Abstract: Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug-gene interaction data. We obtained drug-gene interaction data from an earlier version of DrugBank, built a drug-gene interaction network, and projected it as a drug-drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug-gene interaction data to generate a comprehensive drug repurposing hint list.

Abstract: Thalidomide is effective in patients with refractory cutaneous lupus erythematosus (CLE). However, the mechanism of action is not completely understood, and its use is limited by its potential, severe side-effects. Immune cell subset analysis in thalidomide's CLE responder patients showed a reduction of circulating and tissue cytotoxic T-cells with an increase of iNKT cells and a shift towards a Th2 response. We conducted an RNA-sequencing study using CLE skin biopsies performing a Therapeutic Performance Mapping System (TMPS) analysis in order to generate a predictive model of its mechanism of action and to identify new potential therapeutic targets. Integrating RNA-seq data, public databases, and literature, TMPS analysis generated mathematical models which predicted that thalidomide acts via two CRBN-CRL4A- (CRL4(CRBN)) dependent pathways: IRF4/NF-B and AMPK1/mTOR. Skin biopsies showed a significant reduction of IRF4 and mTOR in post-treatment samples by immunofluorescence. In vitro experiments confirmed the effect of thalidomide downregulating IRF4 in PBMCs and mTOR in keratinocytes, which converged in an NF-B reduction that led to a resolution of the inflammatory lesion. These results emphasize the anti-inflammatory role of thalidomide in CLE treatment, providing novel molecular targets for the development of new therapies that could avoid thalidomide's side effects while maintaining its efficacy.

Abstract: BACKGROUND: The incidence of diabetes mellitus (DM) and depression is increasing year by year around the world, bringing a serious burden to patients and their families. Jiao-tai-wan (JTW), a well-known traditional Chinese medicine (TCM), has been approved to have hypoglycemic and antidepressant effects, respectively, but whether JTW has such dual effects and its potential mechanisms is still unknown. This study is to evaluate the dual therapeutic effects of JTW on chronic restraint stress (CRS)-induced DM combined with depression mice, and to explore the underlying mechanisms through network pharmacology. METHODS: CRS was used on db/db mice for 21 days to induce depression-like behaviors, so as to obtain the DM combined with depression mouse model. Mice were treated with 0.9% saline (0.1 ml/10 g), JTW (3.2 mg/kg) and Fluoxetine (2.0 mg/kg), respectively. The effect of JTW was accessed by measuring fasting blood glucose (FBG) levels, conducting behavioral tests and observing histopathological change. The ELISA assay was used to evaluate the levels of inflammatory cytokines and the UHPLC-MS/MS method was used to determine the depression-related neurotransmitters levels in serum. The mechanism exploration of JTW against DM and depression were performed via a network pharmacological method. RESULTS: The results of blood glucose measurement showed that JTW has a therapeutic effect on db/db mice. Behavioral tests and the levels of depression-related neurotransmitters proved that JTW can effectively ameliorate depression-like symptoms in mice induced by CRS. In addition, JTW can also improve the inflammatory state and reduce the number of apoptotic cells in the hippocampus. According to network pharmacology, 28 active compounds and 484 corresponding targets of JTW, 1407 DM targets and 1842 depression targets were collected by screening the databases, and a total of 117 targets were obtained after taking the intersection. JTW plays a role in reducing blood glucose level and antidepressant mainly through active compounds such as quercetin, styrene, cinnamic acid, ethyl cinnamate, (R)-Canadine, palmatine and berberine, etc., the key targets of its therapeutic effect include INS, AKT1, IL-6, VEGF-A, TNF and so on, mainly involved in HIF-1 signal pathway, pathways in cancer, Hepatitis B, TNF signal pathway, PI3K-Akt signal pathway and MAPK signaling pathway, etc. CONCLUSION: Our experimental study showed that JTW has hypoglycemic and antidepressant effects. The possible mechanism was explored by network pharmacology, reflecting the characteristics of multi-component, multi-target and multi-pathway, which provides a theoretical basis for the experimental research and clinical application of JTW in the future.

Abstract: Gemcitabine is a nucleoside analog effective against several solid tumors. Standard treatment consists of an intravenous infusion over 30 min. This is an invasive, uncomfortable and often painful method, involving recurring visits to the hospital and costs associated with medical staff and equipment. Gemcitabine's activity is significantly limited by numerous factors, including metabolic inactivation, rapid systemic clearance of gemcitabine and transporter deficiency-associated resistance. As such, there have been research efforts to improve gemcitabine-based therapy efficacy, as well as strategies to enhance its oral bioavailability. In this work, gemcitabine in vitro and clinical data were analyzed and in silico tools were used to study the pharmacokinetics of gemcitabine after oral administration following different regimens. Several physiologically based pharmacokinetic (PBPK) models were developed using simulation software GastroPlus, predicting the PK parameters and plasma concentration-time profiles. The integrative biomedical data analyses presented here are promising, with some regimens of oral administration reaching higher AUC in comparison to the traditional IV infusion, supporting this route of administration as a viable alternative to IV infusions. This study further contributes to personalized health care based on potential new formulations for oral administration of gemcitabine, as well nanotechnology-based drug delivery systems.

