Abstract: Wu-tou Decoction (WTD) is a classic herbal formula in traditional Chinese medicine for the treatment of joint diseases, neuropathic pain (NP) and inflammatory pain. In this study we investigated whether WTD produced analgesic action in a mouse spinal nerve ligation (SNL) model and elucidated the underlying molecular mechanisms. Mice were subjected to SNL and orally treated with WTD (3.15, 6.30 or 12.60 g.kg(-1).d(-1)) for 21 d. SNL induced mechanical hyperalgesia and heat hyperalgesia characterized by rapid and persistent pain hypersensitivity. In addition, the expression levels of IL-1beta, TNF-alpha, CCL2 and CXCL1 in the spinal cord dorsal horn were dramatically increased on the 10(th) d post-surgery. Oral administration of WTD dose-dependently suppressed both mechanical and heat hyperalgesia as well as the expression levels of inflammatory cytokines in the spinal cord dorsal horn on the 21(st) d post-surgery. Then whole-genome microarray analyses were conducted to detect the gene expression profiles of spinal cord dorsal horn in SNL mice with or without WTD treatment. After construction of the WTD-SNL-network and topological analysis, a list of candidate target genes of WTD acting on SNL-induced NP was identified and found to be functionally enriched in several glial cell activation-related pathways and neuroinflammatory pathways. Our data have clarified the gene expression patterns in the mouse spinal cord under the NP condition. We also demonstrate the analgesic action of WTD through suppression of glial cell activation and neuroinflammation, which suggest the potential of WTD as a promising candidate for the treatment of NP.

Abstract: Uncovering modular structure in networks is fundamental for systems in biology, physics, and engineering. Community detection identifies candidate modules as hypotheses, which then need to be validated through experiments, such as mutagenesis in a biological laboratory. Only a few communities can typically be validated, and it is thus important to prioritize which communities to select for downstream experimentation. Here we develop CRANK, a mathematically principled approach for prioritizing network communities. CRANK efficiently evaluates robustness and magnitude of structural features of each community and then combines these features into the community prioritization. CRANK can be used with any community detection method. It needs only information provided by the network structure and does not require any additional metadata or labels. However, when available, CRANK can incorporate domain-specific information to further boost performance. Experiments on many large networks show that CRANK effectively prioritizes communities, yielding a nearly 50-fold improvement in community prioritization.

Abstract: Many xenobiotics can bind to off-target receptors and cause toxicity via the dysregulation of downstream transcription factors. Identification of subsequent off-target toxicity in these chemicals has often required extensive chemical testing in animal models. An alternative, integrated in vitro/in silico approach for predicting toxic off-target functional responses is presented to refine in vitro receptor identification and reduce the burden on in vivo testing. As part of the methodology, mathematical modeling is used to mechanistically describe processes that regulate transcriptional activity following receptor-ligand binding informed by transcription factor signaling assays. Critical reactions in the signaling cascade are identified to highlight potential perturbation points in the biochemical network that can guide and optimize additional in vitro testing. A physiologically based pharmacokinetic model provides information on the timing and localization of different levels of receptor activation informing whole-body toxic potential resulting from off-target binding.

Abstract: Oxamate and pyruvate are isoelectronic molecules. They both quench tryptophan fluorescence with Stern-Volmer constants of 16 and 20 M(-1), respectively, which are comparable to that of arcrylamide, a commonly used probe for protein structure. On the other hand, it is well known that neither the carboxylate group of these molecules nor the amide group is a good quencher. To find the mechanism of the quenching by oxamate and pyruvate, density functional theory computations with a polarizable continuum model, solvation based on density, and explicit waters, were performed. Results indicate that both molecules can be an electron acceptor via photoinduced electron transfer. There are two requirements. First, the carboxylate and amide moieties must be in direct contact to bring about noticeable quenching. The conjugation between the amide (or the keto) group and the carboxylate group leads to a lower pi* orbital, which is the lowest unoccupied molecular orbital (LUMO), and can then accept an electron from the excited tryptophan. Second, since oxamate and pyruvate ions have high electron density, hydrogen bonds with waters, which can be simulated by an explicit water model, are essential. Their LUMO energies are strongly influenced by water in aqueous solution. The above findings demonstrate how tryptophan fluorescence gets quenched in aqueous solution. The findings may be important in dealing with those problems where frontier orbitals are considered, especially with molecules having high electron density.

