Abstract: BACKGROUND: Childhood-onset asthma is highly affected by genetic components. In recent years, many genome-wide association studies (GWAS) have reported a large group of genetic variants and susceptible genes associated with asthma-related phenotypes including childhood-onset asthma. However, the regulatory mechanisms of these genetic variants for childhood-onset asthma susceptibility remain largely unknown. METHODS: In the current investigation, we conducted a two-stage designed Sherlock-based integrative genomics analysis to explore the cis- and/or trans-regulatory effects of genome-wide SNPs on gene expression as well as childhood-onset asthma risk through incorporating a large-scale GWAS data (N = 314,633) and two independent expression quantitative trait loci (eQTL) datasets (N = 1890). Furthermore, we applied various bioinformatics analyses, including MAGMA gene-based analysis, pathway enrichment analysis, drug/disease-based enrichment analysis, computer-based permutation analysis, PPI network analysis, gene co-expression analysis and differential gene expression analysis, to prioritize susceptible genes associated with childhood-onset asthma. RESULTS: Based on comprehensive genomics analyses, we found 31 genes with multiple eSNPs to be convincing candidates for childhood-onset asthma risk; such as, PSMB9 (cis-rs4148882 and cis-rs2071534) and TAP2 (cis-rs9267798, cis-rs4148882, cis-rs241456, and trans-10,447,456). These 31 genes were functionally interacted with each other in our PPI network analysis. Our pathway enrichment analysis showed that numerous KEGG pathways including antigen processing and presentation, type I diabetes mellitus, and asthma were significantly enriched to involve in childhood-onset asthma risk. The co-expression patterns among 31 genes were remarkably altered according to asthma status, and 25 of 31 genes (25/31 = 80.65%) showed significantly or suggestively differential expression between asthma group and control group. CONCLUSIONS: We provide strong evidence to highlight 31 candidate genes for childhood-onset asthma risk, and offer a new insight into the genetic pathogenesis of childhood-onset asthma.

Abstract: Drug-drug interactions (DDIs) with oral anticoagulants may lead to under-anticoagulation and increased risk of thromboembolism. Although warfarin is susceptible to numerous DDIs, few studies have examined DDIs resulting in thromboembolism or those involving direct-acting oral anticoagulants (DOACs). We aimed to identify medications that increase the rate of hospitalization for thromboembolic events when taken concomitantly with oral anticoagulants. We conducted a high-throughput pharmacoepidemiologic screening study using Optum Clinformatics Data Mart, 2000-2016. We performed self-controlled case series studies among adult users of oral anticoagulants (warfarin, dabigatran, rivaroxaban, apixaban, and edoxaban) with at least one hospitalization for a thromboembolic event. Among eligible patients, we identified all oral medications frequently co-prescribed with oral anticoagulants as potential interacting precipitants. Conditional Poisson regression was used to estimate rate ratios comparing precipitant exposed vs. unexposed time for each anticoagulant-precipitant pair. To minimize within-person confounding by indication for the precipitant, we used pravastatin as a negative control object drug. Multiple estimation was adjusted using semi-Bayes shrinkage. We screened 1,622 oral anticoagulant-precipitant drug pairs and identified 226 (14%) drug pairs associated with statistically significantly elevated risk of thromboembolism. Using pravastatin as the negative control object drug, this list was reduced to 69 potential DDI signals for thromboembolism, 33 (48%) of which were not documented in the DDI knowledge databases Lexicomp and/or Micromedex. There were more DDI signals associated with warfarin than DOACs. This study reproduced several previously documented oral anticoagulant DDIs and identified potential DDI signals that deserve to be examined in future etiologic studies.

