Abstract: Identification of the plasma proteomic changes of Coronavirus disease 2019 (COVID-19) is essential to understanding the pathophysiology of the disease and developing predictive models and novel therapeutics. We performed plasma deep proteomic profiling from 332 COVID-19 patients and 150 controls and pursued replication in an independent cohort (297 cases and 76 controls) to find potential biomarkers and causal proteins for three COVID-19 outcomes (infection, ventilation, and death). We identified and replicated 1,449 proteins associated with any of the three outcomes (841 for infection, 833 for ventilation, and 253 for death) that can be query on a web portal ( https://covid.proteomics.wustl.edu/ ). Using those proteins and machine learning approached we created and validated specific prediction models for ventilation (AUC>0.91), death (AUC>0.95) and either outcome (AUC>0.80). These proteins were also enriched in specific biological processes, including immune and cytokine signaling (FDR = 3.72x10 (-14) ), Alzheimer's disease (FDR = 5.46x10 (-10) ) and coronary artery disease (FDR = 4.64x10 (-2) ). Mendelian randomization using pQTL as instrumental variants nominated BCAT2 and GOLM1 as a causal proteins for COVID-19. Causal gene network analyses identified 141 highly connected key proteins, of which 35 have known drug targets with FDA-approved compounds. Our findings provide distinctive prognostic biomarkers for two severe COVID-19 outcomes (ventilation and death), reveal their relationship to Alzheimer's disease and coronary artery disease, and identify potential therapeutic targets for COVID-19 outcomes.

Abstract: Depending on the patients' genotype, the same drug may have different efficacies or side effects. With the cost of genomic analysis decreasing and reliability of analysis methods improving, vast amount of genomic information has been made available. Several studies in pharmacology have been based on genomic information to select the optimal drug, determine the dose, predict efficacy, and prevent side effects. This paper reviews the tissue specificity and genomic information of cancer. If the tissue specificity of cancer is low, cancer is induced in various organs based on a single gene mutation. Basket trials can be performed for carcinomas with low tissue specificity, confirming the efficacy of one drug for a single gene mutation in various carcinomas. Conversely, if the tissue specificity of cancer is high, cancer is induced in only one organ based on a single gene mutation. An umbrella trial can be performed for carcinomas with a high tissue specificity. Some drugs are effective for patients with a specific genotype. A companion diagnostic strategy that prescribes a specific drug for patients selected with a specific genotype is also reviewed. Genomic information is used in pharmacometrics to identify the relationship among pharmacokinetics, pharmacodynamics, and biomarkers of disease treatment effects. Utilizing genomic information, sophisticated clinical trials can be designed that will be better suited to the patients of specific genotypes. Genomic information also provides prospects for innovative drug development. Through proper genomic information management, factors relating to drug response and effects can be determined by selecting the appropriate data for analysis and by understanding the structure of the data. Selecting pre-processing and appropriate machine-learning libraries for use as machine-learning input features is also necessary. Professional curation of the output result is also required. Personalized medicine can be realized using a genome-based customized clinical trial design.

Abstract: Pressure injuries, also known as pressure ulcers, are regions of localized damage to the skin and/or underlying tissue. Repeated rounds of ischemia-reperfusion (I/R) have a major causative role for tissue damage in pressure injury. Ischemia prevents oxygen/nutrient supply, and restoration of blood flow induces a burst of reactive oxygen species that damages blood vessels, surrounding tissues and can halt blood flow return. Minimizing the consequences of repeated I/R is expected to provide a protective effect against pressure injury. Sulfaphenazole (SP), an off patent sulfonamide antibiotic, is a potent CYP 2C6 and CYP 2C9 inhibitor, functioning to decrease post-ischemic vascular dysfunction and increase blood flow. The therapeutic effect of SP on pressure injury was therefore investigated in apolipoprotein E knockout mice, a model of aging susceptible to ischemic injury, which were subjected to repeated rounds of I/R-induced skin injury. SP reduced overall severity, improved wound closure and increased wound tensile strength compared to vehicle-treated controls. Saliently, SP restored tissue perfusion in and around the wound rapidly to pre-injury levels, decreased tissue hypoxia, and reduced both inflammation and fibrosis. SP also demonstrated bactericidal activity through enhanced M1 macrophage activity. The efficacy of SP in reducing thermal injury severity was also demonstrated. SP is therefore a potential therapeutic option for pressure injury and other ischemic skin injuries.

