Abstract: INTRODUCTION: Postmortem redistribution (PMR), a well-described phenomenon in forensic toxicology for certain drugs, can result in increased central blood concentrations relative to peripheral blood concentrations. Delta(9)-tetrahydrocannabinol (THC), the primary psychoactive component in cannabis or marijuana, is the illicit substance most commonly implicated in driving under the influence of drugs (DUID) cases and fatally-injured drivers. No investigation of PMR of THC in human blood has been reported to date. METHODS: Matched heart and iliac postmortem blood specimens were collected from 19 medical examiner cases (16 Males, 3 Females) with positive cannabinoid urine immunoassay screens. THC, its equipotent metabolite 11-hydroxy-THC (11-OH-THC) and non-psychoactive metabolite 11-nor-9-carboxy-THC (THCCOOH) were quantified by two-dimensional gas chromatography-mass spectrometry with cryofocusing, with 0.5 ng/mL limits of quantification (LOQ) for all analytes. RESULTS: 10 cases had quantifiable THC and 11-OH-THC; THCCOOH was present in all 19. Median (range) heart:iliac blood ratios were 1.5 for THC (range: 0.3-3.1); 1.6 for 11-OH-THC (range: 0.3-2.7); and 1.8 for THCCOOH (range: 0.5-3.0). DISCUSSION: Cannabinoids, in general, exhibited a mean and median central:peripheral (C:P) concentration ratio of less than 2 following death. A trend was observed for greater PMR with increasing postmortem interval between death and sampling. To our knowledge, these are the first data on THC PMR in humans, providing important scientific data to aid in the interpretation of postmortem cannabinoid concentrations in medico-legal investigations.

Abstract: To explore the extent to which current knowledge about the organelle-targeting features of small molecules may be applicable toward controlling the accumulation and distribution of exogenous chemical agents inside cells, molecules with known subcellular localization properties (as reported in the scientific literature) were compiled into a single data set. This data set was compared to a reference data set of approved drug molecules derived from the DrugBank database, and to a reference data set of random organic molecules derived from the PubChem database. Cheminformatic analysis revealed that molecules with reported subcellular localizations were comparably diverse. However, the calculated physicochemical properties of molecules reported to accumulate in different organelles were markedly overlapping. In relation to the reference sets of DrugBank and PubChem molecules, molecules with reported subcellular localizations were biased toward larger, more complex chemical structures possessing multiple ionizable functional groups and higher lipophilicity. Stratifying molecules based on molecular weight revealed that many physicochemical properties' trends associated with specific organelles were reversed in smaller vs larger molecules. Most likely, these reversed trends are due to the different transport mechanisms determining the subcellular localization of molecules of different sizes. Molecular weight can be dramatically altered by tagging molecules with fluorophores or by incorporating organelle targeting motifs. Generally, in order to better exploit structure-localization relationships, subcellular targeting strategies would benefit from analysis of the biodistribution effects resulting from variations in the size of the molecules.

Abstract: Repositioning existing drugs for new therapeutic uses is an efficient approach to drug discovery. We have developed a computational drug repositioning pipeline to perform large-scale molecular docking of small molecule drugs against protein drug targets, in order to map the drug-target interaction space and find novel interactions. Our method emphasizes removing false positive interaction predictions using criteria from known interaction docking, consensus scoring, and specificity. In all, our database contains 252 human protein drug targets that we classify as reliable-for-docking as well as 4621 approved and experimental small molecule drugs from DrugBank. These were cross-docked, then filtered through stringent scoring criteria to select top drug-target interactions. In particular, we used MAPK14 and the kinase inhibitor BIM-8 as examples where our stringent thresholds enriched the predicted drug-target interactions with known interactions up to 20 times compared to standard score thresholds. We validated nilotinib as a potent MAPK14 inhibitor in vitro (IC50 40 nM), suggesting a potential use for this drug in treating inflammatory diseases. The published literature indicated experimental evidence for 31 of the top predicted interactions, highlighting the promising nature of our approach. Novel interactions discovered may lead to the drug being repositioned as a therapeutic treatment for its off-target's associated disease, added insight into the drug's mechanism of action, and added insight into the drug's side effects.

