Genomics Inform. 2023 Mar;21(1):e6. doi: 10.5808/gi.22070. Epub 2023 Mar 31.

ABSTRACT

Glaucoma is the second leading cause of irreversible blindness, and primary open-angle glaucoma (POAG) is the most common type. Due to inadequate diagnosis, treatment is often not administered until symptoms occur. Hence, approaches enabling earlier prediction or diagnosis of POAG are necessary. We aimed to identify novel drugs for glaucoma through bioinformatics and network analysis. Data from 36 samples, obtained from the trabecular meshwork of healthy individuals and patients with POAG, were acquired from a dataset. Next, differentially expressed genes (DEGs) were identified to construct a protein-protein interaction (PPI) network. In both stages, the genes were enriched by studying the critical biological processes and pathways related to POAG. Finally, a drug-gene network was constructed, and novel drugs for POAG treatment were proposed. Genes with p < 0.01 and |log fold change| > 0.3 (1,350 genes) were considered DEGs and utilized to construct a PPI network. Enrichment analysis yielded several key pathways that were upregulated or downregulated. For example, extracellular matrix organization, the immune system, neutrophil degranulation, and cytokine signaling were upregulated among immune pathways, while signal transduction, the immune system, extracellular matrix organization, and receptor tyrosine kinase signaling were downregulated. Finally, novel drugs including metformin hydrochloride, ixazomib citrate, and cisplatin warrant further analysis of their potential roles in POAG treatment. The candidate drugs identified in this computational analysis require in vitro and in vivo validation to confirm their effectiveness in POAG treatment. This may pave the way for understanding life-threatening disorders such as cancer.

PMID:37037464 | DOI:10.5808/gi.22070

J Bone Miner Metab. 2023 Apr 10. doi: 10.1007/s00774-023-01428-9. Online ahead of print.

ABSTRACT

INTRODUCTION: Tetracyclines (TCs) embrace a class of broad-spectrum antibiotics with unrelated effects at sub-antimicrobial levels, including an effective anti-inflammatory activity and stimulation of osteogenesis, allowing their repurposing for different clinical applications. Recently, sarecycline (SA)-a new-generation molecule with a narrower antimicrobial spectrum-was clinically approved due to its anti-inflammatory profile and reduced adverse effects verified with prolonged use. Notwithstanding, little is known about its osteogenic potential, previously verified for early generation TCs.

MATERIALS AND METHODS: Accordingly, the present study is focused on the assessment of the response of human bone marrow-derived mesenchymal stromal cells (hBMSCs) to a concentration range of SA, addressing the metabolic activity, morphology and osteoblastic differentiation capability, further detailing the modulation of Wnt, Hedgehog, and Notch signaling pathways. In addition, an ex vivo organotypic bone development system was established in the presence of SA and characterized by microtomographic and histochemical analysis.

RESULTS: hBMSCs cultured with SA presented a significantly increased metabolic activity compared to control, with an indistinguishable cell morphology. Moreover, RUNX2 expression was upregulated 2.5-fold, and ALP expression was increased around sevenfold in the presence of SA. Further, GLI2 expression was significantly upregulated, while HEY1 and HNF1A were downregulated, substantiating Hedgehog and Notch signaling pathways' modulation. The ex vivo model developed in the presence of SA presented a significantly enhanced collagen deposition, extended migration areas of osteogenesis, and an increased bone mineral content, substantiating an increased osteogenic development.

CONCLUSION: Summarizing, SA is a promising candidate for drug repurposing within therapies envisaging the enhancement of bone healing/regeneration.

PMID:37036531 | DOI:10.1007/s00774-023-01428-9

Microbiol Spectr. 2023 Apr 10:e0447422. doi: 10.1128/spectrum.04474-22. Online ahead of print.

