Abstract: Eradication of pharmaceutical drugs from the global ecosystem has received remarkable attention due to the extensive horrible consequences on the human immunological system and the high rate of human deaths. The urgent need for drug eradication became the dominant priority for many research institutions worldwide due to the sharp increase of antimicrobial resistance (AMR) in the human body, which inhibits drug effectiveness and leads ultimately to death. Nanohybrid GO/O-CNTs was fabricated from graphene oxide (GO) cross-linked via calcium ions (Ca(2+)) with oxidized carbon nanotubes (O-CNTs) to eradicate the well-known ciprofloxacin antibiotic drug from aqueous solutions. The ciprofloxacin drug is medically prescribed in millions of medical prescriptions every year and typically exists in domestic and wastewaters. Characterization of the nanohybrid GO/O-CNTs was carried out through spectroscopic (Fourier Transform Infrared (FTIR) and X-ray diffraction (XRD)), thermal (Thermogravimetric analysis (TGA) and derivative thermogravimetry (DTG)), and microscopic (scanning electron microscopy (SEM)) techniques. Optimum parameters for the drug eradication process from aqueous solutions were verified and selected as follows: contact time = 4 h, pH = 6.0, temperature = 290 K, %CaCl2 = 0.5%, GO/O-CNT ratio = 4:1, and adsorbent mass = 1.0 mg. The equilibrium data were fitted to different adsorption isotherms, and the Langmuir isotherm provided the best fit to our data. Dynamic studies demonstrated a pseudo-second-order removal process for the ciprofloxacin drug, and thermodynamic parameters confirmed exothermic drug adsorption (-27.07 kJ/mol) as well as a physisorption process. For the sake of fighting against the generated AMR, our working strategy demonstrated a removal efficiency of 99.2% of the ciprofloxacin drug and drug uptake as high as 512 mg/g.

Abstract: The global depression population is showing a significant increase. Hemerocallis fulva L. is a common Traditional Chinese Medicine (TCM). Its flower buds are known to have ability to clear away heat and dampness, detoxify, and relieve depression. Ancient TCM literature shows that its roots have a beneficial effect in calming the spirit and even the temper in order to reduce the feeling of melancholy. Therefore, it is inferred that the root of Hemerocallis fulva L. can be used as a therapeutic medicine for depression. This study aims to uncover the pharmacological mechanism of the antidepressant effect of Hemerocallis Radix (HR) through network pharmacology method. During the analysis, 11 active components were obtained and screened using ADME-absorption, distribution, metabolism, and excretion- method. Furthermore, 267 HR targets and 740 depressive disorder (DD) targets were gathered from various databases. Then protein-protein interaction (PPI) network of HR and DD targets were constructed and cluster analysis was applied to further explore the connection between the targets. In addition, gene ontology (GO) enrichment and pathway analysis was applied to further verify that the biological process related to the target protein is associated with the occurrence of depression disorder. In conclusion, the most important bioactive components-anthraquinone, kaempferol, and vanillic acid-can alleviate depression symptoms by regulating MAOA, MAOB, and ESR1. The proposed network pharmacology strategy provides an integrating method to explore the therapeutic mechanism of multi-component drugs on a systematic level.

Abstract: The identification of physical interactions between drug candidate compounds and target biomolecules is an important process in drug discovery. Since conventional screening procedures are expensive and time consuming, computational approaches are employed to provide aid by automatically predicting novel drug-target interactions (DTIs). In this study, we propose a large-scale DTI prediction system, DEEPScreen, for early stage drug discovery, using deep convolutional neural networks. One of the main advantages of DEEPScreen is employing readily available 2-D structural representations of compounds at the input level instead of conventional descriptors that display limited performance. DEEPScreen learns complex features inherently from the 2-D representations, thus producing highly accurate predictions. The DEEPScreen system was trained for 704 target proteins (using curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against the state-of-the-art on multiple benchmark datasets to indicate the effectiveness of the proposed approach and verified selected novel predictions through molecular docking analysis and literature-based validation. Finally, JAK proteins that were predicted by DEEPScreen as new targets of a well-known drug cladribine were experimentally demonstrated in vitro on cancer cells through STAT3 phosphorylation, which is the downstream effector protein. The DEEPScreen system can be exploited in the fields of drug discovery and repurposing for in silico screening of the chemogenomic space, to provide novel DTIs which can be experimentally pursued. The source code, trained "ready-to-use" prediction models, all datasets and the results of this study are available at ; https://github.com/cansyl/DEEPscreen.

