Abstract: PURPOSE: Low posaconazole plasma concentrations (PPCs) are frequently encountered in allogeneic hematopoietic stem cell transplant (HSCT) patients, due to variable gastrointestinal absorption. In this study, the impact of intestinal mucositis on posaconazole exposure is investigated. PATIENTS AND METHODS: A prospective pharmacokinetic study was performed including allogeneic HSCT patients receiving posaconazole prophylaxis with the oral suspension or tablets. Steady state PPCs were determined using high-performance liquid chromatography-fluorescence detection at the day of transplantation (=day 0), day +7, and +14. Citrulline was measured using liquid chromatography-tandem mass spectrometry to evaluate severity of mucositis, at baseline (day -7 or -6), and at day 0, +7 and +14. Additionally, citrulline plasma concentrations and steady state trough PPCs were determined in hematological patients without HSCT or mucositis. RESULTS: Thirty-four HSCT patients received posaconazole oral suspension together with 25 cL of Coca Cola, 6 HSCT patients received posaconazole tablets and 33 hematological patients not receiving HSCT received posaconazole oral suspension. The median (interquartile range) average PPC was 0.26 mg/L (0.17-0.43), 0.67 mg/L (0.27-1.38), and 1.08 mg/L (0.96-1.38), with suspension in HSCT patients, suspension in hematological patients and tablets in HSCT patients, respectively. A higher trough PPC was encountered with the oral suspension when citrulline plasma concentrations were above 10 mumol/L compared to values below 10 mumol/L (p < 0.001), whereas for tablets, average PPCs remained high with citrulline plasma concentrations below or above 10 mumol/L (p = 0.64). CONCLUSION: Posaconazole tablets should be preferred to suspension in HSCT patients immediately after transplantation to prevent insufficient plasma exposure due to intestinal mucositis.

Abstract: BACKGROUND: Parkinson disease (PD) is a severe neurodegenerative disease without curative drugs. The highly complex and heterogeneous disease mechanisms are still unclear. Detecting novel PD associated genes not only contributes in revealing the disease pathogenesis, but also facilitates discovering new targets for drugs. METHODS: We propose a phenome-based gene prediction strategy to identify disease-associated genes for PD. We integrated multiple disease phenotype networks, a gene functional relationship network, and known PD genes to predict novel candidate genes. Then we investigated the translational potential of the predicted genes in drug discovery. RESULTS: In a cross validation analysis, the average rank for 15 known PD genes is within top 0.8 %. We also tested the algorithm with an independent validation set of 669 PD-associated genes detected by genome-wide association studies. The top ranked genes predicted by our approach are enriched for these validation genes. In addition, our approach prioritized the target genes for FDA-approved PD drugs and the drugs that have been tested for PD in clinical trials. Pathway analysis shows that the prioritized drug target genes are closely associated with PD pathogenesis. The result provides empirical evidence that our computational gene prediction approach identifies novel candidate genes for PD, and has the potential to lead to rapid drug discovery.

Abstract: AIM: Fungi are valuable resources for bioactive secondary metabolites. However, the chemical space of fungal secondary metabolites has been studied only on a limited basis. Herein, we report a comprehensive chemoinformatic analysis of a unique set of 207 fungal metabolites isolated and characterized in a USA National Cancer Institute funded drug discovery project. RESULTS: Comparison of the molecular complexity of the 207 fungal metabolites with approved anticancer and nonanticancer drugs, compounds in clinical studies, general screening compounds and molecules Generally Recognized as Safe revealed that fungal metabolites have high degree of complexity. Molecular fingerprints showed that fungal metabolites are as structurally diverse as other natural products and have, in general, drug-like physicochemical properties. CONCLUSION: Fungal products represent promising candidates to expand the medicinally relevant chemical space. This work is a significant expansion of an analysis reported years ago for a smaller set of compounds (less than half of the ones included in the present work) from filamentous fungi using different structural properties.

Abstract: Local concentrations of mutations are well known in human cancers. However, their three-dimensional spatial relationships in the encoded protein have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and intermolecular clusters, some of which showed tumor and/or tissue specificity. In addition, we identified 369 rare mutations in genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium-recurrence mutations in genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all mapping within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high-throughput phosphorylation data and cell-line-based experimental evaluation. Finally, mutation-drug cluster and network analysis predicted over 800 promising candidates for druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Abstract: Nuclear receptor (NR) signaling pathways impact cellular function in a broad variety of tissues in both normal physiology and disease states. The complex tissue-specific biology of these pathways is an enduring impediment to the development of clinical NR small-molecule modulators that combine therapeutically desirable effects in specific target tissues with suppression of off-target effects in other tissues. Supporting the important primary research in this area is a variety of web-based resources that assist researchers in gaining an appreciation of the molecular determinants of the pharmacology of a NR pathway in a given tissue. In this study, selected representative examples of these tools are reviewed, along with discussions on how current and future generations of tools might optimally adapt to the future of NR signaling research.