ABSTRACT

The increasing occurrence of extensively drug-resistant and pan-drug-resistant K. pneumoniae has posed a serious threat to global public health. Therefore, new antimicrobial strategies are urgently needed to combat these resistant K. pneumoniae-related infections. Drug repurposing and combination are two effective strategies to solve this problem. By a high-throughput screening assay of FDA-approved drugs, we found that the potential small molecule 9-aminoacridine (9-AA) could be used as an antimicrobial alone or synergistically with rifampin (RIF) against extensively/pan-drug-resistant K. pneumoniae. In addition, 9-AA could overcome the shortcomings of RIF by reducing the occurrence of resistance. Mechanistic studies revealed that 9-AA interacted with bacterial DNA and disrupted the proton motive force in K. pneumoniae. Through liposomeization and combination with RIF, the cytotoxicity of 9-AA was significantly reduced without affecting its antimicrobial activity. In addition, we demonstrated the in vivo antimicrobial activity of 9-AA combined with RIF without detectable toxicity. In summary, 9-AA has the potential to be an antimicrobial agent or a RIF adjuvant for the treatment of multidrug-resistant K. pneumoniae infections. IMPORTANCE Klebsiella pneumoniae is a leading cause of clinically acquired infections. The increasing occurrence of drug-resistant K. pneumoniae has posed a serious threat to global public health. We found that the potential small molecule 9-AA could be used as an antimicrobial alone or synergistically with RIF against drug-resistant K. pneumoniae in vitro and with low resistance occurrence. The combination of 9-AA or 9-AA liposomes with RIF possesses effective antimicrobial activity in vivo without detected toxicity. 9-AA exerted its antimicrobial activity by interacting with specific bacterial DNA and disrupting the proton motive force in K. pneumoniae. In summary, we found that 9-AA has the potential to be developed as a new antibacterial agent and adjuvant for RIF. Therefore, our study can reduce the risk of antimicrobial resistance and provide an option for the exploitation of new clinical drugs and a theoretical basis for the research on a new antimicrobial agent.

PMID:37036368 | DOI:10.1128/spectrum.04474-22

Front Med (Lausanne). 2023 Mar 23;10:1159453. doi: 10.3389/fmed.2023.1159453. eCollection 2023.

ABSTRACT

INTRODUCTION: Inverse signals produced from disproportional analyses using spontaneous drug adverse event reports can be used for drug repositioning purposes. The purpose of this study is to predict drug candidates using a computational method that integrates reported drug adverse event data, disease-specific gene expression profiles, and drug-induced gene expression profiles.

METHODS: Drug and adverse events from 2015 through 2020 were downloaded from the United States Food and Drug Administration Adverse Event Reporting System (FAERS). The reporting odds ratio (ROR), information component (IC) and empirical Bayes geometric mean (EBGM) were used to calculate the inverse signals. Psoriasis was selected as the target disease. Disease specific gene expression profiles were obtained by the meta-analysis of the Gene Expression Omnibus (GEO). The reverse gene expression scores were calculated using the Library of Integrated Network-based Cellular Signatures (LINCS) and their correlations with the inverse signals were obtained.

RESULTS: Reversal genes and the candidate compounds were identified. Additionally, these correlations were validated using the relationship between the reverse gene expression scores and the half-maximal inhibitory concentration (IC50) values from the Chemical European Molecular Biology Laboratory (ChEMBL).

CONCLUSION: Inverse signals produced from a disproportional analysis can be used for drug repositioning and to predict drug candidates against psoriasis.

PMID:37035327 | PMC:PMC10076533 | DOI:10.3389/fmed.2023.1159453

Front Microbiol. 2023 Mar 22;14:1052352. doi: 10.3389/fmicb.2023.1052352. eCollection 2023.