Abstract: Detecting whether a pair of biomolecules associate is of great significance in the study of molecular biology. Hence, computational methods are urgently needed as guidance for practice. However, most of the previous prediction models influenced by reductionism focused on isolated research objects, which have their own inherent defects. Inspired by holism, a machine-learning-based framework called MAN-node2vec is proposed to predict multi-type relationships in the molecular associations network (MAN). Specifically, we constructed a large-scale MAN composed of 1,023 miRNAs, 1,649 proteins, 769 long non-coding RNAs (lncRNAs), 1,025 drugs, and 2,062 diseases. Then, each biomolecule in MAN can be represented as a vector by its attribute learned by k-mer, etc. and its behavior learned by node2vec. Finally, the random forest classifier is applied to carry out the relationship prediction task. The proposed model achieved a reliable performance with 0.9677 areas under the curve (AUCs) and 0.9562 areas under the precision curve (AUPRs) under 5-fold cross-validation. Also, additional experiments proved that the proposed global model shows more competitive performance than the traditional local method. All of these provided a systematic insight for understanding the synergistic interactions between various molecules and diseases. It is anticipated that this work can bring beneficial inspiration and advance to related systems biology and biomedical research.

Abstract: Drug repositioning offers new clinical applications for existing drugs with shorter approval processes and lower costs and risks than de novo experimental drug development. The Fufang-Biejia-Ruangan pill (FBRP) is the first clinically approved anti-fibrosis herbal formula in China. Whether FBRP could be used to treat hepatocellular carcinoma (HCC) remains unclear. Herein, a total of 161 FBRP candidate targets against HCC were identified according to the topological importance in the "hepatic fibrosis-cirrhosis-cancer axis-related gene-FBRP putative target" network, and mostly enriched in phosphatidylinositol 3-kinase (PI3K)/AKT/nuclear factor kappaB (NF-kappaB) signaling. Experimentally, FBRP inhibited liver fibrosis and prevented the development of neoplastic lesions at the early stages of hepatocarcinogenesis in a diethylnitrosamine-induced rat HCC model. FBRP inhibited tumor cell proliferation, induced tumor-specific cell death, and suppressed tumor progression in HCC rats while preventing the activation of PI3K, AKT and IKKappaB proteins, reducing the nuclear accumulation of NFKappaB1 protein, and decreasing the downstream expression of proteins. Consistently, FBRP suppressed HCC cell proliferation and induced cell cycle arrest in vitro. Co-treatment of FBRP with PI3K inhibitor exhibited an additive inhibitory effect on PI3K/AKT/NF-kappaB activation. Collectively, our data showed the potentials of FBRP in hepatic fibrosis microenvironment regulation and tumor prevention, suggesting that FBRP may be a promising candidate drug for reduction of fibrogenesis and prevention of HCC.

Abstract: The SLC22 family of OATs, OCTs, and OCTNs is emerging as a central hub of endogenous physiology. Despite often being referred to as "drug" transporters, they facilitate the movement of metabolites and key signaling molecules. An in-depth reanalysis supports a reassignment of these proteins into eight functional subgroups, with four new subgroups arising from the previously defined OAT subclade: OATS1 (SLC22A6, SLC22A8, and SLC22A20), OATS2 (SLC22A7), OATS3 (SLC22A11, SLC22A12, and Slc22a22), and OATS4 (SLC22A9, SLC22A10, SLC22A24, and SLC22A25). We propose merging the OCTN (SLC22A4, SLC22A5, and Slc22a21) and OCT-related (SLC22A15 and SLC22A16) subclades into the OCTN/OCTN-related subgroup. Using data from GWAS, in vivo models, and in vitro assays, we developed an SLC22 transporter-metabolite network and similar subgroup networks, which suggest how multiple SLC22 transporters with mono-, oligo-, and multi-specific substrate specificity interact to regulate metabolites. Subgroup associations include: OATS1 with signaling molecules, uremic toxins, and odorants, OATS2 with cyclic nucleotides, OATS3 with uric acid, OATS4 with conjugated sex hormones, particularly etiocholanolone glucuronide, OCT with neurotransmitters, and OCTN/OCTN-related with ergothioneine and carnitine derivatives. Our data suggest that the SLC22 family can work among itself, as well as with other ADME genes, to optimize levels of numerous metabolites and signaling molecules, involved in organ crosstalk and inter-organismal communication, as proposed by the remote sensing and signaling theory.

