Abstract: The problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot of computational algorithms have been developed to predict side effects with different types of drug information. However, there is still a lack of methods which could integrate heterogeneous data to predict side effects and select important features at the same time. Here, we propose a novel computational framework based on multi-view and multi-label learning for side effect prediction. Four different types of drug features are collected and graph model is constructed from each feature profile. After that, all the single view graphs are combined to regularize the linear regression functions which describe the relationships between drug features and side effect labels. L1 penalties are imposed on the regression coefficient matrices in order to select features relevant to side effects. Additionally, the correlations between side effect labels are also incorporated into the model by graph Laplacian regularization. The experimental results show that the proposed method could not only provide more accurate prediction for side effects but also select drug features related to side effects from heterogeneous data. Some case studies are also supplied to illustrate the utility of our method for prediction of drug side effects.

Abstract: BACKGROUND: Development of new drugs is a time-consuming and costly process, and the cost is still increasing in recent years. However, the number of drugs approved by FDA every year per dollar spent on development is declining. Drug repositioning, which aims to find new use of existing drugs, attracts attention of pharmaceutical researchers due to its high efficiency. A variety of computational methods for drug repositioning have been proposed based on machine learning approaches, network-based approaches, matrix decomposition approaches, etc. RESULTS: We propose a novel computational method for drug repositioning. We construct and decompose three-dimensional tensors, which consist of the associations among drugs, targets and diseases, to derive latent factors reflecting the functional patterns of the three kinds of entities. The proposed method outperforms several baseline methods in recovering missing associations. Most of the top predictions are validated by literature search and computational docking. Latent factors are used to cluster the drugs, targets and diseases into functional groups. Topological Data Analysis (TDA) is applied to investigate the properties of the clusters. We find that the latent factors are able to capture the functional patterns and underlying molecular mechanisms of drugs, targets and diseases. In addition, we focus on repurposing drugs for cancer and discover not only new therapeutic use but also adverse effects of the drugs. In the in-depth study of associations among the clusters of drugs, targets and cancer subtypes, we find there exist strong associations between particular clusters. CONCLUSIONS: The proposed method is able to recover missing associations, discover new predictions and uncover functional clusters of drugs, targets and diseases. The clustering of drugs, targets and diseases, as well as the associations among the clusters, provides a new guiding framework for drug repositioning.

Abstract: MOTIVATION: Traditional drug discovery and development are often time-consuming and high risk. Repurposing/repositioning of approved drugs offers a relatively low-cost and high-efficiency approach toward rapid development of efficacious treatments. The emergence of large-scale, heterogeneous biological networks has offered unprecedented opportunities for developing in silico drug repositioning approaches. However, capturing highly non-linear, heterogeneous network structures by most existing approaches for drug repositioning has been challenging. RESULTS: In this study, we developed a network-based deep-learning approach, termed deepDR, for in silico drug repurposing by integrating 10 networks: one drug-disease, one drug-side-effect, one drug-target and seven drug-drug networks. Specifically, deepDR learns high-level features of drugs from the heterogeneous networks by a multi-modal deep autoencoder. Then the learned low-dimensional representation of drugs together with clinically reported drug-disease pairs are encoded and decoded collectively via a variational autoencoder to infer candidates for approved drugs for which they were not originally approved. We found that deepDR revealed high performance [the area under receiver operating characteristic curve (AUROC) = 0.908], outperforming conventional network-based or machine learning-based approaches. Importantly, deepDR-predicted drug-disease associations were validated by the ClinicalTrials.gov database (AUROC = 0.826) and we showcased several novel deepDR-predicted approved drugs for Alzheimer's disease (e.g. risperidone and aripiprazole) and Parkinson's disease (e.g. methylphenidate and pergolide). AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/ChengF-Lab/deepDR. SUPPLEMENTARY INFORMATION: Supplementary data are available online at Bioinformatics.

Abstract: MOTIVATION: Breast cancer is the second leading cause of cancer death among women. Tumors, even of the same histopathological subtype, exhibit a high genotypic diversity that impedes therapy stratification and that hence must be accounted for in the treatment decision-making process. RESULTS: Here, we present ClinOmicsTrailbc, a comprehensive visual analytics tool for breast cancer decision support that provides a holistic assessment of standard-of-care targeted drugs, candidates for drug repositioning and immunotherapeutic approaches. To this end, our tool analyzes and visualizes clinical markers and (epi-)genomics and transcriptomics datasets to identify and evaluate the tumor's main driver mutations, the tumor mutational burden, activity patterns of core cancer-relevant pathways, drug-specific biomarkers, the status of molecular drug targets and pharmacogenomic influences. In order to demonstrate ClinOmicsTrailbc's rich functionality, we present three case studies highlighting various ways in which ClinOmicsTrailbc can support breast cancer precision medicine. ClinOmicsTrailbc is a powerful integrated visual analytics tool for breast cancer research in general and for therapy stratification in particular, assisting oncologists to find the best possible treatment options for their breast cancer patients based on actionable, evidence-based results. AVAILABILITY AND IMPLEMENTATION: ClinOmicsTrailbc can be freely accessed at https://clinomicstrail.bioinf.uni-sb.de. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract: The first step of automating composition patent drafting is to draft the claims around a Markush structure with substituents. Currently, this process depends heavily on experienced attorneys or patent agents, and few tools are available. IntelliPatent was created to accelerate this process. Users can simply upload a series of analogs of interest, and IntelliPatent will automatically extract the general structural scaffold and generate the patent claim text. The program can also extend the patent claim by adding commonly seen R groups from historical lists of the top 30 selling drugs in the US for all R substituents. The program takes MDL SD file formats as inputs, and the invariable core structure and variable substructures will be identified as the initial scaffold and R groups in the output Markush structure. The results can be downloaded in MS Word format (.docx). The suggested claims can be quickly generated with IntelliPatent. This web-based tool is freely accessible at https://intellipatent.cmdm.tw/.

Abstract: Carbohydrates and their conjugates are the most abundant natural products, with diverse and highly important biological roles. Synthetic glycoconjugates are versatile tools used to probe biological systems and interfere with them. In an endeavor to provide an efficient route to glycomimetics comprising structurally diverse carbohydrate units, we describe herein a robust, stereoselective, multicomponent approach. Isopropylidene-protected carbohydrate-derived aldehydes and ketones were utilized in the Passerini reaction, giving different glycosylated structures in high yields and diastereoselectivities up to 90:10 diastereomeric ratio (d.r). Access to highly valuable building blocks based on alpha-hydroxy C-glycosyl acids or more complex systems was elaborated by simple post-condensation methodologies.

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.