Abstract: Streptococcus agalactiae is an invasive multi-host pathogen that causes invasive diseases mainly in newborns, elderly, and individuals with underlying health complications. In fish, S. agalactiae causes streptococcosis, which is characterized by septicemia and neurological signs, and leads to great economic losses to the fish farming industry worldwide. These bacteria can be classified into different serotypes based on capsular antigens, and into different sequence types (ST) based on multilocus sequence typing (MLST). In 2015, serotype III ST283 was identified to be associated with a foodborne invasive disease in non-pregnant immunocompetent humans in Singapore, and the infection was related to raw fish consumption. In addition, a serotype III strain isolated from tilapia in Brazil has been reported to be resistant to five antibiotic classes. This specific serotype can serve as a reservoir of resistance genes and pose a serious threat to public health. Thus, new approaches for the control and treatment of S. agalactiae infections are needed. In the present study, 24 S. agalactiae serotype III complete genomes, isolated from human and fish hosts, were compared. The core genome was identified, and, using bioinformatics tools and subtractive criteria, five proteins were identified as potential drug targets. Furthermore, 5,008 drug-like natural compounds were virtually screened against the identified targets. The ligands with the best binding properties are suggested for further in vitro and in vivo analysis.

Abstract: Banxia Houpu decoction (BXHPD) has been used to treat depression in clinical practice for centuries. However, the pharmacological mechanisms of BXHPD still remain unclear. Network Pharmacology (NP) approach was used to explore the potential molecular mechanisms of BXHPD in treating depression. Potential active compounds of BXHPD were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform Database. STRING database was used to build a interaction network between the active compounds and target genes associated with depression. The topological features of nodes were visualized and calculated. Significant pathways and biological functions were identified using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. A total of 44 active compounds were obtained from BXHPD, and 121 potential target genes were considered to be therapeutically relevant. Pathway analysis indicated that MAPK signaling pathway, ErbB signaling pathway, HIF-1 signaling pathway and PI3K-Akt pathway were significant pathways in depression. They were mainly involved in promoting nerve growth and nutrition and alleviating neuroinflammatory conditions. The result provided some potential ways for modern medicine in the treatment of depression.

Abstract: Transdermal drug delivery is a key technology for administering drugs. However, most devices are "one-size-fits-all", even though drug diffusion through the skin varies significantly from person-to-person. For next-generation devices, personalization for optimal drug release would benefit from an augmented insight into the drug release and percutaneous uptake kinetics. Our objective was to quantify the changes in transdermal fentanyl uptake with regards to the patient's age and the anatomical location where the patch was placed. We also explored to which extent the drug flux from the patch could be altered by miniaturizing the contact surface area of the patch reservoir with the skin. To this end, we used validated mechanistic modeling of fentanyl diffusion, storage, and partitioning in the epidermis to quantify drug release from the patch and the uptake within the skin. A superior spatiotemporal resolution compared to experimental methods enabled in-silico identification of peak concentrations and fluxes, and the amount of stored drug and bioavailability. The patients' drug uptake showed a 36% difference between different anatomical locations after 72 h, but there was a strong interpatient variability. With aging, the drug uptake from the transdermal patch became slower and less potent. A 70-year-old patient received 26% less drug over the 72-h application period, compared to an 18-year-old patient. Additionally, a novel concept of using micron-sized drug reservoirs was explored in silico. These reservoirs induced a much higher local flux (microg cm(-2) h(-1)) than conventional patches. Up to a 200-fold increase in the drug flux was obtained from these small reservoirs. This effect was mainly caused by transverse diffusion in the stratum corneum, which is not relevant for much larger conventional patches. These micron-sized drug reservoirs open new ways to individualize reservoir design and thus transdermal therapy. Such computer-aided engineering tools also have great potential for in-silico design and precise control of drug delivery systems. Here, the validated mechanistic models can serve as a key building block for developing digital twins for transdermal drug delivery systems.

