Marc Tessier-Lavigne

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407 appearances 1 recordings 1 series first heard Mar 2026 last heard 12 Mar

Marc Tessier-Lavigne’s voice in public audio — every appearance, attributed to the second.

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Vanguards of Healthcare by Bloomberg Intelligence · Xaira’s War on Trial-and-Error Drug Development · 12 Mar 2026 podcast

And for the small companies, I do think that there are opportunities to lead in development of the technology, but also great opportunities to partner with pharma partners to bring together the technology from the small company with both the expertise and the, as you point out, the data that the large pharma might have.

So I see this more as an opportunity for synergies rather than a, you know, sort of winner take all kind of situation, which is the way you phrased the question.

We're certainly very mindful of that.

You know, I think all of us as startups don't want to, you know, invest our resources in activities that, you know, we're just replicating what others have done previously.

We're trying to create, for example, novel data sets for foundation models and biology.

By the way, those kinds of data sets don't exist in any pharma right now, just to be clear.

So for the target identification side of the equation, the data pretty much has to be generated.

What pharma can bring to the table there often is deep insights into certain therapeutic areas, great models, a lot of expertise and experience in therapy.

studying those models both in rarefied settings in vitro and in a petri dish, but also in the living organism, and to match them with the kind of technology that we're applying in our studies.

So I think there's opportunity for synergy there, but I don't think this is a case of pharma having all the answers.

I agree with you on the clinical trial side.

Many pharmas have terrific experience in clinical trials.

and have a lot of data, often the data is not in the right format.

It may be possible to convert it for use.

But there are two things about the data also in the clinical setting that are worth pointing out.

The first is that the data that are going to be most valuable, again, will be prospective data where you have made a decision in advance to test patients in one setting or, you know, for example, with one drug or not having a drug.

And then

measuring deeply the response in the relevant tissues.

Sometimes that's been done in the pharma, sometimes it has not, that they don't have the biospecimens from the patients in order to feed into the AI models to try to identify who's likely to respond and who's not likely not to respond.

So there may be data that are useful.

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