CORona Drug InTEractions database
Comparative Computational Study of SARS-CoV-2 Receptors Antagonists from Already Approved Drugs
Micael Davi, Lima de Oliveira, Kelson Mota, Teixeira de Oliveira
Abstract
According to the World Health Organisation, on March 27, 2020, the number of confirmed cases of COVID-19 has already exceeded 509.000 with about of 23.000 deaths worldwide. Given this, the impact of COVID-19 on humanity cannot be overlooked, and basic research are urgently needed. This research aims to find antagonists already approved for another diseases, that may inhibit activity of the main protease (Mpro) of the SARS-CoV-2 virus, as well as modulate the ACE2 receptors, largely found in lung cells and reduce viral replication by inhibiting NSP12 RNA Polymerase. Docking molecular simulations were realized among a total of 28 ligands published in the literature against COVID-19. Docking studies were made with algorithm of AutoDock Vina 1.1.2 software. A structure-based virtual screening was performed with MTiOpenScreen. Subsequently, the physical-chemical and pharmacokinetic parameters were analyzed with SwissADME in order to select only the most promising ones. Finally, simulations of molecular dynamics with elapsed time of 4 nanoseconds (ns) were analysed in order to better understand the action of drugs to the detriment of the limitations of molecular docking. This work has shown that, in comparative terms, Simeprevir, Paritaprevir, Remdesivir and Baricitinib are currently among the most promising in remission of symptoms from the disease. Hydroxy-chloroquine, Chloroquine and Azithromicin were not showed effective, as monotherapies, against COVID-19 or lung cell receptors. Nevertheless, it has not been able to reach conclusive results due to the limitations of computational techniques that do not take into account numerous empirical parameters.
Source: ChemRxiv
Related molecules
| Target | Target affiliation | Drug | Type | Result |
|---|---|---|---|---|
| Target | Target affiliation | Drug | Type | Result |
| Name | Synonyms | Genes | Origin |
|---|---|---|---|
| Name | Synonyms | Genes | Origin |
| Name | Synonyms | PubChem | DrugBank | RCSB PDB | ATC |
|---|---|---|---|---|---|
| Name | Synonyms | PubChem | DrugBank | RCSB PDB | ATC |
| Title | Authors | DOI | Source | Article type | Date |
|---|---|---|---|---|---|
| Title | Authors | DOI | Source | Article type | Date |
| Title | Status | Phases | Start Date | Prim. Comp. Date | Comp. Date | First Post. Date |
|---|---|---|---|---|---|---|
| Title | Status | Phases | Start Date | Prim. Comp. Date | Comp. Date | First Post. Date |
CORDITE (CORona Drug InTEractions database) collects and aggregates data from PubMed, MedRxiv, BioRxiv, ChemRxiv and PMC for SARS-CoV-2. Its main focus is set on drug interactions either addressing viral proteins or human proteins that could be used to treat COVID. It collects and provides up-to-date information on computational predictions, in vitro, as well as in vivo study data.
The information provided is for research only and we cannot guarantee the correctness of the data.
Please contact dominik.heider@uni-muenster.de for further information.
Programmable access
There is an open API for access programmatically to the database. The API will print a JSON output:
- Interactions
https://cordite-api.uni-muenster.de/api.php?action=list&table=interaction
- Targets
https://cordite-api.uni-muenster.de/api.php?action=list&table=target
- Drugs
https://cordite-api.uni-muenster.de/api.php?action=list&table=drug
- Publications
https://cordite-api.uni-muenster.de/api.php?action=list&table=publication
- Clinical trials
https://cordite-api.uni-muenster.de/api.php?action=list&table=clinical_trial