Abstract: Understanding how the human microbiome affects human health has consequences for treating disease and minimizing unwanted side effects in clinical research. Here, we present MetaMed (http://metamed.rwebox.com/index), a novel and integrative system-wide correlation mapping system to link bacterial functions and medicine therapeutics, providing novel hypotheses for deep investigation of microbe therapeutic effects on human health. Furthermore, comprehensive relationships between microbes living in the environment and drugs were discovered, providing a rich source for discovering microbiota metabolites with great potential for pharmaceutical applications.

Abstract: Natural products (NPs) inspire the design and synthesis of novel biologically relevant chemical matter, for instance through biology-oriented synthesis (BIOS). However, BIOS is limited by the partial coverage of NP-like chemical space by the guiding NPs. The design and synthesis of "pseudo NPs" overcomes these limitations by combining NP-inspired strategies with fragment-based compound design through de novo combination of NP-derived fragments to unprecedented compound classes not accessible through biosynthesis. We describe the development and biological evaluation of pyrano-furo-pyridone (PFP) pseudo NPs, which combine pyridone- and dihydropyran NP fragments in three isomeric arrangements. Cheminformatic analysis indicates that the PFPs reside in an area of NP-like chemical space not covered by existing NPs but rather by drugs and related compounds. Phenotypic profiling in a target-agnostic "cell painting" assay revealed that PFPs induce formation of reactive oxygen species and are structurally novel inhibitors of mitochondrial complex I.

Abstract: Exhaled breath condensate (EBC) collection provides a promising matrix for bioanalysis of endogenous biomarkers of health and also for exogenous compounds like drugs. There is little information regarding drugs and their metabolites contained in breath, as well as their pharmacokinetics. In this present work, we use a simple and non-invasive technique to collect EBC from chronic pain patients using different analgesic opioid drugs to manage pain. Six patients received continuous infusion of morphine and hydromorphone intravenously (IV), together with other analgesic drugs (IV and orally). Repeated sampling of serum and EBC was done at two time points separated by 90 min. The EBC was collected using a glass tube surrounded by dry ice, and an ethanol solvent wash of the glass was performed after EBC extraction to retrieve the apolar compounds stuck to the glass surface. All samples were analyzed with liquid chromatography coupled to mass spectrometry (LC-MS/MS) to identify possible metabolites present in the sample, and to quantify the drugs being used. Several metabolites, such as normorphine (norM), norhydromorphone (norHM) and dihydromorphone (diHM) were detected in both fractions, while hydromorphone 3-glucuronide (HM 3G) was only detected in the solvent rinse fraction. Results were correlated to explain the pharmacokinetics of the main drugs administered. This pilot study presented promising correlations between drug concentrations in blood and breath at different time points for norM, norHM and HM 3G.

Abstract: SUMMARY: Making use of accumulated genetic knowledge for clinical practice is our next goal in human genetics. Here we introduce GREP (Genome for REPositioning drugs), a standalone python software to quantify an enrichment of the user-defined set of genes in the target of clinical indication categories and to capture potentially repositionable drugs targeting the gene set. We show that genes identified by the large-scale genome-wide association studies were robustly enriched in the approved drugs to treat the trait of interest. This enrichment analysis was also highly applicable to other sets of biological genes such as those identified by gene expression studies and genes somatically mutated in cancers. This software should accelerate investigators to reposition drugs to other indications with the guidance of known genomics. AVAILABILITY AND IMPLEMENTATION: GREP is available at https://github.com/saorisakaue/GREP as a python source code. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: MOTIVATION: Drug combinations that simultaneously suppress multiple cancer driver signaling pathways increase therapeutic options and may reduce drug resistance. We have developed a computational systems biology tool, DrugComboExplorer, to identify driver signaling pathways and predict synergistic drug combinations by integrating the knowledge embedded in vast amounts of available pharmacogenomics and omics data. RESULTS: This tool generates driver signaling networks by processing DNA sequencing, gene copy number, DNA methylation and RNA-seq data from individual cancer patients using an integrated pipeline of algorithms, including bootstrap aggregating-based Markov random field, weighted co-expression network analysis and supervised regulatory network learning. It uses a systems pharmacology approach to infer the combinatorial drug efficacies and synergy mechanisms through drug functional module-induced regulation of target expression analysis. Application of our tool on diffuse large B-cell lymphoma and prostate cancer demonstrated how synergistic drug combinations can be discovered to inhibit multiple driver signaling pathways. Compared with existing computational approaches, DrugComboExplorer had higher prediction accuracy based on in vitro experimental validation and probability concordance index. These results demonstrate that our network-based drug efficacy screening approach can reliably prioritize synergistic drug combinations for cancer and uncover potential mechanisms of drug synergy, warranting further studies in individual cancer patients to derive personalized treatment plans. AVAILABILITY AND IMPLEMENTATION: DrugComboExplorer is available at https://github.com/Roosevelt-PKU/drugcombinationprediction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: MOTIVATION: Drug repurposing is a potential alternative to the classical drug discovery pipeline. Repurposing involves finding novel indications for already approved drugs. In this work, we present a novel machine learning-based method for drug repurposing. This method explores the anti-similarity between drugs and a disease to uncover new uses for the drugs. More specifically, our proposed method takes into account three sources of information: (i) large-scale gene expression profiles corresponding to human cell lines treated with small molecules, (ii) gene expression profile of a human disease and (iii) the known relationship between Food and Drug Administration (FDA)-approved drugs and diseases. Using these data, our proposed method learns a similarity metric through a supervised machine learning-based algorithm such that a disease and its associated FDA-approved drugs have smaller distance than the other disease-drug pairs. RESULTS: We validated our framework by showing that the proposed method incorporating distance metric learning technique can retrieve FDA-approved drugs for their approved indications. Once validated, we used our approach to identify a few strong candidates for repurposing. AVAILABILITY AND IMPLEMENTATION: The R scripts are available on demand from the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: Tuberculosis (TB) is a global health concern, and this situation has further worsened due to the emergence of drug-resistant strains and the failure of BCG vaccine to impart protection. There is an imperative need to develop highly sensitive, specific diagnostic tools, novel therapeutics, and vaccines for the eradication of TB. In the present study, a chemical screen of a pharmacologically active compound library was performed to identify antimycobacterial compounds. The phenotypic screen identified a few novel small-molecule inhibitors, including NU-6027, a known CDK-2 inhibitor. We demonstrate that NU-6027 inhibits Mycobacterium bovis BCG growth in vitro and also displayed cross-reactivity with Mycobacterium tuberculosis protein kinase D (PknD) and protein kinase G (PknG). Comparative structural and sequence analysis along with docking simulation suggest that the unique binding site stereochemistry of PknG and PknD accommodates NU-6027 more favorably than other M. tuberculosis Ser/Thr protein kinases. Further, we also show that NU-6027 treatment induces the expression of proapoptotic genes in macrophages. Finally, we demonstrate that NU-6027 inhibits M. tuberculosis growth in both macrophage and mouse tissues. Taken together, these results indicate that NU-6027 can be optimized further for the development of antimycobacterial agents.

