populated with various compounds.

Abstract: Drug abuse (DA) and addiction is a complex illness, broadly viewed as a neurobiological impairment with genetic and environmental factors that influence its development and manifestation. Abused substances can disrupt the activity of neurons by interacting with many proteins, particularly G-protein coupled receptors (GPCRs). A few medicines that target the central nervous system (CNS) can also modulate DA related proteins, such as GPCRs, which can act in conjunction with the controlled psychoactive substance(s) and increase side effects. To fully explore the molecular interaction networks that underlie DA and to effectively modulate the GPCRs in these networks with small molecules for DA treatment, we built a drug-abuse domain specific chemogenomics knowledgebase (DA-KB) to centralize the reported chemogenomics research information related to DA and CNS disorders in an effort to benefit researchers across a broad range of disciplines. We then focus on the analysis of GPCRs as many of them are closely related with DA. Their distribution in human tissues was also analyzed for the study of side effects caused by abused drugs. We further implement our computational algorithms/tools to explore DA targets, DA mechanisms and pathways involved in polydrug addiction and to explore polypharmacological effects of the GPCR ligands. Finally, the polypharmacology effects of GPCRs-targeted medicines for DA treatment were investigated and such effects can be exploited for the development of drugs with polypharmacophore for DA intervention. The chemogenomics database and the analysis tools will help us better understand the mechanism of drugs abuse and facilitate to design new medications for system pharmacotherapy of DA.

Abstract: The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line's response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning.

Abstract: The human ATP-binding cassette family C member 6 (ABCC6) gene encodes an ABC transporter protein (ABCC6), primarily expressed in liver and kidney. Mutations in the ABCC6 gene cause pseudoxanthoma elasticum (PXE), an autosomal recessive connective tissue disease characterized by ectopic mineralization of the elastic fibers. The pathophysiology underlying PXE is incompletely understood, which can at least partly be explained by the undetermined nature of the ABCC6 substrates as well as the unknown substrate recognition and binding sites. Several compounds, including anionic glutathione conjugates (N-ethylmaleimide; NEM-GS) and leukotriene C4 (LTC4) were shown to be modestly transported in vitro; conversely, vitamin K3 (VK3) was demonstrated not to be transported by ABCC6. To predict the possible substrate binding pockets of the ABCC6 transporter, we generated a 3D homology model of ABCC6 in both open and closed conformation, qualified for molecular docking and virtual screening approaches. By docking 10 reported in vitro substrates in our ABCC6 3D homology models, we were able to predict the substrate binding residues of ABCC6. Further, virtual screening of 4651 metabolites from the Human Serum Metabolome Database against our open conformation model disclosed possible substrates for ABCC6, which are mostly lipid and biliary secretion compounds, some of which are found to be involved in mineralization. Docking of these possible substrates in the closed conformation model also showed high affinity. Virtual screening expands this possibility to explore more compounds that can interact with ABCC6, and may aid in understanding the mechanisms leading to PXE.

Abstract: Oceanic dissolved organic matter (DOM) is an assemblage of reduced carbon compounds, which results from biotic and abiotic processes. The biotic processes consist in either release or uptake of specific molecules by marine organisms. Heterotrophic bacteria have been mostly considered to influence the DOM composition by preferential uptake of certain compounds. However, they also secrete a variety of molecules depending on physiological state, environmental and growth conditions, but so far the full set of compounds secreted by these bacteria has never been investigated. In this study, we analyzed the exo-metabolome, metabolites secreted into the environment, of the heterotrophic marine bacterium Pseudovibrio sp. FO-BEG1 via ultra-high resolution mass spectrometry, comparing phosphate limited with phosphate surplus growth conditions. Bacteria belonging to the Pseudovibrio genus have been isolated worldwide, mainly from marine invertebrates and were described as metabolically versatile Alphaproteobacteria. We show that the exo-metabolome is unexpectedly large and diverse, consisting of hundreds of compounds that differ by their molecular formulae. It is characterized by a dynamic recycling of molecules, and it is drastically affected by the physiological state of the strain. Moreover, we show that phosphate limitation greatly influences both the amount and the composition of the secreted molecules. By assigning the detected masses to general chemical categories, we observed that under phosphate surplus conditions the secreted molecules were mainly peptides and highly unsaturated compounds. In contrast, under phosphate limitation the composition of the exo-metabolome changed during bacterial growth, showing an increase in highly unsaturated, phenolic, and polyphenolic compounds. Finally, we annotated the detected masses using multiple metabolite databases. These analyses suggested the presence of several masses analogue to masses of known bioactive compounds. However, the annotation was successful only for a minor part of the detected molecules, underlining the current gap in knowledge concerning the biosynthetic ability of marine heterotrophic bacteria.

Abstract: Traditional Chinese medicine (TCM) herbal formulae can be valuable therapeutic strategies and drug discovery resources. However, the active ingredients and action mechanisms of most TCM formulae remain unclear. Therefore, the identification of potent ingredients and their actions is a major challenge in TCM research. In this study, we used a network pharmacology approach we previously developed to help determine the potential antidiabetic ingredients from the traditional Ge-Gen-Qin-Lian decoction (GGQLD) formula. We predicted the target profiles of all available GGQLD ingredients to infer the active ingredients by clustering the target profile of ingredients with FDA-approved antidiabetic drugs. We also applied network target analysis to evaluate the links between herbal ingredients and pharmacological actions to help explain the action mechanisms of GGQLD. According to the predicted results, we confirmed that a novel antidiabetic ingredient from Puerariae Lobatae radix (Ge-Gen), 4-Hydroxymephenytoin, increased the insulin secretion in RIN-5F cells and improved insulin resistance in 3T3-L1 adipocytes. The network pharmacology strategy used here provided a powerful means for identifying bioactive ingredients and mechanisms of action for TCM herbal formulae, including Ge-Gen-Qin-Lian decoction.