A comprehensive drug repurposing study for COVID19 treatment: novel putative dihydroorotate dehydrogenase inhibitors show association to serotonin-dopamine receptors

Cell-specific genome-scale metabolic modeling of SARS-CoV-2-infected lung to identify antiviral enzymes

Computational systems biology plays a key role in the discovery of suitable antiviral targets. We designed a cell-specific, constraint-based modeling technique for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected lungs. We used the gene sequence of the alpha variant of SARS-CoV-2 to build a viral biomass reaction (VBR). We also used the mass proportion of lipids between the viral biomass and its host cell to estimate the stoichiometric coefficients of viral lipids in the reaction. We then integrated the VBR, the gene expression of the alpha variant of SARS-CoV-2, and the generic human metabolic network Recon3D to reconstruct a cell-specific genome-scale metabolic model. An antiviral target discovery (AVTD) platform was introduced using this model to identify therapeutic drug targets for combating COVID-19. The AVTD platform not only identified antiviral genes for eliminating viral replication but also predicted side effects of treatments. Our computational results revealed that knocking out dihydroorotate dehydrogenase (DHODH) might reduce the synthesis rate of cytidine-5'-triphosphate and uridine-5'-triphosphate, which terminate the viral building blocks of DNA and RNA for SARS-CoV-2 replication. Our results also indicated that DHODH is a promising antiviral target that causes minor side effects, which is consistent with the results of recent reports. Moreover, we discovered that the genes that participate in the de novo biosynthesis of glycerophospholipids and ceramides become unidentifiable if the VBR does not involve the stoichiometry of lipids.

Effects of astragaloside IV on glucocorticoid-induced avascular necrosis of the femoral head via regulating Akt-related pathways

We investigated the role of astragaloside IV (AS-IV) in preventing glucocorticoid-induced avascular necrosis of the femoral head (ANFH) and the underlying molecular mechanisms. Network pharmacology was used to predict the molecular targets of AS-IV. Molecular dynamic simulations were performed to explore the binding mechanism and interaction mode between AS-IV and Akt. Rat models of glucocorticoid-induced ANFH with AS-IV intervention were established, and osteogenesis, angiogenesis, apoptosis and oxidative stress were evaluated before and after blocking the PI3K/Akt pathway with LY294002. The effects of glucocorticoid and AS-IV on bone marrow mesenchymal stem cells and human umbilical vein endothelial cells incubated with and without LY294002 were determined. Downregulated p-Akt expression could be detected in the femoral heads of glucocorticoid-induced ANFH patients and rats. AS-IV increased trabecular bone integrity and vessel density of the femoral head in the model rats. AS-IV increased Akt phosphorylation and upregulated osteogenesis-, angiogenesis-, apoptosis- and oxidative stress-related proteins and mRNA and downregulated Bax, cleaved caspase-3 and cytochrome c levels. AS-IV promoted human umbilical vein endothelial cell migration, proliferation and tube formation ability; bone marrow mesenchymal stem cell proliferation; and osteogenic differentiation under glucocorticoid influence. AS-IV inhibited apoptosis. LY294002 inhibited these effects. AS-IV prevented glucocorticoid-induced ANFH by promoting osteogenesis and angiogenesis via the Akt/Runx2 and Akt/HIF-1α/VEGF pathways, respectively, and suppressing apoptosis and oxidative stress via the Akt/Bad/Bcl-2 and Akt/Nrf2/HO-1 pathways, respectively.

Schizophrenia as metabolic disease. What are the causes?

Schizophrenia (SZ) is a devastating neurodevelopmental disease with an accelerated ageing feature. The criteria of metabolic disease firmly fit with those of schizophrenia. Disturbances in energy and mitochondria are at the core of complex pathology. Genetic and environmental interaction creates changes in redox, inflammation, and apoptosis. All the factors behind schizophrenia interact in a cycle where it is difficult to discriminate between the cause and the effect. New technology and advances in the multi-dispensary fields could break this cycle in the future.

Discovery of potential anti-SARS-CoV-2 drugs based on large-scale screening in vitro and effect evaluation in vivo

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a global crisis. Clinical candidates with high efficacy, ready availability, and that do not develop resistance are in urgent need. Despite that screening to repurpose clinically approved drugs has provided a variety of hits shown to be effective against SARS-CoV-2 infection in cell culture, there are few confirmed antiviral candidates in vivo. In this study, 94 compounds showing high antiviral activity against SARS-CoV-2 in Vero E6 cells were identified from 2,580 FDA-approved small-molecule drugs. Among them, 24 compounds with low cytotoxicity were selected, and of these, 17 compounds also effectively suppressed SARS-CoV-2 infection in HeLa cells transduced with human ACE2. Six compounds disturb multiple processes of the SARS-CoV-2 life cycle. Their prophylactic efficacies were determined in vivo using Syrian hamsters challenged with SARS-CoV-2 infection. Seven compounds reduced weight loss and promoted weight regain of hamsters infected not only with the original strain but also the D614G variant. Except for cisatracurium, six compounds reduced hamster pulmonary viral load, and IL-6 and TNF-α mRNA when assayed at 4 d postinfection. In particular, sertraline, salinomycin, and gilteritinib showed similar protective effects as remdesivir in vivo and did not induce antiviral drug resistance after 10 serial passages of SARS-CoV-2 in vitro, suggesting promising application for COVID-19 treatment.

