Abstract: Background: Glioma accounts for 80% of malignant brain tumors, but its etiologic determinants remain elusive. Despite genetic susceptibility loci identified by genome-wide association study (GWAS), the agnostic approach leaves open the possibility that other susceptibility genes remain to be discovered. Here we conduct a gene-centric integrative GWAS (iGWAS) of glioma risk that combines transcriptomics and genetics. Methods: We synthesized a brain transcriptomics dataset (n = 354), a GWAS dataset (n = 4203), and an advanced glioma tumor transcriptomic dataset (n = 483) to conduct an iGWAS. Using the expression quantitative trait loci (eQTL) dataset, we built models to predict gene expression for the GWAS data, based on eQTL genotypes. With the predicted gene expression, iGWAS analyses were performed using a novel statistical method. Gene signature risk score was constructed using a penalized logistic regression model. Results: A total of 30527 transcripts were analyzed using the iGWAS approach. Four novel glioma susceptibility genes were identified with internal and external validation, including DRD5 (P = 3.0 x 10-79), WDR1 (P = 8.4 x 10-77), NOMO1 (P = 1.3 x 10-25), and PDXDC1 (P = 8.3 x 10-24). The genotype-predicted transcription pattern between cases and controls is consistent with that between tumor and its matched normal tissue. The genotype-based 4-gene signature improved the classification between glioma cases and controls based on age, gender, and population stratification, with area under the receiver operating characteristic curve increasing from 0.77 to 0.85 (P = 8.1 x 10-23). Conclusion: A new genotype-based gene signature of glioma was identified using a novel iGWAS approach, which integrates multiplatform genomic data as well as different genetic association studies.

Abstract: AIMS: Clonidine is used for sedation in the paediatric intensive care unit. Extracorporeal membrane oxygenation (ECMO) provides temporary support if respiratory and cardiac function is threatened. ECMO influences the pharmacokinetics of drugs. Clonidine during paediatric ECMO cannot be effectively titrated as PK data are lacking. The aim of this study is to describe clonidine PK in a particular ECMO system and propose dosing guidelines for children on this particular ECMO circuit. METHODS: All children below the age of 18 years who received clonidine during ECMO were eligible. The pharmacokinetic analysis was conducted by nonlinear mixed effect modelling, which enables to establish the separate influences of determinants on drug blood level and to provide individualized dosing. RESULTS: Twenty-two patients, median age 1 month (IQR 6.4) and weight at inclusion 4 kg (IQR 3.1) were included of whom 90% in addition to ECMO received pre-emptive continuous venovenous hemofiltration to optimize fluid balance. The clonidine clearance rate was two-fold that measured in patients not on ECMO. Clearance increased steeply with postnatal age: at days 6, 8 and 10, respectively 30%, 50% and 70% of the adult clearance rate was reached. The use of diuretics was associated with a lower clearance. The volume of distribution increased by 55% during ECMO support. CONCLUSION: Our findings suggest that a higher dose of clonidine may be needed during ECMO. The PK parameters on ECMO and the dosing guidelines proposed hold the potential to improve sedation practices on ECMO but need to be repeated with different ECMO systems.

Abstract: Intercommunication of Dopamine Receptors (DRs) with their associate protein partners is crucial to maintain regular brain function in human. Majority of the brain disorders arise due to malfunctioning of such communication process. Hence, contributions of genetic factors, as well as phenotypic indications for various neurological and psychiatric disorders are often attributed as sharing in nature. In our earlier research article entitled "Human Dopamine Receptors Interaction Network (DRIN): a systems biology perspective on topology, stability and functionality of the network" (Podder et al., 2014) [1], we had depicted a holistic interaction map of human Dopamine Receptors. Given emphasis on the topological parameters, we had characterized the functionality along with the vulnerable properties of the network. In support of this, we hereby provide an additional data highlighting the genetic overlapping of various brain disorders in the network. The data indicates the sharing nature of disease genes for various neurological and psychiatric disorders in dopamine receptors connecting protein-protein interactions network. The data also indicates toward an alternative approach to prioritize proteins for overlapping brain disorders as valuable drug targets in the network.

Abstract: The P-glycoprotein (Pgp) transporter reduces the penetration of a chemically diverse range of neurotherapeutics at the blood-brain barrier, but the molecular features of drugs and drug-Pgp interactions that drive transport remain to be clarified. In particular, the triptan neurotherapeutics, eletriptan (ETT) and sumatriptan (STT), were identified to have a >10-fold difference in transport rates despite being from the same drug class. Consistent with these transport differences, ETT activated Pgp-mediated ATP hydrolysis approximately 2-fold, whereas STT slightly inhibited Pgp-mediated ATP hydrolysis by approximately 10%. The interactions between them were also noncompetitive, suggesting that they occupy different binding sites on the transporter. Despite these differences, protein fluorescence spectroscopy revealed that the drugs have similar affinity to the transporter. NMR with Pgp and the drugs showed that they have distinct interactions with the transporter. Tertiary conformational changes probed by acrylamide quenching of Pgp tryptophan fluorescence with the drugs and a nonhydrolyzable ATP analog implied that the STT-bound Pgp must undergo larger conformational changes to hydrolyze ATP than ETT-bound Pgp. These results and previous transport studies were used to build a conformationally driven model for triptan transport with Pgp where STT presents a higher conformational barrier for ATP hydrolysis and transport than ETT.