Abstract: BACKGROUND: A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19. METHODS: In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results. RESULTS: In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs. CONCLUSIONS: Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

Abstract: MOTIVATION: Drug repurposing is a potential alternative to the traditional drug discovery process. Drug repurposing can be formulated as a recommender system that recommends novel indications for available drugs based on known drug-disease associations. This paper presents a method based on non-negative matrix factorization (NMF-DR) to predict the drug-related candidate disease indications. This work proposes a recommender system-based method for drug repurposing to predict novel drug indications by integrating drug and diseases related data sources. For this purpose, this framework first integrates two types of disease similarities, the associations between drugs and diseases, and the various similarities between drugs from different views to make a heterogeneous drug-disease interaction network. Then, an improved non-negative matrix factorization-based method is proposed to complete the drug-disease adjacency matrix with predicted scores for unknown drug-disease pairs. RESULTS: The comprehensive experimental results show that NMF-DR achieves superior prediction performance when compared with several existing methods for drug-disease association prediction. AVAILABILITY: The program is available at https://github.com/sshaghayeghs/NMF-DR.

Abstract: BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a devastating disease with a high clinical burden. The molecular signatures of IPF were analyzed to distinguish molecular subgroups and identify key driver genes and therapeutic targets. METHODS: Thirteen datasets of lung tissue transcriptomics including 585 IPF patients and 362 normal controls were obtained from the databases and subjected to filtration of differentially expressed genes (DEGs). A functional enrichment analysis, agglomerative hierarchical clustering, network-based key driver analysis, and diffusion scoring were performed, and the association of enriched pathways and clinical parameters was evaluated. RESULTS: A total of 2,967 upregulated DEGs was filtered during the comparison of gene expression profiles of lung tissues between IPF patients and healthy controls. The core molecular network of IPF featured p53 signaling pathway and cellular senescence. IPF patients were classified into two molecular subgroups (C1, C2) via unsupervised clustering. C1 was more enriched in the p53 signaling pathway and ciliated cells and presented a worse prognostic score, while C2 was more enriched for cellular senescence, profibrosing pathways, and alveolar epithelial cells. The p53 signaling pathway was closely correlated with a decline in forced vital capacity and carbon monoxide diffusion capacity and with the activation of cellular senescence. CDK1/2, CKDNA1A, CSNK1A1, HDAC1/2, FN1, VCAM1, and ITGA4 were the key regulators as evidence by high diffusion scores in the disease module. Currently available and investigational drugs showed differential diffusion scores in terms of their target molecules. CONCLUSIONS: An integrative molecular analysis of IPF lungs identified two molecular subgroups with distinct pathobiological characteristics and clinical prognostic scores. Inhibition against CDKs or HDACs showed great promise for controlling lung fibrosis. This approach provided molecular insights to support the prediction of clinical outcomes and the selection of therapeutic targets in IPF patients.

Abstract: Isavuconazole is a newer broad-spectrum triazole approved for the treatment of invasive fungal disease. The objective of this study was to conduct a population pharmacokinetic and pharmacodynamic analysis of isavuconazole in a retrospective cohort of hospitalized patients. A nonlinear mixed-effect approach with Monte Carlo simulations was conducted to assess the probability of target attainment (PTA) of an area under the concentration-time curve (AUC24 h)/minimum inhibitory concentration (MIC) ratio of 33.4 (defined as efficacy threshold against A. fumigatus and A. flavus) associated with a maintenance dose (MD) of 100, 200 and 300 mg daily after loading. The cumulative fraction of response (CFR) against the EUCAST MIC distributions of A. fumigatus and A. flavus was calculated as well. The proportion of trough concentrations (Ctrough) exceeding a defined threshold of toxicity (>5.13 mg/L) was estimated. A total of 50 patients, with a median age of 61.5 years, provided 199 plasma isavuconazole concentrations. Invasive pulmonary aspergillosis was the prevalent type of infection and accounted for 80% (40/50) of cases. No clinical covariates were retained by the model. With the standard MD of 200 mg daily, CFRs were always >/=90% during the first two months of treatment. The risk of Ctrough < 1.0 mg/L was around 1%, and that of Ctrough > 5.13 mg/L was 27.7 and 39.2% at 28 and 60 days, respectively, due to isavuconazole accumulation over time. Our findings suggest that TDM for isavuconazole should not be considered as mandatory as for the other mold-active azoles voriconazole and posaconazole.

