Abstract: Pravastatin, used for lowering cholesterol and further decreasing blood lipid, has been frequently detected in the contaminated freshwaters, whereas its long-term exposure effects on non-target aquatic invertebrates remains undetermined. Therefore, the purpose of this study was to evaluate the toxic effects of pravastatin (PRA) with the concentration gradients (0, 0.5, 50, 5000 mug/L) on a model water flea Daphnia magna (D. magna) over 21 d based on phenotypic and genome-wide transcriptomic analyses. After 21 d, exposure to PRA at 5000 mug/L significantly reduced the body length and increased the number of offspring. The 76, 167, and 499 differentially expressed genes (DEGs) were identified by using absolute log2 fold change < 1 and adj p < 0.05 as a cutoff in the 0.5, 50, and 5000 mug/L PRA treatment groups, respectively. Three pathways, including xenobiotic metabolism, insect hormone biosynthesis pathway, and energy metabolism were significantly (p < 0.05) enriched after exposure to PRA. These suggested that the upregulation of genes in insect biosynthetic hormone pathway increased the juvenile hormone III content, which further reduced the body length of D. magna. The positive effect of methyl farnesoate synthesis on the ovarian may result in the increased number of offspring. Furthermore, energy tended to be allocated to detoxification process and survival under stress conditions, as the amount of energy that an individual can invest in maintenance and growth is limited. Taken together, our results unraveled the toxic mechanism of cardiovascular and lipid pharmaceuticals in aquatic invertebrate.

Abstract: BACKGROUND: Long non-coding RNAs (lncRNAs) play a crucial role in diverse biological processes and have been confirmed to be concerned with various diseases. Largely uncharacterized of the physiological role and functions of lncRNA remains. MicroRNAs (miRNAs), which are usually 20-24 nucleotides, have several critical regulatory parts in cells. LncRNA can be regarded as a sponge to adsorb miRNA and indirectly regulate transcription and translation. Thus, the identification of lncRNA-miRNA associations is essential and valuable. RESULTS: In our work, we present DWLMI to infer the potential associations between lncRNAs and miRNAs by representing them as vectors via a lncRNA-miRNA-disease-protein-drug graph. Specifically, DeepWalk can be used to learn the behavior representation of vertices. The methods of fingerprint, k-mer and MeSH descriptors were mainly used to learn the attribute representation of vertices. By combining the above two kinds of information, unknown lncRNA-miRNA associations can be predicted by the random forest classifier. Under the five-fold cross-validation, the proposed DWLMI model obtained an average prediction accuracy of 95.22% with a sensitivity of 94.35% at the AUC of 98.56%. CONCLUSIONS: The experimental results demonstrated that DWLMI can effectively predict the potential lncRNA-miRNA associated pairs, and the results can provide a new insight for related non-coding RNA researchers in the field of combing biology big data with deep learning.

Abstract: Concurrent use of multiple drugs can lead to unexpected adverse drug reactions. The interaction between drugs can be confirmed by routine in vitro and clinical trials. However, it is difficult to test the drug-drug interactions widely and effectively before the drugs enter the market. Therefore, the prediction of drug-drug interactions has become one of the research priorities in the biomedical field. In recent years, researchers have been using deep learning to predict drug-drug interactions by exploiting drug structural features and graph theory, and have achieved a series of achievements. A drug-drug interaction prediction model SmileGNN is proposed in this paper, which can be characterized by aggregating the structural features of drugs constructed by SMILES data and the topological features of drugs in knowledge graphs obtained by graph neural networks. The experimental results show that the model proposed in this paper combines a variety of data sources and has a better prediction performance compared with existing prediction models of drug-drug interactions. Five out of the top ten predicted new drug-drug interactions are verified from the latest database, which proves the credibility of SmileGNN.

