Understanding translational biomarkers: from discovery to the clinic
Understanding translational biomarkers: from discovery to the clinic
JOHN SMERAGLIA: I'm John Smeraglia. I work for UCB, and my role is to head up the translational biomarker and bioanalysis group. Effectively, what that means is that me and my group are involved in bioanalytical activities from discovery all the way out to post-marketing. That could be PK analysis. It could be immunogenicity analysis or translational biomarkers.
Segment:1 What are translational biomarkers?.
JOHN SMERAGLIA: Fundamentally, translational biomarkers are typically proteins, but they could be any range of measures that aid our understanding of disease biology or disease progression, effectively how the drug is interacting with the target.
JOHN SMERAGLIA: Is it engaging the target through target engagement? Is it solely occupying a target but not seeing any downstream effect, demonstrating target occupancy without engagement? Or alternatively, what are the clinical response associated with that target engagement? And ultimately, they lead us to then drive strategies around patient selection or patient stratification for our clinical studies.
JOHN SMERAGLIA: Translational biomarkers fit into the bioanalytical landscape
Segment:2 How do translational biomarkers fit into the bioanalytical landscape?.
JOHN SMERAGLIA: in many different ways. But effectively, most critically, they are associated with the learn phase of drug discovery, and let that be preclinical, driving into clinical, or for that matter, taking that knowledge forward into clinical studies for patient selection. And, of course, really what that means, ultimately, is developing assays that are capable of measuring a range of different biomarker endpoints within a study.
Segment:3 How do you decide on selecting the appropriate biomarkers and analytical methodology in your studies?.
JOHN SMERAGLIA: It comes down to what is the key question that we're trying to answer within the biomarker study. Specifically, are we trying to determine if, in fact, we have a marker that helps us to understand the prognosis of disease? Or does it help us understand the mechanism of action of disease? And then, of course, through those key questions, through those hypotheses, we'll then drive a analytical strategy that's aligned to those key questions, those hypotheses.
JOHN SMERAGLIA: And then from a method perspective, that will define the degree of sensitivity required, the amount of selectivity or specificity that's necessary. And, of course, if the hypothesis is to demonstrate down regulation, then sensitivity becomes a really critical element. If however, we're seeing large changes, then actually the degree of variability of a method may not have such a significant implication on the overall biomarker strategy.
Segment:4 What are the key challenges working with translational biomarkers?.
JOHN SMERAGLIA: There are a range of different challenges. Some of those challenges are analytically focused. Some of them have far more to do with our understanding, thinking about situations where we have very limited understanding of the target or the mechanism of a particular disease. A great deal of investigative work needs to be established and performed to understand the fundamental aspects of the biology.
JOHN SMERAGLIA: From there, then we can start building towards those hypotheses that are so very important to decisions that we're going to make within a biomarker study. And, of course, we'll then take that into the channel from an analytical perspective, the challenge could be that the very limited technologies can answer that specific question. Experience and skill sets of the bioanalytical scientists are fundamental to ensure we do that.
JOHN SMERAGLIA: Driving really quite bespoke and specific method development, these biomarker analysis and biomarker assays are very different from PK assays. The purpose of them is fundamentally different. So therefore, oftentimes, we're in a novel environment, novel space as far as knowledge is concerned, new technologies, and we're having to drive very bespoke and specific methods. And, therefore, the reliance on a skilled bioanalytical scientist who can develop a method is fundamental to success.
Segment:5 What strategies are being used to improve the transition of biomarkers from discovery to the clinic?.
JOHN SMERAGLIA: There are a range of interfaces, and actually, many of them are very fundamental to success. In the earliest phases, you're looking very closely with your discovery colleagues to understand disease biology, to understand mechanism of disease, then also with regard to therapeutic areas. The same protein can be used across multiple different therapeutic areas. However, to define that question, to ensure that we understand what we're trying to achieve within the study, we need to link in our discovery colleagues, our therapeutic area colleagues, as well as our clinical development colleagues.
JOHN SMERAGLIA: And, in fact, what often happens is those questions evolve and develop through the stages of drug discovery, initially, relatively open-ended searching for different markers, ultimately focusing in, in many cases, focusing in on which therapies are most appropriate for which subjects, therefore, using it as a tool for patient stratification or, for that matter, patient selection.
Segment:6 How are translational biomarker studies regulated? .
JOHN SMERAGLIA: So it's a really interesting question because it's not a straightforward answer.
JOHN SMERAGLIA: And what I mean by that, it all depends on what you can use the data for, what is effectively the context of use. If the context of use is linked toward the discovery activities and more for internal decision making, do we have an asset that is valuable? Are we seeing the level of engagement that we need? Then the regulatory component is a relatively light touch. It's more about the scientific integrity of the data.
JOHN SMERAGLIA: However, as you progress through development and you want to use that data to support submission activities, then the regulatory component becomes far more significant. And, therefore, there are a range of guidance that are out there that define how you characterize variability in patient populations, how you characterize variability in the assay themselves. And ultimately, through that process, you make certain statements or certain label claims that are supported by data.
JOHN SMERAGLIA: And, of course, that data must be compliant with the regulatory interface-- or the regulatory infrastructure. Having said that, an example of that would be the use of biomarker data to find PK/PD models. You're now building a mathematical model based on the analytical data. Therefore, the reliance from a regulatory perspective is fundamental.
JOHN SMERAGLIA: And, of course, what you would do is to use a great deal of rigor in terms of documentation exercises, as well as the regulatory infrastructure to support PK/PD models. [MUSIC PLAYING]