Abstract: The Transporter Classification Database (TCDB; http://www.tcdb.org) serves as a common reference point for transport protein research. The database contains more than 10,000 non-redundant proteins that represent all currently recognized families of transmembrane molecular transport systems. Proteins in TCDB are organized in a five level hierarchical system, where the first two levels are the class and subclass, the second two are the family and subfamily, and the last one is the transport system. Superfamilies that contain multiple families are included as hyperlinks to the five tier TC hierarchy. TCDB includes proteins from all types of living organisms and is the only transporter classification system that is both universal and recognized by the International Union of Biochemistry and Molecular Biology. It has been expanded by manual curation, contains extensive text descriptions providing structural, functional, mechanistic and evolutionary information, is supported by unique software and is interconnected to many other relevant databases. TCDB is of increasing usefulness to the international scientific community and can serve as a model for the expansion of database technologies. This manuscript describes an update of the database descriptions previously featured in NAR database issues.

Abstract: The Small Molecule Pathway Database (SMPDB, http://www.smpdb.ca) is a comprehensive, colorful, fully searchable and highly interactive database for visualizing human metabolic, drug action, drug metabolism, physiological activity and metabolic disease pathways. SMPDB contains >600 pathways with nearly 75% of its pathways not found in any other database. All SMPDB pathway diagrams are extensively hyperlinked and include detailed information on the relevant tissues, organs, organelles, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Since its last release in 2010, SMPDB has undergone substantial upgrades and significant expansion. In particular, the total number of pathways in SMPDB has grown by >70%. Additionally, every previously entered pathway has been completely redrawn, standardized, corrected, updated and enhanced with additional molecular or cellular information. Many SMPDB pathways now include transporter proteins as well as much more physiological, tissue, target organ and reaction compartment data. Thanks to the development of a standardized pathway drawing tool (called PathWhiz) all SMPDB pathways are now much more easily drawn and far more rapidly updated. PathWhiz has also allowed all SMPDB pathways to be saved in a BioPAX format. Significant improvements to SMPDB's visualization interface now make the browsing, selection, recoloring and zooming of pathways far easier and far more intuitive. Because of its utility and breadth of coverage, SMPDB is now integrated into several other databases including HMDB and DrugBank.

Abstract: Building molecular correlates of drug resistance in cancer and exploiting them for therapeutic intervention remains a pressing clinical need. To identify factors that impact drug resistance herein we built a model that couples inherent cell-based response toward drugs with transcriptomes of resistant/sensitive cells. To test this model, we focused on a group of genes called metastasis suppressor genes (MSGs) that influence aggressiveness and metastatic potential of cancers. Interestingly, modeling of 84 000 drug response transcriptome combinations predicted multiple MSGs to be associated with resistance of different cell types and drugs. As a case study, on inducing MSG levels in a drug resistant breast cancer line resistance to anticancer drugs caerulomycin, camptothecin and topotecan decreased by more than 50-60%, in both culture conditions and also in tumors generated in mice, in contrast to control un-induced cells. To our knowledge, this is the first demonstration of engineered reversal of drug resistance in cancer cells based on a model that exploits inherent cellular response profiles.

Abstract: BACKGROUND AND OBJECTIVE: Milrinone is the drug of choice for the treatment and prevention of low cardiac output syndrome (LCOS) in paediatric patients after open heart surgery across Europe. Discrepancies, however, among prescribing guidance, clinical studies and practice pattern require clarification to ensure safe and effective prescribing. However, the clearance prediction equations derived from classical pharmacokinetic modelling provide limited support as they have recently failed a clinical practice evaluation. Therefore, the objective of this study was to evaluate current milrinone dosing using physiology-based pharmacokinetic (PBPK) modelling and simulation to complement the existing pharmacokinetic knowledge and propose optimised dosing regimens as a basis for improving the standard of care for paediatric patients. METHODS: A PBPK drug-disease model using a population approach was developed in three steps from healthy young adults to adult patients and paediatric patients with and without LCOS after open heart surgery. Pre- and postoperative organ function values from adult and paediatric patients were collected from literature and integrated into a disease model as factorial changes from the reference values in healthy adults aged 20-40 years. The disease model was combined with the PBPK drug model and evaluated against existing pharmacokinetic data. Model robustness was assessed by parametric sensitivity analysis. In the next step, virtual patient populations were created, each with 1,000 subjects reflecting the average adult and paediatric patient characteristics with regard to age, sex, bodyweight and height. They were integrated into the PBPK drug-disease model to evaluate the effectiveness of current milrinone dosing in achieving the therapeutic target range of 100-300 ng/mL milrinone in plasma. Optimised dosing regimens were subsequently developed. RESULTS: The pharmacokinetics of milrinone in healthy young adults as well as adult and paediatric patients were accurately described with an average fold error of 1.1 +/- 0.1 (mean +/- standard deviation) and mean relative deviation of 1.5 +/- 0.3 as measures of bias and precision, respectively. Normalised maximum sensitivity coefficients for model input parameters ranged from -0.84 to 0.71, which indicated model robustness. The evaluation of milrinone dosing across different paediatric age groups showed a non-linear age dependence of total plasma clearance and exposure differences of a factor 1.4 between patients with and without LCOS for a fixed dosing regimen. None of the currently used dosing regimens for milrinone achieved the therapeutic target range across all paediatric age groups and adult patients, so optimised dosing regimens were developed that considered the age-dependent and pathophysiological differences. CONCLUSION: The PBPK drug-disease model for milrinone in paediatric patients with and without LCOS after open heart surgery highlights that age, disease and surgery differently impact the pharmacokinetics of milrinone, and that current milrinone dosing for LCOS is suboptimal to maintain the therapeutic target range across the entire paediatric age range. Thus, optimised dosing strategies are proposed to ensure safe and effective prescribing.