Abstract: Colorectal cancer is one of the commoner digestive tract malignant tumor types, and its incidence and mortality rate are high. Accumulating evidence indicates that longchain noncoding RNAs (lncRNAs) and proteincoding RNAs interact with each other by competing with the same micro(mi)RNA response element (MREs) and serve an important role in the regulation of gene expression in a variety of tumor types. However, the regulatory mechanism and prognostic role of lncRNAmediated competing endogenous (ce)RNA networks in colon cancer have yet to be elucidated. The expression profiles of mRNAs, lncRNAs and miRNAs from 471 colon cancer and 41 paracancerous tissue samples were downloaded from The Cancer Genome Atlas database. A lncRNAmiRNAmRNA ceRNA network in colon cancer was constructed and comprised 17 hub lncRNAs, 87 hub miRNA and 144 hub mRNAs. The topological properties of the network were analyzed, and the random walk algorithm was used to identify the nodes significantly associated with colon cancer. Survival analysis using the UALCAN database indicated that 2/17 lncRNAs identified [metastasisassociated lung adenocarcinoma transcript (MALAT1) and maternally expressed gene 3 (MEG3)] and 5/144 mRNAs [FES upstream region (FURIN), nuclear factor of activated Tcells 5 (NFAT5), RNA Binding Motif Protein 12B (RBM12B), Ras related GTP binding A (RRAGA) and WD repeat domain phosphoinositideinteracting protein 2 (WIPI2)] were significantly associated with the overall survival of patients with colon cancer, and may therefore be used as potential prognostic biomarkers of colon cancer. According to extracted lncRNAmiRNAmRNA interaction pairs, the GSE26334 dataset was used to confirm that the lncRNA MALAT1/miR1295p/NFAT5 axis may represent a novel regulatory mechanism concerning the progression of colon cancer. The clusterProfiler package was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in colon cancer. Finally, drugs that significantly interact with the core genes identified in colon cancer were predicted using a hypergeometric test. Of these, fostamatinib was identified to be a targeted drug for colon cancer therapy. The present findings provide a novel perspective for improved understanding of the lncRNAassociated ceRNA network and may facilitate the development of novel targeted therapeutics in colon cancer.

Abstract: The purpose of this study was to develop solid dispersions of fenretinide(4HPR), incorporate them into poly(lactic-co-glycolic)(PLGA) millicylindrical implants, and evaluate the resulting implants in vitro and in vivo for future applications in oral cancer chemoprevention. Due to the extreme hydrophobicity of 4HPR, 4HPR-polyvinylpyrrolidone (PVP) amorphous solid dispersions(ASDs) were prepared for solubility enhancement. The optimal PVP-4HPR ratio of 9/1(w/w) provided a 50-fold solubility enhancement in aqueous media, which was sustained over 1 week. PVP-4HPR ASD particles were loaded into PLGA millicylinders and drug release was evaluated in vitro in PBST and in vivo by recovery from subcutaneous injection in rats. While initial formulations of PLGA PVP-4HPR millicylinders only released 10% 4HPR in vitro after 28 days, addition of the plasticizer triethyl-o-acetyl-citrate(TEAC) into PVP-4HPR ASDs resulted in a 5.6-fold total increase in drug release. Remarkably, the TEAC-PVP-4HPR PLGA implants demonstrated slow, continuous, and nearly complete release over 1 month in vivo compared to a 25% release for our previously reported formulation incorporating solubilizers and pore-forming agents. Hence, a combination of PLGA plasticizer and ASD formation provides an avenue for long-term controlled release in vivo for the exceptionally difficult drug to formulate, 4HPR, and a suitable formulation for future evaluation in rodent models of oral cancer.

Abstract: The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.

Abstract: Drug repurposing is an effective means for rapid drug discovery. The aim of this study was to develop and validate a computational methodology based on Literature-Wide Association Studies (LWAS) of PubMed to repurpose existing drugs for a rare inflammatory breast cancer (IBC). We have developed a methodology that conducted LWAS based on the text mining technology Word2Vec. 3.80 million "cancer"-related PubMed abstracts were processed as the corpus for Word2Vec to derive vector representation of biological concepts. These vectors for drugs and diseases served as the foundation for creating similarity maps of drugs and diseases, respectively, which were then employed to find potential therapy for IBC. Three hundred and thirty-six (336) known drugs and three hundred and seventy (370) diseases were expressed as vectors in this study. Nine hundred and seventy (970) previously known drug-disease association pairs among these drugs and diseases were used as the reference set. Based on the hypothesis that similar drugs can be used against similar diseases, we have identified 18 diseases similar to IBC, with 24 corresponding known drugs proposed to be the repurposing therapy for IBC. The literature search confirmed most known drugs tested for IBC, with four of them being novel candidates. We conclude that LWAS based on the Word2Vec technology is a novel approach to drug repurposing especially useful for rare diseases.