Abstract: Bacillus firmus nematicidal bacterial strains are used to control plant parasitic nematode infestation of crops in agricultural production. Proteases are presumed to be the primary nematode virulence factors in nematicidal B. firmus degrading the nematode cuticle and other organs. We determined and compared the whole genome sequences of two nematicidal strains. Comparative genomics with a particular focus on possible virulence determinants revealed a wider range of possible virulence factors in a B. firmus isolate from a commercial bionematicide and a wild type Bacillus sp. isolate with nematicidal activity. The resulting 4.6 Mb B. firmus I-1582 and 5.3 Mb Bacillus sp. ZZV12-4809 genome assemblies contain respectively 18 and 19 homologs to nematode-virulent proteases, two nematode-virulent chitinase homologs in ZZV12-4809 and 28 and 36 secondary metabolite biosynthetic clusters, projected to encode antibiotics, small peptides, toxins and siderophores. The results of this study point to the genetic capability of B. firmus and related species for nematode virulence through a range of direct and indirect mechanisms.

Abstract: Uncaria alkaloids are the major bioactive chemicals found in the Uncaria genus, which have a long history of clinical application in treating cardiovascular and mental diseases in traditional Chinese medicine (TCM). However, there are gaps in understanding the multiple targets, pathways, and biological activities of Uncaria alkaloids. By constructing the interactions among drug-targets-diseases, network pharmacology provides a systemic methodology and a novel perspective to present the intricate connections among drugs, potential targets, and related pathways. It is a valuable tool for studying TCM drugs with multiple indications, and how these multi-indication drugs are affected by complex interactions in the biological system. To better understand the mechanisms and targets of Uncaria alkaloids, we built an integrated analytical platform based on network pharmacology, including target prediction, protein-protein interaction (PPI) network, topology analysis, gene enrichment analysis, and molecular docking. Using this platform, we revealed the underlying mechanisms of Uncaria alkaloids' anti-hypertensive effects and explored the possible application of Uncaria alkaloids in preventing Alzheimer's disease. These results were further evaluated and refined using biological experiments. Our study provides a novel strategy for understanding the holistic pharmacology of TCM, as well as for exploring the multi-indication properties of TCM beyond its traditional applications.

Abstract: Autotaxin (ATX) is considered as an interesting drug target for the therapy of several diseases. The goal of the research was to detect new ATX inhibitors which have novel scaffolds by using virtual screening. First, based on two diverse receptor-ligand complexes, 14 pharmacophore models were developed, and the 14 models were verified through a big test database. Those pharmacophore models were utilized to accomplish virtual screening. Next, for the purpose of predicting the probable binding poses of compounds and then carrying out further virtual screening, docking-based virtual screening was performed. Moreover, an excellent 3D QSAR model was established, and 3D QSAR-based virtual screening was applied for predicting the activity values of compounds which got through the above two-round screenings. A correlation coefficient r(2), which equals 0.988, was supplied by the 3D QSAR model for the training set, and the correlation coefficient r(2) equaling 0.808 for the test set means that the developed 3D QSAR model is an excellent model. After the filtering was done by the combinatory virtual screening, which is based on the pharmacophore modelling, docking study, and 3D QSAR modelling, we chose nine potent inhibitors with novel scaffolds finally. Furthermore, two potent compounds have been particularly discussed.

Abstract: In the title compound, C18H18ClN3O2S, the dihedral angle between the fused pyrazole and pyridine rings is 3.81 (9) degrees . The benzene ring forms dihedral angles of 35.08 (10) and 36.26 (9) degrees with the pyrazole and pyridine rings, respectively. In the crystal, weak C-Hcdots, three dots, centeredO hydrogen bonds connect mol-ecules along [100].