Leveraging publicly available coronavirus data to identify new therapeutic targets for COVID-19

Bioinformation Analysis of Differential Expression Proteins in Different Processes of COVID-19

COVID-19 is a highly infectious respiratory disease whose progression has been associated with multiple factors. From SARS-CoV-2 infection to death, biomarkers capable of predicting different disease processes are needed to help us further understand the molecular progression of COVID-19 disease. The aim is to find differentially expressed proteins that are associated with the progression of COVID-19 disease or can be potential biomarkers, and to provide a reference for further understanding of the molecular mechanisms of COVID-19 occurrence, progression, and treatment. Data-independent Acquisition (DIA) proteomics to obtain sample protein expression data, using R language screening differentially expressed proteins. Gene Ontology and Kyoto Encyclopedia for Genes and Genomes analysis was performed on differential proteins and protein-protein interaction (PPI) network was constructed to screen key proteins. A total of 47 differentially expressed proteins were obtained from COVID-19 incubation patients and healthy population (L/H), mainly enriched in platelet-related functions, and complement and coagulation cascade reaction pathways, such as platelet degranulation and platelet aggregation. A total of 42 differential proteins were obtained in clinical and latent phase patients (C/L), also mainly enriched in platelet-related functions and in complement and coagulation cascade reactions, platelet activation pathways. A total of 10 differential proteins were screened in recovery and clinical phase patients (R/C), mostly immune-related proteins. The differentially expressed proteins in different stages of COVID-19 are mostly closely associated with coagulation, and key differential proteins, such as FGA, FGB, FGG, ACTB, PFN1, VCL, SERPZNCL, APOC3, LTF, and DEFA1, have the potential to be used as early diagnostic markers.

Interactome-Based Machine Learning Predicts Potential Therapeutics for COVID-19

COVID-19, the disease caused by SARS-CoV-2, has been disrupting our lives for more than two years now. SARS-CoV-2 interacts with human proteins to pave its way into the human body, thereby wreaking havoc. Moreover, the mutating variants of the virus that take place in the SARS-CoV-2 genome are also a cause of concern among the masses. Thus, it is very important to understand human-spike protein-protein interactions (PPIs) in order to predict new PPIs and consequently propose drugs for the human proteins in order to fight the virus and its different mutated variants, with the mutations occurring in the spike protein. This fact motivated us to develop a complete pipeline where PPIs and drug-protein interactions can be predicted for human-SARS-CoV-2 interactions. In this regard, initially interacting data sets are collected from the literature, and noninteracting data sets are subsequently created for human-SARS-CoV-2 by considering only spike glycoprotein. On the other hand, for drug-protein interactions both interacting and noninteracting data sets are considered from DrugBank and ChEMBL databases. Thereafter, a model based on a sequence-based feature is used to code the protein sequences of human and spike proteins using the well-known Moran autocorrelation technique, while the drugs are coded using another well-known technique, viz., PaDEL descriptors, to predict new human-spike PPIs and eventually new drug-protein interactions for the top 20 predicted human proteins interacting with the original spike protein and its different mutated variants like Alpha, Beta, Delta, Gamma, and Omicron. Such predictions are carried out by random forest as it is found to perform better than other predictors, providing an accuracy of 90.53% for human-spike PPI and 96.15% for drug-protein interactions. Finally, 40 unique drugs like eicosapentaenoic acid, doxercalciferol, ciclesonide, dexamethasone, methylprednisolone, etc. are identified that target 32 human proteins like ACACA, DST, DYNC1H1, etc.

A Phase 2 Randomized, Double-Blind, Placebo-controlled Trial of Oral Camostat Mesylate for Early Treatment of COVID-19 Outpatients Showed Shorter Illness Course and Attenuation of Loss of Smell and Taste

Importance

Early treatment of mild SARS-CoV-2 infection might lower the risk of clinical deterioration in COVID-19.

Objective

To determine whether oral camostat mesylate would reduce upper respiratory SARS-CoV-2 viral load in newly diagnosed outpatients with mild COVID-19, and would lead to improvement in COVID-19 symptoms.

Design

From June, 2020 to April, 2021, we conducted a randomized, double-blind, placebo-controlled phase 2 trial.

Setting

Single site, academic medical center, outpatient setting in Connecticut, USA.

Participants

Of 568 COVID-19 positive potential adult participants diagnosed within 3 days of study entry and assessed for eligibility, 70 were randomized and 498 were excluded (198 did not meet eligibility criteria, 37 were not interested, 265 were excluded for unknown or other reasons). The primary inclusion criteria were a positive SARS-CoV-2 nucleic acid amplification result in adults within 3 days of screening regardless of COVID-19 symptoms.

Intervention

Treatment was 7 days of oral camostat mesylate, 200 mg po four times a day, or placebo.

Main Outcomes and Measures

The primary outcome was reduction of 4-day log10 nasopharyngeal swab viral load by 0.5 log10 compared to placebo. The main prespecified secondary outcome was reduction in symptom scores as measured by a quantitative Likert scale instrument, Flu-PRO-Plus modified to measure changes in smell/taste measured using FLU-PRO-Plus.

Results

Participants receiving camostat had statistically significant lower quantitative symptom scores (FLU-Pro-Plus) at day 6, accelerated overall symptom resolution and notably improved taste/smell, and fatigue beginning at onset of intervention in the camostat mesylate group compared to placebo. Intention-to-treat analysis demonstrated that camostat mesylate was not associated with a reduction in 4-day log10 NP viral load compared to placebo.

Conclusions and relevance

The camostat group had more rapid resolution of COVID-19 symptoms and amelioration of the loss of taste and smell. Camostat compared to placebo was not associated with reduction in nasopharyngeal SARS-COV-2 viral load. Additional clinical trials are warranted to validate the role of camostat mesylate on SARS-CoV-2 infection in the treatment of mild COVID-19.

Trial registration

Clinicaltrials.gov, NCT04353284 (04/20/20)(https://clinicaltrials.gov/ct2/show/NCT04353284?term=camostat+%2C+yale&draw=2&rank=1).