Enfuvirtide for COVID-19

c19early.org

COVID-19 Treatment Clinical Evidence

COVID-19 involves the interplay of 400+ viral and host proteins and factors, providing many therapeutic targets. c19early analyzes 6,000+ studies for 210+ treatments—over 17 million hours of research. Only three high-profit early treatments are approved in the US. In reality, many treatments reduce risk, with 25 low-cost treatments approved across 163 countries.

Naso/oropharyngeal treatment Effective Treatment directly to the primary source of initial infection.
Healthy lifestyles Protective Exercise, sunlight, a healthy diet, and good sleep all reduce risk.
Immune support Effective Vitamins A, C, D, and zinc show reduced risk, as with other viruses.
Thermotherapy Effective Methods for increasing internal body temperature, enhancing immune system function.
Systemic agents Effective Many systemic agents reduce risk, and may be required when infection progresses.
High-profit systemic agents Conditional Effective, but with greater access and cost barriers.
Monoclonal antibodies Limited Utility Effective but rarely used—high cost, variant dependence, IV/SC admin.
Acetaminophen Harmful Increased risk of severe outcomes and mortality.
Remdesivir Harmful Increased mortality with longer followup. Increased kidney and liver injury, cardiac disorders.

Enfuvirtide may be beneficial for COVID-19 according to the studies below. COVID-19 involves the interplay of 400+ viral and host proteins and factors providing many therapeutic targets. Scientists have proposed 11,000+ potential treatments. c19early.org analyzes 210+ treatments. We have not reviewed Enfuvirtide in detail.

Studies suggesting benefit with Enfuvirtide

Qu et al., A new integrated framework for the identification of potential virus–drug associations, Frontiers in Microbiology, doi:10.3389/fmicb.2023.1179414

IntroductionWith the increasingly serious problem of antiviral drug resistance, drug repurposing offers a time-efficient and cost-effective way to find potential therapeutic agents for disease. Computational models have the ability to quickly predict potential reusable drug candidates to treat diseases.MethodsIn this study, two matrix decomposition-based methods, i.e., Matrix Decomposition with Heterogeneous Graph Inference (MDHGI) and Bounded Nuclear Norm Regularization (BNNR), were integrated to predict anti-viral drugs. Moreover, global leave-one-out cross-validation (LOOCV), local LOOCV, and 5-fold cross-validation were implemented to evaluate the performance of the proposed model based on datasets of DrugVirus that consist of 933 known associations between 175 drugs and 95 viruses.ResultsThe results showed that the area under the receiver operating characteristics curve (AUC) of global LOOCV and local LOOCV are 0.9035 and 0.8786, respectively. The average AUC and the standard deviation of the 5-fold cross-validation for DrugVirus datasets are 0.8856 ± 0.0032. We further implemented cross-validation based on MDAD and aBiofilm, respectively, to evaluate the performance of the model. In particle, MDAD (aBiofilm) dataset contains 2,470 (2,884) known associations between 1,373 (1,470) drugs and 173 (140) microbes. In addition, two types of case studies were carried out further to verify the effectiveness of the model based on the DrugVirus and MDAD datasets. The results of the case studies supported the effectiveness of MHBVDA in identifying potential virus-drug associations as well as predicting potential drugs for new microbes.

Liu et al., DRAVP: A Comprehensive Database of Antiviral Peptides and Proteins, Viruses, doi:10.3390/v15040820

Viruses with rapid replication and easy mutation can become resistant to antiviral drug treatment. With novel viral infections emerging, such as the recent COVID-19 pandemic, novel antiviral therapies are urgently needed. Antiviral proteins, such as interferon, have been used for treating chronic hepatitis C infections for decades. Natural-origin antimicrobial peptides, such as defensins, have also been identified as possessing antiviral activities, including direct antiviral effects and the ability to induce indirect immune responses to viruses. To promote the development of antiviral drugs, we constructed a data repository of antiviral peptides and proteins (DRAVP). The database provides general information, antiviral activity, structure information, physicochemical information, and literature information for peptides and proteins. Because most of the proteins and peptides lack experimentally determined structures, AlphaFold was used to predict each antiviral peptide’s structure. A free website for users (http://dravp.cpu-bioinfor.org/, accessed on 30 August 2022) was constructed to facilitate data retrieval and sequence analysis. Additionally, all the data can be accessed from the web interface. The DRAVP database aims to be a useful resource for developing antiviral drugs.