Abstract: The acute and sub-chronic effects of four cytostatic drugs-5-fluorouracil (5-FU), cisplatin (CisPt), etoposide (ET) and imatinib mesylate (IM)-on zebrafish (Danio rerio) were investigated. Acute tests were carried out in a static system in accordance with the OECD guideline 203 for adult fish and the draft guideline for fish embryos (FET test) in order to find the LC50 values of the four cytostatic drugs. Early-life stage toxicity test on zebrafish was conducted according the OECD guideline 210 using the cytostatic drugs 5-FU and IM in a semistatic system with the objective of investigating the sub-chronic effects of the cytostatic drugs on fish. In adult fish, the cytostatic drugs 5-FU and ET did not pass the limit test, thus, are considered non-toxic. In case of cisplatin, LC50 was calculated at 64.5 mg L(-1), whereas in case of IM, LC50 was at 70.8 mg L(-1). In the FET test, LC50 of 5-FU at 72-h post fertilization (hpf) was 2441.6 mg L(-1). In case of CisPt, LC50 was 349.9 mg L(-1) at 48 hpf and it progressively decreased to 81.3 mg L(-1) at 120 hpf. In addition, CisPt caused a significant delay in the hatch of larvae. In case of ET, LC50 values were not calculable as they were higher than 300 mg L(-1) at which concentration the substance crystallized in the solution. LC50 values of IM were 48 hpf; 158.3 mg L(-1) , 72 hpf; 141.6 mg L(-1), 96 hpf; 118.0 mg L(-1), and 120 hpf; 65.9 mg L(-1). In the Early-life Stage Test with 5-FU, embryonic deformities were not detected during the tests. Regarding mortalities, the 10 mg L(-1) concentration can be considered as LOEC, as statistically significant difference in mortalities was detected in this group alone. Concerning dry body weight and standard length, 1 mg L(-1) is the LOEC. In case of IM, the highest tested concentration (10 mg L(-1)) can be considered LOEC for mortalities, however, the treatment did not have an effect on the other investigated parameters (dry and wet weight, standard length). All four cytostatic drugs were characterized by low toxicity in zebrafish in acute and sub-chronic tests.

Abstract: Dihydropteroate synthase (DHPS) is a known sulfa drug target in malaria treatment, existing as a bifunctional enzyme together with hydroxymethyldihydropterin pyrophosphokinase (HPPK). Polymorphisms in key residues of Plasmodium falciparum DHPS (PfDHPS) have been characterized and linked to sulfa drug resistance in malaria. Genetic sequencing of P. vivax dhps (Pvdhps) from clinical isolates has shown several polymorphisms at the positions equivalent to those in the Pfdhps genes conferring sulfa drug resistance, suggesting a mechanism for sulfa drug resistance in P. vivax similar to that seen in P. falciparum To characterize the role of polymorphisms in the PvDHPS in sulfa drug resistance, various mutants of recombinant PvHPPK-DHPS enzymes were expressed and characterized. Moreover, due to the lack of a continuous in vitro culture system for P. vivax parasites, a surrogate P. berghei model expressing Pvhppk-dhps genes was established to demonstrate the relationship between sequence polymorphisms and sulfa drug susceptibility and to test the activities of PvDHPS inhibitors on the transgenic parasites. Both enzyme activity and transgenic parasite growth were sensitive to sulfadoxine to different degrees, depending on the number of mutations that accumulated in DHPS. Ki values and 50% effective doses were higher for mutant PvDHPS enzymes than the wild-type enzymes. Altogether, the study provides the first evidence of sulfa drug resistance at the molecular level in P. vivax Furthermore, the enzyme inhibition assay and the in vivo screening system can be useful tools for screening new compounds for their activities against PvDHPS.