ABSTRACT

Infectious diseases caused by filarial nematodes are major health problems for humans and animals globally. Current treatment using anti-helminthic drugs requires a long treatment period and is only effective against the microfilarial stage. Most species of filarial nematodes harbor a specific strain of Wolbachia bacteria, which are essential for the survival, development, and reproduction of the nematodes. This parasite-bacteria obligate symbiosis offers a new angle for the cure of filariasis. In this study, we utilized publicly available genome data and putative protein sequences from seven filarial nematode species and their symbiotic Wolbachia to screen for protein-protein interactions that could be a novel target against multiple filarial nematode species. Genome-wide in silico screening was performed to predict molecular interactions based on co-evolutionary signals. We identified over 8,000 pairs of gene families that show evidence of co-evolution based on high correlation score and low false discovery rate (FDR) between gene families and obtained a candidate list that may be keys in filarial nematode-Wolbachia interactions. Functional analysis was conducted on these top-scoring pairs, revealing biological processes related to various signaling processes, adult lifespan, developmental control, lipid and nucleotide metabolism, and RNA modification. Furthermore, network analysis of the top-scoring genes with multiple co-evolving pairs suggests candidate genes in both Wolbachia and the nematode that may play crucial roles at the center of multi-gene networks. A number of the top-scoring genes matched well to known drug targets, suggesting a promising drug-repurposing strategy that could be applicable against multiple filarial nematode species.

PMID:37032902 | PMC:PMC10073474 | DOI:10.3389/fmicb.2023.1052352

Expert Opin Drug Discov. 2023 Apr 9:1-11. doi: 10.1080/17460441.2023.2199980. Online ahead of print.

ABSTRACT

INTRODUCTION: Drugs that have demonstrated good activity against any member of the Orthopoxvirus genus are good candidates for repurposing studies against the mpox virus (MPXV). The conserved biology of poxviruses has proven beneficial from a clinical virology perspective. Evolutionarily conserved proteins tend to function in a highly similar way. Indeed, the smallpox vaccine was found to be 85% effective in protecting humans from mpox virus infection. Similarly, tecovirimat, the drug of choice for smallpox infections, was recently repurposed as a treatment option for mpox cases in Europe.

AREA COVERED: This review article focuses on drug repurposing strategies to combat the newly emerged MPXV outbreak. The viral and host cell protein targets are challenged with a bunch of drugs and drug-like molecules in silico, in vitro, and in vivo. Some drugs show promising results and can be repurposed to eradicate MPXV infection. The authors also highlight potential limitations and provide their expert perspectives.

EXPERT OPINION: Overall, it is clear that we cannot solely rely on the conventional drug discovery pipeline to find new treatments, despite advances in computational and experimental advances in the last few decades. Drug repurposing has successfully identified good candidate drugs against MPXV as it is one of the Orthopoxvirus genus family. Tecovirimat, brincidofovir, and cidofovir have shown promising results in preventing virus propagation. Consequently, drug repurposing represents an important strategy for the fast identification of new therapeutic options.

PMID:37032577 | DOI:10.1080/17460441.2023.2199980

Int J Antimicrob Agents. 2023 Apr 6:106807. doi: 10.1016/j.ijantimicag.2023.106807. Online ahead of print.

ABSTRACT

Cryptococcus gattii and C. neoformans are the main etiological agents of cryptococcosis, an invasive mycosis treated with amphotericin B, 5-fluorocytosine, and fluconazole. However, this limited arsenal is toxic and associated with antifungal resistance. Cryptococcosis and malaria pathogens are eukaryotic organisms and have a high incidence in Sub-Saharan Africa. The antimalarials (ATMs) halofantrine (HAL) and amodiaquine (AQ) block Plasmodium heme polymerase, while artesunate (ART) induces oxidative stresses. Considering that Cryptococcus spp. is susceptible to reactive oxygen species and that iron is essential for metabolism, we tested the repurposing of ATMs to treat cryptococcosis. ATMs reduced fungal growth, induced oxidative and nitrosative stresses, and altered ergosterol content, melanin production, and polysaccharide capsule size in C. neoformans and C. gattii, revealing a dynamic effect on fungal physiology. A comprehensive chemical-genetic analysis using two mutant libraries demonstrated that the deletion of genes involved in synthesizing components of the plasma membrane and cell wall, and oxidative stress responses are essential for fungal susceptibility to ATMs. Interestingly, the amphotericin B (AMB) fungicidal concentrations were ∼ 10 times lower when combined with ATMs, demonstrating a synergistic interaction. Further, the combinations showed reduced toxicity to murine macrophages. Finally, HAL+AMB and AQ+AMB efficiently reduced lethality and fungal burden in the lungs and brain, in murine cryptococcosis. These findings provide perspectives for further studies with ATMs against cryptococcosis and other fungal infections.

