Erlotinib for COVID-19
c19early.org
COVID-19 Treatment Clinical Evidence
COVID-19 involves the interplay of 500+ viral and host proteins and factors, providing many therapeutic targets.
c19early analyzes 6,000+ studies for 220+ 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.
Erlotinib may be beneficial for
COVID-19 according to the studies below.
COVID-19 involves the interplay of 500+ viral and host proteins and factors providing many therapeutic targets.
Scientists have proposed 11,000+ potential treatments.
c19early.org analyzes
220+ treatments.
We have not reviewed erlotinib in detail.
, An integrative meta-analysis of SARS-CoV-2 RNA–protein interactomes identifies conserved host factors shared with other RNA viruses, Briefings in Functional Genomics, doi:10.1093/bfgp/elag001
Abstract RNA viruses cause substantial global disease burden and depend on host RNA-binding proteins and translation machinery. However, it remains unclear which host factors are robustly engaged across independent Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) RNA interactome studies and to what extent these factors are shared with other RNA viruses. Here, we perform an integrative meta-analysis of eight published SARS-CoV-2 RNA–protein interactomes and compare them with corresponding Influenza A virus, Zika virus, and Dengue virus datasets to define conserved host networks and prioritize candidate host-directed antiviral targets. By integrating multiple datasets and applying ClusterProfiler together with curated pathway resources (KEGG, Reactome, WikiPathways, and Gene Ontology), we systematically characterize the functional landscape of SARS-CoV-2 RNA–protein interactions. The consensus SARS-CoV-2 interactome is enriched for mRNA processing, translation, RNA surveillance and innate immune functions. Cross-viral comparison identifies 275 host proteins shared across all four RNA viruses, forming interconnected modules that include key translation factors (EEF1A1, EIF4A1, EIF3H) and RNA-binding proteins (Nucleolin, ILF3). Drug–target annotation prioritizes 21 proteins with 35 approved or investigational modulators for host-directed antiviral repurposing. Together, these findings generate a consensus map of conserved host dependencies and highlight prioritized targets for future mechanistic and translational studies. Research Highlights Integrated SARS-CoV-2 datasets and compared with, Influenza A virus, Zika virus, Dengue virus. Identified 275 host proteins shared across these four pathogens. Conserved proteins were enriched in translation, RNA processing, and innate immune pathways. Prioritized 21 host targets and 35 drugs for antiviral repurposing.
, 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.