Abstract: Due to the limitations of effective treatments, avian influenza A H5N1 virus is the most lethal influenza virus strain that causes severe acute lung injury (ALI). To develop effective drugs ameliorating H5N1-induced ALI, we explore an RNA interference (RNAi) screening method to monitor changes in cell death induced by H5N1 infection. We performed RNAi screening on 19,424 genes in A549 lung epithelial cells and examined cell death induced by H5N1 infection. These screens identified 1,137 host genes for which knockdown altered cell viability by over 20%. DrugBank searches of these 1,137 host genes identified 146 validated druggable target genes with 372 drug candidates. We obtained 104 commercially available drugs with 65 validated target genes and examined their improvement of cell viability following H5N1 infection. We identified 28 drugs that could significantly recover cell viability following H5N1 infection and tested 10 in an H5N1-induced-ALI mouse model. The neurological drug ifenprodil and the anticancer drug flavopiridol markedly decreased leukocyte infiltration and lung injury scores in infected mouse lungs, significantly ameliorated edema in infected mouse lung tissues, and significantly improved the survival of H5N1-infected mice. Ifenprodil is an antagonist of the N-methyl-d-aspartate (NMDA) receptor, which is linked to inflammation and lung injury. Flavopiridol is an inhibitor of cyclin-dependent kinase 4 (CDK4), which is linked to leukocyte migration and lung injury. These results suggest that ifenprodil and flavopiridol represent novel remedies against potential H5N1 epidemics in addition to their proven indications. Furthermore, our strategy for identifying repurposable drugs could be a general approach for other diseases.IMPORTANCE Drug repurposing is a quick and economical strategy for developing new therapies with approved drugs. H5N1 is a highly pathogenic avian influenza virus subtype that can cause severe acute lung injury (ALI) and a high mortality rate due to limited treatments. The use of RNA interference (RNAi) is a reliable approach to identify essential genes in diseases. In most genomewide RNAi screenings, virus replication is the readout of interference. Since H5N1 virus infection could induce significant cell death and the percentage of cell death is associated with virus lethality, we designed a genomewide RNAi screening method to identify repurposable drugs against H5N1 virus with cell death as the readout. We discovered that the neurological drug ifenprodil and the anticancer drug flavopiridol could effectively ameliorate murine ALI after influenza A H5N1 virus infection, suggesting that they might be novel remedies for H5N1 virus-induced ALI in addition to the traditional indications.

Abstract: Cytogenetically normal acute myeloid leukemia (CN-AML) presents with diverse outcomes in different patients and is categorized as an intermediate prognosis group. It is important to identify prognostic factors for CN-AML risk stratification. In this study, using the TCGA CN-AML dataset, we found that the scavenger receptor stabilin-1 (STAB1) is a prognostic factor for poor outcomes and validated it in three other independent CN-AML datasets. The high STAB1 expression (STAB1(high)) group had shorter event-free survival compared with the low STAB1 expression (STAB1(low)) group in both the TCGA dataset (n = 79; p = 0.0478) and GEO: GSE6891 dataset (n = 187; p = 0.0354). Differential expression analysis between the STAB1(high) and STAB1(low) groups revealed that upregulated genes in the STAB1(high) group were enriched in pathways related to cell adhesion and migration and immune responses. We confirmed that STAB1 suppression inhibits cell growth in KG1a and NB4 leukemia cells. Expression correlation analyses between STAB1 and cancer drug targets suggested that patients in the STAB1(low) group are more sensitive to the BCL2 inhibitor venetoclax, and we confirmed it in cell lines. In conclusion, we identified STAB1 as a prognostic factor for CN-AML in multiple datasets, explored its underlying mechanism, and provided potential therapeutic indications.