PMID:37030471 | DOI:10.1016/j.ijantimicag.2023.106807

Expert Opin Ther Pat. 2023 Apr 7. doi: 10.1080/13543776.2023.2201432. Online ahead of print.

NO ABSTRACT

PMID:37029480 | DOI:10.1080/13543776.2023.2201432

J Immunother Cancer. 2023 Apr;11(4):e004150. doi: 10.1136/jitc-2021-004150.

ABSTRACT

BACKGROUND: Immune responses against tumors are subject to negative feedback regulation. Immune checkpoint inhibitors (ICIs) blocking Programmed cell death protein 1 (PD-1), a receptor expressed on T cells, or its ligand PD-L1 have significantly improved the treatment of cancer, in particular malignant melanoma. Nevertheless, responses and durability are variables, suggesting that additional critical negative feedback mechanisms exist and need to be targeted to improve therapeutic efficacy.

METHODS: We used different syngeneic melanoma mouse models and performed PD-1 blockade to identify novel mechanisms of negative immune regulation. Genetic gain-of-function and loss-of-function approaches as well as small molecule inhibitor applications were used for target validation in our melanoma models. We analyzed mouse melanoma tissues from treated and untreated mice by RNA-seq, immunofluorescence and flow cytometry to detect changes in pathway activities and immune cell composition of the tumor microenvironment. We analyzed tissue sections of patients with melanoma by immunohistochemistry as well as publicly available single-cell RNA-seq data and correlated target expression with clinical responses to ICIs.

RESULTS: Here, we identified 11-beta-hydroxysteroid dehydrogenase-1 (HSD11B1), an enzyme that converts inert glucocorticoids into active forms in tissues, as negative feedback mechanism in response to T cell immunotherapies. Glucocorticoids are potent suppressors of immune responses. HSD11B1 was expressed in different cellular compartments of melanomas, most notably myeloid cells but also T cells and melanoma cells. Enforced expression of HSD11B1 in mouse melanomas limited the efficacy of PD-1 blockade, whereas small molecule HSD11B1 inhibitors improved responses in a CD8+ T cell-dependent manner. Mechanistically, HSD11B1 inhibition in combination with PD-1 blockade augmented the production of interferon-γ by T cells. Interferon pathway activation correlated with sensitivity to PD-1 blockade linked to anti-proliferative effects on melanoma cells. Furthermore, high levels of HSD11B1, predominantly expressed by tumor-associated macrophages, were associated with poor responses to ICI therapy in two independent cohorts of patients with advanced melanomas analyzed by different methods (scRNA-seq, immunohistochemistry).

CONCLUSION: As HSD11B1 inhibitors are in the focus of drug development for metabolic diseases, our data suggest a drug repurposing strategy combining HSD11B1 inhibitors with ICIs to improve melanoma immunotherapy. Furthermore, our work also delineated potential caveats emphasizing the need for careful patient stratification.

PMID:37028818 | DOI:10.1136/jitc-2021-004150

Acta Trop. 2023 Apr 5:106920. doi: 10.1016/j.actatropica.2023.106920. Online ahead of print.

