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Genomics has multiplied the number of targets for new therapeutic interventions, but this has not yet lead to a marked increase of pharma pipeline outputs. The complexity of protein function in higher order biological systems is often underestimated. Translation from in vitro and in vivo results to the human setting frequently fails due to unforeseen toxicity and efficacy issues. Biosimulation addresses these issues by capturing the complex dynamics of interacting molecules and cells in mechanistic, predictive models. A central concept is that of the virtual patient, an encapsulation of a specific pathophysiological behaviour in a biosimulation model. The authors describe how virtual patients are being used in target identification, target validation and clinical development, and discuss challenges for the acceptance of biosimulation methods.

The completeness of experimentally observed NOE restraints of a set of 97 NMR protein structures deposited in the PDB has been assessed. Completeness is defined as the ratio of the number of experimentally observed NOEs and the number of 'expected NOEs'. A practical definition of 'expected NOEs' based on inter-proton distances in the structures up to a given cut-off distance is proposed. The average completeness for the set of 97 structures is 68, 48, and 26% up to 3, 4, and 5 A cut-off distances, respectively. For recent state-of-the-art structures these numbers are approximately 90, 75, and 45%. Almost 20% of the observed NOEs are between atoms that are further than 5 A apart in the final structures. The completeness is independent of the relative surface accessibility and does not depend strongly on residue type, secondary structure or local precision, although the number of observed NOEs in these classes varies considerably. The completeness of NOE restraints is a useful quality criterion in the course of structure refinement. The completeness per residue is more informative than the number of NOEs per residue, which makes it a useful tool to assess the quality of the NMR data set in relation to the resulting structures.

Background Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic side-effects, e.g. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of insulin resistance and which genes are affected by glucocorticoids. Medline abstracts contain many studies about insulin resistance and the molecular effects of glucocorticoids and thus are a good resource to study these effects. Results We developed CoPubGene a method to automatically identify gene-disease associations in Medline abstracts. We used this method to create a literature network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes. With this approach we found several genes that already are considered markers of GC induced IR, such as phosphoenolpyruvate carboxykinase (PCK) and glucose-6-phosphatase, catalytic subunit (G6PC). In addition, we found genes involved in steroid synthesis that have not yet been recognized as mediators of GC induced IR. Conclusions With this approach we are able to construct a robust informative literature network of insulin resistance related genes that gave new insights to better understand the mechanisms behind GC induced IR. The method has been set up in a generic way so it can be applied to a wide variety of disease networks.

BACKGROUNDGlucocorticoids, such as prednisolone, are widely used anti-inflammatory drugs, but therapy is hampered by a broad range of metabolic side effects including skeletal muscle wasting and insulin resistance. Therefore, development of improved synthetic glucocorticoids that display similar efficacy as prednisolone but reduced side effects is an active research area. For efficient development of such new drugs, in vivo biomarkers, which can predict glucocorticoid metabolic side effects in an early stage, are needed. In this study, we aim to provide the first description of the metabolic perturbations induced by acute and therapeutic treatments with prednisolone in humans using urine metabolomics and to derive potential biomarkers for prednisolone-induced metabolic effects. METHODS A randomized, double blind, placebo controlled trial was conducted in healthy men consisting of 2 protocols. In protocol 1, volunteers received placebo (n=11) or prednisolone (7.5 mg (n=11), 15 mg (n=13) or 30 mg (n=12)) orally once daily for 15 days. In protocol 2, volunteers (n=6) received placebo at day 0 and 75 mg prednisolone at day 1. We collected 24 h urine and serum samples at baseline (day 0), after a single dose (day 1) and after prolonged treatment (day 15) and obtained mass-spectrometry based urine and serum metabolic profiles. RESULTSAt day 1, high dose prednisolone treatment increased levels of 13 and 10 proteinogenic amino acids in urine and serum respectively, as well as levels of 3-methylhistidine, providing evidence for an early manifestation of glucocorticoid-induced muscle wasting. Prednisolone treatment also strongly increased urinary carnitine derivatives at day 1 but not at day 15, which might reflect adaptive mechanisms under prolonged treatment. Finally, urinary levels of proteinogenic amino acids at day 1 and of N-methylnicotinamide at day 15 significantly correlated with the homeostatic model assessment of insulin resistance (HOMA-IR) and might represent biomarkers for prednisoloneinduced insulin resistance. CONCLUSIONThis study provides evidence that urinary metabolomics represents a non-invasive way of monitoring the effect of glucocorticoids on muscle protein catabolism after a single dose and can derive new biomarkers of glucocorticoid-induced insulin resistance. It might therefore help the development of improved synthetic glucocorticoids.

High throughput microarray analyses result in many differentially expressed genes that are potentially responsible for the biological process of interest. In order to identify biological similarities between genes, publications from MEDLINE were identified in which pairs of gene names and combinations of gene name with specific keywords were co-mentioned. MEDLINE search strings for 15,621 known genes and 3,731 keywords were generated and validated. PubMed IDs were retrieved from MEDLINE and relative probability of co-occurrences of all gene-gene and gene-keyword pairs determined. To assess gene clustering according to literature co-publication, 150 genes consisting of 8 sets with known connections (same pathway, same protein complex, or same cellular localization, etc.) were run through the program. Receiver operator characteristics (ROC) analyses showed that most gene sets were clustered much better than expected by random chance. To test grouping of genes from real microarray data, 221 differentially expressed genes from a microarray experiment were analyzed with CoPub Mapper, which resulted in several relevant clusters of genes with biological process and disease keywords. In addition, all genes versus keywords were hierarchical clustered to reveal a complete grouping of published genes based on co-occurrence. The CoPub Mapper program allows for quick and versatile querying of co-published genes and keywords and can be successfully used to cluster predefined groups of genes and microarray data.