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Using a diverse set of SARS-CoV-2 transcriptomic signatures, the US researchers used their previously developed drug repositioning line to identify potential drug candidates for the treatment of coronavirus disease (COVID- 19). The study is currently available at bioRxiv * prepress server.
While many efforts are currently underway to identify potential therapies targeting various aspects of COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), there is still a lack of clinically proven approaches available in our arsenal. .
Drug repositioning is one of the approaches that has been widely explored during this pandemic. More specifically, high-throughput lines of research have been proposed as viable strategies for testing drug candidates as they are rapidly identified.
A similar approach has been taken by a group of scientists led by Dr. Brian L. Le of the Department of Pediatrics and the Bakar Institute for Computational Health Sciences at the University of California, San Francisco.
Assess inhibitory effects
In this study, the researchers applied their existing computational drug repositioning channel to SARS-CoV-2 differential gene expression signatures derived from publicly available RNA sequencing data (RNA-seq). The transcriptomic data have been generated from different types of tissues, which means that they could predict the therapeutics for each signature and then combine the results.
“We used three independent gene expression signatures, each of which consisted of lists of genes differentially expressed between SARS-CoV-2 samples and their respective controls,” the study authors explain in more detail their methodological approach in this bioRxiv paper.
Furthermore, validity was confirmed by showing 16 of the inhibitory effects of its pharmacological effects, which could significantly reverse multiple SARS-CoV-2 profiles in in vivo antiviral trials. Two types of cell lines were used: Calu-3 (human lung cell line) and 293T kidney cells that overexpress ACE2 (293T-ACE2 cells).
Finally, using a range-based pattern matching strategy based on the non-parametric and distribution-free Kolmogorov-Smirnov statistic, the signatures were queried against the drug profiles of Connectivity Map (CMap), a resource that uses expression data. transcriptional to test relationships between diseases, cell physiology and therapeutics.
At least two of the firms shared twenty-five common drug hits (p = 0.0334), with four consensus drug hits (bacampicillin, clofazimine, haloperidol, valproic acid) in the three firms (p = 0.0599) (A ). Characterized common hits when examining protein interactions in humans. Known DrugBank32 Drug Targets and Predicted Additional Targets Using the Set of Similarities Approach (SEA) 33. Visualized known DrugBank interactions in a network (Figure 3B)
Revealing potential drug candidates
Overall, using a diverse set of SARS-CoV-2 transcriptomic signatures in two human cell line assays, the researchers identified 25 potential therapeutic candidates out of 102 unique drug hits. Additional validation experiments revealed antiviral activity of SARS-CoV-2 for 11 out of 16 drug doses.
After further refining these exciting results, three of our four consensus drugs were shown to demonstrate significant antiviral efficacy. More specifically, haloperidol showed reproducible inhibition in Calu-3 cells, while clofazimine and bacampicillin were shown to inhibit viral activity in 293T-ACE2 cells.
Several inhibitors showed micromolar to submicromolar antiviral efficacy, including cyclosporine, cyclopirox, and methixen. The findings were also explored in the context of other computational drug reuse efforts for COVID-19.
“These initial results are encouraging as we continue to work toward further analysis of these drugs envisioned as potential therapies for the treatment of COVID-19,” say the study authors.
The undeniable predictive power
This study confirms the power of these predictive methods and successfully identified different clinically approved drugs with potential for reuse for the treatment of COVID-19, many of which can be administered orally.
Therefore, the diversity of signatures found and the overlapping of highlighted drugs accentuates the utility of the current line to reveal drugs that have therapeutic effects for a wide range of SARS-CoV-2 infection effects.
Still, several limitations of this type of approach must be recognized. First, the data generated from cell lines may not correctly represent the biological changes and multiple responses in human infection. Additionally, CMap drug profiles were generated from cell line data.
Encouraged by these initial results, future research efforts on the topic should collect samples from a larger group of patients to obtain a stronger gene expression signature and better inform treatment predictions.
*Important news
bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be considered conclusive, guide clinical practice / health-related behavior, or be treated as established information.