The spike-ACE2 binding assay: An in vitro platform for evaluating vaccination efficacy and for screening SARS-CoV-2 inhibitors and neutralizing antibodies

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 has become a worldwide pandemic, and there is a pressing need for the rapid development of novel therapeutic strategies. SARS-CoV-2 viral entry is mediated by interaction between the receptor binding domain (RBD) of the SARS-CoV-2 Spike protein and host cellular receptor, human angiotensin converting enzyme 2 (ACE2). The lack of a high throughput screening (HTS) platform for candidate drug screening means that no targeted COVID-19 treatments have been developed to date. To overcome this limitation, we developed a novel, rapid, simple, and HTS binding assay platform to screen potential inhibitors of the RBD-ACE2 complex. Three “neutralizing” mouse monoclonal antibodies capable of blocking the RBD-ACE2 interaction were identified using our binding assay and pseudovirus neutralization assay followed by further validation with the Focus Reduction Neutralization Test (FRNT), which analyzes the neutralization capacity of samples in the presence of live SARS-CoV-2. Furthermore, the consistency of our binding assay and FRNT results (R² = 0.68) was demonstrated by patients' serum, of which were COVID-19 positive (n = 34) and COVID-19 negative (n = 76). Several small molecules selected for their potential to inhibit the Spike-ACE2 complex in silico were also confirmed with the binding assay. In addition, we have evaluated vaccine efficacy using binding assay platform and validated through pseudovirus neutralization assay. The correlation between binding assay & psuedovirus assay of the post vaccinated serum showed well correlated (R² = 0.09) Moreover, our binding assay platform successfully validated different Spike RBD mutants. These results indicate that our binding assay can be used as a platform for in vitro screening of small molecules and monoclonal antibodies, and high-throughput assessment of antibody levels after vaccination. When conducting drug screening, computer virtual screening lacks actual basis, construction of pseudoviruses is relatively complicated, and even FRNT requires a P3 laboratory. There are few methods to determine the competitiveness of the target drug and SRBD or ACE2. Our binding assay can fill this gap and accelerate the process and efficiency of COVID-19 drug screening.

DrugDevCovid19: An Atlas of Anti-COVID-19 Compounds Derived by Computer-Aided Drug Design

Since the outbreak of SARS-CoV-2, numerous compounds against COVID-19 have been derived by computer-aided drug design (CADD) studies. They are valuable resources for the development of COVID-19 therapeutics. In this work, we reviewed these studies and analyzed 779 compounds against 16 target proteins from 181 CADD publications. We performed unified docking simulations and neck-to-neck comparison with the solved co-crystal structures. We computed their chemical features and classified these compounds, aiming to provide insights for subsequent drug design. Through detailed analyses, we recommended a batch of compounds that are worth further study. Moreover, we organized all the abundant data and constructed a freely available database, DrugDevCovid19, to facilitate the development of COVID-19 therapeutics.

DeepKG: an end-to-end deep learning-based workflow for biomedical knowledge graph extraction, optimization and applications

SUMMARY

DeepKG is an end-to-end deep learning-based workflow that helps researchers automatically mine valuable knowledge in biomedical literature. Users can utilize it to establish customized knowledge graphs in specified domains, thus facilitating in-depth understanding on disease mechanisms and applications on drug repurposing and clinical research. To improve the performance of DeepKG, a cascaded hybrid information extraction framework is developed for training model of 3-tuple extraction, and a novel AutoML-based knowledge representation algorithm (AutoTransX) is proposed for knowledge representation and inference. The system has been deployed in dozens of hospitals and extensive experiments strongly evidence the effectiveness. In the context of 144 900 COVID-19 scholarly full-text literature, DeepKG generates a high-quality knowledge graph with 7980 entities and 43 760 3-tuples, a candidate drug list, and relevant animal experimental studies are being carried out. To accelerate more studies, we make DeepKG publicly available and provide an online tool including the data of 3-tuples, potential drug list, question answering system, visualization platform.

AVAILABILITY AND IMPLEMENTATION

All the results are publicly available at the website (http://covidkg.ai/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Recent omics-based computational methods for COVID-19 drug discovery and repurposing

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is the main reason for the increasing number of deaths worldwide. Although strict quarantine measures were followed in many countries, the disease situation is still intractable. Thus, it is needed to utilize all possible means to confront this pandemic. Therefore, researchers are in a race against the time to produce potential treatments to cure or reduce the increasing infections of COVID-19. Computational methods are widely proving rapid successes in biological related problems, including diagnosis and treatment of diseases. Many efforts in recent months utilized Artificial Intelligence (AI) techniques in the context of fighting the spread of COVID-19. Providing periodic reviews and discussions of recent efforts saves the time of researchers and helps to link their endeavors for a faster and efficient confrontation of the pandemic. In this review, we discuss the recent promising studies that used Omics-based data and utilized AI algorithms and other computational tools to achieve this goal. We review the established datasets and the developed methods that were basically directed to new or repurposed drugs, vaccinations and diagnosis. The tools and methods varied depending on the level of details in the available information such as structures, sequences or metabolic data.