The chemotherapy drug pralatrexate, originally developed to treat lymphoma, may be effective in patients with the coronavirus disease 2019 (Covid-19), according to a study published on Thursday by the journal PLOS Computational Biology.
That’s based on the results generated by a new computational drug screening approach created by researchers in China and lab experiments conducted by their colleagues at the Shenzhen Institutes of Advanced Technology in Shenzhen, China, the researchers said.
“From our data, pralatrexate is able to potently inhibit [Covid-19] replication [better] … than remdesivir,” study co-author Yanjie Wei, executive director of the Center for High Performance Computing at the Shenzhen Institutes, said.
Since the start of the global pandemic earlier this year, researchers have sought to repurpose existing drugs for use in infected patients, in an effort to develop an effective treatment more quickly.
Computational models can help identify potential drugs for repurposing by simulating how they would interact with the coronavirus that causes Covid-19, according to Wei and his colleagues.
For this study, the researchers combined multiple computational techniques with deep-learning technologies that simulate drug-virus interactions.
They used this hybrid approach to screen 1,906 existing drugs for their potential ability to limit or stop the replication of Covid-19 – the growth of the virus in the body – by targeting a viral protein called RNA-dependent RNA polymerase, they said.
The novel screening approach identified four promising drugs, which then were tested against the virus in lab experiments, according to the researchers.
Two of the drugs, pralatrexate and azithromycin, successfully inhibited replication of the virus, with pralatrexate seemingly more effective than remdesivir, a drug that is used to treat some Covid-19 patients, the researchers said.
“However, more animal or clinical tests in humans are needed [before] these drugs can be used in the treatment of [Covid-19],” Wei said. SOVEREIGNPH