Abstract: BACKGROUND: Patients suffering from osteoporosis show an increased number of adipocytes in their bone marrow, concomitant with a reduction in the pool of human mesenchymal stem cells (hMSCs) that are able to differentiate into osteoblasts, thus leading to suppressed osteogenesis. METHODS: In order to be able to interfere with this process, we have investigated in-vitro culture conditions whereby adipogenic differentiation of hMSCs is impaired and osteogenic differentiation is promoted. By means of gene expression microarray analysis, we have investigated genes which are potential targets for prevention of fat cell differentiation. RESULTS: Our data show that BMP2 promotes both adipogenic and osteogenic differentiation of hMSCs, while transforming growth factor beta (TGFbeta) inhibits differentiation into both lineages. However, when cells are cultured under adipogenic differentiation conditions, which contain cAMP-enhancing agents such as IBMX of PGE2, TGFbeta promotes osteogenic differentiation, while at the same time inhibiting adipogenic differentiation. Gene expression and immunoblot analysis indicated that IBMX-induced suppression of HDAC5 levels plays an important role in the inhibitory effect of TGFbeta on osteogenic differentiation. By means of gene expression microarray analysis, we have investigated genes which are downregulated by TGFbeta under adipogenic differentiation conditions and may therefore be potential targets for prevention of fat cell differentiation. We thus identified nine genes for which FDA-approved drugs are available. Our results show that drugs directed against the nuclear hormone receptor PPARG, the metalloproteinase ADAMTS5, and the aldo-keto reductase AKR1B10 inhibit adipogenic differentiation in a dose-dependent manner, although in contrast to TGFbeta they do not appear to promote osteogenic differentiation. CONCLUSIONS: The approach chosen in this study has resulted in the identification of new targets for inhibition of fat cell differentiation, which may not only be relevant for prevention of osteoporosis, but also of obesity.

Abstract: BACKGROUND: Glioma is the most common brain tumor and it has very high mortality rate due to its infiltration and heterogeneity. Precise classification of glioma subtype is essential for proper therapeutic treatment and better clinical prognosis. However, the molecular mechanism of glioma is far from clear and the classical classification methods based on traditional morphologic and histopathologic knowledge are subjective and inconsistent. Recently, classification methods based on molecular characteristics are developed with rapid progress of high throughput technology. METHODS: In the present study, we designed a novel integrated gene coexpression analysis approach, which involves differential coexpression and differential regulation analysis (DCEA and DRA), to investigate glioma prognostic biomarkers and molecular subtypes based on six glioma transcriptome data sets. RESULTS: We revealed a novel three-transcription-factor signature including AHR, NFIL3 and ZNF423 for glioma molecular subtypes. This three-TF signature clusters glioma patients into three major subtypes (ZG, NG and IG subtypes) which are significantly different in patient survival as well as transcriptomic patterns. Notably, ZG subtype is featured with higher expression of ZNF423 and has better prognosis with younger age at diagnosis. NG subtype is associated with higher expression of NFIL3 and AHR, and has worse prognosis with elder age at diagnosis. According to our inferred differential networking information and previously reported signalling knowledge, we suggested testable hypotheses on the roles of AHR and NFIL3 in glioma carcinogenesis. CONCLUSIONS: With so far the least biomarkers, our approach not only provides a novel glioma prognostic molecular classification scheme, but also helps to explore its dysregulation mechanisms. Our work is extendable to prognosis-related classification and signature identification in other cancer researches.

Abstract: BACKGROUND: Information about drug-drug interactions (DDIs) supported by scientific evidence is crucial for establishing computational knowledge bases for applications like pharmacovigilance. Since new reports of DDIs are rapidly accumulating in the scientific literature, text-mining techniques for automatic DDI extraction are critical. We propose a novel approach for automated pharmacokinetic (PK) DDI detection that incorporates syntactic and semantic information into graph kernels, to address the problem of sparseness associated with syntactic-structural approaches. First, we used a novel all-path graph kernel using shallow semantic representation of sentences. Next, we statistically integrated fine-granular semantic classes into the dependency and shallow semantic graphs. RESULTS: When evaluated on the PK DDI corpus, our approach significantly outperformed the original all-path graph kernel that is based on dependency structure. Our system that combined dependency graph kernel with semantic classes achieved the best F-scores of 81.94 % for in vivo PK DDIs and 69.34 % for in vitro PK DDIs, respectively. Further, combining shallow semantic graph kernel with semantic classes achieved the highest precisions of 84.88 % for in vivo PK DDIs and 74.83 % for in vitro PK DDIs, respectively. CONCLUSIONS: We presented a graph kernel based approach to combine syntactic and semantic information for extracting pharmacokinetic DDIs from Biomedical Literature. Experimental results showed that our proposed approach could extract PK DDIs from literature effectively, which significantly enhanced the performance of the original all-path graph kernel based on dependency structure.