Abstract: Background: Drug repositioning, development of new uses for marketed drugs, is an effective way to discover new antitumor compounds. In this study, we used a new method, filtering compounds via molecular docking to find key targets combination. Methods: The data of gene expression in cancer and normal tissues of colorectal, breast, and liver cancer were obtained from The Cancer Genome Atlas Project (TCGA). The key targets combination was obtained from the protein-protein interaction network (PPI network) and the correlation analysis of the targets. Molecular docking was used to reposition the drugs which were obtained from DrugBank. MTT proliferation assay and animal experiments were used to verify the activity of candidate compounds. Flow cytometric analysis of proliferation, cell cycle and apoptosis, slice analysis, gene regulatory network, and Western blot were performed to elucidate the mechanism of drug action. Results: CDK1 and AURKB were identified as a pair of key targets by the analysis of different expression gene from TCGA. Three compounds, linagliptin, mupirocin, and tobramycin, from 12 computationally predicted compounds, were verified to inhibit cell viability in HCT116 (colorectal), MCF7 (breast), and HepG2 (liver) cancer cells. Linagliptin, a hypoglycemic drug, was proved to inhibit cell proliferation by cell cycle arrest and induce apoptosis in HCT116 cells, and suppress tumor growth in nude mice bearing HCT116 cells. Linagliptin reduced the tumor size and decreased the expression of Ki67, a nuclear protein expressed in all proliferative cells. Gene regulatory network and Western blot analysis suggested that linagliptin inhibited tumor cell proliferation and promoted cell apoptosis through suppressing the expression and phosphorylation of Rb, plus down-regulating the expression of Pro-caspase3 and Bcl-2, respectively. Conclusion: The combination of key targets based on the protein-protein interaction network that were built by the different gene expression of TCGA data to reposition the marketed drugs turned out to be a new approach to discover new antitumor drugs. Hypoglycemic drug linagliptin could potentially lead to novel therapeutics for the treatment of tumors, especially for colorectal cancer. Gene regulatory network is a valuable method for predicting and explaining the mechanism of drugs action.

Abstract: Drug-drug interactions (DDIs) are one of the indispensable factors leading to adverse event reactions. Considering the unique structure of AERS (Food and Drug Administration Adverse Event Reporting System (FDA AERS)) reports, we changed the scope of the window value in the original skip-gram algorithm, then propose a language concept representation model and extract features of drug name and reaction information from large-scale AERS reports. The validation of our scheme was tested and verified by comparing with vectors originated from the cooccurrence matrix in tenfold cross-validation. In the verification of description enrichment of the DrugBank DDI database, accuracy was calculated for measurement. The average area under the receiver operating characteristic curve of logistic regression classifiers based on the proposed language model is 6% higher than that of the cooccurrence matrix. At the same time, the average accuracy in five severe adverse event classes is 88%. These results indicate that our language model can be useful for extracting drug and reaction features from large-scale AERS reports.

Abstract: Human coronaviruses (HCoVs), including severe acute respiratory syndrome coronavirus (SARS-CoV) and 2019 novel coronavirus (2019-nCoV, also known as SARS-CoV-2), lead global epidemics with high morbidity and mortality. However, there are currently no effective drugs targeting 2019-nCoV/SARS-CoV-2. Drug repurposing, representing as an effective drug discovery strategy from existing drugs, could shorten the time and reduce the cost compared to de novo drug discovery. In this study, we present an integrative, antiviral drug repurposing methodology implementing a systems pharmacology-based network medicine platform, quantifying the interplay between the HCoV-host interactome and drug targets in the human protein-protein interaction network. Phylogenetic analyses of 15 HCoV whole genomes reveal that 2019-nCoV/SARS-CoV-2 shares the highest nucleotide sequence identity with SARS-CoV (79.7%). Specifically, the envelope and nucleocapsid proteins of 2019-nCoV/SARS-CoV-2 are two evolutionarily conserved regions, having the sequence identities of 96% and 89.6%, respectively, compared to SARS-CoV. Using network proximity analyses of drug targets and HCoV-host interactions in the human interactome, we prioritize 16 potential anti-HCoV repurposable drugs (e.g., melatonin, mercaptopurine, and sirolimus) that are further validated by enrichment analyses of drug-gene signatures and HCoV-induced transcriptomics data in human cell lines. We further identify three potential drug combinations (e.g., sirolimus plus dactinomycin, mercaptopurine plus melatonin, and toremifene plus emodin) captured by the "Complementary Exposure" pattern: the targets of the drugs both hit the HCoV-host subnetwork, but target separate neighborhoods in the human interactome network. In summary, this study offers powerful network-based methodologies for rapid identification of candidate repurposable drugs and potential drug combinations targeting 2019-nCoV/SARS-CoV-2.

Abstract: The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.

