Abstract: A critical goal of pharmacogenomics research is to identify genes that can explain variation in drug response. We have previously reported a method that creates a genome-scale ranking of genes likely to interact with a drug. The algorithm uses information about drug structure and indications of use to rank the genes. Although the algorithm has good performance, its performance depends on a curated set of drug-gene relationships that is expensive to create and difficult to maintain. In this work, we assess the utility of text mining in extracting a network of drug-gene relationships automatically. This provides a valuable aggregate source of knowledge, subsequently used as input into the algorithm that ranks potential pharmacogenes. Using a drug-gene network created from sentence-level co-occurrence in the full text of scientific articles, we compared the performance to that of a network created by manual curation of those articles. Under a wide range of conditions, we show that a knowledge base derived from text-mining the literature performs as well as, and sometimes better than, a high-quality, manually curated knowledge base. We conclude that we can use relationships mined automatically from the literature as a knowledgebase for pharmacogenomics relationships. Additionally, when relationships are missed by text mining, our system can accurately extrapolate new relationships with 77.4% precision.

Abstract: Despite the dramatic increase of global spending on drug discovery and development, the approval rate for new drugs is declining, due chiefly to toxicity and undesirable side effects. Simultaneously, the growth of available biomedical data in the postgenomic era has provided fresh insight into the nature of redundant and compensatory drug-target pathways. This stagnation in drug approval can be overcome by the novel concept of polypharmacology, which is built on the fundamental concept that drugs modulate multiple targets. Polypharmacology can be studied with molecular networks that integrate multidisciplinary concepts including cheminformatics, bioinformatics, and systems biology. In silico techniques such as structure- and ligand-based approaches can be employed to study molecular networks and reduce costs by predicting adverse drug reactions and toxicity in the early stage of drug development. By amalgamating strides in this informatics-driven era, designing polypharmacological drugs with molecular network technology exemplifies the next generation of therapeutics with less of-target properties and toxicity. In this review, we will first describe the challenges in drug discovery, and showcase successes using multitarget drugs toward diseases such as cancer and mood disorders. We will then focus on recent development of in silico polypharmacology predictions. Finally, our technologies in molecular network analysis will be presented.

Abstract: BACKGROUND: Adenocarcinomas of the tongue are rare and represent the minority (20 to 25%) of salivary gland tumors affecting the tongue. We investigated the utility of massively parallel sequencing to characterize an adenocarcinoma of the tongue, before and after treatment. RESULTS: In the pre-treatment tumor we identified 7,629 genes within regions of copy number gain. There were 1,078 genes that exhibited increased expression relative to the blood and unrelated tumors and four genes contained somatic protein-coding mutations. Our analysis suggested the tumor cells were driven by the RET oncogene. Genes whose protein products are targeted by the RET inhibitors sunitinib and sorafenib correlated with being amplified and or highly expressed. Consistent with our observations, administration of sunitinib was associated with stable disease lasting 4 months, after which the lung lesions began to grow. Administration of sorafenib and sulindac provided disease stabilization for an additional 3 months after which the cancer progressed and new lesions appeared. A recurring metastasis possessed 7,288 genes within copy number amplicons, 385 genes exhibiting increased expression relative to other tumors and 9 new somatic protein coding mutations. The observed mutations and amplifications were consistent with therapeutic resistance arising through activation of the MAPK and AKT pathways. CONCLUSIONS: We conclude that complete genomic characterization of a rare tumor has the potential to aid in clinical decision making and identifying therapeutic approaches where no established treatment protocols exist. These results also provide direct in vivo genomic evidence for mutational evolution within a tumor under drug selection and potential mechanisms of drug resistance accrual.

Abstract: The molecular understanding of phenotypes caused by drugs in humans is essential for elucidating mechanisms of action and for developing personalized medicines. Side effects of drugs (also known as adverse drug reactions) are an important source of human phenotypic information, but so far research on this topic has been hampered by insufficient accessibility of data. Consequently, we have developed a public, computer-readable side effect resource (SIDER) that connects 888 drugs to 1450 side effect terms. It contains information on frequency in patients for one-third of the drug-side effect pairs. For 199 drugs, the side effect frequency of placebo administration could also be extracted. We illustrate the potential of SIDER with a number of analyses. The resource is freely available for academic research at http://sideeffects.embl.de.

Abstract: The structural elucidation of small molecules using mass spectrometry plays an important role in modern life sciences and bioanalytical approaches. This review covers different soft and hard ionization techniques and figures of merit for modern mass spectrometers, such as mass resolving power, mass accuracy, isotopic abundance accuracy, accurate mass multiple-stage MS(n) capability, as well as hybrid mass spectrometric and orthogonal chromatographic approaches. The latter part discusses mass spectral data handling strategies, which includes background and noise subtraction, adduct formation and detection, charge state determination, accurate mass measurements, elemental composition determinations, and complex data-dependent setups with ion maps and ion trees. The importance of mass spectral library search algorithms for tandem mass spectra and multiple-stage MS(n) mass spectra as well as mass spectral tree libraries that combine multiple-stage mass spectra are outlined. The successive chapter discusses mass spectral fragmentation pathways, biotransformation reactions and drug metabolism studies, the mass spectral simulation and generation of in silico mass spectra, expert systems for mass spectral interpretation, and the use of computational chemistry to explain gas-phase phenomena. A single chapter discusses data handling for hyphenated approaches including mass spectral deconvolution for clean mass spectra, cheminformatics approaches and structure retention relationships, and retention index predictions for gas and liquid chromatography. The last section reviews the current state of electronic data sharing of mass spectra and discusses the importance of software development for the advancement of structure elucidation of small molecules. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s12566-010-0015-9) contains supplementary material, which is available to authorized users.