Abstract: Cancer stem cells (CSCs) are in general characterized by higher resistance to cell death and cancer therapies than non-stem differentiated cancer cells. However, we and others have recently revealed using glioma stem cells (GSCs) as a model that, unexpectedly, CSCs have specific vulnerabilities that make them more sensitive to certain drugs compared with their differentiated counterparts. We aimed in this study to discover novel drugs targeting such Achilles' heels of GSCs as anti-GSC drug candidates to be used for the treatment of glioblastoma, the most therapy-resistant form of brain tumors. Here we report that domatinostat (4SC-202), a class I HDAC inhibitor, is one such candidate. At concentrations where it showed no or minimal growth inhibitory effect on differentiated GSCs and normal cells, domatinostat effectively inhibited the growth of GSCs mainly by inducing apoptosis. Furthermore, GSCs that survived domatinostat treatment lost their self-renewal capacity. These results suggested that domatinostat is a unique drug that selectively eliminates GSCs not only physically by inducing cell death but also functionally by inhibiting their self-renewal. Our findings also imply that class I HDACs and/or LSD1, another target of domatinostat, may possibly have a specific role in the maintenance of GSCs and therefore could be an attractive target in the development of anti-GSC therapies.

Abstract: Broad-spectrum antiviral agents that are effective against many viruses are difficult to develop, as the key molecules, as well as the biochemical pathways by which they cause infection, differ largely from one virus to another. This was more strongly highlighted by the COVID-19 pandemic, which found health systems all over the world largely unprepared and proved that the existing armamentarium of antiviral agents is not sufficient to address viral threats with pandemic potential. The clinical protocols for the treatment of COVID-19 are currently based on the use of inhibitors of the inflammatory cascade (dexamethasone, baricitinib), or inhibitors of the cytopathic effect of the virus (monoclonal antibodies, molnupiravir or nirmatrelvir/ritonavir), using different agents. There is a critical need for an expanded armamentarium of orally bioavailable small-molecular medicinal agents, including those that possess dual antiviral and anti-inflammatory (AAI) activity that would be readily available for the early treatment of mild to moderate COVID-19 in high-risk patients. A multidisciplinary approach that involves the use of in silico screening tools to identify potential drug targets of an emerging pathogen, as well as in vitro and in vivo models for the determination of a candidate drug's efficacy and safety, are necessary for the rapid and successful development of antiviral agents with potentially dual AAI activity. Characterization of candidate AAI molecules with physiologically based pharmacokinetics (PBPK) modeling would provide critical data for the accurate dosing of new therapeutic agents against COVID-19. This review analyzes the dual mechanisms of AAI agents with potential anti-SARS-CoV-2 activity and discusses the principles of PBPK modeling as a conceptual guide to develop new pharmacological modalities for the treatment of COVID-19.

Abstract: BACKGROUND: The traditional Chinese medicine Gegen Qinlian Decoction (GQD), as well as metformin, had been reported with anti-diabetic effects in clinical practice. OBJECTIVE: To verify whether these two medicines effectively ameliorate hyperglycemia caused by deficiency of islet beta-cell mass which occurs in both type 1 and type 2 diabetes. METHODS: SD rats were injected with a single dose of STZ (55 mg/kg) to induce beta-cell destruction. The rats were then divided into control, diabetes, GQD and metformin group. GQD and metformin groups were administered with GQD extract or metformin for 6 weeks. The islet alpha-cell or beta-cell mass changes were tested by immunohistochemical and immunofluorescent staining. The potential targets and mechanisms of GQD and metformin on cell proliferation were tested using in silico network pharmacology. Real-time PCR was performed to test the expression of islet cells related genes and targets related genes. RESULTS: Both GQD and metformin did not significantly reduce the FBG level caused by beta-cell mass reduction, but alleviated liver and pancreas histopathology. Both GQD and metformin did not change the insulin positive cell mass but increased alpha-cell proliferation of the diabetic rats. Gene expression analysis showed that GQD and metformin significantly increased the targets gene cyclin-dependent kinase 4 (Cdk4) and insulin receptor substrate (Irs1) level. CONCLUSION: This research indicates that GQD and metformin significantly increased the alpha-cell proliferation of beta-cell deficiency induced diabetic rats by restoring Cdk4 and Irs1 gene expression.

