Indinavir for COVID-19

The rapid spread of the new coronavirus disease (COVID-19) caused by SARS-CoV-2 has become a global pandemic. Although specific vaccines are available and natural drugs are being researched, supportive care and specific treatments to alleviate symptoms and improve patient quality of life remain critical. Chinese medicine (CM) has been employed in China due to the similarities between the epidemiology, genomics, and pathogenesis of SARS-CoV-2 and SARS-CoV. Moreover, the integration of other traditional oriental medical systems into the broader framework of integrative medicine can offer a powerful approach to managing the disease. Additionally, it has been reported that integrated medicine has better effects and does not increase adverse drug reactions in the context of COVID-19. This article examines preventive measures, potential infection mechanisms, and immune responses in Western medicine (WM), as well as the pathophysiology based on principles of complementary medicine (CM). The convergence between WM and CM approaches, such as the importance of maintaining a strong immune system and promoting preventive care measures, is also addressed. Current treatment options, traditional therapies, and classical prescriptions based on empirical knowledge are also explored, with individual patient circumstances taken into account. An analysis of the potential benefits and challenges associated with the integration of complementary and Western medicine (WM) in the treatment of COVID-19 can provide valuable guidance, enrichment, and empowerment for future research endeavors.

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.