Chinese Researchers Identify Lymphoma Drug as Potential COVID-19 Treatment
Researchers at China’s Shenzhen Institutes of Advanced Technology have found that pralatrexate, a chemotherapy drug used to treat lymphoma, may hold potential as a COVID-19 treatment and that it outperforms remdesivir in inhibiting the virus.
The researchers, who published their findings in the Public Library of Science (PLOS) Computational Biology journal, used computational techniques that simulated drug-virus interactions to identify coronavirus treatment candidates in a group of 1,906 approved drugs. The drugs were assessed specifically because they target RNA-dependent RNA polymerase (RdRP), a viral protein that plays a large role in the replication of the SARS-CoV-2 virus.
They found that Acrotech Biopharma’s Folotyn (pralatrexate injection), a chemotherapy drug approved by the FDA for treating lymphoma, strongly helped curb viral replication after further evaluating it in lab experiments. Significantly, they found that the drug was a stronger viral inhibitor than Gilead Sciences’ Veklury (remdesivir), the first and only antiviral fully approved by the FDA for treating hospitalized COVID-19 patients.
“For the first time, our study discovered that pralatrexate is able to potently inhibit SARS-CoV-2 replication with a stronger inhibitory activity than remdesivir within the same experimental conditions,” they said.
Although the chemo drug “could potentially be repurposed to treat COVID-19,” the medicine can cause serious side effects and is meant for patients with terminal lymphoma, “so immediate use for COVID-19 patients is not guaranteed,” the researchers said.
The study also found that azithromycin, a widely used antibiotic, effectively inhibited replication of the virus, though the UK’s large-scale RECOVERY trial recently dropped the treatment after finding it was ineffective for hospitalized coronavirus patients (DID, Dec. 15, 2020). Two other potential candidates, sofosbuvir and amoxicillin, were also identified by the researchers’ computational model.
Read the PLOS article here: bit.ly/2LgfAsG. — James Miessler