Fostamatinib for COVID-19

Abstract Motivation Since December 2019, the newly identified coronavirus SARS-CoV-2 has caused a massive health crisis worldwide and resulted in over 70 000 COVID-19 infections so far. Clinical drugs targeting SARS-CoV-2 are urgently needed to decrease the high fatality rate of confirmed COVID-19 patients. Traditional de novo drug discovery needs more than 10 years, so drug repurposing seems the best option currently to find potential drugs for treating COVID-19. Results Compared with traditional non-covalent drugs, covalent drugs have attracted escalating attention recent years due to their advantages in potential specificity upon careful design, efficiency and patient burden. We recently developed a computational protocol named as SCAR (steric-clashes alleviating receptors) for discovering covalent drugs. In this work, we used the SCAR protocol to identify possible covalent drugs (approved or clinically tested) targeting the main protease (3CLpro) of SARS-CoV-2. We identified 11 potential hits, among which at least six hits were exclusively enriched by the SCAR protocol. Since the preclinical or clinical information of these identified drugs is already available, they might be ready for being clinically tested in the treatment of COVID-19. Contact senliu.ctgu@gmail.com

AbstractCoronaviruses (CoVs) circulate in humans and animals, and expand their host range by zoonotic and anthroponotic transmissions. Endemic human CoVs, such as 229E and OC43 cause limited respiratory disease, and elicit short term anti-viral immunity favoring recurrent infections. Yet, severe acute respir-atory syndrome (SARS)-CoV-2 spreads across the globe with unprecedented impact on societies and economics. The world lacks broadly effective and affordable anti-viral agents to fight the pandemic and reduce the death toll. Here, we developed an image-based multicycle replication assay for focus for-mation of α-coronavirus hCoV-229E-eGFP infected cells for screening with a chemical library of 5440 compounds arrayed in 384 well format. The library contained about 39% clinically used compounds, 26% in phase I, II or III clinical trials, and 34% in preclinical development. Hits were counter-selected against toxicity, and challenged with hCoV-OC43 and SARS-CoV-2 in tissue culture and human bronchial and nasal epithelial explant cultures from healthy donors. Fifty three compounds inhibited hCoV-229E-GFP, 39 of which at 50% effective concentrations (EC50) < 2μM, and were at least 2-fold separated from toxicity. Thirty nine of the 53 compounds inhibited the replication of hCoV-OC43, while SARS-CoV-2 was inhibited by 11 compounds in at least two of four tested cell lines. Six of the 11 compounds are FDA-approved, one of which is used in mouth wash formulations, and five are systemic and orally available. Here, we demonstrate that methylene blue (MB) and mycophenolic acid (MPA), two broadly available low cost compounds, strongly inhibited shedding of infectious SARS-CoV-2 at the apical side of the cultures, in either pre- or post-exposure regimens, with somewhat weaker effects on viral RNA release indicated by RT-qPCR measurements. Our study illustrates the power of full cycle screens in repurposing clinical compounds against SARS-CoV-2. Importantly, both MB and MPA reportedly act as immunosuppressants, making them interesting candidates to counteract the cytokine storms affecting COVID-19 patients.

Abstract Background The active pursuit of network medicine for drug repurposing, particularly for combating Covid-19, has stimulated interest in the concept of structural controllability in cellular networks. We sought to extend this theory, focusing on the defense rather than control of the cell against viral infections. Accordingly, we extended structural controllability to total structural controllability and introduced the concept of control hubs. Perturbing any control hub may render the cell uncontrollable by exogenous stimuli like viral infections, so control hubs are ideal drug targets. Results We developed an efficient algorithm to identify all control hubs, applying it to a largest homogeneous network of human protein interactions, including interactions between human and SARS-CoV-2 proteins. Our method recognized 65 druggable control hubs with enriched antiviral functions. Utilizing these hubs, we categorized potential drugs into four groups: antiviral and anti-inflammatory agents, drugs acting on the central nervous system, dietary supplements, and compounds enhancing immunity. An exemplification of our approach’s effectiveness, Fostamatinib, a drug initially developed for chronic immune thrombocytopenia, is now in clinical trials for treating Covid-19. Preclinical trial data demonstrated that Fostamatinib could reduce mortality rates, ICU stay length, and disease severity in Covid-19 patients. Conclusions Our findings confirm the efficacy of our novel strategy that leverages control hubs as drug targets. This approach provides insights into the molecular mechanisms of potential therapeutics for Covid-19, making it a valuable tool for interpretable drug discovery. Our new approach is general and applicable to repurposing drugs for other diseases.

