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Register a new userA Birds-eye (Re)View of Acid-suppression Drugs, COVID-19, and the Highly Variable Literature
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We consider the recent surge of information on the potential benefits of acid-suppression drugs in the context of COVID-19, with an eye on the variability (and confusion) across the reported findings--at least as regards the popular antacid famotidine. The inconsistencies reflect contradictory conclusions from independent clinical-based studies that took roughly similar approaches, in terms of experimental design (retrospective, cohort-based, etc.) and statistical analyses (propensity-score matching and stratification, etc.). The confusion has significant ramifications in choosing therapeutic interventions: e.g., do potential benefits of famotidine indicate its use in a particular COVID-19 case? Beyond this pressing therapeutic issue, conflicting information on famotidine must be resolved before its integration in ontological and knowledge graph-based frameworks, which in turn are useful in drug repurposing efforts. To begin systematically structuring the rapidly accumulating information, in the hopes of clarifying and reconciling the discrepancies, we consider the contradictory information along three proposed axes: (1) a context-of-disease axis, (2) a degree-of-[therapeutic]-benefit axis, and (3) a mechanism-of-action axis. We suspect that incongruencies in how these axes have been (implicitly) treated in past studies has led to the contradictory indications for famotidine and COVID-19. We also trace the evolution of information on acid-suppression agents as regards the transmission, severity, and mortality of COVID-19, given the many literature reports that have accumulated. By grouping the studies conceptually and thematically, we identify three eras in the progression of our understanding of famotidine and COVID-19. Harmonizing these findings is a key goal for both clinical standards-of-care (COVID and beyond) as well as ontological and knowledge graph-based approaches.
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Coronavirus disease (COVID-19) is an infectious disease discovered in 2019 and currently in outbreak across the world. Lung injury with severe respiratory failure is the leading cause of death in COVID-19, brought by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, there still lacks efficient treatment for COVID-19 induced lung injury and acute respiratory failure. Inhibition of Angiotensin-converting enzyme 2 (ACE2) caused by spike protein of SARS-CoV-2 is the most plausible mechanism of lung injury in COVID-19. We propose two candidate drugs, COL-3 (a chemically modified tetracycline) and CGP-60474 (a cyclin-dependent kinase inhibitor), for treating lung injuries in COVID-19, based on their abilities to reverse the gene expression patterns in HCC515 cells treated with ACE2 inhibitor and in human COVID-19 patient lung tissues. Further bioinformatics analysis shows that twelve significantly enriched pathways (P-value <0.05) overlap between HCC515 cells treated with ACE2 inhibitor and human COVID-19 patient lung tissues, including signaling pathways known to be associated with lung injury such as TNF signaling, MAPK signaling and Chemokine signaling pathways. All these twelve pathways are targeted in COL-3 treated HCC515 cells, in which genes such as RHOA, RAC2, FAS, CDC42 have reduced expression. CGP-60474 shares eleven of twelve pathways with COL-3 with common target genes such as RHOA. It also uniquely targets genes related to lung injury, such as CALR and MMP14. In summary, this study shows that ACE2 inhibition is likely part of the mechanisms leading to lung injury in COVID-19, and that compounds such as COL-3 and CGP-60474 have the potential as repurposed drugs for its treatment.
The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected near 5 million people and led to over 0.3 million deaths. Currently, there is no specific anti-SARS-CoV-2 medication. New drug discovery typically takes more than ten years. Drug repositioning becomes one of the most feasible approaches for combating COVID-19. This work curates the largest available experimental dataset for SARS-CoV-2 or SARS-CoV main protease inhibitors. Based on this dataset, we develop validated machine learning models with relatively low root mean square error to screen 1553 FDA-approved drugs as well as other 7012 investigational or off-market drugs in DrugBank. We found that many existing drugs might be potentially potent to SARS-CoV-2. The druggability of many potent SARS-CoV-2 main protease inhibitors is analyzed. This work offers a foundation for further experimental studies of COVID-19 drug repositioning.
The titled subject has attracted much interest. Here we summarize the substantial results obtained by a physical model of protein evolution based on hydropathic domain dynamics. In a recent Letter eighteen biologists suggested that the titled subject should be studied in a way inclusive of broad expertise (1). There is an even broader view that has been developed over several decades by physicists (2,3). This view is based on analyzing amino acid sequences of proteins. These sequences are now available on-line at Uniprot, and represent a treasure-trove of data (4).
COVID-19 infections have well described systemic manifestations, especially respiratory problems. There are currently no specific treatments or vaccines against the current strain. With higher case numbers, a range of neurological symptoms are becoming apparent. The mechanisms responsible for these are not well defined, other than those related to hypoxia and microthrombi. We speculate that sustained systemic immune activation seen with SARS-CoV-2 may also cause secondary autoimmune activation in the CNS. Patients with chronic neurological diseases may be at higher risk because of chronic secondary respiratory disease and potentially poor nutritional status. Here, we review the impact of COVID-19 on people with chronic neurological diseases and potential mechanisms. We believe special attention to protecting people with neurodegenerative disease is warranted. We are concerned about a possible delayed pandemic in the form of an increased burden of neurodegenerative disease after acceleration of pathology by systemic COVID-19 infections.
The all-pervasive lens that humans ordinarily use to watch and analyze the pandemic is time. This article considers an alternative. Instead of tracking incidence as a function of time, new cases are counted as a function of cumulative cases. This resource-centric perspective, which is more natural and physically justified, is the perspective of the virus. In this article, we demonstrate the relevance of this approach by characterizing an outbreak as an independent increments Gaussian process that fluctuates about a deterministic curve, called the incidence-cumulative cases (ICC) curve. We illustrate these concepts on Influenza A and COVID-19 outbreaks in the US. The novel perspective presented here reveals universal properties of disease spread that would otherwise remain hidden.
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