Orlistat for COVID-19

New and emerging pathogens, such as SARS-CoV2 have highlighted the requirement for threat agnostic therapies. Some antibiotics or antivirals can demonstrate broad-spectrum activity against pathogens in the same family or genus but efficacy can quickly reduce due to their specific mechanism of action and for the ability of the disease causing agent to evolve. This has led to the generation of antimicrobial resistant strains, making infectious diseases more difficult to treat. Alternative approaches therefore need to be considered, which include exploring the utility of Host-Directed Therapies (HDTs). This is a growing area with huge potential but difficulties arise due to the complexity of disease profiles. For example, a HDT given early during infection may not be appropriate or as effective when the disease has become chronic or when a patient is in intensive care. With the growing understanding of immune function, a new generation of HDT for the treatment of disease could allow targeting specific pathways to augment or diminish the host response, dependent upon disease profile, and allow for bespoke therapeutic management plans. This review highlights promising and approved HDTs that can manipulate the immune system throughout the spectrum of disease, in particular to viral and bacterial pathogens, and demonstrates how the advantages of HDT will soon outweigh the potential side effects.

Lipids perform a series of cellular functions, establishing cell and organelles’ boundaries, organizing signaling platforms, and creating compartments where specific reactions occur. Moreover, lipids store energy and act as secondary messengers whose distribution is tightly regulated. Disruption of lipid metabolism is associated with many diseases, including those caused by viruses. In this scenario, lipids can favor virus replication and are not solely used as pathogens’ energy source. In contrast, cells can counteract viruses using lipids as weapons. In this review, we discuss the available data on how coronaviruses profit from cellular lipid compartments and why targeting lipid metabolism may be a powerful strategy to fight these cellular parasites. We also provide a formidable collection of data on the pharmacological approaches targeting lipid metabolism to impair and treat coronavirus infection.

Abstract Motivation The coronavirus disease 2019 (COVID-19) remains a global public health emergency. Although people, especially those with underlying health conditions, could benefit from several approved COVID-19 therapeutics, the development of effective antiviral COVID-19 drugs is still a very urgent problem. Accurate and robust drug response prediction to a new chemical compound is critical for discovering safe and effective COVID-19 therapeutics. Results In this study, we propose DeepCoVDR, a novel COVID-19 drug response prediction method based on deep transfer learning with graph transformer and cross-attention. First, we adopt a graph transformer and feed-forward neural network to mine the drug and cell line information. Then, we use a cross-attention module that calculates the interaction between the drug and cell line. After that, DeepCoVDR combines drug and cell line representation and their interaction features to predict drug response. To solve the problem of SARS-CoV-2 data scarcity, we apply transfer learning and use the SARS-CoV-2 dataset to fine-tune the model pretrained on the cancer dataset. The experiments of regression and classification show that DeepCoVDR outperforms baseline methods. We also evaluate DeepCoVDR on the cancer dataset, and the results indicate that our approach has high performance compared with other state-of-the-art methods. Moreover, we use DeepCoVDR to predict COVID-19 drugs from FDA-approved drugs and demonstrate the effectiveness of DeepCoVDR in identifying novel COVID-19 drugs. Availability and implementation https://github.com/Hhhzj-7/DeepCoVDR.