Abstract: Current wastewater treatment technologies do not remove many unregulated hydrophilic compounds, and there is growing interest that low levels of these compounds, referred to as emerging contaminants, may impact human health and the environment. A probabilistic-designed monitoring network was employed to infer the extent of Florida's ambient freshwaters containing the wastewater (Includes reuse water, septic systems leachate, and wastewater treatment effluent.) indicators sucralose, acetaminophen, carbamazepine, and primidone and those containing the widely used pesticide imidacloprid. Extent estimates with 95% confidence bounds are provided for canals, rivers, streams, small and large lakes, and unconfined aquifers containing ultra-trace concentrations of these compounds as based on analyses of 2015 sample surveys utilizing 528 sites. Sucralose is estimated to occur in greater than 50% of the canal, river, stream, and large lake resource extents. The pharmaceuticals acetaminophen, carbamazepine, and primidone are most prevalent in rivers, with approximately 30% of river kilometers estimated to contain at least one of these compounds. Imidacloprid is estimated to occur in 50% or greater of the canal and river resource extents, and it is the only compound found to exceed published toxicity or environmental effects standards. Geospatial analyses show sucralose detection frequencies within Florida's drainage basins to be significantly related to the percentage of urban land use (R(2) = 0.36, p < 0.001), and imidacloprid detection frequencies to be significantly related to the percentage of urban and agricultural land use (R(2) = 0.47, p < 0.001). The extent of the presence of these compounds highlights the need for additional emerging contaminant studies especially those examining effects on aquatic biota.

Abstract: Current pharmaceutical research and development (R&D) is a high-risk investment which is usually faced with some unexpected even disastrous failures in different stages of drug discovery. One main reason for R&D failures is the efficacy and safety deficiencies which are related largely to absorption, distribution, metabolism and excretion (ADME) properties and various toxicities (T). Therefore, rapid ADMET evaluation is urgently needed to minimize failures in the drug discovery process. Here, we developed a web-based platform called ADMETlab for systematic ADMET evaluation of chemicals based on a comprehensively collected ADMET database consisting of 288,967 entries. Four function modules in the platform enable users to conveniently perform six types of drug-likeness analysis (five rules and one prediction model), 31 ADMET endpoints prediction (basic property: 3, absorption: 6, distribution: 3, metabolism: 10, elimination: 2, toxicity: 7), systematic evaluation and database/similarity searching. We believe that this web platform will hopefully facilitate the drug discovery process by enabling early drug-likeness evaluation, rapid ADMET virtual screening or filtering and prioritization of chemical structures. The ADMETlab web platform is designed based on the Django framework in Python, and is freely accessible at http://admet.scbdd.com/ .

Abstract: Recently, many biological experiments have indicated that microRNAs (miRNAs) are a newly discovered small molecule (SM) drug targets that play an important role in the development and progression of human complex diseases. More and more computational models have been developed to identify potential associations between SMs and target miRNAs, which would be a great help for disease therapy and clinical applications for known drugs in the field of medical research. In this study, we proposed a computational model of triple layer heterogeneous network based small molecule-MiRNA association prediction (TLHNSMMA) to uncover potential SM-miRNA associations by integrating integrated SM similarity, integrated miRNA similarity, integrated disease similarity, experimentally verified SM-miRNA associations and miRNA-disease associations into a heterogeneous graph. To evaluate the performance of TLHNSMMA, we implemented global and two types of local leave-one-out cross validation as well as fivefold cross validation to compare TLHNSMMA with one previous classical computational model (SMiR-NBI). As a result, for Dataset 1, TLHNSMMA obtained the AUCs of 0.9859, 0.9845, 0.7645 and 0.9851 +/- 0.0012, respectively; for Dataset 2, the AUCs are in turn 0.8149, 0.8244, 0.6057 and 0.8168 +/- 0.0022. As the result of case studies shown, among the top 10, 20 and 50 potential SM-related miRNAs, there were 2, 7 and 14 SM-miRNA associations confirmed by experiments, respectively. Therefore, TLHNSMMA could be effectively applied to the prediction of SM-miRNA associations.

Abstract: Multiple approaches to quantitative structure-activity relationship (QSAR) modeling using various statistical or machine learning techniques and different types of chemical descriptors have been developed over the years. Oftentimes models are used in consensus to make more accurate predictions at the expense of model interpretation. We propose a simple, fast, and reliable method termed Multi-Descriptor Read Across (MuDRA) for developing both accurate and interpretable models. The method is conceptually related to the well-known kNN approach but uses different types of chemical descriptors simultaneously for similarity assessment. To benchmark the new method, we have built MuDRA models for six different end points (Ames mutagenicity, aquatic toxicity, hepatotoxicity, hERG liability, skin sensitization, and endocrine disruption) and compared the results with those generated with conventional consensus QSAR modeling. We find that models built with MuDRA show consistently high external accuracy similar to that of conventional QSAR models. However, MuDRA models excel in terms of transparency, interpretability, and computational efficiency. We posit that due to its methodological simplicity and reliable predictive accuracy, MuDRA provides a powerful alternative to a much more complex consensus QSAR modeling. MuDRA is implemented and freely available at the Chembench web portal ( https://chembench.mml.unc.edu/mudra ).

Abstract: The past decades have witnessed a paradigm shift from the traditional drug discovery shaped around the idea of “one target, one disease” to polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is to target common biological pathways involved in diseases via endopharmacology (multiple targets, multiple diseases). In this study, we present proximal pathway enrichment analysis (PxEA) for pinpointing drugs that target common disease pathways towards network endopharmacology. PxEA uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs by their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. Using PxEA, we show that many drugs indicated for autoimmune disorders are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two conditions that have recently gained attention due to the increased comorbidity among patients.