Abstract: One of the prominent challenges in precision medicine is to select the most appropriate treatment strategy for each patient based on the personalized information. The availability of massive data about drugs and cell lines facilitates the possibility of proposing efficient computational models for predicting anticancer drug response. In this study, we propose ADRML, a model for Anticancer Drug Response Prediction using Manifold Learning to systematically integrate the cell line information with the drug information to make accurate predictions about drug therapeutic. The proposed model maps the drug response matrix into the lower-rank spaces that lead to obtaining new perspectives about cell lines and drugs. The drug response for a new cell line-drug pair is computed using the low-rank features. The evaluation of ADRML performance on various types of cell lines and drug information, in addition to the comparisons with previously proposed methods, shows that ADRML provides accurate and robust predictions. Further investigations about the association between drug response and pathway activity scores reveal that the predicted drug responses can shed light on the underlying drug mechanism. Also, the case studies suggest that the predictions of ADRML about novel cell line-drug pairs are validated by reliable pieces of evidence from the literature. Consequently, the evaluations verify that ADRML can be used in accurately predicting and imputing the anticancer drug response.

Abstract: The emergence of the new coronavirus (nCoV-19) has impacted human health on a global scale, while the interaction between the virus and the host is the foundation of the disease. The viral genome codes a cluster of proteins, each with a unique function in the event of host invasion or viral development. Under the current adverse situation, we employ virtual screening tools in searching for drugs and natural products which have been already deposited in DrugBank in an attempt to accelerate the drug discovery process. This study provides an initial evaluation of current drug candidates from various reports using our systemic in silico drug screening based on structures of viral proteins and human ACE2 receptor. Additionally, we have built an interactive online platform (https://shennongproject.ai/) for browsing these results with the visual display of a small molecule docked on its potential target protein, without installing any specialized structural software. With continuous maintenance and incorporation of data from laboratory work, it may serve not only as the assessment tool for the new drug discovery but also an educational web site for the public.

Abstract: Network theory provides one of the most potent analysis tools for the study of complex systems. In this paper, we illustrate the network-based perspective in drug research and how it is coherent with the new paradigm of drug discovery. We first present data sources from which networks are built, then show some examples of how the networks can be used to investigate drug-related systems. A section is devoted to network-based inference applications, i.e., prediction methods based on interactomes, that can be used to identify putative drug-target interactions without resorting to 3D modeling. Finally, we present some aspects of Boolean networks dynamics, anticipating that it might become a very potent modeling framework to develop in silico screening protocols able to simulate phenotypic screening experiments. We conclude that network applications integrated with machine learning and 3D modeling methods will become an indispensable tool for computational drug discovery in the next years.

Abstract: BACKGROUND This study used a network pharmacology approach to identify the pharmacological mechanisms of a traditional Chinese medicine derived from Trachelospermum jasminoides (Lindl.) Lem. in patients with rheumatoid arthritis (RA). MATERIAL AND METHODS Known compounds of T. jasminoides were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the Shanghai Institute of Organic Chemistry of Chinese Academy of Science, Chemistry (CASC) database, and a literature search. Putative targets of identified compounds were predicted by SwissTargetPrediction. RA-related targets were achieved from the Therapeutic Target database, Drugbank database, Pharmacogenomics Knowledgebase, and Online Mendelian Inheritance in Man database. The protein-protein interaction (PPI) network was built by STRING. CluGO was utilized for Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis. RESULTS A total of 354 potential targets were predicted for the 17 bioactive compounds in T. jasminoides; 69 of these targets overlapped with RA-related targets. A PPI network was composed and 2 clusters of 59 and 42 nodes each were excavated. GO and KEGG enrichment analysis of the overlapping targets and the 2 clusters was mainly grouped into immunity, inflammation, estrogen, anxiety, and depression processes. CONCLUSIONS Our study illustrated that T. jasminoides alleviates RA through the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway, and other immune and inflammatory-related processes. It also may exert effects in regulating cell differentiation and potentially has anti-anxiety, anti-depression, and estrogen-like effects.