Abstract: FOXO transcription factors are critical regulators of cell homeostasis and steer cell death, differentiation and longevity in mammalian cells. By combined pharmacophore-modeling-based in silico and fluorescence polarization-based screening we identified small molecules that physically interact with the DNA-binding domain (DBD) of FOXO3 and modulate the FOXO3 transcriptional program in human cells. The mode of interaction between compounds and the FOXO3-DBD was assessed via NMR spectroscopy and docking studies. We demonstrate that compounds S9 and its oxalate salt S9OX interfere with FOXO3 target promoter binding, gene transcription and modulate the physiologic program activated by FOXO3 in cancer cells. These small molecules prove the druggability of the FOXO-DBD and provide a structural basis for modulating these important homeostasis regulators in normal and malignant cells.

Abstract: Drug repurposing is the application of approved drugs to treat diseases separate and distinct from their original indications. Herein, we define the scope of all practical precision drug repurposing using DrugBank, a publicly available database of pharmacological agents, and BioVU, a large, de-identified DNA repository linked to longitudinal electronic health records at Vanderbilt University Medical Center. We present a method of repurposing candidate prioritization through integration of pharmacodynamic and marketing variables from DrugBank with quality control thresholds for genomic data derived from the DNA samples within BioVU. Through the synergy of delineated "target-action pairs," along with target genomics, we identify approximately 230 "pairs" that represent all practical opportunities for genomic drug repurposing. From this analysis, we present a pipeline of 14 repurposing candidates across 7 disease areas that link to our repurposability platform and present high potential for randomized controlled trial startup in upcoming months.

Abstract: Social media has been identified as a promising potential source of information for pharmacovigilance. The adoption of social media data has been hindered by the massive and noisy nature of the data. Initial attempts to use social media data have relied on exact text matches to drugs of interest, and therefore suffer from the gap between formal drug lexicons and the informal nature of social media. The Reddit comment archive represents an ideal corpus for bridging this gap. We trained a word embedding model, RedMed, to facilitate the identification and retrieval of health entities from Reddit data. We compare the performance of our model trained on a consumer-generated corpus against publicly available models trained on expert-generated corpora. Our automated classification pipeline achieves an accuracy of 0.88 and a specificity of >0.9 across four different term classes. Of all drug mentions, an average of 79% (+/-0.5%) were exact matches to a generic or trademark drug name, 14% (+/-0.5%) were misspellings, 6.4% (+/-0.3%) were synonyms, and 0.13% (+/-0.05%) were pill marks. We find that our system captures an additional 20% of mentions; these would have been missed by approaches that rely solely on exact string matches. We provide a lexicon of misspellings and synonyms for 2978 drugs and a word embedding model trained on a health-oriented subset of Reddit.

Abstract: The aim of the present study was to evaluate the potential network of arsenic trioxide (ATO) target genes in pancreatic cancer. The DrugBank, STITCH, cBioPortal, Kaplan-Meier plotter and Oncomine websites were used to analyze the association of ATO and its target genes with pancreatic cancer. Initially, 19 ATO target genes were identified, along with their associated protein-protein interaction networks and Kyoto Encyclopedia of Genes and Genomes pathways. ATO was found to be associated with multiple types of cancer, and the most common solid cancer was pancreatic cancer. A total of 6 ATO target genes (namely AKT1, CCND1, CDKN2A, IKBKB, MAPK1 and MAPK3) were found to be associated with pancreatic cancer. Next, the mutation information of the 6 ATO target genes in pancreatic cancer was collected. A total of 20 ATO interacting genes were identified, which were mainly involved in hepatitis B, prostate cancer, pathways in cancer, glioma and chronic myeloid leukemia. Finally, the genes CCND1 and MAPK1 were detected to be prognostic factors in patients with pancreatic cancer. In conclusion, bioinformatics analysis may help elucidate the molecular mechanisms underlying the involvement of ATO in pancreatic cancer, enabling more effective treatment of this disease.