ABSTRACT

Benznidazole and nifurtimox are the drugs currently used for the treatment of Chagas disease, however its side effects may affect patient adherence. In the search for new alternative therapies, we previously identified isotretinoin (ISO), an FDA-approved drug widely used for the treatment of severe acne through a drug repurposing strategy. ISO shows a strong activity against Trypanosoma cruzi parasites in the nanomolar range, and its mechanism of action is through the inhibition of T. cruzi polyamine and amino acid transporters from the Amino Acid/Auxin Permeases (AAAP) family. In this work, a murine model of chronic Chagas disease (C57BL/6J mice), intraperitoneally infected with T. cruzi Nicaragua isolate (DTU TcI), were treated with different oral administrations of ISO: daily doses of 5 mg/kg/day for 30 days and weekly doses of 10 mg/kg during 13 weeks. The efficacy of the treatments was evaluated by monitoring blood parasitemia by qPCR, anti-T. cruzi antibodies by ELISA, and cardiac abnormalities by electrocardiography. No parasites were detected in blood after any of the ISO treatments. The electrocardiographic study of the untreated chronic mice showed a significant decrease in heart rate, while in the treated mice this negative chronotropic effect was not observed. Atrioventricular nodal conduction time in untreated mice was significantly longer than in treated animals. Mice treated even with ISO 10 mg/kg dose every 7 days, showed a significant reduction in anti-T. cruzi IgG levels. In conclusion, the intermittent administration of ISO 10 mg/kg would improve myocardial compromise during the chronic stage.

PMID:37028584 | DOI:10.1016/j.actatropica.2023.106920

IEEE/ACM Trans Comput Biol Bioinform. 2023 Mar 8;PP. doi: 10.1109/TCBB.2023.3254163. Online ahead of print.

ABSTRACT

The computational drug repositioning aims to discover new uses for marketed drugs, which can accelerate the drug development process and play an important role in the existing drug discovery system. However, the number of validated drug-disease associations is scarce compared to the number of drugs and diseases in the real world. Too few labeled samples will make the classification model unable to learn effective latent factors of drugs, resulting in poor generalization performance. In this work, we propose a multi-task self-supervised learning framework for computational drug repositioning. The framework tackles label sparsity by learning a better drug representation. Specifically, we take the drug-disease association prediction problem as the main task, and the auxiliary task is to use data augmentation strategies and contrast learning to mine the internal relationships of the original drug features, so as to automatically learn a better drug representation without supervised labels. And through joint training, it is ensured that the auxiliary task can improve the prediction accuracy of the main task. More precisely, the auxiliary task improves drug representation and serving as additional regularization to improve generalization. Furthermore, we design a multi-input decoding network to improve the reconstruction ability of the autoencoder model. We evaluate our model using three real-world datasets. The experimental results demonstrate the effectiveness of the multi-task self-supervised learning framework, and its predictive ability is superior to the state-of-the-art model.

PMID:37028367 | DOI:10.1109/TCBB.2023.3254163

Crit Care Explor. 2023 Apr 3;5(4):e0893. doi: 10.1097/CCE.0000000000000893. eCollection 2023 Apr.

ABSTRACT

COVID-19 highlighted the need for use of real-world data (RWD) in critical care as a near real-time resource for clinical, research, and policy efforts. Analysis of RWD is gaining momentum and can generate important evidence for policy makers and regulators. Extracting high quality RWD from electronic health records (EHRs) requires sophisticated infrastructure and dedicated resources. We sought to customize freely available public tools, supporting all phases of data harmonization, from data quality assessments to de-identification procedures, and generation of robust, data science ready RWD from EHRs. These data are made available to clinicians and researchers through CURE ID, a free platform which facilitates access to case reports of challenging clinical cases and repurposed treatments hosted by the National Center for Advancing Translational Sciences/National Institutes of Health in partnership with the Food and Drug Administration. This commentary describes the partnership, rationale, process, use case, impact in critical care, and future directions for this collaborative effort.

PMID:37025303 | PMC:PMC10072311 | DOI:10.1097/CCE.0000000000000893

Methods Mol Biol. 2023;2658:127-143. doi: 10.1007/978-1-0716-3155-3_9.

ABSTRACT

Drug target identification is an essential component to antifungal drug development. Many methods, including large chemical library screening, natural product screening, and drug repurposing efforts, can identify compounds with favorable in vitro antifungal activity. However, these approaches will often identify compounds with no known mechanism of action. Herein, we describe a method utilizing the human fungal pathogen Cryptococcus neoformans to identify antifungal drug targets through the isolation of spontaneous resistant mutants, antifungal testing, whole-genome sequencing, and variant analysis.