Abstract: Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global health burden. Despite considerable efforts, the underlying mechanisms have not yet been comprehensively understood. In this study, a systematic approach was utilized to identify the microRNA signature in DN and to introduce novel drug targets (DTs) in DN. Using microarray profiling followed by qPCR confirmation, 13 and 6 differentially expressed (DE) microRNAs were identified in the kidney cortex and medulla, respectively. The microRNA-target interaction networks for each anatomical compartment were constructed and central nodes were identified. Moreover, enrichment analysis was performed to identify key signaling pathways. To develop a strategy for DT prediction, the human proteome was annotated with 65 biochemical characteristics and 23 network topology parameters. Furthermore, all proteins targeted by at least one FDA-approved drug were identified. Next, mGMDH-AFS, a high-performance machine learning algorithm capable of tolerating massive imbalanced size of the classes, was developed to classify DT and non-DT proteins. The sensitivity, specificity, accuracy, and precision of the proposed method were 90%, 86%, 88%, and 89%, respectively. Moreover, it significantly outperformed the state-of-the-art (P-value = 0.05) and showed very good diagnostic accuracy and high agreement between predicted and observed class labels. The cortex and medulla networks were then analyzed with this validated machine to identify potential DTs. Among the high-rank DT candidates are Egfr, Prkce, clic5, Kit, and Agtr1a which is a current well-known target in DN. In conclusion, a combination of experimental and computational approaches was exploited to provide a holistic insight into the disorder for introducing novel therapeutic targets.

Abstract: Chemical pesticides have an immense role in curbing the infection of plant viruses and soil-borne pathogens of high valued crops. However, the usage of chemical pesticides also contributes to the development of resistance among pathogens. Hence, attempts were made in this study to identify a suitable bacterial antagonist for managing viral and fungal pathogens infecting crop plants. Based on our earlier investigations, we identified Bacillus amyloliquefaciens VB7 as a potential antagonist for managing Sclerotinia sclerotiorum infecting carnation, tobacco streak virus infecting cotton and groundnut bud necrosis infecting tomato. Considering the multifaceted action of B. amyloliquefaciens VB7, attempts were made for whole-genome sequencing to assess the antiviral activity against tomato spotted wilt virus infecting chrysanthemum and antifungal action against Fusarium oxysporum f. sp. cubense (Foc). Genome annotation of the isolate B. amyloliquefaciens VB7 was confirmed as B. velezensis VB7 with accession number CP047587. Genome analysis revealed the presence of 9,231,928 reads with an average read length of 149 bp. Assembled genome had 1 contig, with a total length of 3,021,183 bp and an average G+C content of 46.79%. The protein-coding sequences (CDS) in the genome was 3090, transfer RNA (tRNA) genes were 85 with 29 ribosomal RNA (rRNA) genes and 21 repeat regions. The genome of B. velezensis VB7 had 506 hypothetical proteins and 2584 proteins with functional assignments. VB7 genome had the presence of flagellin protein FlaA with 987 nucleotides and translation elongation factor TU (Ef-Tu) with 1191 nucleotides. The identified ORFs were 3911 with 47.22% GC content. Non ribosomal pepide synthetase cluster (NRPS) gene clusters in the genome of VB7, coded for the anti-microbial peptides surfactin, butirosin A/butirosin B, fengycin, difficidin, bacillibactin, bacilysin, and mersacidin the Ripp lanthipeptide. Antiviral action of VB7 was confirmed by suppression of local lesion formation of TSWV in the local lesion host cowpea (Co-7). Moreover, combined application of B. velezensis VB7 with phyto-antiviral principles M. Jalapa and H. cupanioides increased shoot length, shoot diameter, number of flower buds per plant, flower diameter, and fresh weight of chrysanthemum. Further, screening for antifungal action of VB7 expressed antifungal action against Foc in vitro by producing VOC/NVOC compounds, including hexadecanoic acid, linoelaidic acid, octadecanoic acid, clindamycin, formic acid, succinamide, furanone, 4H-pyran, nonanol and oleic acid, contributing to the total suppression of Foc apart from the presence of NRPS gene clusters. Thus, our study confirmed the scope for exploring B. velezensis VB7 on a commercial scale to manage tomato spotted wilt virus, groundnut bud necrosis virus, tobacco streak virus, S. sclerotiorum, and Foc causing panama wilt of banana.