Abstract: Diabetes mellitus is a disorder characterized by higher levels of blood glucose due to impaired insulin mechanisms. Alpha glucosidase is a critical drug target implicated in the mechanisms of diabetes mellitus and its inhibition controls hyperglycemia. Since the existing standard synthetic drugs have therapeutic limitations, it is imperative to identify new potent inhibitors of natural product origin which may slow carbohydrate digestion and absorption via alpha glucosidase. Since plant extracts from Calotropis procera have been extensively used in the treatment of diabetes mellitus, the present study used molecular docking and dynamics simulation techniques to screen its constituents against the receptor alpha glucosidase. Taraxasterol, syriogenin, isorhamnetin-3-O-robinobioside and calotoxin were identified as potential novel lead compounds with plausible binding energies of -40.2, -35.1, -34.3 and -34.3 kJ/mol against alpha glucosidase, respectively. The residues Trp(481), Asp(518), Leu(677), Leu(678) and Leu(680) were identified as critical for binding and the compounds were predicted as alpha glucosidase inhibitors. Structurally similar compounds with Tanimoto coefficients greater than 0.7 were reported experimentally to be inhibitors of alpha glucosidase or antidiabetic. The structures of the molecules may serve as templates for the design of novel inhibitors and warrant in vitro assaying to corroborate their antidiabetic potential.

Abstract: The discovery of biofunctional natural products (NPs) has relied on the phenotypic screening of extracts and subsequent laborious work to dereplicate active NPs and define cellular targets. Herein, NPs present as crude extracts, partially purified fractions, and pure compounds were screened directly against molecular target libraries of RNA structural motifs in a library-versus-library fashion. We identified 21 hits with affinity for RNA, including one pure NP, nocathiacin I (NOC-I). The resultant data set of NOC-I-RNA fold interactions was mapped to the human transcriptome to define potential bioactive interactions. Interestingly, one of NOC-I's most preferred RNA folds is present in the nuclease processing site in the oncogenic, noncoding microRNA-18a, which NOC-I binds with low micromolar affinity. This affinity for the RNA translates into the selective inhibition of its nuclease processing in vitro and in prostate cancer cells, in which NOC-I also triggers apoptosis. In principle, adaptation of this combination of experimental and predictive approaches to dereplicate NPs from the other hits (extracts and partially purified fractions) could fundamentally transform the current paradigm and accelerate the discovery of NPs that bind RNA and their simultaneous correlation to biological targets.

Abstract: Schizophrenia is a mental disorder characterized by alterations in cognition, behavior and emotions. Oral olanzapine (OZ) administration is extensively metabolized (~up to 40% of the administrated dose). In addition, OZ is a P-glycoproteins substrate that impairs the blood-brain barrier (BBB) permeability. To direct OZ to the brain and to minimize its systemic side effects, the nasal pathway is recommended. OZ-loaded polymeric micelles nano-carriers were developed using suitable biodegradable excipients. The developed micelles were physicochemically investigated to assess their appropriateness for intranasal delivery and the potential of these carriers for OZ brain targeting. The selected formula will be examined in vivo for improving the anti-schizophrenic effects on a schizophrenia rat model. The binary mixture of P123/P407 has a low CMC (0.001326% w/v), which helps in maintaining the formed micelles' stability upon dilution. The combination effect of P123, P407 and TPGS led to a decrease in micelle size, ranging between 37.5-47.55 nm and an increase in the EE% (ranging between 68.22-86.84%). The selected OZ-PM shows great stability expressed by a suitable negative charge zeta potential value (-15.11 +/- 1.35 mV) and scattered non-aggregated spherical particles with a particle size range of 30-40 nm. OZ-PM maintains sustained drug release at the application site with no nasal cytotoxicity. In vivo administration of the selected OZ-PM formula reveals improved CNS targeting and anti-schizophrenia-related deficits after OZ nasal administration. Therefore, OZ-PM provided safe direct nose-to-brain transport of OZ after nasal administration with an efficient anti-schizophrenic effect.

Abstract: Prostate cancer (PCa) is the second most common cancer in men. Common treatments include active surveillance, surgery, or radiation. Androgen deprivation therapy and chemotherapy are usually reserved for advanced disease or biochemical recurrence, such as castration-resistant prostate cancer (CRPC), but they are not considered curative because PCa cells eventually develop drug resistance. The latter is achieved through various cellular mechanisms that ultimately circumvent the pharmaceutical's mode of action. The need for novel therapeutic approaches is necessary under these circumstances. An alternative way to treat PCa is by repurposing of existing drugs that were initially intended for other conditions. By extrapolating the effects of previously approved drugs to the intracellular processes of PCa, treatment options will expand. In addition, drug repurposing is cost-effective and efficient because it utilizes drugs that have already demonstrated safety and efficacy. This review catalogues the drugs that can be repurposed for PCa in preclinical studies as well as clinical trials.