Abstract: PURPOSE: The aim of this in vitro study was to evaluate potential determinants of drug loss in different ECMO circuits. METHODS: Midazolam, morphine, fentanyl, paracetamol, cefazolin, meropenem and vancomycin were injected into three neonatal roller pump, two paediatric roller pump and two clinically used neonatal roller pump circuits, all with a silicone membrane, and two neonatal centrifugal pump circuits with polypropylene hollow-fibre membranes. Serial blood samples were taken from a post-oxygenator site. Drug recovery was calculated as the ratio between the determined and the theoretical maximum concentration. The latter was obtained by dividing dose by theoretical circuit volume. RESULTS: Average drug recoveries at 180 min in three neonatal silicone membrane roller pump circuits were midazolam 0.62%, morphine 23.9%, fentanyl 0.35%, paracetamol 34.0%, cefazolin 84.3%, meropenem 82.9% and vancomycin 67.8%. There was a significant correlation between the lipophilicity of the drug expressed as log P and the extent of drug absorption, p < 0.001. The recovery of midazolam and fentanyl in centrifugal pump circuits with hollow-fibre membrane oxygenator was significantly higher compared to neonatal roller pump circuits with silicone membranes: midazolam 63.4 versus 0.62%, fentanyl 33.8 versus 0.35%, p < 0.001. Oxygenator size and used circuits do not significantly affect drug losses. CONCLUSIONS: Significant absorption of drugs occurs in the ECMO circuit, correlating with increased lipophilicity of the drug. Centrifugal pump circuits with hollow-fibre membrane oxygenators show less absorption for all drugs, most pronounced for lipophilic drugs. These results suggest that pharmacokinetics and hence optimal doses of these drugs may be altered during ECMO.

Abstract: One of the biggest obstacles to progress in modern pharmaceutical research is the difficulty of integrating all available research findings into effective therapies for humans. Studies of traditionally used pharmacologically active plants and other substances in traditional medicines may be valuable sources of previously unknown compounds with therapeutic actions. However, the integration of findings from traditional medicines can be fraught with difficulties and misunderstandings. This article proposes an approach to use linked open data and Semantic Web technologies to address the heterogeneous data integration problem. The approach is based on our initial experiences with implementing an integrated web of data for a selected use-case, i.e., the identification of plant species used in Chinese medicine that indicate potential antidepressant activities.

Abstract: MOTIVATION: Advances in the field of cheminformatics have been hindered by a lack of freely available tools. We have created Chembench, a publicly available cheminformatics portal for analyzing experimental chemical structure-activity data. Chembench provides a broad range of tools for data visualization and embeds a rigorous workflow for creating and validating predictive Quantitative Structure-Activity Relationship models and using them for virtual screening of chemical libraries to prioritize the compound selection for drug discovery and/or chemical safety assessment. AVAILABILITY: Freely accessible at: http://chembench.mml.unc.edu CONTACT: alex_tropsha@unc.edu

Abstract: Atrial and ventricular tachyarrhythmias can be perpetuated by up-regulation of inward rectifier potassium channels. Thus, it may be beneficial to block inward rectifier channels under conditions in which their function becomes arrhythmogenic (e.g., inherited gain-of-function mutation channelopathies, ischemia, and chronic and vagally mediated atrial fibrillation). We hypothesize that the antimalarial quinoline chloroquine exerts potent antiarrhythmic effects by interacting with the cytoplasmic domains of Kir2.1 (I(K1)), Kir3.1 (I(KACh)), or Kir6.2 (I(KATP)) and reducing inward rectifier potassium currents. In isolated hearts of three different mammalian species, intracoronary chloroquine perfusion reduced fibrillatory frequency (atrial or ventricular), and effectively terminated the arrhythmia with resumption of sinus rhythm. In patch-clamp experiments chloroquine blocked I(K1), I(KACh), and I(KATP). Comparative molecular modeling and ligand docking of chloroquine in the intracellular domains of Kir2.1, Kir3.1, and Kir6.2 suggested that chloroquine blocks or reduces potassium flow by interacting with negatively charged amino acids facing the ion permeation vestibule of the channel in question. These results open a novel path toward discovering antiarrhythmic pharmacophores that target specific residues of the cytoplasmic domain of inward rectifier potassium channels.

Abstract: MOTIVATION: Most of the previous data mining studies based on the NCI-60 dataset, due to its intrinsic cell-based nature, can hardly provide insights into the molecular targets for screened compounds. On the other hand, the abundant information of the compound-target associations in PubChem can offer extensive experimental evidence of molecular targets for tested compounds. Therefore, by taking advantages of the data from both public repositories, one may investigate the correlations between the bioactivity profiles of small molecules from the NCI-60 dataset (cellular level) and their patterns of interactions with relevant protein targets from PubChem (molecular level) simultaneously. RESULTS: We investigated a set of 37 small molecules by providing links among their bioactivity profiles, protein targets and chemical structures. Hierarchical clustering of compounds was carried out based on their bioactivity profiles. We found that compounds were clustered into groups with similar mode of actions, which strongly correlated with chemical structures. Furthermore, we observed that compounds similar in bioactivity profiles also shared similar patterns of interactions with relevant protein targets, especially when chemical structures were related. The current work presents a new strategy for combining and data mining the NCI-60 dataset and PubChem. This analysis shows that bioactivity profile comparison can provide insights into the mode of actions at the molecular level, thus will facilitate the knowledge-based discovery of novel compounds with desired pharmacological properties. AVAILABILITY: The bioactivity profiling data and the target annotation information are publicly available in the PubChem BioAssay database (ftp://ftp.ncbi.nlm.nih.gov/pubchem/Bioassay/).