Abstract: The lymphoid tyrosine phosphatase (LYP), encoded by the PTPN22 gene, has recently been identified as a promising drug target for human autoimmunity diseases. Like the majority of protein-tyrosine phosphatases LYP can adopt two functionally distinct forms determined by the conformation of the WPD-loop. The WPD-loop plays an important role in the catalytic dephosphorylation by protein-tyrosine phosphatases. Here we investigate the binding modes of two chemotypes of small molecule LYP inhibitors with respect to both protein conformations using computational modeling. To evaluate binding in the active form, we built a LYP protein structure model of high quality. Our results suggest that the two different compound classes investigated, bind to different conformations of the LYP phosphatase domain. Binding to the closed form is facilitated by an interaction with Asp195 in the WPD-loop, presumably stabilizing the active conformation. The analysis presented here is relevant for the design of inhibitors that specifically target either the closed or the open conformation of LYP in order to achieve better selectivity over phosphatases with similar binding sites.

Abstract: The scaffold diversity of 7 representative commercial and proprietary compound libraries is explored for the first time using both Murcko frameworks and Scaffold Trees. We show that Level 1 of the Scaffold Tree is useful for the characterization of scaffold diversity in compound libraries and offers advantages over the use of Murcko frameworks. This analysis also demonstrates that the majority of compounds in the libraries we analyzed contain only a small number of well represented scaffolds and that a high percentage of singleton scaffolds represent the remaining compounds. We use Tree Maps to clearly visualize the scaffold space of representative compound libraries, for example, to display highly populated scaffolds and clusters of structurally similar scaffolds. This study further highlights the need for diversification of compound libraries used in hit discovery by focusing library enrichment on the synthesis of compounds with novel or underrepresented scaffolds.

Abstract: BACKGROUND: The speed at which biological datasets are being accumulated stands in contrast to our ability to integrate them meaningfully. Large-scale biological databases containing datasets of genes, proteins, cells, organs, and diseases are being created but they are not connected. Integration of these vast but heterogeneous sources of information will allow the systematic and comprehensive analysis of molecular and clinical datasets, spanning hundreds of dimensions and thousands of individuals. This integration is essential to capitalize on the value of current and future molecular- and cellular-level data on humans to gain novel insights about health and disease. RESULTS: We describe a new open-source Cytoscape plugin named iCTNet (integrated Complex Traits Networks). iCTNet integrates several data sources to allow automated and systematic creation of networks with up to five layers of omics information: phenotype-SNP association, protein-protein interaction, disease-tissue, tissue-gene, and drug-gene relationships. It facilitates the generation of general or specific network views with diverse options for more than 200 diseases. Built-in tools are provided to prioritize candidate genes and create modules of specific phenotypes. CONCLUSIONS: iCTNet provides a user-friendly interface to search, integrate, visualize, and analyze genome-scale biological networks for human complex traits. We argue this tool is a key instrument that facilitates systematic integration of disparate large-scale data through network visualization, ultimately allowing the identification of disease similarities and the design of novel therapeutic approaches.The online database and Cytoscape plugin are freely available for academic use at: http://www.cs.queensu.ca/ictnet.

Abstract: Molecular target identification is of central importance to drug discovery. Here, we developed a computational approach, named bioactivity profile similarity search (BASS), for associating targets to small molecules by using the known target annotations of related compounds from public databases. To evaluate BASS, a bioactivity profile database was constructed using 4296 compounds that were commonly tested in the US National Cancer Institute 60 human tumor cell line anticancer drug screen (NCI-60). Each compound was used as a query to search against the entire bioactivity profile database, and reference compounds with similar bioactivity profiles above a threshold of 0.75 were considered as neighbor compounds of the query. Potential targets were subsequently linked to the identified neighbor compounds by using the known targets of the query compound. About 45% of the predicted compound-target associations were successfully verified retrospectively, suggesting the possible application of BASS in identifying the targets of uncharacterized compounds and thus providing insight into the study of promiscuity and polypharmacology. Furthermore, BASS identified a significant fraction of structurally diverse compounds with similar bioactivities, indicating its feasibility of "scaffold hopping" in searching novel molecules against the target of interest.