Abstract: Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.

Abstract: Tinnitus, phantom sound perception, is a worldwide highly prevalent disorder for which no clear underlying pathology has been established and for which no approved drug is on the market. Thus, there is an urgent need for new approaches to understand this condition. We used a network pharmacology side-effect analysis to search for genes that are involved in tinnitus generation. We analyzed a network of 1,313 drug-target pairs, based on 275 compounds that elicit tinnitus as side effect and their targets reported in databases, and used a quantitative score to identify emergent significant targets that were more common than expected at random. Cyclooxigenase 1 and 2 were significant, which validates our approach, since salicylate is a known tinnitus generator. More importantly, we predict previously unknown tinnitus-related targets. The present results have important implications toward understanding tinnitus pathophysiology and might pave the way toward the design of novel pharmacotherapies.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e97; doi:10.1038/psp.2013.75; published online 29 January 2014.

Abstract: The stereoselective binding of R- and S-propranolol to the metabolic enzyme cytochrome P450 2D6 and its mutant F483A was studied using various computational approaches. Previously reported free-energy differences from Hamiltonian replica exchange simulations, combined with thermodynamic integration, are compared to the one-step perturbation approach, combined with local-elevation enhanced sampling, and an excellent agreement between methods was obtained. Further, the free-energy differences are interpreted in terms of enthalpic and entropic contributions where it is shown that exactly compensating contributions obscure a molecular interpretation of differences in the affinity while various reduced terms allow a more detailed analysis, which agree with heuristic observations on the interactions.

Abstract: Druggability of a protein is its potential to be modulated by drug-like molecules. It is important in the target selection phase. We hypothesize that: (i) known drug-binding sites contain advantageous physicochemical properties for drug binding, or "druggable microenvironments" and (ii) given a target, the presence of multiple druggable microenvironments similar to those seen previously is associated with a high likelihood of druggability. We developed DrugFEATURE to quantify druggability by assessing the microenvironments in potential small-molecule binding sites. We benchmarked DrugFEATURE using two data sets. One data set measures druggability using NMR-based screening. DrugFEATURE correlates well with this metric. The second data set is based on historical drug discovery outcomes. Using the DrugFEATURE cutoffs derived from the first, we accurately discriminated druggable and difficult targets in the second. We further identified novel druggable transcription factors with implications for cancer therapy. DrugFEATURE provides useful insight for drug discovery, by evaluating druggability and suggesting specific regions for interacting with drug-like molecules.CPT: Pharmacometrics Systems Pharmacology (2014) 3, e93; doi:10.1038/psp.2013.66; published online 22 January 2014.

Abstract: BACKGROUND: Development of novel antibacterial drugs is both an urgent healthcare necessity and a partially neglected field. The last decades have seen a substantial decrease in the discovery of novel antibiotics, which combined with the recent thrive of multi-drug-resistant pathogens have generated a scenario of general concern. The procedures involved in the discovery and development of novel antibiotics are economically challenging, time consuming and lack any warranty of success. Furthermore, the return-on-investment for an antibacterial drug is usually marginal when compared to other therapeutics, which in part explains the decrease of private investment. RESULTS: In this work we present antibacTR, a computational pipeline designed to aid researchers in the selection of potential drug targets, one of the initial steps in antibacterial-drug discovery. The approach was designed and implemented as part of two publicly funded initiatives aimed at discovering novel antibacterial targets, mechanisms and drugs for a priority list of Gram-negative pathogens: Acinetobacter baumannii, Escherichia coli, Helicobacter pylori, Pseudomonas aeruginosa and Stenotrophomonas maltophilia. However, at present this list has been extended to cover a total of 74 fully sequenced Gram-negative pathogens. antibacTR is based on sequence comparisons and queries to multiple databases (e.g. gene essentiality, virulence factors) to rank proteins according to their potential as antibacterial targets. The dynamic ranking of potential drug targets can easily be executed, customized and accessed by the user through a web interface which also integrates computational analyses performed in-house and visualizable on-site. These include three-dimensional modeling of protein structures and prediction of active sites among other functionally relevant ligand-binding sites. CONCLUSIONS: Given its versatility and ease-of-use at integrating both experimental annotation and computational analyses, antibacTR may effectively assist microbiologists, medicinal-chemists and other researchers working in the field of antibacterial drug-discovery. The public web-interface for antibacTR is available at 'http://bioinf.uab.cat/antibactr'.

Abstract: Today, herb medicines have become the major source for discovery of novel agents in countermining diseases. However, many of them are largely under-explored in pharmacology due to the limitation of current experimental approaches. Therefore, we proposed a computational framework in this study for network understanding of herb pharmacology via rapid identification of putative ingredient-target interactions in human structural proteome level. A marketing anti-cancer herb medicine in China, Yadanzi (Brucea javanica), was chosen for mechanistic study. Total 7,119 ingredient-target interactions were identified for thirteen Yadanzi active ingredients. Among them, about 29.5% were estimated to have better binding affinity than their corresponding marketing drug-target interactions. Further Bioinformatics analyses suggest that simultaneous manipulation of multiple proteins in the MAPK signaling pathway and the phosphorylation process of anti-apoptosis may largely answer for Yadanzi against non-small cell lung cancers. In summary, our strategy provides an efficient however economic solution for systematic understanding of herbs' power.