PMID:37024699 | DOI:10.1007/978-1-0716-3155-3_9

J Chem Inf Model. 2023 Apr 6. doi: 10.1021/acs.jcim.2c01291. Online ahead of print.

ABSTRACT

Drug repurposing or repositioning (DR) refers to finding new therapeutic applications for existing drugs. Current computational DR methods face data representation and negative data sampling challenges. Although retrospective studies attempt to operate various representations, it is a crucial step for an accurate prediction to aggregate these features and bring the associations between drugs and diseases into a unified latent space. In addition, the number of unknown associations between drugs and diseases, which is considered negative data, is much higher than the number of known associations, or positive data, leading to an imbalanced dataset. In this regard, we propose the DrugRep-KG method, which applies a knowledge graph embedding approach for representing drugs and diseases, to address these challenges. Despite the typical DR methods that consider all unknown drug-disease associations as negative data, we select a subset of unknown associations, provided the disease occurs because of an adverse reaction to a drug. DrugRep-KG has been evaluated based on different settings and achieves an AUC-ROC (area under the receiver operating characteristic curve) of 90.83% and an AUC-PR (area under the precision-recall curve) of 90.10%, which are higher than in previous works. Besides, we checked the performance of our framework in finding potential drugs for coronavirus infection and skin-related diseases: contact dermatitis and atopic eczema. DrugRep-KG predicted beclomethasone for contact dermatitis, and fluorometholone, clocortolone, fluocinonide, and beclomethasone for atopic eczema, all of which have previously been proven to be effective in other studies. Fluorometholone for contact dermatitis is a novel suggestion by DrugRep-KG that should be validated experimentally. DrugRep-KG also predicted the associations between COVID-19 and potential treatments suggested by DrugBank, in addition to new drug candidates provided with experimental evidence. The data and code underlying this article are available at https://github.com/CBRC-lab/DrugRep-KG.

PMID:37023229 | DOI:10.1021/acs.jcim.2c01291

Expert Opin Drug Discov. 2023 Apr 6:1-9. doi: 10.1080/17460441.2023.2198700. Online ahead of print.

ABSTRACT

INTRODUCTION: Over the last decades, there has been substantial debate around the apparent drop in productivity in the pharmaceutical sector. The development of second or further medical uses for known drugs is a possible answer to expedite the development of new therapeutic solutions. Computational methods are among the main strategies for exploring drug repurposing opportunities in a systematic manner.

AREAS COVERED: This article reviews three general approximations to systematically discover new therapeutic uses for existing drugs: disease-, target-, and drug-centric approaches, along with some recently reported computational methods associated with them.

EXPERT OPINION: Computational methods are essential for organizing and analyzing the large volume of available biomedical data, which has grown exponentially in the era of big data. The clearest trend in the field involves the use of integrative approaches where different types of data are combined into multipartite networks. Every aspect of computer-guided drug repositioning has currently incorporated state-of-the-art machine learning tools to boost their pattern recognition and predictive capabilities. Remarkably, a majority of the recently reported platforms are publicly available as web apps or open-source software. The introduction of nationwide electronic health records provides invaluable real-world data to detect unknown relationships between approved drug treatments and diseases.

PMID:37021703 | DOI:10.1080/17460441.2023.2198700

J Biomol Struct Dyn. 2023 Apr 5:1-39. doi: 10.1080/07391102.2023.2192797. Online ahead of print.