Network medicine has been pursued for Covid-19 drug repurposing. One such approach adopts structural controllability, a theory for controlling a network (the cell). Motivated to protect the cell from viral infections, we extended this theory to total controllability and introduced a new concept of control hubs. Perturbation to any control hub renders the cell uncontrollable by exogenous stimuli, e.g., viral infections, so control hubs are ideal drug targets. We developed an efficient algorithm for finding all control hubs and applied it to the largest homogenous human protein-protein interaction network. Our new method outperforms several popular gene-selection methods, including that based on structural controllability. The final 65 druggable control hubs are enriched with functions of cell proliferation, regulation of apoptosis, and responses to cellular stress and nutrient levels, revealing critical pathways induced by SARS-CoV-2. These druggable control hubs led to drugs in 4 major categories: antiviral and anti-inflammatory agents, drugs on central nerve systems, and dietary supplements and hormones that boost immunity. Their functions also provided deep insights into the therapeutic mechanisms of the drugs for Covid-19 therapy, making the new approach an explainable drug repurposing method. A remarkable example is Fostamatinib that has been shown to lower mortality, shorten the length of ICU stay, and reduce disease severity of hospitalized Covid-19 patients. The drug targets 10 control hubs, 9 of which are kinases that play key roles in cell differentiation and programmed death. One such kinase is RIPK1 that directly interacts with viral protein nsp12, the RdRp of the virus. The study produced many control hubs that were not targets of existing drugs but were enriched with proteins on membranes and the NF-$κ$B pathway, so are excellent candidate targets for new drugs.

According to the World Health Organization (WHO), in the second half of 2022, there are about 606 million confirmed cases of COVID-19 and almost 6,500,000 deaths around the world. A pandemic was declared by the WHO in March 2020 when the new coronavirus spread around the world. The short time between the first cases in Wuhan and the declaration of a pandemic initiated the search for ways to stop the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or to attempt to cure the disease COVID-19. More than ever, research groups are developing vaccines, drugs, and immunobiological compounds, and they are even trying to repurpose drugs in an increasing number of clinical trials. There are great expectations regarding the vaccine’s effectiveness for the prevention of COVID-19. However, producing sufficient doses of vaccines for the entire population and SARS-CoV-2 variants are challenges for pharmaceutical industries. On the contrary, efforts have been made to create different vaccines with different approaches so that they can be used by the entire population. Here, we summarize about 8162 clinical trials, showing a greater number of drug clinical trials in Europe and the United States and less clinical trials in low-income countries. Promising results about the use of new drugs and drug repositioning, monoclonal antibodies, convalescent plasma, and mesenchymal stem cells to control viral infection/replication or the hyper-inflammatory response to the new coronavirus bring hope to treat the disease.

The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified as the pathogenic cause of coronavirus disease 2019 (COVID-19). The RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2 is a potential target for the treatment of COVID-19. An RdRp complex:dsRNA structure suitable for docking simulations was prepared using a cryo-electron microscopy (cryo-EM) structure (PDB ID: 7AAP; resolution, 2.60 Å) that was reported recently. Structural refinement was performed using energy calculations. Structure-based virtual screening was performed using the ChEMBL database. Through 1,838,257 screenings, 249 drugs (37 approved, 93 clinical, and 119 preclinical drugs) were predicted to exhibit a high binding affinity for the RdRp complex:dsRNA. Nine nucleoside triphosphate analogs with anti-viral activity were included among these hit drugs, and among them, remdesivir-ribonucleoside triphosphate and favipiravir-ribonucleoside triphosphate adopted a similar docking mode as that observed in the cryo-EM structure. Additional docking simulations for the predicted compounds with high binding affinity for the RdRp complex:dsRNA suggested that 184 bioactive compounds could be anti-SARS-CoV-2 drug candidates. The hit bioactive compounds mainly consisted of a typical noncovalent major groove binder for dsRNA. Three-layer ONIOM (MP2/6-31G:AM1:AMBER) geometry optimization calculations and frequency analyses (MP2/6-31G:AMBER) were performed to estimate the binding free energy of a representative bioactive compound obtained from the docking simulation, and the fragment molecular orbital calculation at the MP2/6-31G level of theory was subsequently performed for analyzing the detailed interactions. The procedure used in this study represents a possible strategy for discovering anti-SARS-CoV-2 drugs from drug libraries that could significantly shorten the clinical development period for drug repositioning.

The active global SARS-CoV-2 pandemic caused more than 426 million cases and 5.8 million deaths worldwide. The development of completely new drugs for such a novel disease is a challenging, time intensive process. Despite researchers around the world working on this task, no effective treatments have been developed yet. This emphasizes the importance of drug repurposing, where treatments are found among existing drugs that are meant for different diseases. A common approach to this is based on \emph{knowledge graphs}, that condense relationships between entities like drugs, diseases and genes. Graph neural networks (GNNs) can then be used for the task at hand by predicting links in such knowledge graphs. Expanding on state-of-the-art GNN research, Doshi {\sl et al.} recently developed the \drcov \ model. We further extend their work using additional output interpretation strategies. The best aggregation strategy derives a top-100 ranking of 8,070 candidate drugs, 32 of which are currently being tested in COVID-19-related clinical trials. Moreover, we present an alternative application for the model, the generation of additional candidates based on a given pre-selection of drug candidates using collaborative filtering. In addition, we improved the implementation of the \drcov \ model by significantly shortening the inference and pre-processing time by exploiting data-parallelism. As drug repurposing is a task that requires high computation and memory resources, we further accelerate the post-processing phase using a new emerging hardware --- we propose a new approach to leverage the use of high-capacity Non-Volatile Memory for aggregate drug ranking.