Abstract: Background: The cancer molecular targeted therapy has achieved unprecedented progress in the past decade and is thought to be the most promising direction for cancer treatment in future. As the fast growing of the clinical trials of targeted anticancer agents for different cancer types, it is critical to collect and integrate such information to guide clinical practice. Methods: We constructed the Cancer Molecular Targeted Therapy database (CMTTdb) to store and retrieve molecular targeted therapy data about randomized clinical trials (RCTs) of targeted agents and also accompanied targets, biomarkers, targeted cancer subtypes, etc. Results: Different with some existing resources, CMTTdb particularly focuses on clinical application of the trails. Design of the trails, such as treatment modalities (monotherapy or combination with other therapies), as well as results on clinical efficacy parameters, adverse events are also collected. In this current version, CMTTdb contains data for 1,088 clinical trials which cover 165 agents, 80 targets, 15 cancer types (95 molecular subtypes and 56 histological or cytological subtypes) from public literatures. This database is freely available at http://www.biosino.org/CMTTdb. A user-friendly web interface was designed so that these data can be easily retrieved. Conclusions: CMTTdb will be a valuable source for providing access to information of clinical trials on the rapidly growing number of novel targeted agent and be useful in guiding oncologists for the optimization of the therapy strategy for cancer treatment.

Abstract: This paper presents a new multi-residue method for the quantification of more than 142 anthropogenic compounds of emerging concern (CECs) in various environmental matrices. These CECs are from a wide range of major classes including pharmaceuticals, household, industrial and agricultural. This method utilises ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) for analysis of five matrices (three liquid and two solid) from wastewater treatment processes and the surrounding environment. Relative recoveries were predominantly between 80 and 120%; however, due to the complexity of the matrices used in this work, not all compounds were recovered in all matrices, from 138/142 analytes in surface water to 96/142 analytes in digested solids. Method quantification limits (MQLs) ranged from 0.004 ng L(-1) (bisoprolol in surface water) to 3118 ng L(-1) (creatinine in wastewater treatment work (WwTW) influent). The overall method accuracy was 107.0%, and precision was 13.4%. To test its performance, the method was applied to the range of environmental matrices at WwTWs in South West England. Overall, this method was found to be suitable for application in catchment-based exposure-driven studies, as, of the total number of analytes quantifiable in each matrix, 61% on average was found to be above their corresponding MQL. The results confirm the need for analysing both the liquid and solid compartments within a WwTW to prevent under-reporting of concentrations.

Abstract: Interactions between proteins and small molecules are critical for biological functions. These interactions often occur in small cavities within protein structures, known as ligand-binding pockets. Understanding the physicochemical qualities of binding pockets is essential to improve not only our basic knowledge of biological systems, but also drug development procedures. In order to quantify similarities among pockets in terms of their geometries and chemical properties, either bound ligands can be compared to one another or binding sites can be matched directly. Both perspectives routinely take advantage of computational methods including various techniques to represent and compare small molecules as well as local protein structures. In this review, we survey 12 tools widely used to match pockets. These methods are divided into five categories based on the algorithm implemented to construct binding-site alignments. In addition to the comprehensive analysis of their algorithms, test sets and the performance of each method are described. We also discuss general pharmacological applications of computational pocket matching in drug repurposing, polypharmacology and side effects. Reflecting on the importance of these techniques in drug discovery, in the end, we elaborate on the development of more accurate meta-predictors, the incorporation of protein flexibility and the integration of powerful artificial intelligence technologies such as deep learning.

Abstract: The BIOPEP-UWM database of bioactive peptides (formerly BIOPEP) has recently become a popular tool in the research on bioactive peptides, especially on these derived from foods and being constituents of diets that prevent development of chronic diseases. The database is continuously updated and modified. The addition of new peptides and the introduction of new information about the existing ones (e.g., chemical codes and references to other databases) is in progress. New opportunities include the possibility of annotating peptides containing D-enantiomers of amino acids, batch processing option, converting amino acid sequences into SMILES code, new quantitative parameters characterizing the presence of bioactive fragments in protein sequences, and finding proteinases that release particular peptides.