Rowan GardnerHost
Barry WarkGuest

Give me some color of, of the opportunities and the other data sets that you're building right now.


So if we were to acquire all of those consented samples, we'd be building a new UK biobank roughly every two and a half months.

We don't have to acquire all the samples because in real time, we can link those with longitudinal clinical records and identify those patients that are of interest for a particular data set.

But really, there's an opportunity for translational and, and clinical development teams who need data sets in any disease.

We're, we're always open to those conversations, but really excited about this intersection of complex disease and societal impact right at the scale that we're talking in GLP-1s, right? This is a really unique opportunity.

And I think you're right, and I just love the way you speaking to your ability to assess the value of a sample and your ability to link it before you incur the costs of processing it and turning it into omic data.

Because for many of these large collections, you're paying for them is a challenge.

If you're a biopharma company who needs a visibility or to a data set to ask a question, and we talked about supporting precision medicine development, you're on a timescale.

That speed of execution and the intentionality, again, is giving us opportunities to answer questions in a very timely and powerful way, I think.

So traditionally, historically, there have been prospective clinical trials, very focused, very effective, rich data collection, but slow and expensive.

We can observe patients in the real world, and the challenge is when you're trying to combine- The broad net of real world data with the focused data collection and data generation of a clinical trial, right? If we care about the whole genome of these patients or the whole transcriptome or broad proteomics for these patients, there was a gap.

We couldn't have the best of both worlds, a real world data broad net and speed and efficiency of collection and this research omics data.

Whereas I think Ovation has found this interesting medium point where we can work in the broad real world of patients, but because we're generating our own research data, we get some of the benefits of a clinical trial as well, right? And finding that middle ground, I think, has opened up a whole range of new opportunities.


Give me some color of, of the opportunities and the other data sets that you're building right now.


So if we were to acquire all of those consented samples, we'd be building a new UK biobank roughly every two and a half months.

We don't have to acquire all the samples because in real time, we can link those with longitudinal clinical records and identify those patients that are of interest for a particular data set.

But really, there's an opportunity for translational and, and clinical development teams who need data sets in any disease.

We're, we're always open to those conversations, but really excited about this intersection of complex disease and societal impact right at the scale that we're talking in GLP-1s, right? This is a really unique opportunity.

And I think you're right, and I just love the way you speaking to your ability to assess the value of a sample and your ability to link it before you incur the costs of processing it and turning it into omic data.

Because for many of these large collections, you're paying for them is a challenge.

If you're a biopharma company who needs a visibility or to a data set to ask a question, and we talked about supporting precision medicine development, you're on a timescale.

That speed of execution and the intentionality, again, is giving us opportunities to answer questions in a very timely and powerful way, I think.

So traditionally, historically, there have been prospective clinical trials, very focused, very effective, rich data collection, but slow and expensive.

We can observe patients in the real world, and the challenge is when you're trying to combine- The broad net of real world data with the focused data collection and data generation of a clinical trial, right? If we care about the whole genome of these patients or the whole transcriptome or broad proteomics for these patients, there was a gap.

We couldn't have the best of both worlds, a real world data broad net and speed and efficiency of collection and this research omics data.

Whereas I think Ovation has found this interesting medium point where we can work in the broad real world of patients, but because we're generating our own research data, we get some of the benefits of a clinical trial as well, right? And finding that middle ground, I think, has opened up a whole range of new opportunities.
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