ABSTRACT

The COVID-19 pandemic has caused adverse health (severe respiratory, enteric and systemic infections) and environmental impacts that have threatened public health and the economy worldwide. Drug repurposing and small molecule multi-target directed herbal medicine therapeutic approaches are the most appropriate exploration strategies for SARS-CoV-2 drug discovery. This study identified potential multi-target-directed Parkia bioactive entities against SARS-CoV-2 receptors (S-protein, ACE2, TMPRSS2, RBD/ACE2, RdRp, MPro, and PLPro) using ADMET, drug-likeness, molecular docking (AutoDock, FireDock and HDOCK), molecular dynamics simulation and MM-PBSA tools. One thousand Parkia bioactive entities were screened out by virtual screening and forty-five bioactive phytomolecules were selected based on favorable binding affinity and acceptable pharmacokinetic and pharmacodynamics properties. The binding affinity values of Parkia phyto-ligands (AutoDock: -6.00--10.40 kcal/mol; FireDock: -31.00--62.02 kcal/mol; and HDOCK: -150.0--294.93 kcal/mol) were observed to be higher than the reference antiviral drugs (AutoDock: -5.90--9.10 kcal/mol; FireDock: -35.64--59.35 kcal/mol; and HDOCK: -132.82--211.87 kcal/mol), suggesting a potent modulatory action of Parkia bioactive entities against the SARS-CoV-2. Didymin, rutin, epigallocatechin gallate, epicatechin-3-0-gallate, hyperin, ursolic acid, lupeol, stigmasta-5,24(28)-diene-3-ol, ellagic acid, apigenin, stigmasterol, and campesterol strongly bound with the multiple targets of the SARS-CoV-2 receptors, inhibiting viral entry, attachment, binding, replication, transcription, maturation, packaging and spread. Furthermore, ACE2, TMPRSS2, and MPro receptors possess significant molecular dynamic properties, including stability, compactness, flexibility and total binding energy. Residues GLU-589, and LEU-95 of ACE2, GLN-350, HIS-186, and ASP-257 of TMPRSS2, and GLU-14, MET-49, and GLN-189 of MPro receptors contributed to the formation of hydrogen bonds and binding interactions, playing vital roles in inhibiting the activity of the receptors. Promising results were achieved by developing multi-targeted antiviral Parkia bioactive entities as lead and prospective candidates under a small molecule strategy against SARS-CoV-2 pathogenesis. The antiviral activity of Parkia bioactive entities needs to be further validated by pre-clinical and clinical trials.

PMID:37021347 | DOI:10.1080/07391102.2023.2192797

iScience. 2023 Mar 21;26(4):106460. doi: 10.1016/j.isci.2023.106460. eCollection 2023 Apr 21.

ABSTRACT

The abundance of biomedical knowledge gained from biological experiments and clinical practices is an invaluable resource for biomedicine. The emerging biomedical knowledge graphs (BKGs) provide an efficient and effective way to manage the abundant knowledge in biomedical and life science. In this study, we created a comprehensive BKG called the integrative Biomedical Knowledge Hub (iBKH) by harmonizing and integrating information from diverse biomedical resources. To make iBKH easily accessible for biomedical research, we developed a web-based, user-friendly graphical portal that allows fast and interactive knowledge retrieval. Additionally, we also implemented an efficient and scalable graph learning pipeline for discovering novel biomedical knowledge in iBKH. As a proof of concept, we performed our iBKH-based method for computational in-silico drug repurposing for Alzheimer's disease. The iBKH is publicly available.

PMID:37020958 | PMC:PMC10068563 | DOI:10.1016/j.isci.2023.106460

Oncogene. 2023 Apr 5. doi: 10.1038/s41388-023-02679-6. Online ahead of print.