Abstract: Increasing need for novel drugs and their application for treating diseases are the main reasons for the development of bioinformatics platforms for drug repositioning. The use of existing approved drugs for treating other diseases reduces cost and time needed for a drug to come to clinical use. Different strategies for drug repositioning have been reported. The use of several omics types is becoming increasingly important in drug repositioning. Although there are several public databases intended for drug repositioning, not many successful cases of novel use of drugs have been reported in the literature and transferred to clinical use. Additionally, the study approaches in published literature are very heterogeneous. A classification scheme - Drug Repositioning Evidence Level (DREL) - for drug repositioning projects, according to the level of scientific evidence has been proposed previously. In the present study, we have reviewed main databases and bioinformatics approaches enabling drug repositioning studies. We also reviewed six published studies and evaluated them according to the DREL classification. The evaluated cases used drug repositioning approach for therapy of rheumatoid arthritis, cancer, coronary artery disease, diabetes, and gulf war illness. The drug repositioning study field could benefit from clearer definition in published articles therefore including drug repositioning DREL classification scheme could be included in published original and review studies. Novel bioinformatics approaches to improve prediction of drug-target interactions, continuous updating of the databases, and development of novel validation techniques are needed to facilitate the development of the drug repositioning field. Although there are still many challenges in drug repositioning and personalized medicine, stratification of patients based on their molecular signatures and testing of signature-targeting drugs should improve drug efficacy in clinical trials.

Abstract: The 'big data' concept plays an increasingly important role in many scientific fields. Big data involves more than unprecedentedly large volumes of data that become available. Different criteria characterizing big data must be carefully considered in computational data mining, as we discuss herein focusing on medicinal chemistry. This is a scientific discipline where big data is beginning to emerge and provide new opportunities. For example, the ability of many drugs to specifically interact with multiple targets, termed promiscuity, forms the molecular basis of polypharmacology, a hot topic in drug discovery. Compound promiscuity analysis is an area that is much influenced by big data phenomena. Different results are obtained depending on chosen data selection and confidence criteria, as we also demonstrate.

Abstract: In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia.

Abstract: Regulation of blood glucose requires precise coordination between different endocrine systems and multiple organs. Type 2 diabetes mellitus (T2D) arises from a dysregulated response to elevated glucose levels in the circulation. Globally, the prevalence of T2D has increased dramatically in all age groups. T2D in older adults is associated with higher mortality and reduced functional status, leading to higher rate of institutionalization. Despite the potential healthcare challenges associated with the presence of T2D in the elderly, the pathogenesis and phenotype of late-onset T2D is not well studied. Here we applied untargeted metabolite profiling of urine samples from people with and without late-onset T2D using ultra-performance liquid-chromatography mass-spectrometry (UPLC-MS) to identify urinary biomarkers for late-onset T2D in the elderly. Statistical modeling of measurements and thorough validation of structural assignment using liquid chromatography tandem mass-spectrometry (LC-MS/MS) have led to the identification of metabolite biomarkers associated with late-onset T2D. Lower levels of phenylalanine, acetylhistidine, and cyclic adenosine monophosphate (cAMP) were found in urine samples of T2D subjects validated with commercial standards. Elevated levels of 5'-methylthioadenosine (MTA), which previously has only been implicated in animal model of diabetes, was found in urine of older people with T2D.

Abstract: Genomic alterations involving translocations of the ETS-related gene ERG occur in approximately half of prostate cancer cases. These alterations result in aberrant, androgen-regulated production of ERG protein variants that directly contribute to disease development and progression. This study describes the discovery and characterization of a new class of small molecule ERG antagonists identified through rational in silico methods. These antagonists are designed to sterically block DNA binding by the ETS domain of ERG and thereby disrupt transcriptional activity. We confirmed the direct binding of a lead compound, VPC-18005, with the ERG-ETS domain using biophysical approaches. We then demonstrated VPC-18005 reduced migration and invasion rates of ERG expressing prostate cancer cells, and reduced metastasis in a zebrafish xenograft model. These results demonstrate proof-of-principal that small molecule targeting of the ERG-ETS domain can suppress transcriptional activity and reverse transformed characteristics of prostate cancers aberrantly expressing ERG. Clinical advancement of the developed small molecule inhibitors may provide new therapeutic agents for use as alternatives to, or in combination with, current therapies for men with ERG-expressing metastatic castration-resistant prostate cancer.

Abstract: Drug-induced liver injury (DILI), although rare, is a frequent cause of adverse drug reactions resulting in warnings and withdrawals of numerous medications. Despite the research community's best efforts, current testing strategies aimed at identifying hepatotoxic drugs prior to human trials are not sufficiently powered to predict the complex mechanisms leading to DILI. In our previous studies, we demonstrated lipophilicity and dose to be associated with increased DILI risk and, and in our latest work, we factored reactive metabolites into the algorithm to predict DILI. Given the inconsistency in determining the potential for drugs to cause DILI, the present study comprehensively assesses the relationship between DILI risk and lipophilicity and the extent of metabolism using a large published dataset of 1036 Food and Drug Administration (FDA)-approved drugs by considering five independent DILI annotations. We found that lipophilicity and the extent of metabolism alone were associated with increased risk for DILI. Moreover, when analyzed in combination with high daily dose (>/=100 mg), lipophilicity was statistically significantly associated with the risk of DILI across all datasets (p < 0.05). Similarly, the combination of extensive hepatic metabolism (>/=50%) and high daily dose (>/=100 mg) was also strongly associated with an increased risk of DILI among all datasets analyzed (p < 0.05). Our results suggest that both lipophilicity and the extent of hepatic metabolism can be considered important risk factors for DILI in humans, and that this relationship to DILI risk is much stronger when considered in combination with dose. The proposed paradigm allows the convergence of different published annotations to a more uniform assessment.