Abstract: The antipsychotic drug pimozide has been found to exhibit anticancer effects. Previously, it was demonstrated that pimozide inhibits hepatocellular carcinoma (HCC) cell growth, but its pharmacodynamic characteristics remain unclear. The aim of the present study was to investigate the reversibility and mechanism of the ability of pimozide to inhibit cell proliferation in liver cancer. Cell viability was determined by Cell Counting Kit8 and colony formation assay. The cell cycle distribution was analyzed by flow cytometry with Ki67 and PI staining. ROS production of HCC cells was detected with DCFHDA and inhibited with NAC treatment. Western blot assay was performed to detect the expression of related signaling molecules in HCC cells. Our results showed that pimozide promoted G0/G1 phase arrest in HCC cell lines without significant cell death. Its antiproliferative effects on HCC cells were reversible, consistent with involvement of cell quiescence and reactive oxygen species (ROS) production. Pimozide enhanced inhibition of HCC cell proliferation by sorafenib. In conclusion, elucidation of pimozide's reversible proliferation inhibition in liver cancer and additive activity with a wellestablished anticancer drug warrants further exploration of the potential of pimozide as an adjuvant anticancer therapy.

Abstract: PURPOSE: To develop a system to estimate the risk of herb-drug interactions that includes the available evidence from clinical and laboratory studies, transparently delineates the algorithm for the risk estimation, could be used in practice settings and allows for adaptation and update. METHODS: We systematically searched Drugbank, Transformer, Drug Information Handbook, European and German Pharmacopoeia and MEDLINE for studies on herb-drug interactions of five common medicinal plants (coneflower, ginseng, milk thistle, mistletoe and St. John's wort). A diverse set of data were independently extracted by two researchers and subsequently analysed by a newly developed algorithm. Results are displayed in the form of interaction risk categories. The development of the algorithm was guided by an expert panel consensus process. RESULTS: From 882 publications retrieved by the search, 154 studies were eligible and provided 529 data sets on herbal interactions. The developed algorithm prioritises results from clinical trials over case reports over in vitro investigations and considers type of study, consistency of study results and study outcome for clinical trials as well as identification, permeability, bioavailability, and interaction potency of an identified herbal perpetrator for in vitro investigations. Risk categories were assigned to and dynamically visualised in a colour-coded matrix format. CONCLUSIONS: The novel algorithm allows to transparently generate and dynamically display herb-drug interaction risks based on the available evidence from clinical and laboratory pharmacologic studies. It provides health professionals with readily available and easy updatable information about the risk of pharmacokinetic interactions between herbs and oncologic drugs.

Abstract: Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and prevents adverse drug reactions (ADRs). Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to postmarketing surveillance of ADRs in real-world populations. Future toxicity and safety challenges, including increased polypharmacy and patient diversity, stress the limits of these traditional tools. Massive amounts of newly available data present an opportunity for using artificial intelligence (AI) and machine learning to improve drug safety science. Here, we explore recent advances as applied to preclinical drug safety and postmarketing surveillance with a specific focus on machine and deep learning (DL) approaches.