ABSTRACT

Ovarian cancer is the leading cause of death among gynecological malignancies. Checkpoint blockade immunotherapy has so far only shown modest efficacy in ovarian cancer and platinum-based chemotherapy remains the front-line treatment. Development of platinum resistance is one of the most important factors contributing to ovarian cancer recurrence and mortality. Through kinome-wide synthetic lethal RNAi screening combined with unbiased datamining of cell line platinum response in CCLE and GDSC databases, here we report that Src-Related Kinase Lacking C-Terminal Regulatory Tyrosine And N-Terminal Myristylation Sites (SRMS), a non-receptor tyrosine kinase, is a novel negative regulator of MKK4-JNK signaling under platinum treatment and plays an important role in dictating platinum efficacy in ovarian cancer. Suppressing SRMS specifically sensitizes p53-deficient ovarian cancer cells to platinum in vitro and in vivo. Mechanistically, SRMS serves as a "sensor" for platinum-induced ROS. Platinum treatment-induced ROS activates SRMS, which inhibits MKK4 kinase activity by directly phosphorylating MKK4 at Y269 and Y307, and consequently attenuates MKK4-JNK activation. Suppressing SRMS leads to enhanced MKK4-JNK-mediated apoptosis by inhibiting MCL1 transcription, thereby boosting platinum efficacy. Importantly, through a "drug repurposing" strategy, we uncovered that PLX4720, a small molecular selective inhibitor of B-RafV600E, is a novel SRMS inhibitor that can potently boost platinum efficacy in ovarian cancer in vitro and in vivo. Therefore, targeting SRMS with PLX4720 holds the promise to improve the efficacy of platinum-based chemotherapy and overcome chemoresistance in ovarian cancer.

PMID:37020040 | DOI:10.1038/s41388-023-02679-6

IEEE/ACM Trans Comput Biol Bioinform. 2023 Jan 5;PP. doi: 10.1109/TCBB.2023.3234331. Online ahead of print.

ABSTRACT

Drug repositioning is an important approach for predicting new disease indications of the existing drugs in drug discovery. A great progress has been achieved in drug repositioning. However, effectively utilizing the localized neighborhood interaction features of drug and disease in drug-disease associations remains challenging. This paper proposes a neighborhood interaction-based method called NetPro for drug repositioning via label propagation. In NetPro, we first formulate the known drug-disease associations, various disease and drug similarities from different perspectives to construct drug-drug and disease-disease networks. Meanwhile we employ the nearest neighbors and their interactions in the constructed networks to devise a new approach for computing drug similarity and disease similarity. To implement the prediction of new drugs or diseases, a preprocessing step is applied to renew the known drug-disease associations using our calculated drug and disease similarities. We then employ a label propagation model to predict drug-disease associations by the drug and disease linear neighborhood similarities derived from the renewed drug-disease associations. The experimental results on three benchmark datasets show that NetPro can effectively identify potential drug-disease associations and achieve better prediction performance than the existing methods. Case studies further demonstrate that NetPro is capable of predicting promising candidate disease indications for drugs.

PMID:37018341 | DOI:10.1109/TCBB.2023.3234331

Eur Neuropsychopharmacol. 2023 Apr 4;71:96-108. doi: 10.1016/j.euroneuro.2023.03.011. Online ahead of print.

ABSTRACT

The World Health Organization has proposed that a search be made for alternatives to vaccines for the prevention and treatment of COVID-19, with one such alternative being selective serotonin reuptake inhibitors (SSRIs). This study thus sought to assess: the impact of previous treatment with SSRI antidepressants on the severity of COVID-19 (risk of hospitalisation, admission to an intensive care unit [ICU], and mortality), its influence on susceptibility to SARS-CoV-2 and progression to severe COVID-19. We conducted a population-based multiple case-control study in a region in the north-west of Spain. Data were sourced from electronic health records. Adjusted odds ratios (aORs) and 95%CIs were calculated using multilevel logistic regression. We collected data from a total of 86,602 subjects: 3060 cases PCR+, 26,757 non-hospitalised cases PCR+ and 56,785 controls (without PCR+). Citalopram displayed a statistically significant decrease in the risk of hospitalisation (aOR=0.70; 95% CI 0.49-0.99, p = 0.049) and progression to severe COVID-19 (aOR=0.64; 95% CI 0.43-0.96, p = 0.032). Paroxetine was associated with a statistically significant decrease in risk of mortality (aOR=0.34; 95% CI 0.12 - 0.94, p = 0.039). No class effect was observed for SSRIs overall, nor was any other effect found for the remaining SSRIs. The results of this large-scale, real-world data study indicate that, citalopram, could be a candidate drug for being repurposed as preventive treatment aimed at reducing COVID-19 patients' risk of progressing to severe stages of the disease.

PMID:37094487 | DOI:10.1016/j.euroneuro.2023.03.011