A fresh outbreak of the Nipah virus has been reported in India, raising alarms due to its alarming fatality rate of up to 75%. The World Health Organization has long flagged Nipah as a potential pandemic risk, particularly because of its transmissibility and the absence of targeted vaccines or therapeutic options. Nevertheless, researchers achieved a noteworthy advancement in the quest for a treatment last year, thanks to artificial intelligence.
The focus of the therapeutic development is a specific site on the RNA-dependent RNA polymerase (RdRp) found within the virus's L-protein. By obstructing this area, the virus’s ability to replicate its genetic material can be inhibited. This method is particularly advantageous because human cells lack this enzyme, making it a safer approach that has been employed in treating influenza and various other infections.
The main hurdle lies in discovering the appropriate "key" to effectively target this site. Developing a new molecule from the ground up and navigating it through clinical trials can require as much as a decade. To expedite the process, a global team of researchers utilized an AI model to analyze 2,000 FDA-approved medications. This deep learning model successfully pinpointed around 500 candidates that could potentially interact with the Nipah target, with further evaluations singling out Cangrelor as the leading contender.
Cangrelor demonstrates superior binding to RdRp compared to other known substances, effectively stifling the virus's replication. It is worth noting that Cangrelor received approval from U.S. health authorities in 2015 and is currently utilized in cardiology practices.
However, it's important to highlight that this discovery is based solely on computational modeling. The proposed treatment will need to undergo validation through both in vitro and in vivo studies, including safety assessments to ensure that potential side effects, such as an elevated risk of bleeding, do not become problematic.
Informational material. 18+.
