In conversation with Fierce Biotech’s Chris Hayden, Austin Read, head of PPD™ CorEvitas™ Precision Medicine, Thermo Fisher Scientific, unpacks the evolving landscape of precision therapeutic and diagnostic development. Austin explains how linking biospecimens with longitudinal patient data is transforming translational medicine, particularly beyond oncology into areas like immunology and neurology. He highlights the importance of ethical considerations, operational design and data quality in biospecimen collection, and points to underrepresented diseases and AI-driven innovations as key opportunities for accelerating research. Watch the full interview to hear Austin’s insights on how these advances are reshaping patient care and the future of precision medicine.
Chris Hayden:
Hi everyone. My name is Chris Hayden from Fierce Biotech, and I'm here today with Austin Read, head of PPD CorEvitas Precision Medicine, Thermo Fisher Scientific. Today we're going to be discussing precision therapeutic and diagnostic development.
Austin, I'm interested in your perspective on the ways in which you see biotech companies using biospecimens for precision medicine studies and the opportunities to link longitudinal registry data with biospecimens to advance translational medicine.
So let's dive right in. Thank you for joining us today, Austin.
Austin Read:
Thank you. Happy to be here.
Chris Hayden:
Excellent. We'll jump right into the questions. Okay.
I'd be curious, Austin, to hear what is your view of the current precision medicine landscape and how do you see precision therapeutic and diagnostic development advancing.
Austin Read:
Overall, precision medicine is a medicinal model that's ultimately seeking to stratify patients into different groups based on their measurable characteristics. So think of environmental, lifestyle, biological factors. And then, based on the groups that these patients ultimately fall into, the intention is to inform different either prevention, diagnostic, or treatment decisions as they progress through their journey. The goal ultimately is to move away from a one-size-fits-all model and care approach and delve into what makes you unique and what ultimately is best in the instance for your care in a particular disease.
This isn't a new vision. It dates back almost 30 years at this point, but has been accelerated predominantly through the era of big data and increased availability of various analytical methodologies to try to unfurl different insights for different disease populations.
Particularly this has been most evident in oncology and rare disease indications; when you think of different therapies for different genetic mutations, potentially cell and gene therapy, but more recently has begun expanding outside of those disease areas into more heterogeneous diseases such as immunology and neurology. These disorders still have a ways to go in the context of unwrapping what makes different patient subsets unique and ultimately bringing forth next generation of treatment paradigms for these patients.
When we think about how overall is the process advancing, technology is an ultimate tailwind that's accelerated all innovation. Industry has gotten much smarter in the context of thinking of how do benchside research experiments translate into real-world populations, including the clinical trial design paradigm and focusing on the most relevant outcomes and research approaches to inform those types of pathways. But this is all supplemented by data through real-world research sources, which can range from publicly available data sets, claims, EMR and registries.
And I think more recently you've seen unique designs that we've embraced, which have been proactive registries that not only think about clinical and patient-reported outcomes longitudinally collected from patient subtypes, but then also supplementing those journeys with biospecimens from those same patients, which ultimately creates a much fuller picture of the patient and enables a broader aperture of research initiatives downstream.
Chris Hayden:
You mentioned registries that pair biospecimens with longitudinal clinical outcomes. Can you explain more about these types of registries and the role they can play in advancing precision medicine?
Austin Read:
Registries can vary in all different sizes, shapes, patient populations, cohorts, data types. So there's a lot of variability. When we think about registries, we think of prospective registries that are not only focused on clinical and patient-reported outcomes, but those that are collecting biospecimens alongside those visit points.
Our data has been used in a variety of regulatory cases as an example, whereas in certain instances, like we were talking about, the data source may not always be applicable in some of those instances. So we've evolved more recently to focus more heavily on the biospecimen element of that, particularly in this thematic of how do we play a role in advancing precision medicine and standard of care. This aperture is much broader and the downstream research applications using it for biologic target identification, biomarker discovery work, patient segmentation, and subtyping.
So all of these are really critical in the research paradigm to usher in the next generation of both diagnostic, prognostic and therapeutic interventions for patients and hopefully relieving disease burden.
Chris Hayden:
Yeah, I like that. Austin, what are some of the important considerations for biospecimen collections to successfully enable precision medicine research?
Austin Read:
First and foremost, it starts with ethical considerations around patients, right? You want to make sure that obviously patients have a good understanding and the investigators as well. What are they opting into? Where does any type of perspective, particularly biospecimen collection, fit into the research paradigm? What's the role they're playing, and how is any entity, the sponsor, treating data privacy considerations downstream? So I think it has to start there.
I think secondly is success drivers really around understanding the clinical research paradigm from an operational standpoint. Where is the unmet clinical need? Where is the scientific question to be answered? And then how does a research design fit into that structure to ensure that not only is a study operationally feasible, but respectful of those that are participating in the research initiative to ensure that it ultimately becomes successful?
I think lastly, I'd call out quality and consistency, not just on the biospecimen end of it, but also on any associated data attributes that would accompany those biospecimens. We spent a lot of time thinking about centralized protocols, centralized collection and handling procedures of our biospecimens, and then having really good data accessibility and structuring to ensure that once a research initiative is ultimately complete, it can be available as readily as possible to the end users that are ultimately going to be the ones responsible for bringing these new treatment paradigms to market.
Chris Hayden:
Austin, where do you see opportunity to accelerate precision medicine research?
Austin Read:
Two themes here. Ultimately, there's certainly diseases that are underrepresented in the context of research. As I was mentioning earlier, oncology rare diseases appropriately get a lot of focus and attention. But I think making sure in the spirit of bringing precision medicine to all patients, there certainly needs to be an intentional effort to think about diseases that may not be as front and center as it's been historically. So I think that's one area.
I think the second is certainly technological adoption. Obviously, with the growing rise in application of AI/ML technologies, I think one area that I'd love to see it continue to be applied is not just from a computational perspective, but in a patient recruiting and engagement point of view as a tool. Oftentimes historically, research initiatives that would include biospecimens, whether they be trials or observational studies, often struggle to get representativeness from patient populations. So I think this is a key area that we focus on and think about to ensure that the research that we conduct can be representative for all different patient attributes.
But the computational accelerant that is AI/ML certainly can't be ignored. We've strategically thought about how do we selectively and strategically pre-generate multi-omic data from biospecimens in certain instances, which not only reduces clinical research timelines by provisioning that data to our partners, but also ultimately I think memorializes the value contribution that these patients have by participating within these registries and ensuring that the research impact can be felt often long beyond the point of time where a biospecimen may ultimately be consumed.
Chris Hayden:
That's great. Austin, it's funny, you didn't mention AI until our last question. That's really quite remarkable. So that's great.
This been a fascinating conversation, Austin. I mean, there's a lot of opportunity here, so we really appreciate your time, and thank you so much for joining us today.
Austin Read:
Thank you so much, Chris.