Abstract: The norepinephrine transporter (NET) transports norepinephrine from the synapse into presynaptic neurons, where norepinephrine regulates signaling pathways associated with cardiovascular effects and behavioral traits via binding to various receptors (e.g., beta2-adrenergic receptor). NET is a known target for a variety of prescription drugs, including antidepressants and psychostimulants, and may mediate off-target effects of other prescription drugs. Here, we identify prescription drugs that bind NET, using virtual ligand screening followed by experimental validation of predicted ligands. We began by constructing a comparative structural model of NET based on its alignment to the atomic structure of a prokaryotic NET homolog, the leucine transporter LeuT. The modeled binding site was validated by confirming that known NET ligands can be docked favorably compared to nonbinding molecules. We then computationally screened 6,436 drugs from the Kyoto Encyclopedia of Genes and Genomes (KEGG DRUG) against the NET model. Ten of the 18 high-scoring drugs tested experimentally were found to be NET inhibitors; five of these were chemically novel ligands of NET. These results may rationalize the efficacy of several sympathetic (tuaminoheptane) and antidepressant (tranylcypromine) drugs, as well as side effects of diabetes (phenformin) and Alzheimer's (talsaclidine) drugs. The observations highlight the utility of virtual screening against a comparative model, even when the target shares less than 30% sequence identity with its template structure and no known ligands in the primary binding site.

Abstract: The development of small-molecule therapeutics that target RNA remains a promising field but one hampered with considerable challenges that include programming high affinity, specificity, cell permeability, and favorable pharmacokinetic profiles. Previously, we employed the use of peptoids to modularly display RNA-binding modules to enhance binding affinity and specificity by altering valency and the distance between ligand modules. Herein, factors that affect uptake, localization, and toxicity of peptoids that display a kanamycin derivative into a variety of mammalian cells lines are reported. A series of peptoids that display various spacing modules was synthesized to determine if the spacing module affects permeability and localization. The spacing module does affect cellular permeability into C2C12, A549, HeLa, and MCF7 cell lines but not into Jurkat cells. Moreover, the modularly assembled peptoids carrying the kanamycin cargo localize in the cytoplasm and perinuclear region of C2C12 and A549 cells and throughout HeLa cells, including the nucleus. These studies could contribute to the development of general strategies to afford cell permeable, modularly assembled small molecules that specifically target RNAs present in a variety of cell types.

Abstract: Human P450 protein CYP2C9 is one of the major drug-metabolizing isomers, contributing to the oxidation of 16% of the drugs currently in clinical use. To examine the interaction mechanisms between CYP2C9 and proton pump inhibitions (PPIs), we used molecular docking and molecular dynamics (MD) simulation methods to investigate the conformations and interactions around the binding sites of PPIs/CYPP2C9. Results from molecular docking and MD simulations demonstrate that nine PPIs adopt two different conformations (extended and U-bend structures) at the binding sites and position themselves far above the heme of 2C9. The presence of PPIs changes the secondary structures and residue flexibilities of 2C9. Interestingly, at the binding sites of all PPI-CYP2C9 complexes except for Lan/CYP2C9, there are hydrogen-bonding networks made of PPIs, water molecules, and some residues of 2C9. Moreover, there are strong hydrophobic interactions at all binding sites for PPIs/2C9, which indicate that electrostatic interactions and hydrophobic interactions appear to be important for stabilizing the binding sites of most PPIs/2C9. However, in the case of Lan/2C9, the hydrophobic interactions are more important than the electrostatic interactions for stabilizing the binding site. In addition, an interesting conformational conversion from extended to U-bend structures was observed for pantoprazole, which is attributed to an H-bond interaction in the binding pocket, an internal pi-pi stacking interaction, and an internal electrostatic interaction of pantoprazole.