Background: Drug repurposing is a fast and effective way to develop drugs for an emerging disease such as COVID-19. The main challenges of effective drug repurposing are the discoveries of the right therapeutic targets and the right drugs for combating the disease.Methods: Here, we present a systematic repurposing approach, combining Homopharma and hierarchal systems biology networks (HiSBiN), to predict 327 therapeutic targets and 21,233 drug-target interactions of 1,592 FDA drugs for COVID-19. Among these multi-target drugs, eight candidates (along with pimozide and valsartan) were tested and methotrexate was identified to affect 14 therapeutic targets suppressing SARS-CoV-2 entry, viral replication, and COVID-19 pathologies. Through the use of in vitro (EC₅₀ = 0.4 μM) and in vivo models, we show that methotrexate is able to inhibit COVID-19 via multiple mechanisms.Results: Our in vitro studies illustrate that methotrexate can suppress SARS-CoV-2 entry and replication by targeting furin and DHFR of the host, respectively. Additionally, methotrexate inhibits all four SARS-CoV-2 variants of concern. In a Syrian hamster model for COVID-19, methotrexate reduced virus replication, inflammation in the infected lungs. By analysis of transcriptomic analysis of collected samples from hamster lung, we uncovered that neutrophil infiltration and the pathways of innate immune response, adaptive immune response and thrombosis are modulated in the treated animals.Conclusions: We demonstrate that this systematic repurposing approach is potentially useful to identify pharmaceutical targets, multi-target drugs and regulated pathways for a complex disease. Our findings indicate that methotrexate is established as a promising drug against SARS-CoV-2 variants and can be used to treat lung damage and inflammation in COVID-19, warranting future evaluation in clinical trials.

Autoimmune hypophysitis (AH) is an autoimmune disease of the pituitary for which the pathogenesis is incompletely known. AH is often treated with corticosteroids; however, steroids may lead to considerable side effects. Using a mouse model of AH (experimental autoimmune hypophysitis, EAH), we show that interleukin-1 receptor-associated kinase 1 (IRAK1) is upregulated in the pituitaries of mice that developed EAH. We identified rosoxacin as a specific inhibitor for IRAK1 and found it could treat EAH. Rosoxacin treatment at an early stage (day 0–13) slightly reduced disease severity, whereas treatment at a later stage (day 14–27) significantly suppressed EAH. Further investigation indicated rosoxacin reduced production of autoantigen-specific antibodies. Rosoxacin downregulated production of cytokines and chemokines that may dampen T cell differentiation or recruitment to the pituitary. Finally, rosoxacin downregulated class II major histocompatibility complex expression on antigen-presenting cells that may lead to impaired activation of autoantigen-specific T cells. These data suggest that IRAK1 may play a pathogenic role in AH and that rosoxacin may be an effective drug for AH and other inflammatory diseases involving IRAK1 dysregulation.

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan–Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.

This study aims to evaluate the clinical efficacy of 445 nm blue laser inferior turbinate reduction (BITR) and establish optimal energy parameters for patients with chronic rhinitis (CR) refractory to pharmacotherapy. This was a retrospective cohort study of adult patients with medically refractory CR, a Nasal Obstruction Symptom Evaluation (NOSE) score ≥ 6, and a nasal congestion score ≥ 2. Turbinate hypertrophy was graded using the Camacho classification. Patients were stratified into Group A (Grade 3) and Group B (Grade 4). Primary endpoints were changes in NOSE scores and 12-hour Reflective Total Nasal Symptom Score (12-h rTNSS) at 1, 3, and 6 months. At 6 months, laser energy settings were analyzed in the top 50% of responders to determine optimal settings. Eighty-four patients were analyzed. At 6 months, NOSE scores improved by a mean of 91.1% in Group A and 88.0% in Group B ( p < 0.001 for both). At 3 months, the 12-h rTNSS improved by 58.2% and 54.9%, respectively ( p < 0.001 for both). In the top 50% of responders, the optimal mean energy delivered was 219 J (anterior) and 51 J (middle) for Group A, and 332 J (anterior) and 60 J (middle) for Group B. Postoperative complications were minimal. BITR is a safe and effective office-based procedure for medically refractory CR. To optimize outcomes, we recommend energy settings of approximately 219 J (anterior) and 51 J (middle) for moderate hypertrophy, and 332 J (anterior) and 60 J (middle) for severe hypertrophy.

Protein kinases play a pivotal role in regulating cellular signaling pathways, and their dysregulation is closely associated with numerous diseases, including cancer, autoimmune disorders, and inflammation. Although over 100,000 kinase inhibitors have been developed, only a small fraction has achieved FDA approval, primarily due to off-target effects stemming from the high conservation of kinase binding sites. To address this challenge, we present an ensemble learning framework that integrates both chemical and protein-level information to improve the prediction of selective kinase inhibitors. On the compound side, we construct a 1,048-dimensional feature representation encompassing topological fingerprints, drug-like moieties, atomic composition, and stereochemical descriptors. On the protein side, we develop a 1,700- dimensional representation of kinase binding site environments using multiple sequence alignment and evolutionary conservation information. Comprehensive evaluations across 131 human kinases show that the integration of these features significantly improves model performance, achieving 93.6% accuracy on an independent test set. Furthermore, SHAP-based model interpretation reveals that high-impact features correspond to known binding motifs, such as the P-loop, Hinge region, and DFG motif, as confirmed by crystal structure validation. Lastly, we apply the model to a curated dataset of flavonoid-like compounds, identifying potential natural product-derived kinase inhibitors. This study demonstrates that the proposed integrative approach not only enhances predictive accuracy but also provides interpretable insights into kinase-ligand interactions, offering a promising direction for rational kinase inhibitor design.

Over 400,000 people are estimated to have been exposed to World Trade Center particulate matter (WTC_PM) since the attack on the Twin Towers in Lower Manhattan on September 11, 2001. Epidemiological studies have found that exposure to dust may cause respiratory ailments and cardiovascular diseases. However, limited studies have performed a systematic analysis of transcriptomic data to elucidate the biological responses to WTC_PM exposure and the therapeutic options. Here, we developed an in vivo mouse exposure model of WTC_PM and administered two drugs (i.e., rosoxacin and dexamethasone) to generate transcriptomic data from lung samples. WTC_PM exposure increased the inflammation index, and this index was significantly reduced by both drugs. We analyzed the transcriptomics derived omics data using a hierarchical systems biology model (HiSBiM) with four levels, including system, subsystem, pathway, and gene analyses. Based on the selected differentially expressed genes (DEGs) from each group, WTC_PM and the two drugs commonly affected the inflammatory responses, consistent with the inflammation index. Among these DEGs, the expression of 31 genes was affected by WTC_PM exposure and consistently reversed by the two drugs, and these genes included Psme2, Cldn18, and Prkcd, which are involved in immune- and endocrine-related subsystems and pathways such as thyroid hormone synthesis, antigen processing and presentation, and leukocyte transendothelial migration. Furthermore, the two drugs reduced the inflammatory effects of WTC_PM through distinct pathways, e.g., vascular-associated signaling by rosoxacin, whereas mTOR-dependent inflammatory signaling was found to be regulated by dexamethasone. To the best of our knowledge, this study constitutes the first investigation of transcriptomics data of WTC_PM and an exploration of potential therapies. We believe that these findings provide strategies for the development of promising optional interventions and therapies for airborne particle exposure.