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Published on: April 6, 2016
Determining similarities of COVID-19 - lung cancer drugs and affinity binding mode analysis by graph neural
Cafer Budak1, Vasfiye Mençik2, Veysel Gider2
1Department of Biomedical Engineering, Dicle University, Diyarbakır, Turkey.
Abstract:
COVID-19 is a worldwide health crisis seriously endangering the arsenal of antiviral and antibiotic drugs. It is urgent to find an effective antiviral drug against pandemic caused by the severe acute respiratory syndrome (Sars-Cov-2), which increases global health concerns. As it can be expensive and time-consuming to develop specific antiviral drugs, reuse of FDA-approved drugs that provide an opportunity to rapidly distribute effective therapeutics can allow to provide treatments with known preclinical, pharmacokinetic, pharmacodynamic and toxicity profiles that can quickly enter in clinical trials. In this study, using the structural information of molecules and proteins, a list of repurposed drug candidates was prepared again with the graph neural network-based GEFA model. The data set from the public databases DrugBank and PubChem were used for analysis. Using the Tanimoto/jaccard similarity analysis, a list of similar drugs was prepared by comparing the drugs used in the treatment of COVID-19 with the drugs used in the treatment of other diseases. The resultant drugs were compared with the drugs used in lung cancer and repurposed drugs were obtained again by calculating the binding strength between a drug and a target. The kinase inhibitors (erlotinib, lapatinib, vandetanib, pazopanib, cediranib, dasatinib, linifanib and tozasertib) obtained from the study can be used as an alternative for the treatment of COVID-19, as a combination of blocking agents (gefitinib, osimertinib, fedratinib, baricitinib, imatinib, sunitinib and ponatinib) such as ABL2, ABL1, EGFR, AAK1, FLT3 and JAK1, or antiviral therapies (ribavirin, ritonavir-lopinavir and remdesivir).Communicated by Ramaswamy H. Sarma.
Insights
Repurposing existing drugs offers a rapid strategy against COVID-19. This study identified kinase inhibitors and blocking agents as potential alternative treatments for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.
Area of Science:
- Drug discovery and repurposing
- Computational drug design
- Virology and infectious diseases
Background:
- The COVID-19 pandemic necessitates rapid identification of effective antiviral therapies.
- Developing novel antiviral drugs is time-consuming and expensive.
- Repurposing FDA-approved drugs offers a faster route to clinical application due to existing safety and pharmacokinetic data.
Purpose of the Study:
- To identify potential drug candidates for COVID-19 treatment through drug repurposing.
- To leverage computational methods and structural information for drug discovery.
- To explore existing drugs, particularly kinase inhibitors, for efficacy against SARS-CoV-2.
Main Methods:
- Utilized the graph neural network-based GEFA model with structural information of molecules and proteins.
- Analyzed data from DrugBank and PubChem databases.
- Employed Tanimoto/jaccard similarity analysis to identify similar drugs.
- Calculated binding strength between drugs and targets, focusing on lung cancer-related drugs.
Main Results:
- Identified several kinase inhibitors (e.g., erlotinib, lapatinib) as potential COVID-19 treatments.
- Found that blocking agents targeting kinases like ABL2, EGFR, and JAK1 could be effective.
- The study suggests these repurposed drugs can complement existing antiviral therapies.
Conclusions:
- Kinase inhibitors and specific blocking agents show promise for repurposing in COVID-19 treatment.
- Computational drug repurposing is a viable strategy to accelerate therapeutic development.
- Further clinical investigation of these identified candidates is warranted for COVID-19 management.
