How can AI help develop snakebite antivenom?
In this interview from our sister site BioTechniques, find out about the role of AI in developing snakebite antivenom. Highlights include: generative antibody design with structure-prediction networks, regulatory approval and the future of these AI-designed drugs.
We spoke to Tim Jenkins, an Assistant Professor at the Technical University of Denmark (DTU; Copenhagen, Denmark), about his research incorporating artificial intelligence into antibody discovery programs to develop new snakebite antivenoms.
Snakebites present a neglected public health issue in many tropical and subtropical countries – an estimated 5.4 million people are bitten by snakes each year, and roughly half of those people are injected with venom. Between 81,000 and 138,000 people die as a result of snakebites, while around three times as many are left with permanent disabilities [1].
Although antivenoms exist, there are many barriers to making them safe, effective and accessible to those who need them. Current antivenoms are made following a 100-year-old method of injecting a venom of interest into a production animal, such as a horse, waiting up to a year for the animal’s immune system to generate antibodies and then collecting the blood plasma from the animal and purifying it. “It works, but it has a lot of downsides,” Tim explains. The resulting antivenom is not tailored, so it might not target the most clinically relevant toxins and can cause adverse reactions; it’s not pure, so large quantities are required for successful treatment; and the manufacturing pipeline is lengthy, and upscaling is challenging, driving up the cost. These factors make snakebite a huge socio-economic burden, impacting those in poorer, rural communities most. “It can cost a farmer in Africa more than he makes in a year to pay for just the vials of antivenom, not even the hospital treatment.”