Abstract: The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task of drug repurposing hypothesis generation is well-posed as a link prediction problem in a knowledge graph (KG) of interacting of drugs, proteins, genes and disease phenotypes. KGs derived from biomedical literature are semantically rich and up-to-date representations of scientific knowledge. Inference methods on scientific KGs can be confounded by unspecified contexts and contradictions. Extracting context enables incorporation of relevant pharmacokinetic and pharmacodynamic detail, such as tissue specificity of interactions. Contradictions in biomedical KGs may arise when contexts are omitted or due to contradicting research claims. In this review, we describe challenges to creating literature-scale representations of pharmacological knowledge and survey current approaches toward incorporating context and resolving contradictions.

Abstract: The network approach is quickly becoming a fundamental building block of computational methods aiming at elucidating the mechanism of action (MoA) and therapeutic effect of drugs. By modeling the effect of drugs and diseases on different biological networks, it is possible to better explain the interplay between disease perturbations and drug targets as well as how drug compounds induce favorable biological responses and/or adverse effects. Omics technologies have been extensively used to generate the data needed to study the mechanisms of action of drugs and diseases. These data are often exploited to define condition-specific networks and to study whether drugs can reverse disease perturbations. In this review, we describe network data mining algorithms that are commonly used to study drug's MoA and to improve our understanding of the basis of chronic diseases. These methods can support fundamental stages of the drug development process, including the identification of putative drug targets, the in silico screening of drug compounds and drug combinations for the treatment of diseases. We also discuss recent studies using biological and omics-driven networks to search for possible repurposed FDA-approved drug treatments for SARS-CoV-2 infections (COVID-19).

Abstract: Endolysins are bacteriolytic enzymes derived from bacteriophages. They represent an alternative to antibiotics, since they are not susceptible to conventional antimicrobial resistance mechanisms. Since non-human proteins are efficient inducers of specific immune responses, including the IgG response or the development of an allergic response mediated by IgE, we evaluated the general immunogenicity of the highly active antibacterial enzyme, PlyC, in a human population and in a mouse model. The study includes the identification of molecular epitopes of PlyC. The overall assessment of potential hypersensitivity to this protein and PlyC-specific IgE testing was also conducted in mice. PlyC induced efficient IgG production in mice, and the molecular analysis revealed that PlyC-specific IgG interacted with four immunogenic regions identified within the PlyCA subunit. In humans, approximately 10% of the population demonstrated IgG reactivity to the PlyCB subunit only, which is attributed to cross-reactions since this was a naive serum. Of note, in spite of being immunogenic, PlyC induced a normal immune response, without hypersensitivity, since both the animals challenged with PlyC and in the human population PlyC-specific IgE was not detected.

Abstract: Internal validation is the most popular evaluation strategy used for drug-target predictive models. The simple random shuffling in the cross-validation, however, is not always ideal to handle large, diverse and copious datasets as it could potentially introduce bias. Hence, these predictive models cannot be comprehensively evaluated to provide insight into their general performance on a variety of use-cases (e.g. permutations of different levels of connectiveness and categories in drug and target space, as well as validations based on different data sources). In this work, we introduce a benchmark, BETA, that aims to address this gap by (i) providing an extensive multipartite network consisting of 0.97 million biomedical concepts and 8.5 million associations, in addition to 62 million drug-drug and protein-protein similarities and (ii) presenting evaluation strategies that reflect seven cases (i.e. general, screening with different connectivity, target and drug screening based on categories, searching for specific drugs and targets and drug repurposing for specific diseases), a total of seven Tests (consisting of 344 Tasks in total) across multiple sampling and validation strategies. Six state-of-the-art methods covering two broad input data types (chemical structure- and gene sequence-based and network-based) were tested across all the developed Tasks. The best-worst performing cases have been analyzed to demonstrate the ability of the proposed benchmark to identify limitations of the tested methods for running over the benchmark tasks. The results highlight BETA as a benchmark in the selection of computational strategies for drug repurposing and target discovery.