Researchers from the Icahn School of Medicine at Mount Sinai Health System in New York have developed a computational method to identify drugs that could be combat COVID-19. Unlike other research to repurpose drugs to treat infection, this effort focused on inhibiting viral uptake of SARS-CoV-2 in the first place.
In a preprint paper posted to BioRxiv, the researchers explored viral sequences using PCR analysis, RNA sequencing, and bioinformatics. They identified four compounds that could block replication of the novel coronavirus, namely amlodipine, loperamide, terfenadine, and berbamine. They then validated these findings in multiple assays using primate Vero cells infected with SARS-CoV-2, A549 cells, and in human organoids.
"These compounds were found to potently reduce viral load despite having no impact on viral entry or modulation of the host antiviral response in the absence of virus," according to the paper.
"In the absence of a pan-specific coronavirus drug, or a SARS-CoV-2 vaccine, the next most useful tool would be the effective repurposing of FDA-approved drugs," the preprint said. "There is underutilized opportunity to understand the global molecular changes these drugs induce by using available sequencing data from diverse models and cell types."
"You have a bunch of drugs that are blocking the virus in cell culture," said lead researcher Avi Ma'ayan, director of the Mount Sinai Center for Bioinformatics and principal investigator with the academic health system's LymeMIND team of other research into other potential COVID-19 treatments. "But this particular paper is showing a lot of details about why and which drug and … is beginning to understand the molecular mechanism."
"We looked for those drugs that are really matching that signature. There are more drugs that fall into that cloud," Ma'ayan said.
The researchers used a collection of gene expression profiles from the National Institutes of Health's Library of Integrated Network-based Cellular Signatures (LINCS) database that has previously been applied to identify drugs that attenuate the Ebola virus. With SARS-CoV-2, the Mount Sinai team was able to spot transcriptional irregularities by comparing changes in gene expression before and after infection or drug treatment.
In this new work, the Mount Sinai team studied 50 genes that were downregulated by the virus or 50 upregulated by certain drugs. They also looked at the 100 genes most commonly coexpressed by ACE2, known to be the receptor of SARS-CoV-2. This methodology also led the researchers to quercetin, an effector of these ACE2-coexpressed genes.
The research on ACE2 expression was more of a negative control, according to Ma'ayan. "We did look for drugs that can reduce the level of the expression of the receptor and we found a drug that had a high score, but it actually made things worse," he said. "When you test it with the virus, you actually get more viral replication and more virus when you use that drug."
Manual examination of drugs revealed that terfenadine, loperamide, berbamine, trifluoperazine, amlodipine, RS-504393, and chlorpromazine regularly targeted this expression space.
The antidiarrheal loperamide is widely available over the counter. Terfenadrine is an antihistamine, sold as Seldane in the U.S., that was pulled from the market in the 1990s after it was linked to cardiac arrhythmia.
While SARS-CoV-2 appears to prevent antiviral response by masking aberrant RNA, replication of the virus still produces a unique transcriptional footprint, they said, citing a May paper in Cell from Daniel Blanco-Melo, a postdoctoral researcher in Mount Sinai's tenOever Laboratory.
The Ma'ayan work builds on this by attempting to identify drugs that might invert transcriptional signatures to inhibit replication. The tenOever Lab assisted on this new experiment, and Benjamin tenOever, director of Mount Sinai's Virus Engineering Center for Therapeutics and Research (VECToR), is listed as an author on the preprint.
The findings, or "predictions," as Ma'ayan called them, were validated with multiple assays and multiple cell lines. Ma'ayan's lab built the computational model, but tenOever's lab tested the hypotheses about drugs that the model predicted. Ma'ayan said that some predictions were based on earlier work by tenOever's team.
According to Ma'ayan, seven of the eight drugs initially tested completely blocked transmission in monkey Vero cells. Some also worked in human cells.
The prepress paper only mentioned the genetic signatures from the Cell paper, but Ma'ayan said that he and his colleagues looked at signatures from other viruses and compounds, including the controversial hydroxychloroquine, in an effort to determine if that might inhibit SARS-CoV-2 replication.
Ma'ayan said that hydroxychloroquine does work similarly to the drugs his Mount Sinai tested, but requires a much higher concentration than the others. It also has more side effects, so he said that loperamide and amlodipine are likely to be more effective than hydroxychloroquine.
After the prepress article was posted, the researchers found several other published studies that have data from cells infected with the coronavirus, so they now are trying to see if there is a consensus on the drug and pathway predictions. "That we think is critical. We are creating a more comprehensive across-lab analysis," Ma'ayan said.
They are using machine learning to look for common themes among published "hits," according to Ma'ayan. "A lot of people are publishing drugs that are working in cells, but we're trying to synthesize all that information and try to make sense of it, and also explain the mechanisms behind those observations," he said.
Ma'Ayan said that his goal is to get these compounds into human clinical trials, whether at Mount Sinai or elsewhere. He also wants to study combination therapies.
Christina Fliege technical research lead at the NCSA Genomics in the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, said it was important not to "overinterpret" the results because the work involved Gene Ontology analysis. "Gene ontology ... analysis, in general just gives a hint of further experiments," Fliege said.
"That's a problem because the number of genes they're using for Gene Ontology as an input is quite small, which means that whatever you miss will have a big impact on the statistical results," Mainzer added.
Liudmila Mainzer, a technical program manager at NCSA Genomics said that reviewers and others who might consider adopting the methodology or running trials with the identified compounds will want to pay attention to the inputs in this experiment in order to validate the findings.
"Right now, it's very difficult for us to make that interpretation because we don't know the versions of the software they used for annotation and the number of genes they used was very small, so there can be some room for possible misinterpretation," Mainzer cautioned.
However, Mainzer said that the researchers accomplished their main objective of matching gene expression patterns in response to SARS-CoV-2 with the inverse of patterns related to drug response. "As they can make that match, ultimately, it doesn't matter exactly what the Gene Ontology database says because you're not diving into the biological underpinnings of these processes," she said.
"The ultimate validation is, if you apply this drug, does it work? Does it prevent COVID from reproducing?" Mainzer said. "They did the match, they found the drugs, and then they applied them to the cells."
This story first appeared in our sister publication, Genomeweb.
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