Abstract: Background: Many drugs have poor water solubility and so the oral delivery of such drugs is usually associated with limitation of low bioavailability and lack of dose proportionality. Lipid-based liquid crystal (LC) systems are excellent potential formulations for increasing dissolution and bioavailability of drugs. The aim of the present study was to formulate lipid-based LC containing fenofibrate (FFB) as a hydrophobic drug. Materials and Methods: The studied variables included lipid and stabilizer concentrations and the type of stabilizer. The LC formation was identified by the polarized optical microscopic method. The effects of variables on formulation characteristics such as particle size, drug release, and rheological behavior were evaluated. Results: The results showed that the prepared formulations had the particle size between 42 and 503 nm. The drug release profiles showed that FFB had the continuous release from the formulations and the highest dissolution efficiency was seen in formulation prepared by 1.5% of glyceryl monostearate and 0.5% of Pluronic F127 as the stabilizer. The change of stabilizer type from colloidal silica to Pluronic F127 increased the drug release, significantly. Conclusions: In the most formulations of FFB LCs, the DE% was more than the pure drug, and therefore, it seems that the liquid crystalline formulations can be effective for enhancing drug release. Furthermore, drug release rate depended on the stabilizer type so that the presence of colloidal silica caused slower drug release compared to Pluronic F127.

Abstract: Urinary proteome was analyzed and quantified by tandem mass tag (TMT) labeling followed by bioinformatics analysis to study diabetic nephropathy (DN) pathophysiology and to identify biomarkers of a clinical outcome. We included type 2 diabetic normotensive non-obese males with (n = 9) and without (n = 11) incipient DN (microalbuminuria). Sample collection included blood and urine at baseline (control and DN basal) and, in DN patients, after 3 months of losartan treatment (DN treated). Urinary proteome analysis identified 166 differentially abundant proteins between controls and DN patients, 27 comparing DN-treated and DN-basal patients, and 182 between DN-treated patients and controls. The mathematical modeling analysis predicted 80 key proteins involved in DN pathophysiology and 15 in losartan effect, a total of 95 proteins. Out of these 95, 7 are involved in both processes. VCAM-1 and neprilysin stand out of these 7 for being differentially expressed in the urinary proteome. We observed an increase of VCAM-1 urine levels in DN-basal patients compared to diabetic controls and an increase of urinary neprilysin in DN-treated patients with persistent albuminuria; the latter was confirmed by ELISA. Our results point to neprilysin and VCAM-1 as potential candidates in DN pathology and treatment.

Abstract: Rare, or orphan, diseases are conditions afflicting a small subset of people in a population. Although these disorders collectively pose significant health care problems, drug companies require government incentives to develop drugs for rare diseases due to extremely limited individual markets. Computer-aided drug repositioning, i.e., finding new indications for existing drugs, is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Structure-based matching of drug-binding pockets is among the most promising computational techniques to inform drug repositioning. In order to find new targets for known drugs ultimately leading to drug repositioning, we recently developed eMatchSite, a new computer program to compare drug-binding sites. In this study, eMatchSite is combined with virtual screening to systematically explore opportunities to reposition known drugs to proteins associated with rare diseases. The effectiveness of this integrated approach is demonstrated for a kinase inhibitor, which is a confirmed candidate for repositioning to synapsin Ia. The resulting dataset comprises 31,142 putative drug-target complexes linked to 980 orphan diseases. The modeling accuracy is evaluated against the structural data recently released for tyrosine-protein kinase HCK. To illustrate how potential therapeutics for rare diseases can be identified, we discuss a possibility to repurpose a steroidal aromatase inhibitor to treat Niemann-Pick disease type C. Overall, the exhaustive exploration of the drug repositioning space exposes new opportunities to combat orphan diseases with existing drugs. DrugBank/Orphanet repositioning data are freely available to research community at https://osf.io/qdjup/.

Abstract: Understanding the mechanisms underlying drug therapeutic action and toxicity is crucial for the prevention and management of drug adverse reactions, and paves the way for a more efficient and rational drug design. The characterization of drug targets, drug metabolism proteins, and proteins associated to side effects according to their expression patterns, their tolerance to genomic variation and their role in cellular networks, is a necessary step in this direction. In this contribution, we hypothesize that different classes of proteins involved in the therapeutic effect of drugs and in their adverse effects have distinctive transcriptomics, genomics and network features. We explored the properties of these proteins within global and organ-specific interactomes, using multi-scale network features, evaluated their gene expression profiles in different organs and tissues, and assessed their tolerance to loss-of-function variants leveraging data from 60K subjects. We found that drug targets that mediate side effects are more central in cellular networks, more intolerant to loss-of-function variation, and show a wider breadth of tissue expression than targets not mediating side effects. In contrast, drug metabolizing enzymes and transporters are less central in the interactome, more tolerant to deleterious variants, and are more constrained in their tissue expression pattern. Our findings highlight distinctive features of proteins related to drug action, which could be applied to prioritize drugs with fewer probabilities of causing side effects.

