Binding a venom toxin is not the same as neutralizing it, and the hosts lay out the five-step pipeline a toxin binder has to clear before it becomes a usable antivenom: confirm it neutralizes the toxin, confirm it covers the relevant range of venoms, test it in animals, clear it for human safety, and finally show it helps actual patients. They note this full pipeline is estimated at four to seven-plus years, and the scorpion-toxin work discussed earlier has only reached the binding stage, not even step one of neutralization.
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The hosts cite a chart estimating where AI will help most: high impact on generalizing a binder into a universal antivenom and on animal testing, but only moderate to limited impact on human trial preparation and the regulatory approval process.
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They connect this to an earlier discussed story about a company's wet lab, contrasting a setup where AI only designs molecules while human scientists run the physical lab work.
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1:15:49>> Catrini actually put this road map out that we can look at. That's kind of how they sort of define it. So step one, right, is does it neutralize the toxin? We haven't even gotten to step one yet. We've found a binding mechanism and so now step one is making sure that it can actually neutralize the tox because it could bind but not necessarily neutralize it like the actual >> Oh, interesting. You know what I'm saying? I see. Yeah. >> And so there's a difference between those two things. We know it can connect but we don't know that it makes uh that it protects the actual nerve function itself. >> So then number two, does it cover the relevant venoms that we're trying to address? >> Number three, does it work in a body? uh in this case in animal testing. Uh then
1:16:30we get to number stage number four which is is it ready from a safety perspective for human testing >> and then five does it actually help patients once it gets through that developmental process. They estimate that whole pipeline for this specific use case about four to seven years plus. Um and all we're talking about is not even yet having solved step one. >> Step one. Yeah. uh but this is the pipeline that all these AI is going to cure X Y and Z. This is the pipeline they kind of have to go through. And they've identified a few things in their chart about where they think AI is going to be the most impactful. So in proving the neutralization of the venom itself,
1:17:12they're like, I think we think it'll have moderate impact >> in being able to then cover once they know it can neutralize, they think it has the highest impact of being able to then generalize it across uh all these different classes of venom, this universal antivenenom. They also think that in that first stage of animal testing that it can have high impact as it relates to animal testing. But when you get to human preparing for human trials and the trials and acceptance process, it goes to moderate to limited which is obviously very understandable. I think the point here that is sort of being brought up is >> there is there's a real world aspect to this stuff that is just naturally going to be slower unless and until some of
1:17:52the processes the regulatory environment and the way in which we do those downstream processes fundamentally changes. So that's kind of part one of what this OpenAI announcement >> was about. And it's interesting because it dovtales with the wet lab story we talked about a few weeks ago where everyone's like, "Oh, Anthropic has a wet lab." >> Yeah. >> You know, but it was where the AI was designing and the human scientists were in the lab actually conducting the entire process. >> Yeah. >> Um itself. So I want to sort of briefly talk about the timing
Animal BehaviorArtificial IntelligenceNeuroscienceProtein Engineering