Naloxone for COVID-19

Innate immunity is the first line of defense against infections, including the detection and response to SARS-CoV-2. Cells of the innate system are usually activated within hours after pathogen exposure and do not generate conventional immunological memory. In this review, the current knowledge of the innate immune cells and of pattern-recognition receptors in sensing and responding to SARS-CoV-2 to mount a protective response has been shortly reviewed. Subsequently, the evasion strategies of the virus, as the inhibition of IFN-I/III production and autophagic response, counteracting the innate cell activity (including NK cells), have been briefly outlined. In the course of the infection, these strategies are also capable of rendering dysfunctional most innate cells, thus deeply interfering with the onset and maintenance of adaptive immunity. Possible mechanism(s) for the maintenance of dysfunctional innate immune response are also discussed. In this context, the importance of a rapid and robust activation of innate immunity through toll-like receptor (TLR) 4 as a key paradigm central to host defense against COVID-19 pathogenesis is also illustrated. We also discuss how the viral excess plus inflammatory signals upregulating TLR4 on innate cells may initiate a vicious loop which maintains and improves hyperinflammation, leading to the most critical outcomes. Targeting the TLR4 or its signaling pathway may be a promising therapeutic strategy, offering the dual benefits of viral suppression and decreasing inflammation.

AbstractThe search for an effective drug is still urgent for COVID-19 as no drug with proven clinical efficacy is available. Finding the new purpose of an approved or investigational drug, known as drug repurposing, has become increasingly popular in recent years. We propose here a new drug repurposing approach for COVID-19, based on knowledge graph (KG) embeddings. Our approach learns “ensemble embeddings” of entities and relations in a COVID-19 centric KG, in order to get a better latent representation of the graph elements. Ensemble KG-embeddings are subsequently used in a deep neural network trained for discovering potential drugs for COVID-19. Compared to related works, we retrieve more in-trial drugs among our top-ranked predictions, thus giving greater confidence in our prediction for out-of-trial drugs. For the first time to our knowledge, molecular docking is then used to evaluate the predictions obtained from drug repurposing using KG embedding. We show that Fosinopril is a potential ligand for the SARS-CoV-2 nsp13 target. We also provide explanations of our predictions thanks to rules extracted from the KG and instanciated by KG-derived explanatory paths. Molecular evaluation and explanatory paths bring reliability to our results and constitute new complementary and reusable methods for assessing KG-based drug repurposing.