Abstract: INTRODUCTION: The question of whether pioglitazone, an antidiabetic drug, increases the risk of cancer has been debated for some time. Recent studies have shown that pioglitazone use can increase the risk of prostate cancer as well as pancreatic cancer. However, it is unclear whether pioglitazone is a causal risk factor for these cancers. METHODS: In this study, we aimed to explore the direct targets of pioglitazone and genes associated with this drug by querying open platforms in order to construct a biological function network, and then to further evaluate the relationships of pioglitazone with prostate cancer and pancreatic cancer. RESULTS: We first tested our hypothesis using DrugBank and STRING. We identified four direct targets of pioglitazone and 50 pioglitazone-associated genes, which were then selected for KEGG pathway analysis using STRING and WebGestalt. This analysis generated the top 25 KEGG pathways, among which four pathways were related to site-specific cancers, including prostate cancer and pancreatic cancer. Finally, a genomic study using cBioPortal indicated that genomic alterations of two gene sets related to the prostate cancer and pancreatic cancer pathways, respectively, are associated with the acceleration of carcinogenesis. CONCLUSIONS: Pioglitazone is likely to be a causal risk factor for prostate cancer and pancreatic cancer, so this drug should be used with caution. The present research also demonstrates the use of biological function network analysis to effectively explore drug interactions and drug safety profiles.

Abstract: AIMS: Alzheimer's disease (AD) is one of the leading causes of death in elderly people. Its pathogenesis is greatly associated with the abnormality of immune system. However, only a few immune-relevant AD drug target genes have been discovered up to now, and it is speculated that there are still many potential drug target genes of AD (at least immune-relevant genes) to be discovered. Thus, this study was designed to identify novel AD drug target genes and explore their biological properties. METHODS: A combinatorial approach was adopted for the first time to discover AD drug targets by collectively considering ontology inference and network analysis. Moreover, a novel strategy limiting the distance of reasoning and in turn reducing noise interference was further proposed to improve inference performance. Potential AD drug target genes were discovered by integrating information of multiple popular databases (TTD, DrugBank, PharmGKB, AlzGene, and BioGRID). Then, the enrichment analyses of the identified drug targets genes based on nine well-known pathway-related databases were conducted to explore the function of the identified potential drug target genes. RESULTS: Eighteen potential drug target genes were finally identified, and 13 of them had been reported to be closely associated with AD. Enrichment analyses of these identified drug target genes, based on nine pathway-related databases, revealed that the enriched terms were primarily focus on immune-relevant biological processes. Four of those identified drug target genes are involved in the classical complement pathway and process of antigen presenting. CONCLUSION: The well-reproducible results showed the good performance of the combinatorial approach, and the remaining five new targets could be a good starting point for our understanding of the pathogenesis and drug discovery of AD. Moreover, this study supported validity of the combinatorial approach integrating ontology inference with network analysis in the discovery of novel drug target for neurological diseases.

Abstract: Herein, we describe the synthesis of N-aryl phthalimides by metal-free coupling of potassium phthalimide with unsymmetrical aryl(TMP)iodonium tosylate salts. The aryl transfer from the iodonium moiety occurs under electronic control with the electron-rich trimethoxyphenyl group acting as a competent dummy ligand. The yields of N-aryl phthalimides are moderate to high and the coupling reaction is compatible with electron-deficient and sterically encumbered aryl groups.

Abstract: Ouratea fieldingiana (Gardner) Engl is popularly used for wound healing. This study describes the main chemical compounds present in extracts of O. fieldingiana and evaluates their biological potential by investigating antifungal, antioxidant, and anticholinesterase activities. The action mechanism of main antifungal compound was investigated by molecular docking using the enzyme sterol 14-alpha demethylase, CYP51, required for ergosterol biosynthesis. The seeds and leaves were extracted with ethanol in a Soxhlet apparatus and by maceration, respectively. Both extracts were subjected to silica gel column chromatography for isolation of main constituents, followed by purification in sephadex. The structures of compounds were established by (1)H and (13)C-NMR spectroscopy and identified by comparison with literature data as amentoflavone and kaempferol 3-O-rutinoside, respectively. The antioxidant activities of the extracts were determined by the DPPH and ABTS free radical inhibition methods. In general, the extracts with the highest antioxidant activity corresponded to those with higher content of phenolic compounds and flavonoids. The ethanol extracts and two isolated compounds presented relevant antifungal activity against several Candida strains. The in silico findings revealed that the compound amentoflavone coupled with the CYP450 protein due to the low energy stabilization (-9.39 kcal/mol), indicating a possible mechanism of action by inhibition of the ergosterol biosynthesis of Candida fungi.

Abstract: Drug-target interaction (DTI) is the basis of drug discovery. However, it is time-consuming and costly to determine DTIs experimentally. Over the past decade, various computational methods were proposed to predict potential DTIs with high efficiency and low costs. These methods can be roughly divided into several categories, such as molecular docking-based, pharmacophore-based, similarity-based, machine learning-based, and network-based methods. Among them, network-based methods, which do not rely on three-dimensional structures of targets and negative samples, have shown great advantages over the others. In this article, we focused on network-based methods for DTI prediction, in particular our network-based inference (NBI) methods that were derived from recommendation algorithms. We first introduced the methodologies and evaluation of network-based methods, and then the emphasis was put on their applications in a wide range of fields, including target prediction and elucidation of molecular mechanisms of therapeutic effects or safety problems. Finally, limitations and perspectives of network-based methods were discussed. In a word, network-based methods provide alternative tools for studies in drug repurposing, new drug discovery, systems pharmacology and systems toxicology.