Abstract: Static in vitro permeation experiments are commonly used to gain insights into the permeation properties of drug substances but exhibit limitations due to missing physiologic cell stimuli. Thus, fluidic systems integrating stimuli, such as physicochemical fluxes, have been developed. However, as fluidic in vitro studies display higher complexity compared to static systems, analysis of experimental readouts is challenging. Here, the integration of in silico tools holds the potential to evaluate fluidic experiments and to investigate specific simulation scenarios. This study aimed to develop in silico models that describe and predict the permeation and disposition of two model substances in a static and fluidic in vitro system. For this, in vitro permeation studies with a 16HBE cellular barrier under both static and fluidic conditions were performed over 72 h. In silico models were implemented and employed to describe and predict concentration-time profiles of caffeine and diclofenac in various experimental setups. For both substances, in silico modeling identified reduced apparent permeabilities in the fluidic compared to the static cellular setting. The developed in vitro-in silico modeling framework can be expanded further, integrating additional cell tissues in the fluidic system, and can be employed in future studies to model pharmacokinetic and pharmacodynamic drug behavior.

Abstract: BACKGROUND: Many studies link G protein-coupled receptors (GPCRs) to cancer. Some endocrine tumors are unresponsive to standard treatment and/or require long-term and poorly tolerated treatment. This study explored, by bioinformatics analysis, the tumoral profiling of the GPCR transcriptome to identify potential targets in these tumors aiming at drug repurposing. METHODS: We explored the GPCR differentially expressed genes (DEGs) from public datasets (Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA)). The GEO datasets were available for two medullary thyroid cancers (MTCs), eighty-seven pheochromocytomas (PHEOs), sixty-one paragangliomas (PGLs), forty-seven pituitary adenomas and one-hundred-fifty adrenocortical cancers (ACCs). The TCGA dataset covered 92 ACCs. We identified GPCRs targeted by approved drugs from pharmacological databases (ChEMBL and DrugBank). RESULTS: The profiling of dysregulated GPCRs was tumor specific. In MTC, we found 14 GPCR DEGs, including an upregulation of the dopamine receptor (DRD2) and adenosine receptor (ADORA2B), which were the target of many drugs. In PGL, seven GPCR genes were downregulated, including vasopressin receptor (AVPR1A) and PTH receptor (PTH1R), which were targeted by approved drugs. In ACC, PTH1R was also downregulated in both the GEO and TCGA datasets and was the target of osteoporosis drugs. CONCLUSIONS: We highlight specific GPCR signatures across the major endocrine tumors. These data could help to identify new opportunities for drug repurposing.

Abstract: BACKGROUND: Administration of Magnoliae Cortex (MC) could induce remission of cisplatin-induced sarcopenia in mice, however, whether it is effective on sarcopenia patients and the underlying mechanisms remain unclear. METHODS: Sarcopenia related differentially expressed genes were analysed based on three Gene Expression Omnibus (GEO) transcriptome profiling datasets, which was merged and de duplicated with disease databases to obtain sarcopenia related pathogenic genes. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were than performed to analyse the role of proteins encoded by sarcopenia related pathogenic genes and the signal regulatory pathways involved in. The main active components and target proteins of MC were obtained by searching traditional Chinese medicine network databases (TCMSP and BATMAN-TCM). MC and sarcopenia related pathogenic genes shared target proteins were identified by matching the two. A protein-protein interaction network was constructed subsequently, and the core proteins were filtered according to the topological structure. GO and KEGG analysis were performed again to analyse the key target proteins and pathways of MC in the treatment of sarcopenia, and build the herbs-components-targets network, as well as core targets-signal pathways network. Molecular docking technology was used to verify the main compounds-targets. RESULTS: Sarcopenia related gene products primarily involve in aging and inflammation related signal pathways. Seven main active components (Anonaine, Eucalyptol, Neohesperidin, Obovatol, Honokiol, Magnolol, and beta-Eudesmol) and 26 target proteins of MC-sarcopenia, of which 4 were core proteins (AKT1, EGFR, INS, and PIK3CA), were identified. The therapeutic effect of MC on sarcopenia may associate with PI3K-Akt signaling pathway, EGFR tyrosine kinase inhibitor resistance, longevity regulating pathway, and other cellular and innate immune signaling pathways. CONCLUSION: MC contains potential anti-sarcopenia active compounds. These compounds play a role by regulating the proteins implicated in regulating aging and inflammation related signaling pathways, which are crucial in pathogenesis of sarcopenia. Our study provides new insights into the development of a natural therapy for the prevention and treatment of sarcopenia.