Abstract: The harmful impact of xenobiotics on the environment and human health is being more widely recognized; yet, inter- and intraindividual genetic variations among humans modulate the extent of harm, mostly through modulating the outcome of xenobiotic metabolism and detoxification. As the Human Genome Project revealed that host genetic, epigenetic, and regulatory variations could not sufficiently explain the complexity of interindividual variability in xenobiotics metabolism, its sequel, the Human Microbiome Project, is investigating how this variability may be influenced by human-associated microbial communities. Xenobiotic-microbiome relationships are mutual and dynamic. Not only does the human microbiome have a direct metabolizing potential on xenobiotics, but it can also influence the expression of the host metabolizing genes and the activity of host enzymes. On the other hand, xenobiotics may alter the microbiome composition, leading to a state of dysbiosis, which is linked to multiple diseases and adverse health outcomes, including increased toxicity of some xenobiotics. Toxicomicrobiomics studies these mutual influences between the ever-changing microbiome cloud and xenobiotics of various origins, with emphasis on their fate and toxicity, as well the various classes of microbial xenobiotic-modifying enzymes. This review article discusses classic and recent findings in toxicomicrobiomics, with examples of interactions between gut, skin, urogenital, and oral microbiomes with pharmaceutical, food-derived, and environmental xenobiotics. The current state and future prospects of toxicomicrobiomic research are discussed, and the tools and strategies for performing such studies are thoroughly and critically compared.

Abstract: Drug repositioning aims to find new indications for existing drugs in order to reduce drug development cost and time. Currently,there are numerous stories of successful drug repositioning that have been reported and many repurposed drugs are already available on the market. Although drug repositioning is often a product of serendipity, repositioning opportunities can be uncovered systematically. There are three systematic approaches to drug repositioning: disease-centric approach, target-centric and drug-centric. Disease-centric approaches identify close relationships between an old and a new indication. A target-centric approach links a known target and its established drug to a new indication. Lastly, a drug-centric approach connects a known drug to a new target and its associated indication. These three approaches differ in their potential and their limitations, but above all else, in the required start information and computing power. This raises the question of which approach prevails in current drug discovery and what that implies for future developments. To address this question, we systematically evaluated over 100 drugs, 200 target structures and over 300 indications from the Drug Repositioning Database. Each analyzed case was classified as one of the three repositioning approaches. For the majority of cases (more than 60%) the disease-centric definition was assigned. Almost 30% of the cases were classified as target-centric and less than 10% as drug-centric approaches. We concluded that, despite the use of umbrella term "drug" repositioning, disease- and target-centric approaches have dominated the field until now. We propose the use of drug-centric approaches while discussing reasons, such as structure-based repositioning techniques, to exploit the full potential of drug-target-disease connections.

Abstract: As one of the classical traditional Chinese medicine (TCM) prescriptions in treating gynecological tumors, Guizhi Fuling Decoction (GFD) has been used to treat breast cancer (BRCA). Nonetheless, the potential molecular mechanism remains unclear so far. Therefore, systems pharmacology was used in combination with high throughput sequencing-based high throughput screening (HTS(2)) assay and bioinformatic technologies in this study to investigate the molecular mechanisms of GFD in treating BRCA. By computationally analyzing 76 active ingredients in GFD, 38 potential therapeutic targets were predicted and significantly enriched in the "pathways in cancer". Meanwhile, experimental analysis was carried out to examine changes in the expression levels of 308 genes involved in the "pathways in cancer" in BRCA cells treated by five herbs of GFD utilizing HTS(2) platform, and 5 key therapeutic targets, including HRAS, EGFR, PTK2, SOS1, and ITGB1, were identified. The binding mode of active compounds to these five targets was analyzed by molecular docking and molecular dynamics simulation. It was found after integrating the computational and experimental data that, GFD possessed the anti-proliferation, pro-apoptosis, and anti-angiogenesis activities mainly through regulating the PI3K and the MAPK signaling pathways to inhibit BRCA. Besides, consistent with the TCM theory about the synergy of Cinnamomi Ramulus (Guizhi) by Cortex Moutan (Mudanpi) in GFD, both of these two herbs acted on the same targets and pathways. Taken together, the combined application of computational systems pharmacology techniques and experimental HTS(2) platform provides a practical research strategy to investigate the functional and biological mechanisms of the complicated TCM prescriptions.